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Drug gets orphan designation for MDS
The US Food and Drug Administration (FDA) has granted orphan designation to an investigational drug for the treatment of myelodysplastic syndromes (MDS).
The drug, CPI-613, targets metabolic changes that are thought to occur in many cancer cells.
It has demonstrated activity and tolerability in a phase 1 trial of patients with advanced, relapsed/refractory hematologic malignancies.
CPI-613 previously received orphan designation for acute myeloid leukemia (AML) and pancreatic carcinoma.
Orphan designation is granted for drugs intended to treat diseases that affect fewer than 200,000 individuals in the US. This designation gives the makers of CPI-613, Cornerstone Pharmaceuticals, 7 years of US marketing exclusivity once the drug is approved.
The designation also allows the company to apply for government funding to defray trial costs, tax credits for clinical research expenses, and a potential waiver of the FDA’s application user fee.
CPI-613: Mechanism and phase 1 results
CPI-613 induces cancer-specific inhibition of the mitochondrial enzymes pyruvate dehydrogenase (PDH) and alpha ketoglutarate dehydrogenase (KGDH).
Disrupting the function of PDH and KGDH disrupts tumor mitochondrial metabolism. As a result, tumor cells are starved of energy and biosynthetic intermediates, which leads to cell death.
Researchers evaluated CPI-613 in a phase 1 study of patients with advanced, relapsed/refractory hematologic malignancies.
The team, led by Timothy S. Pardee, MD, of Wake Forest Baptist Medical Center in Winston-Salem, North Carolina, presented the results at the 2013 ASCO Annual Meeting as abstract 2516. (Information in the abstract differs slightly from that presented at the meeting.)
The trial was designed to determine the maximum tolerated dose, safety, and anticancer activity of CPI-613 as a single agent.
Twenty-one evaluable patients received CPI-613 on days 1 and 4 for 3 weeks every 28 days. Ten patients received more than 1 cycle of therapy.
The starting dose was 420 mg/m2. Treatment could be continued if the patient experienced clinical benefit. Doses were escalated to a final dose of 3780 mg/m2.
CPI-613 was generally well-tolerated when infused over 2 hours. Patients did not experience worsening cytopenias at any dose level. However, 1-hour infusions led to grade 3 renal failure in 2 patients.
At a dose of 3780 mg/m2, 1 patient had prolonged grade 3 nausea, and 1 patient had grade 3 renal failure. Six patients received a 2-hour infusion of 2940 mg/m2 without dose-limiting toxicities, so the researchers considered this the maximum tolerated dose.
Of the 21 patients, 9 achieved a response of stable disease or better. One MDS patient achieved a complete remission and maintained it over 23 cycles. One AML patient achieved a morphologic leukemia-free state.
A Burkitt lymphoma patient and a cutaneous T-cell lymphoma patient maintained partial responses over 16 and 15 cycles, respectively. Two multiple myeloma patients, 2 MDS patients, and 1 AML patient had stable disease.
“We are very encouraged by the tolerability and signals of activity seen in several patients in this phase 1 study for whom there is no available therapy shown to provide clinical benefit,” Dr Pardee said.
“We look forward to further evaluating CPI-613 in the early relapsed/refractory AML patient setting when administered in combination with a standard chemotherapeutic regimen, as well as in early relapsed or refractory MDS patients, with the hope of improving the outcomes and the quality of life for these patients through the combined use of this mechanistically novel agent.”
The AML study is a phase 1 trial investigating CPI-613 in combination with high-dose cytarabine and mitoxantrone, and the MDS study is a phase 2 trial investigating single-agent CPI-613. 
The US Food and Drug Administration (FDA) has granted orphan designation to an investigational drug for the treatment of myelodysplastic syndromes (MDS).
The drug, CPI-613, targets metabolic changes that are thought to occur in many cancer cells.
It has demonstrated activity and tolerability in a phase 1 trial of patients with advanced, relapsed/refractory hematologic malignancies.
CPI-613 previously received orphan designation for acute myeloid leukemia (AML) and pancreatic carcinoma.
Orphan designation is granted for drugs intended to treat diseases that affect fewer than 200,000 individuals in the US. This designation gives the makers of CPI-613, Cornerstone Pharmaceuticals, 7 years of US marketing exclusivity once the drug is approved.
The designation also allows the company to apply for government funding to defray trial costs, tax credits for clinical research expenses, and a potential waiver of the FDA’s application user fee.
CPI-613: Mechanism and phase 1 results
CPI-613 induces cancer-specific inhibition of the mitochondrial enzymes pyruvate dehydrogenase (PDH) and alpha ketoglutarate dehydrogenase (KGDH).
Disrupting the function of PDH and KGDH disrupts tumor mitochondrial metabolism. As a result, tumor cells are starved of energy and biosynthetic intermediates, which leads to cell death.
Researchers evaluated CPI-613 in a phase 1 study of patients with advanced, relapsed/refractory hematologic malignancies.
The team, led by Timothy S. Pardee, MD, of Wake Forest Baptist Medical Center in Winston-Salem, North Carolina, presented the results at the 2013 ASCO Annual Meeting as abstract 2516. (Information in the abstract differs slightly from that presented at the meeting.)
The trial was designed to determine the maximum tolerated dose, safety, and anticancer activity of CPI-613 as a single agent.
Twenty-one evaluable patients received CPI-613 on days 1 and 4 for 3 weeks every 28 days. Ten patients received more than 1 cycle of therapy.
The starting dose was 420 mg/m2. Treatment could be continued if the patient experienced clinical benefit. Doses were escalated to a final dose of 3780 mg/m2.
CPI-613 was generally well-tolerated when infused over 2 hours. Patients did not experience worsening cytopenias at any dose level. However, 1-hour infusions led to grade 3 renal failure in 2 patients.
At a dose of 3780 mg/m2, 1 patient had prolonged grade 3 nausea, and 1 patient had grade 3 renal failure. Six patients received a 2-hour infusion of 2940 mg/m2 without dose-limiting toxicities, so the researchers considered this the maximum tolerated dose.
Of the 21 patients, 9 achieved a response of stable disease or better. One MDS patient achieved a complete remission and maintained it over 23 cycles. One AML patient achieved a morphologic leukemia-free state.
A Burkitt lymphoma patient and a cutaneous T-cell lymphoma patient maintained partial responses over 16 and 15 cycles, respectively. Two multiple myeloma patients, 2 MDS patients, and 1 AML patient had stable disease.
“We are very encouraged by the tolerability and signals of activity seen in several patients in this phase 1 study for whom there is no available therapy shown to provide clinical benefit,” Dr Pardee said.
“We look forward to further evaluating CPI-613 in the early relapsed/refractory AML patient setting when administered in combination with a standard chemotherapeutic regimen, as well as in early relapsed or refractory MDS patients, with the hope of improving the outcomes and the quality of life for these patients through the combined use of this mechanistically novel agent.”
The AML study is a phase 1 trial investigating CPI-613 in combination with high-dose cytarabine and mitoxantrone, and the MDS study is a phase 2 trial investigating single-agent CPI-613.
The US Food and Drug Administration (FDA) has granted orphan designation to an investigational drug for the treatment of myelodysplastic syndromes (MDS).
The drug, CPI-613, targets metabolic changes that are thought to occur in many cancer cells.
It has demonstrated activity and tolerability in a phase 1 trial of patients with advanced, relapsed/refractory hematologic malignancies.
CPI-613 previously received orphan designation for acute myeloid leukemia (AML) and pancreatic carcinoma.
Orphan designation is granted for drugs intended to treat diseases that affect fewer than 200,000 individuals in the US. This designation gives the makers of CPI-613, Cornerstone Pharmaceuticals, 7 years of US marketing exclusivity once the drug is approved.
The designation also allows the company to apply for government funding to defray trial costs, tax credits for clinical research expenses, and a potential waiver of the FDA’s application user fee.
CPI-613: Mechanism and phase 1 results
CPI-613 induces cancer-specific inhibition of the mitochondrial enzymes pyruvate dehydrogenase (PDH) and alpha ketoglutarate dehydrogenase (KGDH).
Disrupting the function of PDH and KGDH disrupts tumor mitochondrial metabolism. As a result, tumor cells are starved of energy and biosynthetic intermediates, which leads to cell death.
Researchers evaluated CPI-613 in a phase 1 study of patients with advanced, relapsed/refractory hematologic malignancies.
The team, led by Timothy S. Pardee, MD, of Wake Forest Baptist Medical Center in Winston-Salem, North Carolina, presented the results at the 2013 ASCO Annual Meeting as abstract 2516. (Information in the abstract differs slightly from that presented at the meeting.)
The trial was designed to determine the maximum tolerated dose, safety, and anticancer activity of CPI-613 as a single agent.
Twenty-one evaluable patients received CPI-613 on days 1 and 4 for 3 weeks every 28 days. Ten patients received more than 1 cycle of therapy.
The starting dose was 420 mg/m2. Treatment could be continued if the patient experienced clinical benefit. Doses were escalated to a final dose of 3780 mg/m2.
CPI-613 was generally well-tolerated when infused over 2 hours. Patients did not experience worsening cytopenias at any dose level. However, 1-hour infusions led to grade 3 renal failure in 2 patients.
At a dose of 3780 mg/m2, 1 patient had prolonged grade 3 nausea, and 1 patient had grade 3 renal failure. Six patients received a 2-hour infusion of 2940 mg/m2 without dose-limiting toxicities, so the researchers considered this the maximum tolerated dose.
Of the 21 patients, 9 achieved a response of stable disease or better. One MDS patient achieved a complete remission and maintained it over 23 cycles. One AML patient achieved a morphologic leukemia-free state.
A Burkitt lymphoma patient and a cutaneous T-cell lymphoma patient maintained partial responses over 16 and 15 cycles, respectively. Two multiple myeloma patients, 2 MDS patients, and 1 AML patient had stable disease.
“We are very encouraged by the tolerability and signals of activity seen in several patients in this phase 1 study for whom there is no available therapy shown to provide clinical benefit,” Dr Pardee said.
“We look forward to further evaluating CPI-613 in the early relapsed/refractory AML patient setting when administered in combination with a standard chemotherapeutic regimen, as well as in early relapsed or refractory MDS patients, with the hope of improving the outcomes and the quality of life for these patients through the combined use of this mechanistically novel agent.”
The AML study is a phase 1 trial investigating CPI-613 in combination with high-dose cytarabine and mitoxantrone, and the MDS study is a phase 2 trial investigating single-agent CPI-613.
Vendor CPOE for Renal Impairment
Hospitalized patients with renal impairment are vulnerable to adverse drug events (ADEs).[1, 2] Appropriate prescribing for patients with renal insufficiency is challenging because of the complexities of drug therapy within the wide spectrum of kidney disease.[3, 4, 5, 6] Accordingly, computerized physician order entry (CPOE) systems with clinical decision support may help prevent many ADEs by providing timely laboratory information, recommending renally adjusted doses, and by offering assistance with prescribing.[7, 8, 9]
Despite the proposed benefits of CPOE, outcomes vary greatly because of differences in technology.[10, 11, 12, 13] In particular, the type of decision support available to assist medication ordering in the setting of renal disease varies widely among current vendor systems. Given the uncertain benefits of CPOE, especially with the wide range of associated clinical decision support, we conducted this study to determine the impact of these systems on the rates of ADEs among hospitalized patients with kidney disease.
METHODS
This study was approved by the institutional review boards at each study site.
Design and Setting
We conducted a before‐and‐after study to evaluate the impact of newly implemented vendor CPOE systems in 5 community hospitals in Massachusetts. Although we reported on 6 hospitals in our baseline study,[14] 1 of these hospitals later chose not to implement CPOE, and therefore was not included in follow‐up. At the time of this study, 1 of the hospitals (site 3) had not yet achieved hospital‐wide implementation. Although CPOE had been adopted by most medical services at site 3, it had not yet been implemented in the emergency, obstetrical, or surgical departments. Thus, we limited our study to the medical services at this site. For the remaining sites, all admitting services were included with the exception of the psychiatric and neonatal services, which were excluded from both phases because they would have required different detection tools.
Participants
Patients aged 18 years with renal failure, exposed to potentially nephrotoxic and/or renally cleared medications, and admitted to any of the participating hospitals during the study period were eligible for inclusion. Of the patients meeting eligibility criteria, we randomly selected approximately 150 records per hospital in the preimplementation and postimplementation phases for a total sample of 1590 charts. The first phase of this study occurred from January 2005 to August 2006; the second phase began 6 months postimplementation and lasted from October 2008 to September 2010.
Principal Exposure
Each hospital independently selected a vendor CPOE system with variable clinical decision support capabilities: (1) sites 4 and 5 had basic CPOE only with no clinical decision support for renal disease; (2) sites 1 and 2 implemented rudimentary clinical decision support with laboratory display (eg, serum creatinine) whenever common renally related drugs were ordered; and (3) site 3 had the most advanced support in place where, in addition to basic order entry and lab checks, physicians were provided with suggested doses for renally cleared and/or nephrotoxic medications, as well as appropriate drug monitoring for medications with narrow therapeutic indices (eg, suggested dosages and frequencies for vancomycin and automated corollary laboratory monitoring).
Definitions
We screened for the presence of renal failure by a serum creatinine 1.5 mg/dL at the time of admission. However, the duration of renal impairment was not known. We defined 3 levels of renal insufficiency based on the calculated creatinine clearance (CrCl)15: mild (CrCl 5080 mL/min), moderate (1649 mL/min), and severe (15 mL/min). Subjects with a CrCl >80 mL/min were considered to have normal renal function and were excluded. Potentially nephrotoxic and/or renally cleared medications were then identified using an established knowledge base (see Supporting Information, Table 1, in the online version of this article).[16]
| Hospital Site | |||||||
|---|---|---|---|---|---|---|---|
| Baseline Characteristics | All Sites | 1 | 2 | 3 | 4 | 5 | P (Among All Sites)* |
| |||||||
| No. of patients | 815 | 170 | 156 | 143 | 164 | 182 | |
| Age, y, mean (range) | 72.2 (18.0102.0) | 79.2 (33102) | 77.3 (23101) | 65.6 (1898) | 70.7 (1896) | 69.2 (2096) | <0.01 |
| 1844 years, no. (%) | 68 (9.1) | 1 (0.67) | 8 (6.5) | 20 (14.9) | 15 (9.4) | 24 (13.4) | <0.01 |
| 4554 years, no. (%) | 67 (9.0) | 6 (4.0) | 5 (4.1) | 17 (12.7) | 16 (10.0) | 23 (12.9) | |
| 5564 years, no. (%) | 79 (10.6) | 15 (10.0) | 12 (9.8) | 23 (17.2) | 13 (8.1) | 16 (8.9) | |
| 6574 years, no. (%) | 104 (13.9) | 20 (13.3) | 12 (9.8) | 16 (11.9) | 30 (18.8) | 26 (14.5) | |
| 7584 years, no. (%) | 197 (26.4) | 44 (29.3) | 36 (29.3) | 24 (17.9) | 49 (30.6) | 44 (24.6) | |
| 85 years, no. (%) | 231 (31.0) | 64 (42.7) | 50 (40.7) | 34 (25.4) | 37 (23.1) | 46 (25.7) | |
| Sex | |||||||
| Male, no. (%) | 427 (57.0) | 66 (44.0) | 60 (48.8) | 82 (60.7) | 105 (65.2) | 114 (63.7) | <0.01 |
| Female, no. (%) | 321 (43.0) | 84 (56.0) | 63 (51.2) | 53 (39.3) | 56 (34.8) | 65 (36.3) | |
| Race | |||||||
| Caucasian, no. (%) | 654 (87.4) | 129 (86.0) | 118 (95.9) | 126 (93.3) | 129 (80.1) | 152 (84.9) | <0.01 |
| Hispanic, no. (%) | 25 (3.3) | 2 (1.3) | 0 (0) | 1 (0.74) | 13 (8.1) | 9 (5.0) | |
| African American, no. (%) | 45 (6.0) | 12 (8.0) | 4 (3.3) | 5 (3.7) | 13 (8.1) | 11 (6.2) | |
| Native American, no. (%) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
| Asian, no. (%) | 13 (1.7) | 1 (0.81) | 1 (0.81) | 2 (1.5) | 5 (3.1) | 4 (2.2) | |
| Other, no. (%) | 7 (0.94) | 2 (1.3) | 0 (0) | 1 (0.74) | 1 (14.3) | 3 (1.7) | |
| Not recorded, no. (%) | 4 (0.53) | 4 (2.7) | 0 (0) | 0 (0.0) | 0 (0) | 0 (0) | |
| Initial severity of renal dysfunction | |||||||
| Mild, CrCl 5080 mL/min, no. (%) | 60 (7.4) | 4 (2.4) | 5 (3.2) | 5 (3.5) | 14 (8.5) | 32 (17.6) | <0. 01 |
| Moderate, CrCl 1649 mL/min, no. (%) | 388 (47.6) | 84 (49.4) | 71 (45.5) | 80 (55.9) | 76 (46.3) | 77 (42.3) | |
| Severe, CrCl <15 mL/min, no. (%) | 367 (45.0) | 82 (48.2) | 80 (51.3) | 58 (40.6) | 74 (45.1) | 73 (40.1) | |
| LOS, d, median (IQR) | 4.0 (26) | 4.0 (37) | 3.0 (25.5) | 4.0 (27) | 4.0 (27) | 4.0 (26) | 0.02 |
| DRG‐weighted LOS, d, median (IQR) | 5.0 (3.76.7) | 5.5 (46.7) | 5.0 (3.46.2) | 5.6 (4.36.7) | 5.0 (3.36.7) | 5.0 (4.26.7) | 0.27 |
In both phases of our study, only medications that were potentially nephrotoxic and/or renally cleared were included as potential cases; all other drugs were excluded. We defined an ADE as any drug‐related injury. These were considered preventable if they were due to an error at the time of order entry (eg, a doubling of creatinine secondary to an overdose of gentamicin or failure to order corollary drug levels for monitoring). A nonpreventable ADE was any drug‐related injury in which there was no error at the time of order entry (eg, a doubling of creatinine despite appropriate dosing of lisinopril).[17] A medication error was an error anywhere in the process of prescribing, transcribing, dispensing, administering, or monitoring a drug, but with no potential for harm or injury (eg, an order for an oral medication with no route specified when it was clear that the oral route was intended).[18] A potential ADE was an error with the potential to cause harm, but not resulting in injury, either because it was intercepted (eg, an order for ketorolac for a patient with renal failure, but caught by a pharmacist) or because of chance (eg, administering enoxaparin to a patient with severe renal dysfunction but without hemorrhage).
All study investigators underwent standardized training using a curriculum developed by the Center for Patient Safety Research and Practice (
Main Outcome Measures
The primary outcome was the rate of preventable ADEs. Secondary outcomes were the rates of potential ADEs and overall ADEs. All outcomes were related to nephrotoxicity or accumulation of a renally excreted medication.
Data collection and classification strategies were identical in both phases of our study.[14] We reviewed physician orders, medication lists, laboratory reports, admission histories, progress and consultation notes, discharge summaries, and nursing flow sheets, screening for the presence of medication incidents using an adaptation of the Institute for Healthcare Improvement's trigger tool, selected for its high sensitivity, reproducibility, and ease of use.[22, 23] In our adaptation of the tool, we excluded lidocaine, tobramycin, amikacin, and theophylline levels because of their infrequency. For each trigger found, a detailed description of the incident was extracted for detailed review. An example of a trigger is the use of sodium polystyrene, which may possibly indicate an overdose of potassium or a medication side effect.
Subsequently, each case was then independently reviewed by two investigators (A.A.L., M.A., B.C., S.R.S., M.C., N.K., E.Z., and G.S.)each assigned to at least 1 siteand blinded to prescribing physician and hospital to determine whether nephrotoxicity or injury from drug accumulation was present (see Supporting Information, Figure 1, in the online version of this article).[17] First, incidents were classified as ADEs, potential ADEs, or medication errors with no potential for injury. Second, ADEs and potential ADEs were rated according to severity. When nephrotoxic drugs were ordered, event severity was classified according to the elevation in serum creatinine24: increases of 10% were considered potential ADEs (near misses); increases of 10% to 100% were significant ADEs; and increases of 100% were serious ADEs. Changes in creatinine that were not associated with inappropriate medication orders were excluded. For renally excreted drugs with no potential for nephrotoxicity (eg, enoxaparin), we used clinical judgment to classify events as significant (eg, rash), severe (eg, 2‐unit gastrointestinal bleed), life threatening (eg, transfer to an intensive care unit), or fatal categories, as based on earlier work.[25] Disagreements were resolved by consensus. We had a score of 0.70 (95% confidence interval [CI]: 0.61‐0.80) for incident type, indicating excellent overall agreement.
Statistical Analysis
Baseline characteristics between hospitals were compared using the Fisher exact test for categorical variables and 1‐way analysis of variance for continuous variables. The occurrence of each outcome was determined according to site. To facilitate comparisons between sites, rates were expressed as number of events per 100 admissions with 95% CIs. To account for hospital effects in the analysis when comparing pre‐ and postimplementation rates of ADEs and potential ADEs, we developed a fixed‐effects Poisson regression model. To explore the independent effects of each system, a stratified analysis was performed to compare average rates of each outcome observed.
RESULTS
The outcomes of 775 patients in the baseline study were compared with the 815 patients enrolled during the postimplementation phase.[14] Among those in the postimplementation phase (Table 1), the mean age was 72.2 years, and they were predominantly male (57.0%). The demographics of the patients admitted to each of the 5 sites varied widely (P<0.01). Most patients had moderate to severe renal dysfunction.
Overall, the rates of ADEs were similar between the pre‐ and postimplementation phases (8.9/100 vs 8.3/100 admissions, respectively; P=0.74) (Table 2). However, there was a significant decrease in the rate of preventable ADEs, the primary outcome of interest, following CPOE implementation (8.0/100 vs 4.4/100 admissions; P<0.01). A reduction in preventable ADEs was observed in every hospital except site 4, where only basic order entry was introduced. However, there was a significant increase in the rates of nonpreventable ADEs (0.90/100 vs 3.9/100 admissions; P<0.01) and potential ADEs (55.5/100 vs 136.8/100 admissions; P<0.01).
| Rate/100 Admissions (95% CI) | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total No. (%) | All Sites | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | ||||||||||||||
| Event | Pre | Post | Pre | Post | P* | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| ||||||||||||||||||||
| ADEs | 69 (13.8) | 68 (5.7) | 8.9 (7.0 1.2) | 8.3 (6.50.5) | 0.74 | 9.8 (6.015.1) | 10.0 (6.015.5) | 0.96 | 11.0 (6.517.4) | 7.7 (4.1 12.9) | 0.34 | 12.4 (7.5 19.1) | 4.2 (1.7 8.5) | 0.02 | 4.1 (1.68.3) | 13.4 (8.619.8) | 0.01 | 7.1 (3.712.2) | 6.0 (3.110.4) | 0.71 |
| Preventable | 62 | 36 | 8.0 (6.2 10.2) | 4.4 (3.16.0) | <0.01 | 8.2 (4.713.1) | 7.1 (3.811.8) | 0.70 | 10.3 (6.016.5) | 5.8 (2.8 10.4) | 0.17 | 12.4 (7.519.1) | 0 (0 0.03) | <0.01 | 3.4 (1.27.3) | 7.9 (4.413.1) | 0.11 | 5.8 (2.810.5) | 1.1 (0.183.4) | 0.03 |
| Nonpreventable | 7 | 32 | 0.90 (0.39 1.7) | 3.9 (2.75.4) | <0.01 | 1.6 (0.414.3) | 2.9 (1.16.3) | 0.42 | 0.69 (0.043.04) | 1.9 (0.48 5.0) | 0.37 | 0 (00.03) | 4.2 (1.7 8.5) | <0.01 | 0.68 (0.043.0) | 5.5 (2.6 9.9) | 0.05 | 1.3 (0.21, 4.0) | 4.9 (2.48.9) | 0.09 |
| Potential ADEs | 430 (86.2) | 1115 (93.5) | 55.5 (50.4 60.9) | 136.8 (128.9145.0) | <0.01 | 65.0 (54.077.4) | 141.1 (124.1159.8) | <0.01 | 57.2 (45.870.5) | 98.7 (83.9 115.1) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7 121.1) | <0.01 | 59.2 (47.645.8) | 132.9 (116.1151.4) | <0.01 | 49.0 (38.860.9) | 195.1 (175.5216.1) | <0.01 |
| Intercepted | 16 | 24 | 2.1 (1.2 3.2) | 2.9 (1.94.3) | <0.24 | 3.3 (1.36.6) | 4.7 (2.28.8) | 0.50 | 2.1 (0.515.4) | 1.3 (0.21 4.0) | 0.60 | 1.4 (0.234.3) | 2.8 (0.87 6.5) | 0.41 | 2.0 (0.515.3) | 4.9 (2.2 9.1) | 0.20 | 1.3 (0.214.0) | 1.1 (0.183.4) | 0.87 |
| Nonintercepted | 414 | 1091 | 53.4 (48.4 58.7) | 133.9 (126.1142.0) | <0.01 | 61.7 (51.173.8) | 136.5 (119.754.8) | <0.01 | 55.2 43.968.2) | 97.4 (82.8 113.8) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1 118.1) | <0.01 | 57.1 (45.8 70.2) | 128.0 (111.5146.2) | <0.01 | 47.7 (37.759.5) | 194.0 (174.4214.9) | <0.01 |
Stratified Analysis
To account for differences in technology, we performed a stratified analysis (Table 3). As was consistent with the overall study estimates, the rates of nonpreventable ADEs and potential ADEs increased with all 3 interventions. In contrast, we found that the changes in preventable ADE rates were related to the level of clinical decision support, where the greatest benefit was associated with the most sophisticated decision support system (P=0.03 and 0.02 for comparisons between advanced vs rudimentary decision support and basic order entry only, respectively). There was no difference in preventable ADE rates at sites without decision support (4.6/100 vs 4.3/100 admissions; P=0.87); with rudimentary clinical decision support, there was a trend toward a decrease in the preventable ADE rate, which did not meet statistical significance (9.1/100 vs 6.4/100 admissions; P=0.22), and, the greatest reduction was seen with advanced clinical decision support (12.4/100 vs 0/100 admissions; P<0.01).
| Rate per 100 Admissions by Level of Clinical Decision Support (95% CI) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Basic CPOE Only (Sites 4 and 5) | CPOE and Lab Display (Sites 1 and 2) | CPOE, Lab Display, and DrugDosing Check (Site 3) | |||||||
| Incident | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| |||||||||
| ADEs | 5.6 (3.48.7) | 9.5 (6.613.2) | 0.08 | 10.3(7.314.3) | 8.9 (6.012.5) | 0.55 | 12.4 (7.5319.1) | 4.2 (1.78.5) | 0.02 |
| Preventable | 4.6 (2.67.5) | 4.3 (2.56.9) | 0.87 | 9.1 (6.312.8) | 6.4 (4.19.6) | 0.22 | 12.4 (7.5319.1) | 0.00 (00.03) | <0.01 |
| Nonpreventable | 0.99 (0.24 2.6) | 5.2 (3.28.0) | <0.01 | 1.2 (0.382.8) | 2.5 (1.14.6) | 0.24 | 0.00 (00.03) | 4.2 (1.78.5) | <0.01 |
| Potential ADEs | 54.0 (46.162.7) | 165.6 (152.4179.5) | <0.01 | 61.6 (53.570.5) | 120.9 (109.3133.2) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7121.1) | <0.01 |
| Intercepted | 1.7 (0.593.6) | 2.9 (1.45.1) | 0.30 | 2.7 (1.34.9) | 3.1 (1.55.4) | 0.76 | 1.4 (0.234.3) | 2.8 (0.876.5) | 0.42 |
| Nonintercepted | 52.3 (44.660.9) | 162.7 (149.6176.5) | <0.01 | 58.8 (50.967.5) | 117.8 (106.4130.0) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1118.1) | <0.01 |
Severity of Events
We further analyzed our data based on event severity (Table 4). Among preventable ADEs, only 1 fatal event was observed, which occurred after CPOE implementation. Here, a previously opioid‐nave patient received intravenous morphine for malignant pain. Within the first 24 hours, the patient received 70.2 mg of intravenous morphine, resulting in a decreased level of consciousness. The patient expired the following day. Furthermore, following implementation, among preventable ADEs, a reduction in significant events was seen (P=0.02) along with a nonsignificant reduction in the rate of serious events (P=0.06). However, the rate of preventable life‐threatening events was not different (P=0.96). The nonpreventable ADE rate rose during the postimplementation period for both serious (P=0.03) and significant events (P<0.01). The risk of fatal and life‐threatening nonpreventable ADEs did not change. The potential ADE rate increased following implementation for all severities (P<0. 01).
| Preimplementation | Postimplementation | ||||
|---|---|---|---|---|---|
| Incident | No. (%) | Average Rate/100 Admissions (95% CI)* | No. (%) | Average Rate/100 Admissions (95% CI)* | P |
| |||||
| All ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (1.4) | 0.12 (0.0070.54) | 0.52 |
| Lifethreatening | 3 (4.3) | 0.39 (0.101.0) | 3 (4.4) | 0.37 (0.09 0.95) | 0.95 |
| Serious | 34 (49.3) | 4.4 (3.16.0) | 32 (47.1) | 3.9 (2.75.4) | 0.65 |
| Significant | 32 (46.4) | 4.1 (2.95.7) | 32 (47.1) | 3.9 (2.75.4) | 0.84 |
| Total | 69 (100) | 8.9 (7.011.2) | 68 (100) | 8.3 (6.510.5) | 0.74 |
| Preventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (2.7) | 0.00 (00.0045) | 0.52 |
| Lifethreatening | 2 (3.2) | 0.26 (0.040.80) | 2 (5.6) | 0.25 (0.040.76) | 0.96 |
| Serious | 31 (50.0) | 4.0 (2.85.6) | 19 (52.8) | 2.3 (1.43.5) | 0.06 |
| Significant | 29 (46.8) | 3.7 (2.55.3) | 14 (38.9) | 1.7 (0.972.8) | 0.02 |
| Total | 62 (100) | 8.0 (6.210.2) | 36 (100) | 4.4 (3.16.0) | <0.01 |
| Nonpreventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 0 (0) | 0.00 (00.0045) | NS |
| Lifethreatening | 1 (14.2) | 0.13 (0.0070.57) | 1 (3.1) | 0.12 (0.0070.54) | 0.97 |
| Serious | 3 (42.9) | 0.39 (0.101.0) | 13 (40.6) | 1.6 (0.882.6) | 0.03 |
| Significant | 3 (42.9) | 0.39 (0.101.0) | 18 (56.3) | 2.2 (1.33.4) | <0.01 |
| Total | 7 (100) | 0.90 (0.391.7) | 32 (100) | 3.9 (2.75.4) | <0.01 |
| All potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 33 (3.0) | 4.0 (2.85.6) | <0.01 |
| Serious | 233 (54.2) | 30.1 (26.434.1) | 429 (38.4) | 52.6 (47.857.8) | <0.01 |
| Significant | 192 (44.6) | 24.8 (21.428.4) | 653 (58.6) | 80.1 (74.186.4) | <0.01 |
| Total | 430 (100) | 55.5 (50.460.9) | 1115 (100) | 136.8 (128.9145.0) | <0.01 |
| Intercepted potential ADEs | |||||
| Lifethreatening | 0 (0) | 0.00 (00.0047) | 1 (4.2) | 0.12 (0.0070.54) | 0.52 |
| Serious | 5 (31.2) | 0.65 (0.231.4) | 13 (54.2) | 1.6 (0.882.6) | 0.09 |
| Significant | 11 (68.8) | 1.4 (0.74 2.4) | 10 (41.6) | 1.2 (0.622.2) | 0.74 |
| Total | 16 (100) | 2.1 (1.23.2) | 24 (100) | 2.9 (1.94.3) | 0.24 |
| Nonintercepted potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 32 (2.9) | 3.9 (2.75.4) | <0.01 |
| Serious | 228 (55.1) | 29.4 (25.833.4) | 416 (38.1) | 51.0 (46.356.1) | <0.01 |
| Significant | 181 (43.7) | 23.4 (20.126.9) | 643 (58.9) | 78.9 (73.085.2) | <0.01 |
| Total | 414 (100) | 53.4 (48.458.7) | 1091 (100) | 133.9(126.1142.0) | <0.01 |
Case Reviews
In total, there were 36 preventable ADEs identified during the postimplementation phase (Table 5). Of these, inappropriate renal dosing accounted for 26 preventable ADEs, which involved antibiotics (eg, gentamicin‐induced renal failure), opioids (eg, over sedation from morphine), ‐blockers (eg, hypotension from atenolol), angiotensin‐converting enzyme inhibitors (eg, renal failure with hyperkalemia secondary to lisinopril), and digoxin (eg, bradyarrhythmia and toxicity). The use of contraindicated medications resulted in 7 preventable ADEs (eg, prescribing glyburide in the setting of severe renal impairment).[26] The remaining 3 preventable ADEs stemmed from unmonitored use of vancomycin.
| ADEs, Preventable, No. (Rate per 100 Admissions)* | ADEs, Nonpreventable, No. (Rate per 100 Admissions)* | ||||||
|---|---|---|---|---|---|---|---|
| Drug Class | Preimplementation | Postimplementation | P (for Entire Drug Class) | Preimplementation | Postimplementation | P (for Drug Class) | Drugs Involved |
| |||||||
| Cardiovascular | 20 (2.6) | 18 (2.2) | 0.63 | 4 (0.52) | 16 (2.0) | 0.02 | Atenolol, bumetanide, captopril, digoxin, furosemide, hydralazine, hydrochlorothiazide, lisinopril, sotalol, spironolactone |
| Diuretics | 1 (0.13) | 2 (0.25) | 1 (0.13) | 9 (1.1) | |||
| ‐blockers | 0 (0.00) | 2 (0.25) | 1 (0.13) | ||||
| ACE inhibitors and ARBs | 16 (2.1) | 10 (1.2) | 2 (0.26) | 7 (0.86) | |||
| Antiarrhythmic | 3 (0.39) | 3 (0.37) | |||||
| Vasodilator | 0 (0.00) | 1 (0.12) | |||||
| Analgesics | 28 (3.6) | 4 (0.49) | 0.0002 | 1 (0.13) | 5 (0.61) | 0.15 | Acetaminophen and combination pills containing acetaminophen: Percocet (oxycodone and acetaminophen), Tylenol #3 (codeine and acetaminophen), Vicodin (hydrocodone and acetaminophen), fentanyl, hydrocodone, meperidine, morphine, oxycodone |
| Narcotic | 13 (1.7) | 4 (0.49) | 0 (0.00) | 5 (0.61) | |||
| Non‐narcotic | 15 (1.9) | 0 (0.00) | 1 (0.13) | 0 | |||
| Antibiotics | 8 (1.0) | 13 (1.6) | 0.33 | 1 (0.13) | 9 (1.1) | 0.04 | Amikacin, ampicillin and sulbactam, ciprofloxacin, cefazolin, cefuroxime, gatifloxacin, gentamicin, levofloxacin, metronidazole, piperacillin and tazobactam, tobramycin, vancomycin |
| Neurotropic drugs | 2 (0.26) | 0 (0.00) | 0.28 | 0 | 0 | Lithium, midazolam | |
| Sedatives | 1 (0.13) | 0 (0.00) | |||||
| Antipsychotics | 1 (0.13) | 0 (0.00) | |||||
| Diabetes | 0 | 1 (0.12) | 0.52 | 0 | 1 (0.12) | 0.52 | Glipizide, glyburide |
| Oral antidiabetics | 0 | 1 (0.12) | 1 (0.12) | ||||
| Other drugs | 4 (0.52) | 0 (0.00) | 0.13 | 1 (0.13) | 1 (0.12) | 0.97 | Allopurinol, famotidine |
| Gastrointestinal drugs | 1 (0.13) | 0 (0.00) | |||||
| Other | 3 (0.39) | 0 (0.00) | 0 | 1 (0.12) | |||
DISCUSSION
We evaluated the use of vendor CPOE for hospitalized patients with renal disease and found that it was associated with a 45% reduction in preventable ADEs related to nephrotoxicity and accumulation of renally excreted medications. The impact of CPOE appeared to be related to the level of associated clinical decision support, where only the most advanced system was associated with benefit. We observed a significant increase in potential ADEs with all levels of intervention. Overall, these findings suggest that vendor‐developed applications with appropriate decision support can reduce the occurrence of renally related preventable ADEs, but careful implementation is needed if the potential ADE rate is to fall.
Many of the benefits of CPOE come from clinical decision support.[11] When applied to patients with renal impairment, CPOE with clinical decision support has been associated with decreased lengths of stay,[16, 27] reduced use of contraindicated medications,[28, 29, 30] improved dosing and drug monitoring,[16, 31, 32] and improved general prescribing practices.[29, 33] Even so, the observed benefit of CPOE on ADE rates has been variable, with some studies reporting reductions,[27, 34] whereas others are unable to detect differences.[16, 31] These studies, however, limited their case definition of ADEs to strictly declining renal function,[16, 31, 34] or adverse events directly resulting from anti‐infective drugs.[27] In contrast, our study accounted for nephrotoxicity and systemic toxicity from drug accumulation. Using this broader definition, we were able to detect large reductions in the rates of preventable ADEs following CPOE adoption.
Successful decision support is simple, intuitive, and provides speedy information that integrates seamlessly into the clinical workflow.[35, 36] However, information delivery, although necessary, is insufficient for improving safety. For instance, passive alerts are often ignored, deferred, or overridden.[30, 37, 38] Demonstrating this, Quartarolo et al. found that informing physicians of the presence of renal impairment using automated reporting of glomerular filtration rates did not change prescribing behavior.[39] In contrast, providing active feedback (with dosing recommendations) was observed to be more useful in effecting change.[40] Chertow et al. further showed that providing an adjusted dose list with a default dose and frequency at the time of order entry for patients with renal insufficiency improved appropriate ordering and was associated with a decreased length of stay.[16] Altogether, these studies help to explain why only CPOE with clinical decision support equipped to provide renally adjusted dosing and monitoring was associated with a reduction in preventable ADEs in our study.
However, in contrast to reports of internally developed systems,[20, 25] potential ADE rates actually rose during the follow‐up portion of our study. These appeared to be chiefly related to customized order sets with the potential of overdosing drugs through therapeutic duplication, a problem that is commonly known to be associated with CPOE (ie, new orders that overlap with other new or active medication orders, which may be the same drug itself or from within the same drug class, with the risk of overdose).[41, 42] Of note, our findings give rise to several key implications. First, hospitals implementing vendor‐developed CPOE systems may be at greater risk of incurring potential ADEs compared to those using home‐grown systems, which have comparatively gone through more cycles of internal refinement. As such, it is necessary to monitor for issues postimplementation and respond with appropriate changes to achieve successful system performance.[35, 36] Second, although the rate of potential ADEs (near misses) increased, preventable ADEs decreased because some of these errors were intercepted, whereas others were averted simply because of chance. Of note, not all potential ADEs have the same potential for injury; more serious cases are more likely to result in actual ADEs (eg, failure to renally dose acetaminophen likely poses less potential for harm than prescribing a full dose of enoxaparin in the setting of severe renal failure). Third, we found that most potential ADEs could have been averted with a combination of basic (dosing guidance and drug‐drug interactions checks) and advanced decision support (medication‐associated laboratory testing and drug‐disease interactions).[43] Therefore, further refinements to existing software are needed to maximize safety outcomes.
Our study has some limitations. This study was not a randomized controlled trial, and thus is subject to potential confounding. Although 6 hospitals were involved at the study inception,[14] one of these hospitals eventually opted not to implement CPOE, and further declined to participate as a control site. Therefore, we cannot exclude confounding from secular trends because we had no contemporaneous control group. However, the introduction of CPOE was the main medication safety‐oriented intervention during the study interval, thus arguing against major confounding by cointervention. Second, even though it is possible that classification bias may have been introduced between the preimplementation and postimplementation portions of our study, especially given the passage of time, it is unlikely. Study personnel underwent training using a curriculum designed to maintain continuity across projects, minimize individual variability, and optimize reproducibility in data collection and classification, as in a number of previous studies.[14, 17, 19, 20, 21] Third, our study is limited by a heterogeneous intervention, as varying levels of decision support were introduced. However, this reflects usual practice and may be construed as a strength as we were able to describe the impact of different types of decision support. Fourth, we enrolled patients with a large spectrum of renal impairment, and our findings are not specific to any particular subgroup. However, our wide recruitment strategy also enhances the generalizability. Finally, our study was restricted to patients who were exposed to potentially nephrotoxic and/or renally cleared drugs. As such, we could not determine whether advanced decision support helped to eliminate the use of some potentially dangerous medications altogether, as these cases would have been excluded from our study. It is possible, therefore, that our study findings underestimate the true benefit of clinical decision support.
In conclusion, vendor CPOE implementation in 5 community hospitals was associated with a 45% reduction in preventable ADE rates among patients with renal impairment. Measurable benefit was associated with advanced decision support capable of lab display, dosing guidance, and medication‐associated laboratory testing. Although the potential benefits of CPOE systems are far reaching, achieving the desired safety benefits will require appropriate decision support, tracking of problems that arise, and systematic approaches to eliminating them.
Acknowledgments
The authors thank Kathy Zigmont, RN, and Cathy Foskett, RN (Brigham and Women's Hospital, Division of General Internal Medicine and Primary Care) for the chart review and data collection at the participating study sites.
Disclosures: The Rx Foundation and Commonwealth Fund supported the study. They commented on its design, but were not involved in data collection, data management, analysis, interpretation, or writing of the manuscript. Dr. Leung is supported by a Clinical Fellowship Award from Alberta Innovates Health Solutions and by a Fellowship Award from the Canadian Institutes for Health Research. Dr. Schiff received financial support from the FDA CPOE Task Order and the Commonwealth Fund. Ms. Keohane served as a consultant to the American College of Obstetrician and Gynecologists and as a reviewer for the VRQC Program. She received honoraria for a presentation on Patient Safety in 2010, sponsored by Abbott Nutrition International, and a lecture on Nurse Interruptions in Medication Administration by Educational Review Systems. Dr. Coffey received an honorarium from Meditech for speaking on social networking at Physician/CIO Forum in 2009. Dr. Kaufman participates in an advisory group with Siemens Medical Solutions. Dr. Zimlichman received support from the Rx Foundation and the Commonwealth Fund. Dr. Bates holds a minority equity position in the privately held company Medicalis, which develops Web‐based decision support for radiology test ordering, and has served as a consultant to Medicalis. He serves as an advisor to Calgary Scientific, which makes technologies that enable mobility within electronic health records. He is on the clinical advisory board for Patient Safety Systems, which provides a set of approaches to help hospitals improve safety. He has received funding support from the Massachusetts Technology Consortium. Ms. Amato, Dr. Simon, Dr. Cadet, Ms. Seger, and Ms. Yoon have no disclosures relevant to this study.
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- , , , et al. Alert system for inappropriate prescriptions relating to patients' clinical condition. Methods Inf Med. 2009;48(6):566–573.
- , , , , , . Computerized clinical decision support during medication ordering for long‐term care residents with renal insufficiency. J Am Med Inform Assoc. 2009;16(4):480–485.
- , , . A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. J Am Med Inform Assoc. 2005;12(3):269–274.
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Hospitalized patients with renal impairment are vulnerable to adverse drug events (ADEs).[1, 2] Appropriate prescribing for patients with renal insufficiency is challenging because of the complexities of drug therapy within the wide spectrum of kidney disease.[3, 4, 5, 6] Accordingly, computerized physician order entry (CPOE) systems with clinical decision support may help prevent many ADEs by providing timely laboratory information, recommending renally adjusted doses, and by offering assistance with prescribing.[7, 8, 9]
Despite the proposed benefits of CPOE, outcomes vary greatly because of differences in technology.[10, 11, 12, 13] In particular, the type of decision support available to assist medication ordering in the setting of renal disease varies widely among current vendor systems. Given the uncertain benefits of CPOE, especially with the wide range of associated clinical decision support, we conducted this study to determine the impact of these systems on the rates of ADEs among hospitalized patients with kidney disease.
METHODS
This study was approved by the institutional review boards at each study site.
Design and Setting
We conducted a before‐and‐after study to evaluate the impact of newly implemented vendor CPOE systems in 5 community hospitals in Massachusetts. Although we reported on 6 hospitals in our baseline study,[14] 1 of these hospitals later chose not to implement CPOE, and therefore was not included in follow‐up. At the time of this study, 1 of the hospitals (site 3) had not yet achieved hospital‐wide implementation. Although CPOE had been adopted by most medical services at site 3, it had not yet been implemented in the emergency, obstetrical, or surgical departments. Thus, we limited our study to the medical services at this site. For the remaining sites, all admitting services were included with the exception of the psychiatric and neonatal services, which were excluded from both phases because they would have required different detection tools.
Participants
Patients aged 18 years with renal failure, exposed to potentially nephrotoxic and/or renally cleared medications, and admitted to any of the participating hospitals during the study period were eligible for inclusion. Of the patients meeting eligibility criteria, we randomly selected approximately 150 records per hospital in the preimplementation and postimplementation phases for a total sample of 1590 charts. The first phase of this study occurred from January 2005 to August 2006; the second phase began 6 months postimplementation and lasted from October 2008 to September 2010.
Principal Exposure
Each hospital independently selected a vendor CPOE system with variable clinical decision support capabilities: (1) sites 4 and 5 had basic CPOE only with no clinical decision support for renal disease; (2) sites 1 and 2 implemented rudimentary clinical decision support with laboratory display (eg, serum creatinine) whenever common renally related drugs were ordered; and (3) site 3 had the most advanced support in place where, in addition to basic order entry and lab checks, physicians were provided with suggested doses for renally cleared and/or nephrotoxic medications, as well as appropriate drug monitoring for medications with narrow therapeutic indices (eg, suggested dosages and frequencies for vancomycin and automated corollary laboratory monitoring).
Definitions
We screened for the presence of renal failure by a serum creatinine 1.5 mg/dL at the time of admission. However, the duration of renal impairment was not known. We defined 3 levels of renal insufficiency based on the calculated creatinine clearance (CrCl)15: mild (CrCl 5080 mL/min), moderate (1649 mL/min), and severe (15 mL/min). Subjects with a CrCl >80 mL/min were considered to have normal renal function and were excluded. Potentially nephrotoxic and/or renally cleared medications were then identified using an established knowledge base (see Supporting Information, Table 1, in the online version of this article).[16]
| Hospital Site | |||||||
|---|---|---|---|---|---|---|---|
| Baseline Characteristics | All Sites | 1 | 2 | 3 | 4 | 5 | P (Among All Sites)* |
| |||||||
| No. of patients | 815 | 170 | 156 | 143 | 164 | 182 | |
| Age, y, mean (range) | 72.2 (18.0102.0) | 79.2 (33102) | 77.3 (23101) | 65.6 (1898) | 70.7 (1896) | 69.2 (2096) | <0.01 |
| 1844 years, no. (%) | 68 (9.1) | 1 (0.67) | 8 (6.5) | 20 (14.9) | 15 (9.4) | 24 (13.4) | <0.01 |
| 4554 years, no. (%) | 67 (9.0) | 6 (4.0) | 5 (4.1) | 17 (12.7) | 16 (10.0) | 23 (12.9) | |
| 5564 years, no. (%) | 79 (10.6) | 15 (10.0) | 12 (9.8) | 23 (17.2) | 13 (8.1) | 16 (8.9) | |
| 6574 years, no. (%) | 104 (13.9) | 20 (13.3) | 12 (9.8) | 16 (11.9) | 30 (18.8) | 26 (14.5) | |
| 7584 years, no. (%) | 197 (26.4) | 44 (29.3) | 36 (29.3) | 24 (17.9) | 49 (30.6) | 44 (24.6) | |
| 85 years, no. (%) | 231 (31.0) | 64 (42.7) | 50 (40.7) | 34 (25.4) | 37 (23.1) | 46 (25.7) | |
| Sex | |||||||
| Male, no. (%) | 427 (57.0) | 66 (44.0) | 60 (48.8) | 82 (60.7) | 105 (65.2) | 114 (63.7) | <0.01 |
| Female, no. (%) | 321 (43.0) | 84 (56.0) | 63 (51.2) | 53 (39.3) | 56 (34.8) | 65 (36.3) | |
| Race | |||||||
| Caucasian, no. (%) | 654 (87.4) | 129 (86.0) | 118 (95.9) | 126 (93.3) | 129 (80.1) | 152 (84.9) | <0.01 |
| Hispanic, no. (%) | 25 (3.3) | 2 (1.3) | 0 (0) | 1 (0.74) | 13 (8.1) | 9 (5.0) | |
| African American, no. (%) | 45 (6.0) | 12 (8.0) | 4 (3.3) | 5 (3.7) | 13 (8.1) | 11 (6.2) | |
| Native American, no. (%) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
| Asian, no. (%) | 13 (1.7) | 1 (0.81) | 1 (0.81) | 2 (1.5) | 5 (3.1) | 4 (2.2) | |
| Other, no. (%) | 7 (0.94) | 2 (1.3) | 0 (0) | 1 (0.74) | 1 (14.3) | 3 (1.7) | |
| Not recorded, no. (%) | 4 (0.53) | 4 (2.7) | 0 (0) | 0 (0.0) | 0 (0) | 0 (0) | |
| Initial severity of renal dysfunction | |||||||
| Mild, CrCl 5080 mL/min, no. (%) | 60 (7.4) | 4 (2.4) | 5 (3.2) | 5 (3.5) | 14 (8.5) | 32 (17.6) | <0. 01 |
| Moderate, CrCl 1649 mL/min, no. (%) | 388 (47.6) | 84 (49.4) | 71 (45.5) | 80 (55.9) | 76 (46.3) | 77 (42.3) | |
| Severe, CrCl <15 mL/min, no. (%) | 367 (45.0) | 82 (48.2) | 80 (51.3) | 58 (40.6) | 74 (45.1) | 73 (40.1) | |
| LOS, d, median (IQR) | 4.0 (26) | 4.0 (37) | 3.0 (25.5) | 4.0 (27) | 4.0 (27) | 4.0 (26) | 0.02 |
| DRG‐weighted LOS, d, median (IQR) | 5.0 (3.76.7) | 5.5 (46.7) | 5.0 (3.46.2) | 5.6 (4.36.7) | 5.0 (3.36.7) | 5.0 (4.26.7) | 0.27 |
In both phases of our study, only medications that were potentially nephrotoxic and/or renally cleared were included as potential cases; all other drugs were excluded. We defined an ADE as any drug‐related injury. These were considered preventable if they were due to an error at the time of order entry (eg, a doubling of creatinine secondary to an overdose of gentamicin or failure to order corollary drug levels for monitoring). A nonpreventable ADE was any drug‐related injury in which there was no error at the time of order entry (eg, a doubling of creatinine despite appropriate dosing of lisinopril).[17] A medication error was an error anywhere in the process of prescribing, transcribing, dispensing, administering, or monitoring a drug, but with no potential for harm or injury (eg, an order for an oral medication with no route specified when it was clear that the oral route was intended).[18] A potential ADE was an error with the potential to cause harm, but not resulting in injury, either because it was intercepted (eg, an order for ketorolac for a patient with renal failure, but caught by a pharmacist) or because of chance (eg, administering enoxaparin to a patient with severe renal dysfunction but without hemorrhage).
All study investigators underwent standardized training using a curriculum developed by the Center for Patient Safety Research and Practice (
Main Outcome Measures
The primary outcome was the rate of preventable ADEs. Secondary outcomes were the rates of potential ADEs and overall ADEs. All outcomes were related to nephrotoxicity or accumulation of a renally excreted medication.
Data collection and classification strategies were identical in both phases of our study.[14] We reviewed physician orders, medication lists, laboratory reports, admission histories, progress and consultation notes, discharge summaries, and nursing flow sheets, screening for the presence of medication incidents using an adaptation of the Institute for Healthcare Improvement's trigger tool, selected for its high sensitivity, reproducibility, and ease of use.[22, 23] In our adaptation of the tool, we excluded lidocaine, tobramycin, amikacin, and theophylline levels because of their infrequency. For each trigger found, a detailed description of the incident was extracted for detailed review. An example of a trigger is the use of sodium polystyrene, which may possibly indicate an overdose of potassium or a medication side effect.
Subsequently, each case was then independently reviewed by two investigators (A.A.L., M.A., B.C., S.R.S., M.C., N.K., E.Z., and G.S.)each assigned to at least 1 siteand blinded to prescribing physician and hospital to determine whether nephrotoxicity or injury from drug accumulation was present (see Supporting Information, Figure 1, in the online version of this article).[17] First, incidents were classified as ADEs, potential ADEs, or medication errors with no potential for injury. Second, ADEs and potential ADEs were rated according to severity. When nephrotoxic drugs were ordered, event severity was classified according to the elevation in serum creatinine24: increases of 10% were considered potential ADEs (near misses); increases of 10% to 100% were significant ADEs; and increases of 100% were serious ADEs. Changes in creatinine that were not associated with inappropriate medication orders were excluded. For renally excreted drugs with no potential for nephrotoxicity (eg, enoxaparin), we used clinical judgment to classify events as significant (eg, rash), severe (eg, 2‐unit gastrointestinal bleed), life threatening (eg, transfer to an intensive care unit), or fatal categories, as based on earlier work.[25] Disagreements were resolved by consensus. We had a score of 0.70 (95% confidence interval [CI]: 0.61‐0.80) for incident type, indicating excellent overall agreement.
Statistical Analysis
Baseline characteristics between hospitals were compared using the Fisher exact test for categorical variables and 1‐way analysis of variance for continuous variables. The occurrence of each outcome was determined according to site. To facilitate comparisons between sites, rates were expressed as number of events per 100 admissions with 95% CIs. To account for hospital effects in the analysis when comparing pre‐ and postimplementation rates of ADEs and potential ADEs, we developed a fixed‐effects Poisson regression model. To explore the independent effects of each system, a stratified analysis was performed to compare average rates of each outcome observed.
RESULTS
The outcomes of 775 patients in the baseline study were compared with the 815 patients enrolled during the postimplementation phase.[14] Among those in the postimplementation phase (Table 1), the mean age was 72.2 years, and they were predominantly male (57.0%). The demographics of the patients admitted to each of the 5 sites varied widely (P<0.01). Most patients had moderate to severe renal dysfunction.
Overall, the rates of ADEs were similar between the pre‐ and postimplementation phases (8.9/100 vs 8.3/100 admissions, respectively; P=0.74) (Table 2). However, there was a significant decrease in the rate of preventable ADEs, the primary outcome of interest, following CPOE implementation (8.0/100 vs 4.4/100 admissions; P<0.01). A reduction in preventable ADEs was observed in every hospital except site 4, where only basic order entry was introduced. However, there was a significant increase in the rates of nonpreventable ADEs (0.90/100 vs 3.9/100 admissions; P<0.01) and potential ADEs (55.5/100 vs 136.8/100 admissions; P<0.01).
| Rate/100 Admissions (95% CI) | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total No. (%) | All Sites | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | ||||||||||||||
| Event | Pre | Post | Pre | Post | P* | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| ||||||||||||||||||||
| ADEs | 69 (13.8) | 68 (5.7) | 8.9 (7.0 1.2) | 8.3 (6.50.5) | 0.74 | 9.8 (6.015.1) | 10.0 (6.015.5) | 0.96 | 11.0 (6.517.4) | 7.7 (4.1 12.9) | 0.34 | 12.4 (7.5 19.1) | 4.2 (1.7 8.5) | 0.02 | 4.1 (1.68.3) | 13.4 (8.619.8) | 0.01 | 7.1 (3.712.2) | 6.0 (3.110.4) | 0.71 |
| Preventable | 62 | 36 | 8.0 (6.2 10.2) | 4.4 (3.16.0) | <0.01 | 8.2 (4.713.1) | 7.1 (3.811.8) | 0.70 | 10.3 (6.016.5) | 5.8 (2.8 10.4) | 0.17 | 12.4 (7.519.1) | 0 (0 0.03) | <0.01 | 3.4 (1.27.3) | 7.9 (4.413.1) | 0.11 | 5.8 (2.810.5) | 1.1 (0.183.4) | 0.03 |
| Nonpreventable | 7 | 32 | 0.90 (0.39 1.7) | 3.9 (2.75.4) | <0.01 | 1.6 (0.414.3) | 2.9 (1.16.3) | 0.42 | 0.69 (0.043.04) | 1.9 (0.48 5.0) | 0.37 | 0 (00.03) | 4.2 (1.7 8.5) | <0.01 | 0.68 (0.043.0) | 5.5 (2.6 9.9) | 0.05 | 1.3 (0.21, 4.0) | 4.9 (2.48.9) | 0.09 |
| Potential ADEs | 430 (86.2) | 1115 (93.5) | 55.5 (50.4 60.9) | 136.8 (128.9145.0) | <0.01 | 65.0 (54.077.4) | 141.1 (124.1159.8) | <0.01 | 57.2 (45.870.5) | 98.7 (83.9 115.1) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7 121.1) | <0.01 | 59.2 (47.645.8) | 132.9 (116.1151.4) | <0.01 | 49.0 (38.860.9) | 195.1 (175.5216.1) | <0.01 |
| Intercepted | 16 | 24 | 2.1 (1.2 3.2) | 2.9 (1.94.3) | <0.24 | 3.3 (1.36.6) | 4.7 (2.28.8) | 0.50 | 2.1 (0.515.4) | 1.3 (0.21 4.0) | 0.60 | 1.4 (0.234.3) | 2.8 (0.87 6.5) | 0.41 | 2.0 (0.515.3) | 4.9 (2.2 9.1) | 0.20 | 1.3 (0.214.0) | 1.1 (0.183.4) | 0.87 |
| Nonintercepted | 414 | 1091 | 53.4 (48.4 58.7) | 133.9 (126.1142.0) | <0.01 | 61.7 (51.173.8) | 136.5 (119.754.8) | <0.01 | 55.2 43.968.2) | 97.4 (82.8 113.8) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1 118.1) | <0.01 | 57.1 (45.8 70.2) | 128.0 (111.5146.2) | <0.01 | 47.7 (37.759.5) | 194.0 (174.4214.9) | <0.01 |
Stratified Analysis
To account for differences in technology, we performed a stratified analysis (Table 3). As was consistent with the overall study estimates, the rates of nonpreventable ADEs and potential ADEs increased with all 3 interventions. In contrast, we found that the changes in preventable ADE rates were related to the level of clinical decision support, where the greatest benefit was associated with the most sophisticated decision support system (P=0.03 and 0.02 for comparisons between advanced vs rudimentary decision support and basic order entry only, respectively). There was no difference in preventable ADE rates at sites without decision support (4.6/100 vs 4.3/100 admissions; P=0.87); with rudimentary clinical decision support, there was a trend toward a decrease in the preventable ADE rate, which did not meet statistical significance (9.1/100 vs 6.4/100 admissions; P=0.22), and, the greatest reduction was seen with advanced clinical decision support (12.4/100 vs 0/100 admissions; P<0.01).
| Rate per 100 Admissions by Level of Clinical Decision Support (95% CI) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Basic CPOE Only (Sites 4 and 5) | CPOE and Lab Display (Sites 1 and 2) | CPOE, Lab Display, and DrugDosing Check (Site 3) | |||||||
| Incident | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| |||||||||
| ADEs | 5.6 (3.48.7) | 9.5 (6.613.2) | 0.08 | 10.3(7.314.3) | 8.9 (6.012.5) | 0.55 | 12.4 (7.5319.1) | 4.2 (1.78.5) | 0.02 |
| Preventable | 4.6 (2.67.5) | 4.3 (2.56.9) | 0.87 | 9.1 (6.312.8) | 6.4 (4.19.6) | 0.22 | 12.4 (7.5319.1) | 0.00 (00.03) | <0.01 |
| Nonpreventable | 0.99 (0.24 2.6) | 5.2 (3.28.0) | <0.01 | 1.2 (0.382.8) | 2.5 (1.14.6) | 0.24 | 0.00 (00.03) | 4.2 (1.78.5) | <0.01 |
| Potential ADEs | 54.0 (46.162.7) | 165.6 (152.4179.5) | <0.01 | 61.6 (53.570.5) | 120.9 (109.3133.2) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7121.1) | <0.01 |
| Intercepted | 1.7 (0.593.6) | 2.9 (1.45.1) | 0.30 | 2.7 (1.34.9) | 3.1 (1.55.4) | 0.76 | 1.4 (0.234.3) | 2.8 (0.876.5) | 0.42 |
| Nonintercepted | 52.3 (44.660.9) | 162.7 (149.6176.5) | <0.01 | 58.8 (50.967.5) | 117.8 (106.4130.0) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1118.1) | <0.01 |
Severity of Events
We further analyzed our data based on event severity (Table 4). Among preventable ADEs, only 1 fatal event was observed, which occurred after CPOE implementation. Here, a previously opioid‐nave patient received intravenous morphine for malignant pain. Within the first 24 hours, the patient received 70.2 mg of intravenous morphine, resulting in a decreased level of consciousness. The patient expired the following day. Furthermore, following implementation, among preventable ADEs, a reduction in significant events was seen (P=0.02) along with a nonsignificant reduction in the rate of serious events (P=0.06). However, the rate of preventable life‐threatening events was not different (P=0.96). The nonpreventable ADE rate rose during the postimplementation period for both serious (P=0.03) and significant events (P<0.01). The risk of fatal and life‐threatening nonpreventable ADEs did not change. The potential ADE rate increased following implementation for all severities (P<0. 01).
| Preimplementation | Postimplementation | ||||
|---|---|---|---|---|---|
| Incident | No. (%) | Average Rate/100 Admissions (95% CI)* | No. (%) | Average Rate/100 Admissions (95% CI)* | P |
| |||||
| All ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (1.4) | 0.12 (0.0070.54) | 0.52 |
| Lifethreatening | 3 (4.3) | 0.39 (0.101.0) | 3 (4.4) | 0.37 (0.09 0.95) | 0.95 |
| Serious | 34 (49.3) | 4.4 (3.16.0) | 32 (47.1) | 3.9 (2.75.4) | 0.65 |
| Significant | 32 (46.4) | 4.1 (2.95.7) | 32 (47.1) | 3.9 (2.75.4) | 0.84 |
| Total | 69 (100) | 8.9 (7.011.2) | 68 (100) | 8.3 (6.510.5) | 0.74 |
| Preventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (2.7) | 0.00 (00.0045) | 0.52 |
| Lifethreatening | 2 (3.2) | 0.26 (0.040.80) | 2 (5.6) | 0.25 (0.040.76) | 0.96 |
| Serious | 31 (50.0) | 4.0 (2.85.6) | 19 (52.8) | 2.3 (1.43.5) | 0.06 |
| Significant | 29 (46.8) | 3.7 (2.55.3) | 14 (38.9) | 1.7 (0.972.8) | 0.02 |
| Total | 62 (100) | 8.0 (6.210.2) | 36 (100) | 4.4 (3.16.0) | <0.01 |
| Nonpreventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 0 (0) | 0.00 (00.0045) | NS |
| Lifethreatening | 1 (14.2) | 0.13 (0.0070.57) | 1 (3.1) | 0.12 (0.0070.54) | 0.97 |
| Serious | 3 (42.9) | 0.39 (0.101.0) | 13 (40.6) | 1.6 (0.882.6) | 0.03 |
| Significant | 3 (42.9) | 0.39 (0.101.0) | 18 (56.3) | 2.2 (1.33.4) | <0.01 |
| Total | 7 (100) | 0.90 (0.391.7) | 32 (100) | 3.9 (2.75.4) | <0.01 |
| All potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 33 (3.0) | 4.0 (2.85.6) | <0.01 |
| Serious | 233 (54.2) | 30.1 (26.434.1) | 429 (38.4) | 52.6 (47.857.8) | <0.01 |
| Significant | 192 (44.6) | 24.8 (21.428.4) | 653 (58.6) | 80.1 (74.186.4) | <0.01 |
| Total | 430 (100) | 55.5 (50.460.9) | 1115 (100) | 136.8 (128.9145.0) | <0.01 |
| Intercepted potential ADEs | |||||
| Lifethreatening | 0 (0) | 0.00 (00.0047) | 1 (4.2) | 0.12 (0.0070.54) | 0.52 |
| Serious | 5 (31.2) | 0.65 (0.231.4) | 13 (54.2) | 1.6 (0.882.6) | 0.09 |
| Significant | 11 (68.8) | 1.4 (0.74 2.4) | 10 (41.6) | 1.2 (0.622.2) | 0.74 |
| Total | 16 (100) | 2.1 (1.23.2) | 24 (100) | 2.9 (1.94.3) | 0.24 |
| Nonintercepted potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 32 (2.9) | 3.9 (2.75.4) | <0.01 |
| Serious | 228 (55.1) | 29.4 (25.833.4) | 416 (38.1) | 51.0 (46.356.1) | <0.01 |
| Significant | 181 (43.7) | 23.4 (20.126.9) | 643 (58.9) | 78.9 (73.085.2) | <0.01 |
| Total | 414 (100) | 53.4 (48.458.7) | 1091 (100) | 133.9(126.1142.0) | <0.01 |
Case Reviews
In total, there were 36 preventable ADEs identified during the postimplementation phase (Table 5). Of these, inappropriate renal dosing accounted for 26 preventable ADEs, which involved antibiotics (eg, gentamicin‐induced renal failure), opioids (eg, over sedation from morphine), ‐blockers (eg, hypotension from atenolol), angiotensin‐converting enzyme inhibitors (eg, renal failure with hyperkalemia secondary to lisinopril), and digoxin (eg, bradyarrhythmia and toxicity). The use of contraindicated medications resulted in 7 preventable ADEs (eg, prescribing glyburide in the setting of severe renal impairment).[26] The remaining 3 preventable ADEs stemmed from unmonitored use of vancomycin.
| ADEs, Preventable, No. (Rate per 100 Admissions)* | ADEs, Nonpreventable, No. (Rate per 100 Admissions)* | ||||||
|---|---|---|---|---|---|---|---|
| Drug Class | Preimplementation | Postimplementation | P (for Entire Drug Class) | Preimplementation | Postimplementation | P (for Drug Class) | Drugs Involved |
| |||||||
| Cardiovascular | 20 (2.6) | 18 (2.2) | 0.63 | 4 (0.52) | 16 (2.0) | 0.02 | Atenolol, bumetanide, captopril, digoxin, furosemide, hydralazine, hydrochlorothiazide, lisinopril, sotalol, spironolactone |
| Diuretics | 1 (0.13) | 2 (0.25) | 1 (0.13) | 9 (1.1) | |||
| ‐blockers | 0 (0.00) | 2 (0.25) | 1 (0.13) | ||||
| ACE inhibitors and ARBs | 16 (2.1) | 10 (1.2) | 2 (0.26) | 7 (0.86) | |||
| Antiarrhythmic | 3 (0.39) | 3 (0.37) | |||||
| Vasodilator | 0 (0.00) | 1 (0.12) | |||||
| Analgesics | 28 (3.6) | 4 (0.49) | 0.0002 | 1 (0.13) | 5 (0.61) | 0.15 | Acetaminophen and combination pills containing acetaminophen: Percocet (oxycodone and acetaminophen), Tylenol #3 (codeine and acetaminophen), Vicodin (hydrocodone and acetaminophen), fentanyl, hydrocodone, meperidine, morphine, oxycodone |
| Narcotic | 13 (1.7) | 4 (0.49) | 0 (0.00) | 5 (0.61) | |||
| Non‐narcotic | 15 (1.9) | 0 (0.00) | 1 (0.13) | 0 | |||
| Antibiotics | 8 (1.0) | 13 (1.6) | 0.33 | 1 (0.13) | 9 (1.1) | 0.04 | Amikacin, ampicillin and sulbactam, ciprofloxacin, cefazolin, cefuroxime, gatifloxacin, gentamicin, levofloxacin, metronidazole, piperacillin and tazobactam, tobramycin, vancomycin |
| Neurotropic drugs | 2 (0.26) | 0 (0.00) | 0.28 | 0 | 0 | Lithium, midazolam | |
| Sedatives | 1 (0.13) | 0 (0.00) | |||||
| Antipsychotics | 1 (0.13) | 0 (0.00) | |||||
| Diabetes | 0 | 1 (0.12) | 0.52 | 0 | 1 (0.12) | 0.52 | Glipizide, glyburide |
| Oral antidiabetics | 0 | 1 (0.12) | 1 (0.12) | ||||
| Other drugs | 4 (0.52) | 0 (0.00) | 0.13 | 1 (0.13) | 1 (0.12) | 0.97 | Allopurinol, famotidine |
| Gastrointestinal drugs | 1 (0.13) | 0 (0.00) | |||||
| Other | 3 (0.39) | 0 (0.00) | 0 | 1 (0.12) | |||
DISCUSSION
We evaluated the use of vendor CPOE for hospitalized patients with renal disease and found that it was associated with a 45% reduction in preventable ADEs related to nephrotoxicity and accumulation of renally excreted medications. The impact of CPOE appeared to be related to the level of associated clinical decision support, where only the most advanced system was associated with benefit. We observed a significant increase in potential ADEs with all levels of intervention. Overall, these findings suggest that vendor‐developed applications with appropriate decision support can reduce the occurrence of renally related preventable ADEs, but careful implementation is needed if the potential ADE rate is to fall.
Many of the benefits of CPOE come from clinical decision support.[11] When applied to patients with renal impairment, CPOE with clinical decision support has been associated with decreased lengths of stay,[16, 27] reduced use of contraindicated medications,[28, 29, 30] improved dosing and drug monitoring,[16, 31, 32] and improved general prescribing practices.[29, 33] Even so, the observed benefit of CPOE on ADE rates has been variable, with some studies reporting reductions,[27, 34] whereas others are unable to detect differences.[16, 31] These studies, however, limited their case definition of ADEs to strictly declining renal function,[16, 31, 34] or adverse events directly resulting from anti‐infective drugs.[27] In contrast, our study accounted for nephrotoxicity and systemic toxicity from drug accumulation. Using this broader definition, we were able to detect large reductions in the rates of preventable ADEs following CPOE adoption.
Successful decision support is simple, intuitive, and provides speedy information that integrates seamlessly into the clinical workflow.[35, 36] However, information delivery, although necessary, is insufficient for improving safety. For instance, passive alerts are often ignored, deferred, or overridden.[30, 37, 38] Demonstrating this, Quartarolo et al. found that informing physicians of the presence of renal impairment using automated reporting of glomerular filtration rates did not change prescribing behavior.[39] In contrast, providing active feedback (with dosing recommendations) was observed to be more useful in effecting change.[40] Chertow et al. further showed that providing an adjusted dose list with a default dose and frequency at the time of order entry for patients with renal insufficiency improved appropriate ordering and was associated with a decreased length of stay.[16] Altogether, these studies help to explain why only CPOE with clinical decision support equipped to provide renally adjusted dosing and monitoring was associated with a reduction in preventable ADEs in our study.
However, in contrast to reports of internally developed systems,[20, 25] potential ADE rates actually rose during the follow‐up portion of our study. These appeared to be chiefly related to customized order sets with the potential of overdosing drugs through therapeutic duplication, a problem that is commonly known to be associated with CPOE (ie, new orders that overlap with other new or active medication orders, which may be the same drug itself or from within the same drug class, with the risk of overdose).[41, 42] Of note, our findings give rise to several key implications. First, hospitals implementing vendor‐developed CPOE systems may be at greater risk of incurring potential ADEs compared to those using home‐grown systems, which have comparatively gone through more cycles of internal refinement. As such, it is necessary to monitor for issues postimplementation and respond with appropriate changes to achieve successful system performance.[35, 36] Second, although the rate of potential ADEs (near misses) increased, preventable ADEs decreased because some of these errors were intercepted, whereas others were averted simply because of chance. Of note, not all potential ADEs have the same potential for injury; more serious cases are more likely to result in actual ADEs (eg, failure to renally dose acetaminophen likely poses less potential for harm than prescribing a full dose of enoxaparin in the setting of severe renal failure). Third, we found that most potential ADEs could have been averted with a combination of basic (dosing guidance and drug‐drug interactions checks) and advanced decision support (medication‐associated laboratory testing and drug‐disease interactions).[43] Therefore, further refinements to existing software are needed to maximize safety outcomes.
Our study has some limitations. This study was not a randomized controlled trial, and thus is subject to potential confounding. Although 6 hospitals were involved at the study inception,[14] one of these hospitals eventually opted not to implement CPOE, and further declined to participate as a control site. Therefore, we cannot exclude confounding from secular trends because we had no contemporaneous control group. However, the introduction of CPOE was the main medication safety‐oriented intervention during the study interval, thus arguing against major confounding by cointervention. Second, even though it is possible that classification bias may have been introduced between the preimplementation and postimplementation portions of our study, especially given the passage of time, it is unlikely. Study personnel underwent training using a curriculum designed to maintain continuity across projects, minimize individual variability, and optimize reproducibility in data collection and classification, as in a number of previous studies.[14, 17, 19, 20, 21] Third, our study is limited by a heterogeneous intervention, as varying levels of decision support were introduced. However, this reflects usual practice and may be construed as a strength as we were able to describe the impact of different types of decision support. Fourth, we enrolled patients with a large spectrum of renal impairment, and our findings are not specific to any particular subgroup. However, our wide recruitment strategy also enhances the generalizability. Finally, our study was restricted to patients who were exposed to potentially nephrotoxic and/or renally cleared drugs. As such, we could not determine whether advanced decision support helped to eliminate the use of some potentially dangerous medications altogether, as these cases would have been excluded from our study. It is possible, therefore, that our study findings underestimate the true benefit of clinical decision support.
In conclusion, vendor CPOE implementation in 5 community hospitals was associated with a 45% reduction in preventable ADE rates among patients with renal impairment. Measurable benefit was associated with advanced decision support capable of lab display, dosing guidance, and medication‐associated laboratory testing. Although the potential benefits of CPOE systems are far reaching, achieving the desired safety benefits will require appropriate decision support, tracking of problems that arise, and systematic approaches to eliminating them.
Acknowledgments
The authors thank Kathy Zigmont, RN, and Cathy Foskett, RN (Brigham and Women's Hospital, Division of General Internal Medicine and Primary Care) for the chart review and data collection at the participating study sites.
Disclosures: The Rx Foundation and Commonwealth Fund supported the study. They commented on its design, but were not involved in data collection, data management, analysis, interpretation, or writing of the manuscript. Dr. Leung is supported by a Clinical Fellowship Award from Alberta Innovates Health Solutions and by a Fellowship Award from the Canadian Institutes for Health Research. Dr. Schiff received financial support from the FDA CPOE Task Order and the Commonwealth Fund. Ms. Keohane served as a consultant to the American College of Obstetrician and Gynecologists and as a reviewer for the VRQC Program. She received honoraria for a presentation on Patient Safety in 2010, sponsored by Abbott Nutrition International, and a lecture on Nurse Interruptions in Medication Administration by Educational Review Systems. Dr. Coffey received an honorarium from Meditech for speaking on social networking at Physician/CIO Forum in 2009. Dr. Kaufman participates in an advisory group with Siemens Medical Solutions. Dr. Zimlichman received support from the Rx Foundation and the Commonwealth Fund. Dr. Bates holds a minority equity position in the privately held company Medicalis, which develops Web‐based decision support for radiology test ordering, and has served as a consultant to Medicalis. He serves as an advisor to Calgary Scientific, which makes technologies that enable mobility within electronic health records. He is on the clinical advisory board for Patient Safety Systems, which provides a set of approaches to help hospitals improve safety. He has received funding support from the Massachusetts Technology Consortium. Ms. Amato, Dr. Simon, Dr. Cadet, Ms. Seger, and Ms. Yoon have no disclosures relevant to this study.
Hospitalized patients with renal impairment are vulnerable to adverse drug events (ADEs).[1, 2] Appropriate prescribing for patients with renal insufficiency is challenging because of the complexities of drug therapy within the wide spectrum of kidney disease.[3, 4, 5, 6] Accordingly, computerized physician order entry (CPOE) systems with clinical decision support may help prevent many ADEs by providing timely laboratory information, recommending renally adjusted doses, and by offering assistance with prescribing.[7, 8, 9]
Despite the proposed benefits of CPOE, outcomes vary greatly because of differences in technology.[10, 11, 12, 13] In particular, the type of decision support available to assist medication ordering in the setting of renal disease varies widely among current vendor systems. Given the uncertain benefits of CPOE, especially with the wide range of associated clinical decision support, we conducted this study to determine the impact of these systems on the rates of ADEs among hospitalized patients with kidney disease.
METHODS
This study was approved by the institutional review boards at each study site.
Design and Setting
We conducted a before‐and‐after study to evaluate the impact of newly implemented vendor CPOE systems in 5 community hospitals in Massachusetts. Although we reported on 6 hospitals in our baseline study,[14] 1 of these hospitals later chose not to implement CPOE, and therefore was not included in follow‐up. At the time of this study, 1 of the hospitals (site 3) had not yet achieved hospital‐wide implementation. Although CPOE had been adopted by most medical services at site 3, it had not yet been implemented in the emergency, obstetrical, or surgical departments. Thus, we limited our study to the medical services at this site. For the remaining sites, all admitting services were included with the exception of the psychiatric and neonatal services, which were excluded from both phases because they would have required different detection tools.
Participants
Patients aged 18 years with renal failure, exposed to potentially nephrotoxic and/or renally cleared medications, and admitted to any of the participating hospitals during the study period were eligible for inclusion. Of the patients meeting eligibility criteria, we randomly selected approximately 150 records per hospital in the preimplementation and postimplementation phases for a total sample of 1590 charts. The first phase of this study occurred from January 2005 to August 2006; the second phase began 6 months postimplementation and lasted from October 2008 to September 2010.
Principal Exposure
Each hospital independently selected a vendor CPOE system with variable clinical decision support capabilities: (1) sites 4 and 5 had basic CPOE only with no clinical decision support for renal disease; (2) sites 1 and 2 implemented rudimentary clinical decision support with laboratory display (eg, serum creatinine) whenever common renally related drugs were ordered; and (3) site 3 had the most advanced support in place where, in addition to basic order entry and lab checks, physicians were provided with suggested doses for renally cleared and/or nephrotoxic medications, as well as appropriate drug monitoring for medications with narrow therapeutic indices (eg, suggested dosages and frequencies for vancomycin and automated corollary laboratory monitoring).
Definitions
We screened for the presence of renal failure by a serum creatinine 1.5 mg/dL at the time of admission. However, the duration of renal impairment was not known. We defined 3 levels of renal insufficiency based on the calculated creatinine clearance (CrCl)15: mild (CrCl 5080 mL/min), moderate (1649 mL/min), and severe (15 mL/min). Subjects with a CrCl >80 mL/min were considered to have normal renal function and were excluded. Potentially nephrotoxic and/or renally cleared medications were then identified using an established knowledge base (see Supporting Information, Table 1, in the online version of this article).[16]
| Hospital Site | |||||||
|---|---|---|---|---|---|---|---|
| Baseline Characteristics | All Sites | 1 | 2 | 3 | 4 | 5 | P (Among All Sites)* |
| |||||||
| No. of patients | 815 | 170 | 156 | 143 | 164 | 182 | |
| Age, y, mean (range) | 72.2 (18.0102.0) | 79.2 (33102) | 77.3 (23101) | 65.6 (1898) | 70.7 (1896) | 69.2 (2096) | <0.01 |
| 1844 years, no. (%) | 68 (9.1) | 1 (0.67) | 8 (6.5) | 20 (14.9) | 15 (9.4) | 24 (13.4) | <0.01 |
| 4554 years, no. (%) | 67 (9.0) | 6 (4.0) | 5 (4.1) | 17 (12.7) | 16 (10.0) | 23 (12.9) | |
| 5564 years, no. (%) | 79 (10.6) | 15 (10.0) | 12 (9.8) | 23 (17.2) | 13 (8.1) | 16 (8.9) | |
| 6574 years, no. (%) | 104 (13.9) | 20 (13.3) | 12 (9.8) | 16 (11.9) | 30 (18.8) | 26 (14.5) | |
| 7584 years, no. (%) | 197 (26.4) | 44 (29.3) | 36 (29.3) | 24 (17.9) | 49 (30.6) | 44 (24.6) | |
| 85 years, no. (%) | 231 (31.0) | 64 (42.7) | 50 (40.7) | 34 (25.4) | 37 (23.1) | 46 (25.7) | |
| Sex | |||||||
| Male, no. (%) | 427 (57.0) | 66 (44.0) | 60 (48.8) | 82 (60.7) | 105 (65.2) | 114 (63.7) | <0.01 |
| Female, no. (%) | 321 (43.0) | 84 (56.0) | 63 (51.2) | 53 (39.3) | 56 (34.8) | 65 (36.3) | |
| Race | |||||||
| Caucasian, no. (%) | 654 (87.4) | 129 (86.0) | 118 (95.9) | 126 (93.3) | 129 (80.1) | 152 (84.9) | <0.01 |
| Hispanic, no. (%) | 25 (3.3) | 2 (1.3) | 0 (0) | 1 (0.74) | 13 (8.1) | 9 (5.0) | |
| African American, no. (%) | 45 (6.0) | 12 (8.0) | 4 (3.3) | 5 (3.7) | 13 (8.1) | 11 (6.2) | |
| Native American, no. (%) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
| Asian, no. (%) | 13 (1.7) | 1 (0.81) | 1 (0.81) | 2 (1.5) | 5 (3.1) | 4 (2.2) | |
| Other, no. (%) | 7 (0.94) | 2 (1.3) | 0 (0) | 1 (0.74) | 1 (14.3) | 3 (1.7) | |
| Not recorded, no. (%) | 4 (0.53) | 4 (2.7) | 0 (0) | 0 (0.0) | 0 (0) | 0 (0) | |
| Initial severity of renal dysfunction | |||||||
| Mild, CrCl 5080 mL/min, no. (%) | 60 (7.4) | 4 (2.4) | 5 (3.2) | 5 (3.5) | 14 (8.5) | 32 (17.6) | <0. 01 |
| Moderate, CrCl 1649 mL/min, no. (%) | 388 (47.6) | 84 (49.4) | 71 (45.5) | 80 (55.9) | 76 (46.3) | 77 (42.3) | |
| Severe, CrCl <15 mL/min, no. (%) | 367 (45.0) | 82 (48.2) | 80 (51.3) | 58 (40.6) | 74 (45.1) | 73 (40.1) | |
| LOS, d, median (IQR) | 4.0 (26) | 4.0 (37) | 3.0 (25.5) | 4.0 (27) | 4.0 (27) | 4.0 (26) | 0.02 |
| DRG‐weighted LOS, d, median (IQR) | 5.0 (3.76.7) | 5.5 (46.7) | 5.0 (3.46.2) | 5.6 (4.36.7) | 5.0 (3.36.7) | 5.0 (4.26.7) | 0.27 |
In both phases of our study, only medications that were potentially nephrotoxic and/or renally cleared were included as potential cases; all other drugs were excluded. We defined an ADE as any drug‐related injury. These were considered preventable if they were due to an error at the time of order entry (eg, a doubling of creatinine secondary to an overdose of gentamicin or failure to order corollary drug levels for monitoring). A nonpreventable ADE was any drug‐related injury in which there was no error at the time of order entry (eg, a doubling of creatinine despite appropriate dosing of lisinopril).[17] A medication error was an error anywhere in the process of prescribing, transcribing, dispensing, administering, or monitoring a drug, but with no potential for harm or injury (eg, an order for an oral medication with no route specified when it was clear that the oral route was intended).[18] A potential ADE was an error with the potential to cause harm, but not resulting in injury, either because it was intercepted (eg, an order for ketorolac for a patient with renal failure, but caught by a pharmacist) or because of chance (eg, administering enoxaparin to a patient with severe renal dysfunction but without hemorrhage).
All study investigators underwent standardized training using a curriculum developed by the Center for Patient Safety Research and Practice (
Main Outcome Measures
The primary outcome was the rate of preventable ADEs. Secondary outcomes were the rates of potential ADEs and overall ADEs. All outcomes were related to nephrotoxicity or accumulation of a renally excreted medication.
Data collection and classification strategies were identical in both phases of our study.[14] We reviewed physician orders, medication lists, laboratory reports, admission histories, progress and consultation notes, discharge summaries, and nursing flow sheets, screening for the presence of medication incidents using an adaptation of the Institute for Healthcare Improvement's trigger tool, selected for its high sensitivity, reproducibility, and ease of use.[22, 23] In our adaptation of the tool, we excluded lidocaine, tobramycin, amikacin, and theophylline levels because of their infrequency. For each trigger found, a detailed description of the incident was extracted for detailed review. An example of a trigger is the use of sodium polystyrene, which may possibly indicate an overdose of potassium or a medication side effect.
Subsequently, each case was then independently reviewed by two investigators (A.A.L., M.A., B.C., S.R.S., M.C., N.K., E.Z., and G.S.)each assigned to at least 1 siteand blinded to prescribing physician and hospital to determine whether nephrotoxicity or injury from drug accumulation was present (see Supporting Information, Figure 1, in the online version of this article).[17] First, incidents were classified as ADEs, potential ADEs, or medication errors with no potential for injury. Second, ADEs and potential ADEs were rated according to severity. When nephrotoxic drugs were ordered, event severity was classified according to the elevation in serum creatinine24: increases of 10% were considered potential ADEs (near misses); increases of 10% to 100% were significant ADEs; and increases of 100% were serious ADEs. Changes in creatinine that were not associated with inappropriate medication orders were excluded. For renally excreted drugs with no potential for nephrotoxicity (eg, enoxaparin), we used clinical judgment to classify events as significant (eg, rash), severe (eg, 2‐unit gastrointestinal bleed), life threatening (eg, transfer to an intensive care unit), or fatal categories, as based on earlier work.[25] Disagreements were resolved by consensus. We had a score of 0.70 (95% confidence interval [CI]: 0.61‐0.80) for incident type, indicating excellent overall agreement.
Statistical Analysis
Baseline characteristics between hospitals were compared using the Fisher exact test for categorical variables and 1‐way analysis of variance for continuous variables. The occurrence of each outcome was determined according to site. To facilitate comparisons between sites, rates were expressed as number of events per 100 admissions with 95% CIs. To account for hospital effects in the analysis when comparing pre‐ and postimplementation rates of ADEs and potential ADEs, we developed a fixed‐effects Poisson regression model. To explore the independent effects of each system, a stratified analysis was performed to compare average rates of each outcome observed.
RESULTS
The outcomes of 775 patients in the baseline study were compared with the 815 patients enrolled during the postimplementation phase.[14] Among those in the postimplementation phase (Table 1), the mean age was 72.2 years, and they were predominantly male (57.0%). The demographics of the patients admitted to each of the 5 sites varied widely (P<0.01). Most patients had moderate to severe renal dysfunction.
Overall, the rates of ADEs were similar between the pre‐ and postimplementation phases (8.9/100 vs 8.3/100 admissions, respectively; P=0.74) (Table 2). However, there was a significant decrease in the rate of preventable ADEs, the primary outcome of interest, following CPOE implementation (8.0/100 vs 4.4/100 admissions; P<0.01). A reduction in preventable ADEs was observed in every hospital except site 4, where only basic order entry was introduced. However, there was a significant increase in the rates of nonpreventable ADEs (0.90/100 vs 3.9/100 admissions; P<0.01) and potential ADEs (55.5/100 vs 136.8/100 admissions; P<0.01).
| Rate/100 Admissions (95% CI) | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total No. (%) | All Sites | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | ||||||||||||||
| Event | Pre | Post | Pre | Post | P* | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| ||||||||||||||||||||
| ADEs | 69 (13.8) | 68 (5.7) | 8.9 (7.0 1.2) | 8.3 (6.50.5) | 0.74 | 9.8 (6.015.1) | 10.0 (6.015.5) | 0.96 | 11.0 (6.517.4) | 7.7 (4.1 12.9) | 0.34 | 12.4 (7.5 19.1) | 4.2 (1.7 8.5) | 0.02 | 4.1 (1.68.3) | 13.4 (8.619.8) | 0.01 | 7.1 (3.712.2) | 6.0 (3.110.4) | 0.71 |
| Preventable | 62 | 36 | 8.0 (6.2 10.2) | 4.4 (3.16.0) | <0.01 | 8.2 (4.713.1) | 7.1 (3.811.8) | 0.70 | 10.3 (6.016.5) | 5.8 (2.8 10.4) | 0.17 | 12.4 (7.519.1) | 0 (0 0.03) | <0.01 | 3.4 (1.27.3) | 7.9 (4.413.1) | 0.11 | 5.8 (2.810.5) | 1.1 (0.183.4) | 0.03 |
| Nonpreventable | 7 | 32 | 0.90 (0.39 1.7) | 3.9 (2.75.4) | <0.01 | 1.6 (0.414.3) | 2.9 (1.16.3) | 0.42 | 0.69 (0.043.04) | 1.9 (0.48 5.0) | 0.37 | 0 (00.03) | 4.2 (1.7 8.5) | <0.01 | 0.68 (0.043.0) | 5.5 (2.6 9.9) | 0.05 | 1.3 (0.21, 4.0) | 4.9 (2.48.9) | 0.09 |
| Potential ADEs | 430 (86.2) | 1115 (93.5) | 55.5 (50.4 60.9) | 136.8 (128.9145.0) | <0.01 | 65.0 (54.077.4) | 141.1 (124.1159.8) | <0.01 | 57.2 (45.870.5) | 98.7 (83.9 115.1) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7 121.1) | <0.01 | 59.2 (47.645.8) | 132.9 (116.1151.4) | <0.01 | 49.0 (38.860.9) | 195.1 (175.5216.1) | <0.01 |
| Intercepted | 16 | 24 | 2.1 (1.2 3.2) | 2.9 (1.94.3) | <0.24 | 3.3 (1.36.6) | 4.7 (2.28.8) | 0.50 | 2.1 (0.515.4) | 1.3 (0.21 4.0) | 0.60 | 1.4 (0.234.3) | 2.8 (0.87 6.5) | 0.41 | 2.0 (0.515.3) | 4.9 (2.2 9.1) | 0.20 | 1.3 (0.214.0) | 1.1 (0.183.4) | 0.87 |
| Nonintercepted | 414 | 1091 | 53.4 (48.4 58.7) | 133.9 (126.1142.0) | <0.01 | 61.7 (51.173.8) | 136.5 (119.754.8) | <0.01 | 55.2 43.968.2) | 97.4 (82.8 113.8) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1 118.1) | <0.01 | 57.1 (45.8 70.2) | 128.0 (111.5146.2) | <0.01 | 47.7 (37.759.5) | 194.0 (174.4214.9) | <0.01 |
Stratified Analysis
To account for differences in technology, we performed a stratified analysis (Table 3). As was consistent with the overall study estimates, the rates of nonpreventable ADEs and potential ADEs increased with all 3 interventions. In contrast, we found that the changes in preventable ADE rates were related to the level of clinical decision support, where the greatest benefit was associated with the most sophisticated decision support system (P=0.03 and 0.02 for comparisons between advanced vs rudimentary decision support and basic order entry only, respectively). There was no difference in preventable ADE rates at sites without decision support (4.6/100 vs 4.3/100 admissions; P=0.87); with rudimentary clinical decision support, there was a trend toward a decrease in the preventable ADE rate, which did not meet statistical significance (9.1/100 vs 6.4/100 admissions; P=0.22), and, the greatest reduction was seen with advanced clinical decision support (12.4/100 vs 0/100 admissions; P<0.01).
| Rate per 100 Admissions by Level of Clinical Decision Support (95% CI) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Basic CPOE Only (Sites 4 and 5) | CPOE and Lab Display (Sites 1 and 2) | CPOE, Lab Display, and DrugDosing Check (Site 3) | |||||||
| Incident | Pre | Post | P | Pre | Post | P | Pre | Post | P |
| |||||||||
| ADEs | 5.6 (3.48.7) | 9.5 (6.613.2) | 0.08 | 10.3(7.314.3) | 8.9 (6.012.5) | 0.55 | 12.4 (7.5319.1) | 4.2 (1.78.5) | 0.02 |
| Preventable | 4.6 (2.67.5) | 4.3 (2.56.9) | 0.87 | 9.1 (6.312.8) | 6.4 (4.19.6) | 0.22 | 12.4 (7.5319.1) | 0.00 (00.03) | <0.01 |
| Nonpreventable | 0.99 (0.24 2.6) | 5.2 (3.28.0) | <0.01 | 1.2 (0.382.8) | 2.5 (1.14.6) | 0.24 | 0.00 (00.03) | 4.2 (1.78.5) | <0.01 |
| Potential ADEs | 54.0 (46.162.7) | 165.6 (152.4179.5) | <0.01 | 61.6 (53.570.5) | 120.9 (109.3133.2) | <0.01 | 44.8 (34.856.6) | 103.5 (87.7121.1) | <0.01 |
| Intercepted | 1.7 (0.593.6) | 2.9 (1.45.1) | 0.30 | 2.7 (1.34.9) | 3.1 (1.55.4) | 0.76 | 1.4 (0.234.3) | 2.8 (0.876.5) | 0.42 |
| Nonintercepted | 52.3 (44.660.9) | 162.7 (149.6176.5) | <0.01 | 58.8 (50.967.5) | 117.8 (106.4130.0) | <0.01 | 43.4 (33.655.1) | 100.7 (85.1118.1) | <0.01 |
Severity of Events
We further analyzed our data based on event severity (Table 4). Among preventable ADEs, only 1 fatal event was observed, which occurred after CPOE implementation. Here, a previously opioid‐nave patient received intravenous morphine for malignant pain. Within the first 24 hours, the patient received 70.2 mg of intravenous morphine, resulting in a decreased level of consciousness. The patient expired the following day. Furthermore, following implementation, among preventable ADEs, a reduction in significant events was seen (P=0.02) along with a nonsignificant reduction in the rate of serious events (P=0.06). However, the rate of preventable life‐threatening events was not different (P=0.96). The nonpreventable ADE rate rose during the postimplementation period for both serious (P=0.03) and significant events (P<0.01). The risk of fatal and life‐threatening nonpreventable ADEs did not change. The potential ADE rate increased following implementation for all severities (P<0. 01).
| Preimplementation | Postimplementation | ||||
|---|---|---|---|---|---|
| Incident | No. (%) | Average Rate/100 Admissions (95% CI)* | No. (%) | Average Rate/100 Admissions (95% CI)* | P |
| |||||
| All ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (1.4) | 0.12 (0.0070.54) | 0.52 |
| Lifethreatening | 3 (4.3) | 0.39 (0.101.0) | 3 (4.4) | 0.37 (0.09 0.95) | 0.95 |
| Serious | 34 (49.3) | 4.4 (3.16.0) | 32 (47.1) | 3.9 (2.75.4) | 0.65 |
| Significant | 32 (46.4) | 4.1 (2.95.7) | 32 (47.1) | 3.9 (2.75.4) | 0.84 |
| Total | 69 (100) | 8.9 (7.011.2) | 68 (100) | 8.3 (6.510.5) | 0.74 |
| Preventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 1 (2.7) | 0.00 (00.0045) | 0.52 |
| Lifethreatening | 2 (3.2) | 0.26 (0.040.80) | 2 (5.6) | 0.25 (0.040.76) | 0.96 |
| Serious | 31 (50.0) | 4.0 (2.85.6) | 19 (52.8) | 2.3 (1.43.5) | 0.06 |
| Significant | 29 (46.8) | 3.7 (2.55.3) | 14 (38.9) | 1.7 (0.972.8) | 0.02 |
| Total | 62 (100) | 8.0 (6.210.2) | 36 (100) | 4.4 (3.16.0) | <0.01 |
| Nonpreventable ADEs | |||||
| Fatal | 0 (0) | 0.00 (00.0047) | 0 (0) | 0.00 (00.0045) | NS |
| Lifethreatening | 1 (14.2) | 0.13 (0.0070.57) | 1 (3.1) | 0.12 (0.0070.54) | 0.97 |
| Serious | 3 (42.9) | 0.39 (0.101.0) | 13 (40.6) | 1.6 (0.882.6) | 0.03 |
| Significant | 3 (42.9) | 0.39 (0.101.0) | 18 (56.3) | 2.2 (1.33.4) | <0.01 |
| Total | 7 (100) | 0.90 (0.391.7) | 32 (100) | 3.9 (2.75.4) | <0.01 |
| All potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 33 (3.0) | 4.0 (2.85.6) | <0.01 |
| Serious | 233 (54.2) | 30.1 (26.434.1) | 429 (38.4) | 52.6 (47.857.8) | <0.01 |
| Significant | 192 (44.6) | 24.8 (21.428.4) | 653 (58.6) | 80.1 (74.186.4) | <0.01 |
| Total | 430 (100) | 55.5 (50.460.9) | 1115 (100) | 136.8 (128.9145.0) | <0.01 |
| Intercepted potential ADEs | |||||
| Lifethreatening | 0 (0) | 0.00 (00.0047) | 1 (4.2) | 0.12 (0.0070.54) | 0.52 |
| Serious | 5 (31.2) | 0.65 (0.231.4) | 13 (54.2) | 1.6 (0.882.6) | 0.09 |
| Significant | 11 (68.8) | 1.4 (0.74 2.4) | 10 (41.6) | 1.2 (0.622.2) | 0.74 |
| Total | 16 (100) | 2.1 (1.23.2) | 24 (100) | 2.9 (1.94.3) | 0.24 |
| Nonintercepted potential ADEs | |||||
| Lifethreatening | 5 (1.2) | 0.65 (0.231.4) | 32 (2.9) | 3.9 (2.75.4) | <0.01 |
| Serious | 228 (55.1) | 29.4 (25.833.4) | 416 (38.1) | 51.0 (46.356.1) | <0.01 |
| Significant | 181 (43.7) | 23.4 (20.126.9) | 643 (58.9) | 78.9 (73.085.2) | <0.01 |
| Total | 414 (100) | 53.4 (48.458.7) | 1091 (100) | 133.9(126.1142.0) | <0.01 |
Case Reviews
In total, there were 36 preventable ADEs identified during the postimplementation phase (Table 5). Of these, inappropriate renal dosing accounted for 26 preventable ADEs, which involved antibiotics (eg, gentamicin‐induced renal failure), opioids (eg, over sedation from morphine), ‐blockers (eg, hypotension from atenolol), angiotensin‐converting enzyme inhibitors (eg, renal failure with hyperkalemia secondary to lisinopril), and digoxin (eg, bradyarrhythmia and toxicity). The use of contraindicated medications resulted in 7 preventable ADEs (eg, prescribing glyburide in the setting of severe renal impairment).[26] The remaining 3 preventable ADEs stemmed from unmonitored use of vancomycin.
| ADEs, Preventable, No. (Rate per 100 Admissions)* | ADEs, Nonpreventable, No. (Rate per 100 Admissions)* | ||||||
|---|---|---|---|---|---|---|---|
| Drug Class | Preimplementation | Postimplementation | P (for Entire Drug Class) | Preimplementation | Postimplementation | P (for Drug Class) | Drugs Involved |
| |||||||
| Cardiovascular | 20 (2.6) | 18 (2.2) | 0.63 | 4 (0.52) | 16 (2.0) | 0.02 | Atenolol, bumetanide, captopril, digoxin, furosemide, hydralazine, hydrochlorothiazide, lisinopril, sotalol, spironolactone |
| Diuretics | 1 (0.13) | 2 (0.25) | 1 (0.13) | 9 (1.1) | |||
| ‐blockers | 0 (0.00) | 2 (0.25) | 1 (0.13) | ||||
| ACE inhibitors and ARBs | 16 (2.1) | 10 (1.2) | 2 (0.26) | 7 (0.86) | |||
| Antiarrhythmic | 3 (0.39) | 3 (0.37) | |||||
| Vasodilator | 0 (0.00) | 1 (0.12) | |||||
| Analgesics | 28 (3.6) | 4 (0.49) | 0.0002 | 1 (0.13) | 5 (0.61) | 0.15 | Acetaminophen and combination pills containing acetaminophen: Percocet (oxycodone and acetaminophen), Tylenol #3 (codeine and acetaminophen), Vicodin (hydrocodone and acetaminophen), fentanyl, hydrocodone, meperidine, morphine, oxycodone |
| Narcotic | 13 (1.7) | 4 (0.49) | 0 (0.00) | 5 (0.61) | |||
| Non‐narcotic | 15 (1.9) | 0 (0.00) | 1 (0.13) | 0 | |||
| Antibiotics | 8 (1.0) | 13 (1.6) | 0.33 | 1 (0.13) | 9 (1.1) | 0.04 | Amikacin, ampicillin and sulbactam, ciprofloxacin, cefazolin, cefuroxime, gatifloxacin, gentamicin, levofloxacin, metronidazole, piperacillin and tazobactam, tobramycin, vancomycin |
| Neurotropic drugs | 2 (0.26) | 0 (0.00) | 0.28 | 0 | 0 | Lithium, midazolam | |
| Sedatives | 1 (0.13) | 0 (0.00) | |||||
| Antipsychotics | 1 (0.13) | 0 (0.00) | |||||
| Diabetes | 0 | 1 (0.12) | 0.52 | 0 | 1 (0.12) | 0.52 | Glipizide, glyburide |
| Oral antidiabetics | 0 | 1 (0.12) | 1 (0.12) | ||||
| Other drugs | 4 (0.52) | 0 (0.00) | 0.13 | 1 (0.13) | 1 (0.12) | 0.97 | Allopurinol, famotidine |
| Gastrointestinal drugs | 1 (0.13) | 0 (0.00) | |||||
| Other | 3 (0.39) | 0 (0.00) | 0 | 1 (0.12) | |||
DISCUSSION
We evaluated the use of vendor CPOE for hospitalized patients with renal disease and found that it was associated with a 45% reduction in preventable ADEs related to nephrotoxicity and accumulation of renally excreted medications. The impact of CPOE appeared to be related to the level of associated clinical decision support, where only the most advanced system was associated with benefit. We observed a significant increase in potential ADEs with all levels of intervention. Overall, these findings suggest that vendor‐developed applications with appropriate decision support can reduce the occurrence of renally related preventable ADEs, but careful implementation is needed if the potential ADE rate is to fall.
Many of the benefits of CPOE come from clinical decision support.[11] When applied to patients with renal impairment, CPOE with clinical decision support has been associated with decreased lengths of stay,[16, 27] reduced use of contraindicated medications,[28, 29, 30] improved dosing and drug monitoring,[16, 31, 32] and improved general prescribing practices.[29, 33] Even so, the observed benefit of CPOE on ADE rates has been variable, with some studies reporting reductions,[27, 34] whereas others are unable to detect differences.[16, 31] These studies, however, limited their case definition of ADEs to strictly declining renal function,[16, 31, 34] or adverse events directly resulting from anti‐infective drugs.[27] In contrast, our study accounted for nephrotoxicity and systemic toxicity from drug accumulation. Using this broader definition, we were able to detect large reductions in the rates of preventable ADEs following CPOE adoption.
Successful decision support is simple, intuitive, and provides speedy information that integrates seamlessly into the clinical workflow.[35, 36] However, information delivery, although necessary, is insufficient for improving safety. For instance, passive alerts are often ignored, deferred, or overridden.[30, 37, 38] Demonstrating this, Quartarolo et al. found that informing physicians of the presence of renal impairment using automated reporting of glomerular filtration rates did not change prescribing behavior.[39] In contrast, providing active feedback (with dosing recommendations) was observed to be more useful in effecting change.[40] Chertow et al. further showed that providing an adjusted dose list with a default dose and frequency at the time of order entry for patients with renal insufficiency improved appropriate ordering and was associated with a decreased length of stay.[16] Altogether, these studies help to explain why only CPOE with clinical decision support equipped to provide renally adjusted dosing and monitoring was associated with a reduction in preventable ADEs in our study.
However, in contrast to reports of internally developed systems,[20, 25] potential ADE rates actually rose during the follow‐up portion of our study. These appeared to be chiefly related to customized order sets with the potential of overdosing drugs through therapeutic duplication, a problem that is commonly known to be associated with CPOE (ie, new orders that overlap with other new or active medication orders, which may be the same drug itself or from within the same drug class, with the risk of overdose).[41, 42] Of note, our findings give rise to several key implications. First, hospitals implementing vendor‐developed CPOE systems may be at greater risk of incurring potential ADEs compared to those using home‐grown systems, which have comparatively gone through more cycles of internal refinement. As such, it is necessary to monitor for issues postimplementation and respond with appropriate changes to achieve successful system performance.[35, 36] Second, although the rate of potential ADEs (near misses) increased, preventable ADEs decreased because some of these errors were intercepted, whereas others were averted simply because of chance. Of note, not all potential ADEs have the same potential for injury; more serious cases are more likely to result in actual ADEs (eg, failure to renally dose acetaminophen likely poses less potential for harm than prescribing a full dose of enoxaparin in the setting of severe renal failure). Third, we found that most potential ADEs could have been averted with a combination of basic (dosing guidance and drug‐drug interactions checks) and advanced decision support (medication‐associated laboratory testing and drug‐disease interactions).[43] Therefore, further refinements to existing software are needed to maximize safety outcomes.
Our study has some limitations. This study was not a randomized controlled trial, and thus is subject to potential confounding. Although 6 hospitals were involved at the study inception,[14] one of these hospitals eventually opted not to implement CPOE, and further declined to participate as a control site. Therefore, we cannot exclude confounding from secular trends because we had no contemporaneous control group. However, the introduction of CPOE was the main medication safety‐oriented intervention during the study interval, thus arguing against major confounding by cointervention. Second, even though it is possible that classification bias may have been introduced between the preimplementation and postimplementation portions of our study, especially given the passage of time, it is unlikely. Study personnel underwent training using a curriculum designed to maintain continuity across projects, minimize individual variability, and optimize reproducibility in data collection and classification, as in a number of previous studies.[14, 17, 19, 20, 21] Third, our study is limited by a heterogeneous intervention, as varying levels of decision support were introduced. However, this reflects usual practice and may be construed as a strength as we were able to describe the impact of different types of decision support. Fourth, we enrolled patients with a large spectrum of renal impairment, and our findings are not specific to any particular subgroup. However, our wide recruitment strategy also enhances the generalizability. Finally, our study was restricted to patients who were exposed to potentially nephrotoxic and/or renally cleared drugs. As such, we could not determine whether advanced decision support helped to eliminate the use of some potentially dangerous medications altogether, as these cases would have been excluded from our study. It is possible, therefore, that our study findings underestimate the true benefit of clinical decision support.
In conclusion, vendor CPOE implementation in 5 community hospitals was associated with a 45% reduction in preventable ADE rates among patients with renal impairment. Measurable benefit was associated with advanced decision support capable of lab display, dosing guidance, and medication‐associated laboratory testing. Although the potential benefits of CPOE systems are far reaching, achieving the desired safety benefits will require appropriate decision support, tracking of problems that arise, and systematic approaches to eliminating them.
Acknowledgments
The authors thank Kathy Zigmont, RN, and Cathy Foskett, RN (Brigham and Women's Hospital, Division of General Internal Medicine and Primary Care) for the chart review and data collection at the participating study sites.
Disclosures: The Rx Foundation and Commonwealth Fund supported the study. They commented on its design, but were not involved in data collection, data management, analysis, interpretation, or writing of the manuscript. Dr. Leung is supported by a Clinical Fellowship Award from Alberta Innovates Health Solutions and by a Fellowship Award from the Canadian Institutes for Health Research. Dr. Schiff received financial support from the FDA CPOE Task Order and the Commonwealth Fund. Ms. Keohane served as a consultant to the American College of Obstetrician and Gynecologists and as a reviewer for the VRQC Program. She received honoraria for a presentation on Patient Safety in 2010, sponsored by Abbott Nutrition International, and a lecture on Nurse Interruptions in Medication Administration by Educational Review Systems. Dr. Coffey received an honorarium from Meditech for speaking on social networking at Physician/CIO Forum in 2009. Dr. Kaufman participates in an advisory group with Siemens Medical Solutions. Dr. Zimlichman received support from the Rx Foundation and the Commonwealth Fund. Dr. Bates holds a minority equity position in the privately held company Medicalis, which develops Web‐based decision support for radiology test ordering, and has served as a consultant to Medicalis. He serves as an advisor to Calgary Scientific, which makes technologies that enable mobility within electronic health records. He is on the clinical advisory board for Patient Safety Systems, which provides a set of approaches to help hospitals improve safety. He has received funding support from the Massachusetts Technology Consortium. Ms. Amato, Dr. Simon, Dr. Cadet, Ms. Seger, and Ms. Yoon have no disclosures relevant to this study.
- , , . Drug Prescribing in Renal Failure: Dosing Guidelines for Adults and Children: American College of Physicians; 2007.
- , . Management of drug toxicity in patients with renal insufficiency. Nat Rev Nephrol. 2010;6(6):317–318.
- , , , . Use of renal risk drugs in hospitalized patients with impaired renal function—an underestimated problem? Nephrol Dial Transplant. 2006;21(11):3164–3171.
- , , , et al. Medication misuse in hospitalized patients with renal impairment. Int J Qual Health Care. 2003;15(4):331–335.
- , , , . Impact of a renal drug dosing service on dose adjustment in hospitalized patients with chronic kidney disease. Ann Pharmacother. 2009;43(10):1598–1605.
- , . Drug dosing in chronic kidney disease. Med Clin North Am. 2005;89(3):649–687.
- , , , et al. Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA. 1995;274(1):35–43.
- , , , , , . The epidemiology of prescribing errors: the potential impact of computerized prescriber order entry. Arch Intern Med. 2004;164(7):785–792.
- , , . Factors related to errors in medication prescribing. JAMA. 1997;277(4):312–317.
- , , , et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197–1203.
- , , , , . Mixed results in the safety performance of computerized physician order entry. Health Aff (Millwood). 2010;29(4):655–663.
- , , , et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med. 2008;23(4):451–458.
- , , . The impact of computerized physician medication order entry in hospitalized patients—a systematic review. Int J Med Inform. 2008;77(6):365–376.
- , , , et al. Occurrence of adverse, often preventable, events in community hospitals involving nephrotoxic drugs or those excreted by the kidney. Kidney Int. 2009;76(11):1192–1198.
- , . Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31–41.
- , , , et al. Guided medication dosing for inpatients with renal insufficiency. JAMA. 2001;286(22):2839–2844.
- , , , , . Adverse drug events and medication errors: detection and classification methods. Qual Saf Health Care. 2004;13(4):306–314.
- , , , , . Relationship between medication errors and adverse drug events. J Gen Intern Med. 1995;10(4):199–205.
- , , , et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA. 1995;274(1):29–34.
- , , , et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–1316.
- , , , et al. Adverse drug event rates in six community hospitals and the potential impact of computerized physician order entry for prevention. J Gen Intern Med. 2010;25(1):31–38.
- , , . Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care. 2003;12(3):194–200.
- Institute for Healthcare Improvement: IHI Trigger Tool for Measuring Adverse Drug Events. 2011. Available at: http://www.ihi.org/knowledge/Pages/Tools/TriggerToolforMeasuringAdverseDrugEvents.aspx. Accessed February 1, 2013.
- , , , . Renal safety of two analgesics used over the counter: ibuprofen and aspirin. Clin Pharmacol Ther. 1986;40(4):373–377.
- , , , et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6(4):313–321.
- , , . Prolonged sulfonylurea‐induced hypoglycemia in diabetic patients with end‐stage renal disease. Am J Kidney Dis. 2000;35(3):500–505.
- , , , et al. A computer‐assisted management program for antibiotics and other antiinfective agents. N Engl J Med. 1998;338(4):232–238.
- , , , et al. Alert system for inappropriate prescriptions relating to patients' clinical condition. Methods Inf Med. 2009;48(6):566–573.
- , , , , , . Computerized clinical decision support during medication ordering for long‐term care residents with renal insufficiency. J Am Med Inform Assoc. 2009;16(4):480–485.
- , , . A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. J Am Med Inform Assoc. 2005;12(3):269–274.
- , , , , . Effects of clinical decision support on initial dosing and monitoring of tobramycin and amikacin. Am J Health Syst Pharm. 2011;68(7):624–632.
- , , , , , . Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. Ann Emerg Med. 2010;56(6):623–629.
- , , , . Implementation of rules based computerised bedside prescribing and administration: intervention study. BMJ. 2000;320(7237):750–753.
- , , , et al. Effect of computer‐based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994;154(13):1511–1517.
- , , , et al. Ten commandments for effective clinical decision support: making the practice of evidence‐based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523–530.
- , , . Computerized decision support systems: improving patient safety in nephrology. Nat Rev Nephrol. 2011;7(6):348–355.
- , , , et al. Impact of a computerized alert during physician order entry on medication dosing in patients with renal impairment. Proc AMIA Symp. 2002:577–581.
- , , , et al. A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis. 2010;56(5):832–841.
- , , . Reporting of estimated glomerular filtration rate: effect on physician recognition of chronic kidney disease and prescribing practices for elderly hospitalized patients. J Hosp Med. 2007;2(2):74–78.
- , , , , . Drug dosage in patients with renal failure optimized by immediate concurrent feedback. J Gen Intern Med. 2001;16(6):369–375.
- , , , et al. Factors contributing to an increase in duplicate medication order errors after CPOE implementation. J Am Med Inform Assoc. 2011;18(6):774–782.
- , , , et al. Impact of Vendor Computerized Physician Order Entry in Community Hospitals. J Gen Intern Med. 2012;27(7):801–807.
- , , , et al. Medication‐related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29–40.
- , , . Drug Prescribing in Renal Failure: Dosing Guidelines for Adults and Children: American College of Physicians; 2007.
- , . Management of drug toxicity in patients with renal insufficiency. Nat Rev Nephrol. 2010;6(6):317–318.
- , , , . Use of renal risk drugs in hospitalized patients with impaired renal function—an underestimated problem? Nephrol Dial Transplant. 2006;21(11):3164–3171.
- , , , et al. Medication misuse in hospitalized patients with renal impairment. Int J Qual Health Care. 2003;15(4):331–335.
- , , , . Impact of a renal drug dosing service on dose adjustment in hospitalized patients with chronic kidney disease. Ann Pharmacother. 2009;43(10):1598–1605.
- , . Drug dosing in chronic kidney disease. Med Clin North Am. 2005;89(3):649–687.
- , , , et al. Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA. 1995;274(1):35–43.
- , , , , , . The epidemiology of prescribing errors: the potential impact of computerized prescriber order entry. Arch Intern Med. 2004;164(7):785–792.
- , , . Factors related to errors in medication prescribing. JAMA. 1997;277(4):312–317.
- , , , et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197–1203.
- , , , , . Mixed results in the safety performance of computerized physician order entry. Health Aff (Millwood). 2010;29(4):655–663.
- , , , et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med. 2008;23(4):451–458.
- , , . The impact of computerized physician medication order entry in hospitalized patients—a systematic review. Int J Med Inform. 2008;77(6):365–376.
- , , , et al. Occurrence of adverse, often preventable, events in community hospitals involving nephrotoxic drugs or those excreted by the kidney. Kidney Int. 2009;76(11):1192–1198.
- , . Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31–41.
- , , , et al. Guided medication dosing for inpatients with renal insufficiency. JAMA. 2001;286(22):2839–2844.
- , , , , . Adverse drug events and medication errors: detection and classification methods. Qual Saf Health Care. 2004;13(4):306–314.
- , , , , . Relationship between medication errors and adverse drug events. J Gen Intern Med. 1995;10(4):199–205.
- , , , et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA. 1995;274(1):29–34.
- , , , et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–1316.
- , , , et al. Adverse drug event rates in six community hospitals and the potential impact of computerized physician order entry for prevention. J Gen Intern Med. 2010;25(1):31–38.
- , , . Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care. 2003;12(3):194–200.
- Institute for Healthcare Improvement: IHI Trigger Tool for Measuring Adverse Drug Events. 2011. Available at: http://www.ihi.org/knowledge/Pages/Tools/TriggerToolforMeasuringAdverseDrugEvents.aspx. Accessed February 1, 2013.
- , , , . Renal safety of two analgesics used over the counter: ibuprofen and aspirin. Clin Pharmacol Ther. 1986;40(4):373–377.
- , , , et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6(4):313–321.
- , , . Prolonged sulfonylurea‐induced hypoglycemia in diabetic patients with end‐stage renal disease. Am J Kidney Dis. 2000;35(3):500–505.
- , , , et al. A computer‐assisted management program for antibiotics and other antiinfective agents. N Engl J Med. 1998;338(4):232–238.
- , , , et al. Alert system for inappropriate prescriptions relating to patients' clinical condition. Methods Inf Med. 2009;48(6):566–573.
- , , , , , . Computerized clinical decision support during medication ordering for long‐term care residents with renal insufficiency. J Am Med Inform Assoc. 2009;16(4):480–485.
- , , . A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. J Am Med Inform Assoc. 2005;12(3):269–274.
- , , , , . Effects of clinical decision support on initial dosing and monitoring of tobramycin and amikacin. Am J Health Syst Pharm. 2011;68(7):624–632.
- , , , , , . Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. Ann Emerg Med. 2010;56(6):623–629.
- , , , . Implementation of rules based computerised bedside prescribing and administration: intervention study. BMJ. 2000;320(7237):750–753.
- , , , et al. Effect of computer‐based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994;154(13):1511–1517.
- , , , et al. Ten commandments for effective clinical decision support: making the practice of evidence‐based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523–530.
- , , . Computerized decision support systems: improving patient safety in nephrology. Nat Rev Nephrol. 2011;7(6):348–355.
- , , , et al. Impact of a computerized alert during physician order entry on medication dosing in patients with renal impairment. Proc AMIA Symp. 2002:577–581.
- , , , et al. A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis. 2010;56(5):832–841.
- , , . Reporting of estimated glomerular filtration rate: effect on physician recognition of chronic kidney disease and prescribing practices for elderly hospitalized patients. J Hosp Med. 2007;2(2):74–78.
- , , , , . Drug dosage in patients with renal failure optimized by immediate concurrent feedback. J Gen Intern Med. 2001;16(6):369–375.
- , , , et al. Factors contributing to an increase in duplicate medication order errors after CPOE implementation. J Am Med Inform Assoc. 2011;18(6):774–782.
- , , , et al. Impact of Vendor Computerized Physician Order Entry in Community Hospitals. J Gen Intern Med. 2012;27(7):801–807.
- , , , et al. Medication‐related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29–40.
© 2013 Society of Hospital Medicine
Electronic Communication
INTRODUCTION
Coordination of care within a practice, during transitions of care, and between primary and specialty care teams requires more than data exchange; it requires effective communication among healthcare providers.[1, 2, 3] In clinical terms, data exchange, communication, and care coordination are related, but they represent distinct concepts.[4] Data exchange refers to transfer of information between settings, independent of the individuals involved, whereas communication is the multistep process that enables information exchange between two people.[5] Care coordination, as defined by O'Malley, is integration of care in consultation with patients, their families and caregivers across all of a patient's conditions, needs, clinicians and settings.[3]
Strong collaboration among providers has been associated with improved patient outcomes.[2, 6] Yet, despite the significant role of communication in healthcare, communication may not take place at all, even at high‐stakes events like transitions of care,[7, 8] or it may be done poorly at the risk of substantial clinical morbidity and mortality.[9, 10, 11, 12, 13, 14, 15, 16]
Proof of the global effectiveness of health information technology (HIT) to improve patient care is lacking, but data from some studies demonstrate real improvements in quality and safety in specific areas,[17, 18, 19] especially with computerized physician order entry[20] and electronic prescribing.[21]
The limited information about the effect of HIT on communication focuses largely on the anticipated improvements in patient‐physician communication[22, 23, 24, 25, 26, 27]; provider‐to‐provider communication within the electronic domain is not as well understood. A recent review of interventions involving communication devices such as pagers and mobile phones found limited high‐quality evidence in the literature.[28] Clinicians have described what they consider to be key characteristics of clinical electronic communications systems such as security/reliability, cross coverage, overall convenience, and message prioritization.[29] Although the electronic health record (EHR) is expected to assist with this communication,[30] it also has the potential to impede effective communication, leading physicians to resort to more traditional workarounds.[31, 32, 33]
Measuring and improving the use of EHRs nationally were driving forces behind the creation of the Meaningful Use incentive program in the United States.[34] To receive the incentive payments, providers must meet and report on a series of measures set in three stages over the course of five years.[35] In the current state, Meaningful Use does not reward provider‐to‐provider communication within the EHR.[36, 37] The main communication objectives for stages 1 and 2 concentrate on patient‐to‐provider communication, such as patient portals and patient‐to‐provider messaging.[36, 37]
Understanding the current evidence for provider‐to‐provider communication within EHRs, its reported effectiveness, and its shortcomings may help to develop a roadmap for identifying next‐generation solutions to support coordination of care.[38, 39] This review assesses the literature regarding provider‐to‐provider electronic communication tools (as supported within or external to an EHR). It is intended as a comprehensive view of studies reporting quantitative measures of the impact of electronic communication on providers and patients.
METHODS
Definitions and Conceptual Model of Provider‐to‐Provider Communication
We conducted a systematic review of studies of provider‐to‐provider electronic communication. This review included only formal clinical communication between providers and was informed by the Coiera communications paradigm.[5] This paradigm consists of four steps: (1) task identification, when a task is identified and associated with the appropriate individual; (2) connection, when an attempt is made to contact that person; (3) communication, when task‐specific information is exchanged between the parties; and (4) disconnection, when the task reaches some stage of completion.
Literature Review
We examined written electronic communication between providers including e‐mail, text messaging, and instant messaging. We did not review provider‐to‐provider telephone or telehealth communication, as these are not generally supported within EHR systems. Communication in all clinical contexts was included among providers within an individual clinic or hospital and among providers across specialties or practice settings.[40] We excluded physician handoff communication because it has been extensively reviewed elsewhere and because handoff occurs largely through verbal exchange not recorded in the EHR.[41, 42] Communication from clinical information systems to providers, such as automated notification of unacknowledged orders, was also excluded, as it is not within the scope of provider‐to‐provider interaction.
Data Sources and Searches
A comprehensive literature search was conducted in Ovid MEDLINE with the input of a medical librarian, and a parallel search was performed using PubMed. The Ovid MEDLINE query and parallel database search terms are documented in Table 1. Subsearches were conducted in Google Scholar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Academic Search Premier for peer‐reviewed journals. Subsequent studies citing the initially detected articles were found through citation maps.
| Database | Strategy | Items Reviewed |
|---|---|---|
| ||
| Ovid MEDLINE | Query terms: exp medicine/ or physicians or exp outpatient clinics/ or exp hospitals/ AND *communication/ or *computer communication networks/ or *interprofessional relations/ or *continuity of patient care/ AND electronic mail or referral and consultation or text messaging/ or reminder systems. | 1513 |
| PubMed | Healthcare, provider, communication, messaging, e‐mail, texting, text messaging, instant messaging, paging, coordination, referral, EHR, EMR, electronic health record, electronic medical record, electronic, and physician. Excluding patient‐provider and patient‐physician | 340 |
| Google Scholar | Physician‐physician electronic communication excluding physician‐patient | 940 |
| CINAHL | Medical records and communication; or computerized patient records and communication | None |
| Academic Search Premier (peer‐reviewed journals) | Electronic health record and communication | 54 |
| Communication and electronic health record | 80 | |
| Physician‐physician communication | 2 | |
| Physicians and electronic health records | 88 | |
Study Selection
Paper Inclusion Criteria
Requirements included publication in English‐language peer‐reviewed journals. Included studies provided quantitative provider‐to‐provider communication data, provider satisfaction statistics, or EHR communication data. Provider‐to‐staff communication was also included if it fell within the scope of studies of communication between providers.
Paper Exclusion Criteria
Studies excluded in this review were articles that reviewed EHR systems without any focus on communication between providers and those that discussed EHR models and strategies but did not include actual testing and quantitative results. Results that included nontraditional online documents or that were found on nonpeer‐reviewed websites were also discarded. Duplicate records or publications that covered the same study were also removed. The most common reason for exclusion was the lack of quantitative evaluation.
Data Extraction and Quality Assessment
Three authors (Walsh, Siegler, Stetson) reviewed titles and abstracts of resultant studies against inclusion and exclusion criteria (Figure 1). Studies were evaluated qualitatively and findings summarized. Given the heterogeneous nature of data reported, statistical analysis was not possible.
RESULTS
The primary and parallel searches produced 2946 results that were weaned through title review and exclusion of duplicates, nonEnglish‐language, and nonhuman studies to 820 articles for title and abstract review (Figure 1). After careful review of the articles' titles, abstracts, or full content (where appropriate), twenty‐five articles met inclusion criteria and presented data about provider‐to‐provider electronic communication, either within an EHR or through a system designed to promote provider‐to‐provider communication. All of the studies that met inclusion criteria focused on physicians as providers. Five studies (20%) described trial design, three (12%) were pilot studies, and seventeen (68%) were observational studies. Thirteen of twenty‐five articles (52%) described studies conducted in the United States and twelve in Europe.
Most of the studies (56%) focused on electronic referrals between primary care and subspecialty providers. The clinical need was to communicate information on a specific patient with a specialist who shared responsibility for the overall plan of care. Only two studies evaluated curbside consultation, where providers ask for clinical recommendations without formally engaging a specialist in the plan of care for a particular patient. Table 2 summarizes included studies and has been organized with respect to clinical need under evaluation. The major themes that emerged from this review included: studies of penetration of communication tools either within the EHR system (intra‐EHR IT) or external to the EHR (extra‐EHR IT); electronic referrals; curbside consultations; and test results reporting (results notification).
| Primary Author, Year | Design | Intervention | Measurement | Results |
|---|---|---|---|---|
| ||||
| Need: Communicate care across clinical settings (inpatient‐outpatient) | ||||
| Branger, 1992[4, 6] | Observational study | Introduction of electronic messaging system in the Netherlands between hospital and PCPs. | Satisfaction survey data using Likert scale of usefulness. | Free text messaging to exchange patient data was rated very useful or useful by 20 of 27 PCP respondents. |
| Reponen, 2004[66] | Observational study | Finnish study of electronic referrals XML messages between EHRs or secure web links. | User questionnaire. No description of respondents was provided. | Internists surveyed estimated that electronic referrals accelerate the referral process by 1 week. |
| Need: Communicate care across specialties (primary care physicians‐specialists) | ||||
| Kooijman, 1998[67] | Observational study | Survey of 45 PCPs who received notes from specialists via Electronic Data Interchange. | User questionnaire with 5‐point Likert scale of satisfaction, from 1 (much better) to 5 (much worse). | Highest satisfaction scores for speed (1.51.8) and efficiency (1.51.7) for electronic messages, with lower scores for reliability (2.52.7) and clarity (2.5). |
| Harno, 2000[4][8] | Nonrandomized trial | Eight‐month prospective comparative study in Finland of outpatient clinics in hospitals with and without intranet referral systems. | Comparison of numbers of electronic referrals, clinic visits, costs. | There were 43% of electronic referrals and 79% of outpatient referrals that resulted in outpatient visits. A 3‐fold increase in productivity overall and 7‐fold reduction in visit costs per patient using e‐mail consultation. |
| Moorman, 2001[4][7] | Observational study | Supersedes Branger, 1999.[68] Analyzes intra‐EHR communications between PCPs and consultant in Netherlands re: diabetes management of patients (19941998). | Descriptive statistics of number of messages, content, whether message had been copied into EMR; survey of PCPs (12 of 15 responded). | Decline in integration by PCPs of messages in the EHR from 75% to 51% over first 3 years. Despite this, most PCPs wanted to extend messaging to other patient groups. |
| Bergus, 2006[69] | Observational study | Follow‐up of Bergus, 1998[54]; evaluated formulation of clinical referrals to specialists at the University of Iowa by retrospective review of e‐mail transcripts. | Analyzed taxonomy of clinical questions; assessed need for clinical consultation of 1618 clinical questions. | Specialists less likely to recommend clinic consultation if referral specified the clinical task (OR: 0.36, P<0.001), intervention (OR: 0.62, P=0.004), or outcome (OR: 0.49, P<0.001). This effect was independent of clinical content (P>0.05). |
| Dennison, 2006[70] | Pilot study | Construction of an electronic referral pro forma to facilitate referral of patients to colorectal surgeons. | Descriptive statistics. Comparisons of patient attendance rate, delays to booking and to actual appointment between 54 electronic referrals and 189 paper referrals. | Compared to paper referrals, electronic referrals were booked more quickly (same day vs 1 week later on average) and patients had lower nonattendance rates (8.5% vs 22.5%). Both results stated as statistically significant, but P values were not provided. |
| Shaw, 2007[49] | Observational study | Dermatology electronic referral in England. | Content of 131 electronic vs 139 paper referrals to dermatologists(NHS Choose and Book).[71] | Paper superior to electronic for clinical data such as current treatments (included in 68% of paper vs 39% of electronic referrals, P<0.001); electronic superior for demographic data. |
| Gandhi, 2008[50] | Nonrandomized trial | Electronic referral tool in the Partners Healthcare System in Massachusetts that included a structured referral‐letter generator and referral status tracker. Assigned to 1 intervention site and 1 control site. | Survey assessment. Fifty‐four of 117 PCPs responded (46%), 235 of 430 specialists responded (55%), 143 out of 210 patients responded (69%). | Intervention group showed high voluntary adoption (99%), higher information transfer rates prior to subspecialty visit (62% vs 12%), and lower rates of conflicting information being given to patients (6% vs 20%). |
| John, 2008[72] | Pilot study | Validation study of the Lower Gastrointestinal e‐RP (through the Choose and Book System in the United Kingdom) intended to improve yield of colon cancers diagnosed and to reduce delays in diagnosis. | Comparison of actual to simulated referral patterns through e‐RP for 300 patients divided into colorectal cancer, 2‐week wait suspected cancer, and routine referral groups. | e‐RP was more accurate than traditional referral at upgrading patients who had cancer to the appropriate suspected cancer referral group (85% vs 43%, P=0.002). |
| Kim, 2009[73] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Included reply functionality and ability to forward messaging to a scheduler for calendaring. | Impact of electronic referral system as measured by questionnaire to referring providers. A total of 298/368 participated (24 clinics); 53.5% attending physicians. | Electronic referrals improved overall quality of care (reported by 72%), guidance of presubspecialty visit (73%), and the ability to track referrals (89%). Small change in access for urgent issues (35% better, 49% reported no change). |
| Scott, 2009[74] | Pilot study | Pilot of urgent electronic referral system from PCPs to oncologists at South West Wales Cancer Centre. | Satisfaction statistics (10‐point Likert scale) collected from PCPs via interview. | Over 6 months, 99 referrals submitted; 81% were processed within 1 hour with high satisfaction scores. |
| Were, 2009[75] | Nonrandomized trial | Geriatrics consultants were provided system to make electronic recommendations (consultant‐recommended orders) in the native CPOE system along with consult notes in the intervention vs consult notes alone in the control. | Rates of implementation of consultant recommendations. Qualitative survey of users of the new system. | Higher total number of recommendations (247 vs 192, P<0.05) and higher implementation rates of consultant‐recommended orders in the intervention group vs control (78% vs 59%, P=0.01). High satisfaction scores on 5‐point Likert scale for the intervention system with good survey response rate (83%). |
| Dixon, 2010[52] | Observational study | Comparison of 2 extra‐EHR systems (NHS Choose and Book, Dutch ZorgDomein) for booking referrals. Patients choose doctor or hospital and the system transfers demographic and clinical information between PCP and specialist. | National data, patient and provider surveys, focus groups, observational studies. Focus was on patient choice, but evaluations included all aspects of the systems. | Resistance from PCPs during implementation; 78% of ZorgDomein PCPs felt referrals took more time; general displeasure on the part of specialists re: quality of referrals, although not quantified. |
| Patterson, 2010[51] | Observational study | E‐mail referral system to a neurologist in Northern Ireland. Referrals were template based and recorded as clinical episode in the patient administration system. Comparison of this system to conventional referrals to another neurologist. | Evaluated effectiveness, cost, safety for period 20022007. | Decreased referral wait times (4 vs 13 weeks) and 35% cost reduction per patient for the e‐mail referral vs conventional referrals. |
| No diminution in safety. Limitation: single neurologist participated. | ||||
| Singh, 2011[76] | Observational study | Chart review of electronic referrals to specialist practices in a Veterans Affairs outpatient system. | Follow‐up actions taken by subspecialists within 30 days of receiving referral. | An intra‐EHR referral system was still affected by communication breakdowns. Of 61,931 referrals, 36.4% were discontinued for inappropriate or incomplete referral requests. |
| Kim‐Hwang, 2010[77] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Follow‐up to Kim, 2009.[73] | Survey of medical and surgical subspecialty consultants. | Statistically significant differences in clarity of consult request in both medical and surgical clinics, in decreased inappropriate referrals in surgical clinics, in decreased use of follow‐up appointments by surgical specialists, and in decreased avoidable follow‐up surgical visits. |
| Warren, 2011[53] | Observational study | Electronic referrals from general medical practices to public referral network of Hutt Hospital in New Zealand (20072010). | Retrospective analysis of transactional data from messaging system and from general inpatient tracking system. Qualitative data collection via interviews. | Estimated 71% of 10,367 referrals were electronic referrals over 3 years. Statistically significant improvement in referral latency without change in staffing. Clinicians appreciate shared transparency of referrals but cite usability issues as barriers. |
| Need: Curbside consults (primary care physicians‐specialists) | ||||
| Bergus, 1998[54] | Observational study | Evaluation of the ECS for curbside consultations between family physicians and subspecialists. | Descriptive statistics of usage data; survey of users. | Median response time 16.1 hours; 92% of questions answered; almost 90% concerned specific patients. Both groups expressed satisfaction. |
| Abbott, 2002[55] | Observational study | Evaluation of Department of Defense Ask a Doc physician‐to‐physicians e‐mail consultation system over network of 21 states (19982000). | Descriptive statistics; qualitative assessment. | There were 3121 consultations. Average response time <12 hours. Minimal cost and effort to initiate and sustain. Felt to mirror clinical practice. Barriers were security and assignation of credit for consultation. |
| Need: Communication of results (primary care physicians ‐specialists) | ||||
| Singh, 2007[5][6] | Nonrandomized trial | Concurrent prospective evaluation of responses to 1017 critical imaging alert notifications in a Veterans Affairs outpatient system (2006). Radiologists generated alerts. Included receipt system. | Measured percentage of unacknowledged alerts and imaging lost to follow‐up. | There were 368 of 1017 transmitted alerts unacknowledged (36%); 45 were completely lost to follow‐up. There were 0.2% outpatient imaging results lost to follow‐up overall. |
| Singh, 2009[5][7] | Nonrandomized trial | Concurrent evaluation of responses to 1196 critical imaging alert notifications in a Veterans Affairs outpatient system (20072008). Similar coding system to Singh, 2007.[56] | Measured percentage of alerts acknowledged, timely follow‐up; compared electronic alerts alone to combination of alerts and phone calls or admission. | Percentage of alerts acknowledged did not differ by type of communication; combination of electronic alerts with phone follow‐up (OR: 0.12, P<0.001) or admission (OR: 0.22, P<0.001) decreased likelihood of delayed follow‐up. Alerts to 2 providers increased the likelihood of delayed follow‐up (OR: 1.99, P=0.03). |
| Abujudeh, 2009[5][8] | Observational study | Retrospective review of e‐mailbased alert system for abnormal imaging results at Massachusetts General Hospital 20052007. E‐mail alerting by radiologist to ordering physician of nonurgent findings. | Descriptive statistics; survey of referring physicians (12/26). | There were 56,691 out of 1,540,254 reports for important but not urgent findings; 93.3% generated e‐mail message (6.7% failure rate); 80% of alerts were viewed. Higher satisfaction for e‐mail alerts over conventional methods (eg, facsimile) for nonurgent but important findings. |
| Need: Communicate within 1 care setting (primary care physicians) | ||||
| Lanham, 2012[78] | Observational study | Comparison of practice‐level EHR use with communication patterns among physicians, nurses, medical assistants, practice managers, and nonclinical staff within individual practices in Texas. | Observation and semistructured interviews. Within‐practice communication patterns were categorized as fragmented or cohesive. Practice‐level EHR use was categorized as homogeneous or heterogeneous. | Clinical practices with cohesive within‐practice communication patterns were associated with homogeneous patterns of practice‐level EHR use. |
| Murphy, 2012[79] | Observational study | Review of note‐based messaging within the EHR in outpatient clinics of large tertiary Veterans Affairs facility. Clinic staff send additional signature request alerts linked to parent notes in the EHR to primary care physicians. | Reason for and origin of alerts. Parent note linked to alert was also reviewed for 3 value attributes: urgency; potential harm if alert was missed; subjective value to PCP of the alert. | Of the alerts reviewed, 53.7% of 525 were deemed of high value but required PCPs to review significant amounts of extraneous text (80.3% of words in parent notes) to get relevant information. Most alerts (40%) were medication, prescription, or refill related. |
Extra‐EHR IT
A review of electronic communication in 2000 examined electronic communication among primary care physicians but notably did not distinguish between communication and data exchange.[43] Of the thirty included publications in that review, seventeen publications dealt with electronically communicated information in general; the remaining studies focused on notifications of test results or transitions of care, reports from specialists, or electronic communication as replacement of traditional referral.[43] Although many studies of electronic communication described positive benefits, few included objective data, and most did not analyze provider‐to‐provider communication specifically. A survey of IT use outside of the EHR in 2006 documented that approximately 30% of clinicians used e‐mail to communicate with other clinicians, fewer than those who consulted on‐line journals (40.8%), but many more than those who communicated with patients by e‐mail at that time (3.6%).[44]
Intra‐EHR IT
A comparison of two physician surveys of EHR use in Massachusetts (the first in 2005 and the second in 2007) documented an increase in the percentage of practices with an EHR, from 23% to 35%; in those practices with EHRs, only the use of electronic prescribing increased over time. Use of secure electronic referrals or messaging including secure e‐mail remained unchanged; of note, referrals and messaging were considered a singular clinical function in that study. Between 2005 and 2007, referrals or clinical messaging were available in 62% and 63% of EHR systems, respectively, and they were used most or all of the time by 29% to 33% of the physicians who had an EHR.[45]
Electronic Referrals
Fourteen articles focused on electronic referrals. Two had a prepost or longitudinal study design,[46, 47] and five included a control group.[48, 49, 50, 51] The rest were descriptive. In most cases, electronic referral improved the transfer of information, especially when standardized message templates were created. Use of electronic referral appeared to result in reduced waiting time for appointments and enabled more efficient triage.
Barriers to integration of electronic referral in the EHR were also assessed. An intra‐EHR communication system requiring a primary care physician to integrate information e‐mailed by the consultant into the record showed the percentage of integrated notes decreasing over time.[47] Practitioners had mixed feelings about the system; although the majority (92% of respondents) felt that the system improved patient care and wanted to extend messaging to other patient groups, they also felt that electronic messaging decreased the ease of reviewing data (83%) and confused tasks and responsibilities (59%). A study of British and Dutch electronic referral systems described significant resistance on the part of practitioners to electronic referrals and concern on the part of specialists about the quality of referrals.[52] Another study demonstrated improvement in quality of demographic data but degradation in quality of clinical information when referrals were submitted electronically.[49] A recent transactional analysis of electronic referrals in New Zealand showed high uptake and reduced referral latency compared to conventional referral; clinicians cited usability concerns as the major barrier to use.[53]
Curbside Consultations via E‐mail
Two studies evaluated curbside consultations via e‐mail and documented high provider satisfaction and rapid turnaround.[54, 55] The preliminary nature of these studies raises questions of sustainability and long‐term implementation.
Results Notification
Three studies focused on test‐result reporting from radiologists. In these studies, a radiologist could designate a result as high priority and have an e‐mail notification sent to the ordering physicians.[56, 57, 58] Urgent results were relayed by telephone. Lack of acknowledgement of alerts impacted the results of every study, and in one of these studies, alerting two physicians, rather than just one, decreased the likelihood that the results would be followed up.[57] Providers did prefer e‐mail to fax notification.[58]
DISCUSSION
The principal findings of the literature review demonstrate the paucity of quantitative data surrounding provider‐to‐provider communication. The majority of studies focused on physicians as providers without emphasis on other provider types on the care team. Most of the quantitative studies investigated electronic referrals. Data collected largely represented measures of provider satisfaction and process measures. Few quantitative studies used established models or measures of team coordination or communication.
This study extends the work of others by compiling a comprehensive view of electronic provider‐to‐provider communication. A recent review of devices for clinical communication tells a part of the story,[28] and our review adds a comprehensive, device‐agnostic look at the systems physicians and other providers use every day.
Limitations of this review include the small number of eligible studies and a homogenous provider type (physicians). The latter is both an important finding and a limitation to generalizability of our results. Reviewed studies were in English only. The literature review by its nature is subject to publication bias.
Intra‐EHR communication cannot serve all purposes, and is it not a panacea for effective care coordination. One recent qualitative study warns about the pitfalls of electronic communication. Interviews with physicians from twenty‐six practices elicited some concerns about the resulting decrease in face‐to‐face communication that has resulted from the adoption of electronic communication tools.[32] This finding brings implications: (1) a false sense of security may reduce verbal communications when they are needed mostduring emergencies or when caring for complex patients who require detailed, nuanced discussion; and (2) fewer conversations within a practice can reduce both knowledge sharing and basic social interactions necessary for the maintenance of a collaboration. Last, privacy and confidentiality are top priorities. Common electronic communication tools are susceptible to security breaches,[47, 59] and innovations within this domain must conform to Health Insurance Portability and Accountability Act of 1996 and Health Information Technology for Economic and Clinical Health Act regulations.[60]
Although electronic communication is not a complete solution for clinical collaboration, it is difficult to use face‐to‐face communication and telephone communication to convey large amounts of patient information while simultaneously generating a record of the transaction. Moreover, paging functions, telephone calls, and face‐to‐face encounters can be highly interruptive, increasing cognitive load, burdening working memory, and shifting attention from the task at hand.[14] Interruptions contribute to inefficiency and to the potential for errors.[61]
Effective coordination of care for the chronically ill is one of the essential goals of the health system; it is an ongoing process that depends on constant, effective communication. Bates and Bitton have recognized this and described the crucial role that HIT will play in creating an effective medical home by enumerating seven domains of HIT especially in need of research.[62] In particular, they note that effective team care and care transitions will depend on an EHR that promotes both implicit and real‐time communication: it will be essential to develop communication tools that allow practices to record goals shared by providers and patients alike, and to track medical interventions and progress.[62]
Future research could investigate a number of open questions. Overall, an emphasis should be placed on rigorous qualitative and quantitative evaluation of electronic communication. Process measures, such as length of stay, hospital readmission rates, and measures of care coordination, should be framed ultimately with respect to patient health outcomes. Such data are beginning to be reported.[63]
It is unclear which types of communications would be best served within the EHR and which should remain external to it. Instant communication or chat has not been studied sufficiently to show a demonstrable impact on patient care. Cross‐coverage and team identification within the EHR can be further studied with respect to workflows and best practices. Studies using structured observation or time‐and‐motion analysis could provide insight into use cases and workflows that providers implement to discuss patients. Future research should incorporate established models of communication[5] and coordination.[64] Data on unintended consequences or harms of provider‐to‐provider electronic communication have been limited, and this area should be considered in subsequent work. Finally, although the scope of this review focused on communication between providers, transformative electronic communication systems should bridge communication gaps between providers and patients as well.
As adoption of EHRs in US hospitals has increased from 15.1% of US hospitals in 2010 to 26.6% in 2011 for any type of EHR and 3.6% to 8.7% for comprehensive EHRs,[65] it is worth noting that Meaningful Use, as it stands, incentivizes patient‐provider communication, but not communication between providers. Inclusion of certification criteria focused on provider‐to‐provider communication may spur additional innovation.
CONCLUSIONS
The optimal features to support electronic communication between providers remain under‐assessed, although there is preliminary evidence for the acceptability of electronic referrals. Without better understanding of electronic communication on workflow, provider satisfaction, and patient outcomes, the impact of such tools on coordination of complex medical care will be an open question, and it remains an important one to answer.
Acknowledgments
The authors would like to express their gratitude to Dr. Thomas Payne, Medical Director of IT Services at the University of Washington, for sharing his expertise, and to Marina Chilov, medical librarian at Columbia University, for her assistance with the literature search. The authors would like to thank Paul Sun, MA, for his assistance with the literature review.
Disclosures: This work was funded by 5K22LM8805 (PDS) and T15 LM007079 (CW, SC) grants. Dr. Stetson serves on the advisory board of the Allscripts Enterprise EHR.
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INTRODUCTION
Coordination of care within a practice, during transitions of care, and between primary and specialty care teams requires more than data exchange; it requires effective communication among healthcare providers.[1, 2, 3] In clinical terms, data exchange, communication, and care coordination are related, but they represent distinct concepts.[4] Data exchange refers to transfer of information between settings, independent of the individuals involved, whereas communication is the multistep process that enables information exchange between two people.[5] Care coordination, as defined by O'Malley, is integration of care in consultation with patients, their families and caregivers across all of a patient's conditions, needs, clinicians and settings.[3]
Strong collaboration among providers has been associated with improved patient outcomes.[2, 6] Yet, despite the significant role of communication in healthcare, communication may not take place at all, even at high‐stakes events like transitions of care,[7, 8] or it may be done poorly at the risk of substantial clinical morbidity and mortality.[9, 10, 11, 12, 13, 14, 15, 16]
Proof of the global effectiveness of health information technology (HIT) to improve patient care is lacking, but data from some studies demonstrate real improvements in quality and safety in specific areas,[17, 18, 19] especially with computerized physician order entry[20] and electronic prescribing.[21]
The limited information about the effect of HIT on communication focuses largely on the anticipated improvements in patient‐physician communication[22, 23, 24, 25, 26, 27]; provider‐to‐provider communication within the electronic domain is not as well understood. A recent review of interventions involving communication devices such as pagers and mobile phones found limited high‐quality evidence in the literature.[28] Clinicians have described what they consider to be key characteristics of clinical electronic communications systems such as security/reliability, cross coverage, overall convenience, and message prioritization.[29] Although the electronic health record (EHR) is expected to assist with this communication,[30] it also has the potential to impede effective communication, leading physicians to resort to more traditional workarounds.[31, 32, 33]
Measuring and improving the use of EHRs nationally were driving forces behind the creation of the Meaningful Use incentive program in the United States.[34] To receive the incentive payments, providers must meet and report on a series of measures set in three stages over the course of five years.[35] In the current state, Meaningful Use does not reward provider‐to‐provider communication within the EHR.[36, 37] The main communication objectives for stages 1 and 2 concentrate on patient‐to‐provider communication, such as patient portals and patient‐to‐provider messaging.[36, 37]
Understanding the current evidence for provider‐to‐provider communication within EHRs, its reported effectiveness, and its shortcomings may help to develop a roadmap for identifying next‐generation solutions to support coordination of care.[38, 39] This review assesses the literature regarding provider‐to‐provider electronic communication tools (as supported within or external to an EHR). It is intended as a comprehensive view of studies reporting quantitative measures of the impact of electronic communication on providers and patients.
METHODS
Definitions and Conceptual Model of Provider‐to‐Provider Communication
We conducted a systematic review of studies of provider‐to‐provider electronic communication. This review included only formal clinical communication between providers and was informed by the Coiera communications paradigm.[5] This paradigm consists of four steps: (1) task identification, when a task is identified and associated with the appropriate individual; (2) connection, when an attempt is made to contact that person; (3) communication, when task‐specific information is exchanged between the parties; and (4) disconnection, when the task reaches some stage of completion.
Literature Review
We examined written electronic communication between providers including e‐mail, text messaging, and instant messaging. We did not review provider‐to‐provider telephone or telehealth communication, as these are not generally supported within EHR systems. Communication in all clinical contexts was included among providers within an individual clinic or hospital and among providers across specialties or practice settings.[40] We excluded physician handoff communication because it has been extensively reviewed elsewhere and because handoff occurs largely through verbal exchange not recorded in the EHR.[41, 42] Communication from clinical information systems to providers, such as automated notification of unacknowledged orders, was also excluded, as it is not within the scope of provider‐to‐provider interaction.
Data Sources and Searches
A comprehensive literature search was conducted in Ovid MEDLINE with the input of a medical librarian, and a parallel search was performed using PubMed. The Ovid MEDLINE query and parallel database search terms are documented in Table 1. Subsearches were conducted in Google Scholar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Academic Search Premier for peer‐reviewed journals. Subsequent studies citing the initially detected articles were found through citation maps.
| Database | Strategy | Items Reviewed |
|---|---|---|
| ||
| Ovid MEDLINE | Query terms: exp medicine/ or physicians or exp outpatient clinics/ or exp hospitals/ AND *communication/ or *computer communication networks/ or *interprofessional relations/ or *continuity of patient care/ AND electronic mail or referral and consultation or text messaging/ or reminder systems. | 1513 |
| PubMed | Healthcare, provider, communication, messaging, e‐mail, texting, text messaging, instant messaging, paging, coordination, referral, EHR, EMR, electronic health record, electronic medical record, electronic, and physician. Excluding patient‐provider and patient‐physician | 340 |
| Google Scholar | Physician‐physician electronic communication excluding physician‐patient | 940 |
| CINAHL | Medical records and communication; or computerized patient records and communication | None |
| Academic Search Premier (peer‐reviewed journals) | Electronic health record and communication | 54 |
| Communication and electronic health record | 80 | |
| Physician‐physician communication | 2 | |
| Physicians and electronic health records | 88 | |
Study Selection
Paper Inclusion Criteria
Requirements included publication in English‐language peer‐reviewed journals. Included studies provided quantitative provider‐to‐provider communication data, provider satisfaction statistics, or EHR communication data. Provider‐to‐staff communication was also included if it fell within the scope of studies of communication between providers.
Paper Exclusion Criteria
Studies excluded in this review were articles that reviewed EHR systems without any focus on communication between providers and those that discussed EHR models and strategies but did not include actual testing and quantitative results. Results that included nontraditional online documents or that were found on nonpeer‐reviewed websites were also discarded. Duplicate records or publications that covered the same study were also removed. The most common reason for exclusion was the lack of quantitative evaluation.
Data Extraction and Quality Assessment
Three authors (Walsh, Siegler, Stetson) reviewed titles and abstracts of resultant studies against inclusion and exclusion criteria (Figure 1). Studies were evaluated qualitatively and findings summarized. Given the heterogeneous nature of data reported, statistical analysis was not possible.
RESULTS
The primary and parallel searches produced 2946 results that were weaned through title review and exclusion of duplicates, nonEnglish‐language, and nonhuman studies to 820 articles for title and abstract review (Figure 1). After careful review of the articles' titles, abstracts, or full content (where appropriate), twenty‐five articles met inclusion criteria and presented data about provider‐to‐provider electronic communication, either within an EHR or through a system designed to promote provider‐to‐provider communication. All of the studies that met inclusion criteria focused on physicians as providers. Five studies (20%) described trial design, three (12%) were pilot studies, and seventeen (68%) were observational studies. Thirteen of twenty‐five articles (52%) described studies conducted in the United States and twelve in Europe.
Most of the studies (56%) focused on electronic referrals between primary care and subspecialty providers. The clinical need was to communicate information on a specific patient with a specialist who shared responsibility for the overall plan of care. Only two studies evaluated curbside consultation, where providers ask for clinical recommendations without formally engaging a specialist in the plan of care for a particular patient. Table 2 summarizes included studies and has been organized with respect to clinical need under evaluation. The major themes that emerged from this review included: studies of penetration of communication tools either within the EHR system (intra‐EHR IT) or external to the EHR (extra‐EHR IT); electronic referrals; curbside consultations; and test results reporting (results notification).
| Primary Author, Year | Design | Intervention | Measurement | Results |
|---|---|---|---|---|
| ||||
| Need: Communicate care across clinical settings (inpatient‐outpatient) | ||||
| Branger, 1992[4, 6] | Observational study | Introduction of electronic messaging system in the Netherlands between hospital and PCPs. | Satisfaction survey data using Likert scale of usefulness. | Free text messaging to exchange patient data was rated very useful or useful by 20 of 27 PCP respondents. |
| Reponen, 2004[66] | Observational study | Finnish study of electronic referrals XML messages between EHRs or secure web links. | User questionnaire. No description of respondents was provided. | Internists surveyed estimated that electronic referrals accelerate the referral process by 1 week. |
| Need: Communicate care across specialties (primary care physicians‐specialists) | ||||
| Kooijman, 1998[67] | Observational study | Survey of 45 PCPs who received notes from specialists via Electronic Data Interchange. | User questionnaire with 5‐point Likert scale of satisfaction, from 1 (much better) to 5 (much worse). | Highest satisfaction scores for speed (1.51.8) and efficiency (1.51.7) for electronic messages, with lower scores for reliability (2.52.7) and clarity (2.5). |
| Harno, 2000[4][8] | Nonrandomized trial | Eight‐month prospective comparative study in Finland of outpatient clinics in hospitals with and without intranet referral systems. | Comparison of numbers of electronic referrals, clinic visits, costs. | There were 43% of electronic referrals and 79% of outpatient referrals that resulted in outpatient visits. A 3‐fold increase in productivity overall and 7‐fold reduction in visit costs per patient using e‐mail consultation. |
| Moorman, 2001[4][7] | Observational study | Supersedes Branger, 1999.[68] Analyzes intra‐EHR communications between PCPs and consultant in Netherlands re: diabetes management of patients (19941998). | Descriptive statistics of number of messages, content, whether message had been copied into EMR; survey of PCPs (12 of 15 responded). | Decline in integration by PCPs of messages in the EHR from 75% to 51% over first 3 years. Despite this, most PCPs wanted to extend messaging to other patient groups. |
| Bergus, 2006[69] | Observational study | Follow‐up of Bergus, 1998[54]; evaluated formulation of clinical referrals to specialists at the University of Iowa by retrospective review of e‐mail transcripts. | Analyzed taxonomy of clinical questions; assessed need for clinical consultation of 1618 clinical questions. | Specialists less likely to recommend clinic consultation if referral specified the clinical task (OR: 0.36, P<0.001), intervention (OR: 0.62, P=0.004), or outcome (OR: 0.49, P<0.001). This effect was independent of clinical content (P>0.05). |
| Dennison, 2006[70] | Pilot study | Construction of an electronic referral pro forma to facilitate referral of patients to colorectal surgeons. | Descriptive statistics. Comparisons of patient attendance rate, delays to booking and to actual appointment between 54 electronic referrals and 189 paper referrals. | Compared to paper referrals, electronic referrals were booked more quickly (same day vs 1 week later on average) and patients had lower nonattendance rates (8.5% vs 22.5%). Both results stated as statistically significant, but P values were not provided. |
| Shaw, 2007[49] | Observational study | Dermatology electronic referral in England. | Content of 131 electronic vs 139 paper referrals to dermatologists(NHS Choose and Book).[71] | Paper superior to electronic for clinical data such as current treatments (included in 68% of paper vs 39% of electronic referrals, P<0.001); electronic superior for demographic data. |
| Gandhi, 2008[50] | Nonrandomized trial | Electronic referral tool in the Partners Healthcare System in Massachusetts that included a structured referral‐letter generator and referral status tracker. Assigned to 1 intervention site and 1 control site. | Survey assessment. Fifty‐four of 117 PCPs responded (46%), 235 of 430 specialists responded (55%), 143 out of 210 patients responded (69%). | Intervention group showed high voluntary adoption (99%), higher information transfer rates prior to subspecialty visit (62% vs 12%), and lower rates of conflicting information being given to patients (6% vs 20%). |
| John, 2008[72] | Pilot study | Validation study of the Lower Gastrointestinal e‐RP (through the Choose and Book System in the United Kingdom) intended to improve yield of colon cancers diagnosed and to reduce delays in diagnosis. | Comparison of actual to simulated referral patterns through e‐RP for 300 patients divided into colorectal cancer, 2‐week wait suspected cancer, and routine referral groups. | e‐RP was more accurate than traditional referral at upgrading patients who had cancer to the appropriate suspected cancer referral group (85% vs 43%, P=0.002). |
| Kim, 2009[73] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Included reply functionality and ability to forward messaging to a scheduler for calendaring. | Impact of electronic referral system as measured by questionnaire to referring providers. A total of 298/368 participated (24 clinics); 53.5% attending physicians. | Electronic referrals improved overall quality of care (reported by 72%), guidance of presubspecialty visit (73%), and the ability to track referrals (89%). Small change in access for urgent issues (35% better, 49% reported no change). |
| Scott, 2009[74] | Pilot study | Pilot of urgent electronic referral system from PCPs to oncologists at South West Wales Cancer Centre. | Satisfaction statistics (10‐point Likert scale) collected from PCPs via interview. | Over 6 months, 99 referrals submitted; 81% were processed within 1 hour with high satisfaction scores. |
| Were, 2009[75] | Nonrandomized trial | Geriatrics consultants were provided system to make electronic recommendations (consultant‐recommended orders) in the native CPOE system along with consult notes in the intervention vs consult notes alone in the control. | Rates of implementation of consultant recommendations. Qualitative survey of users of the new system. | Higher total number of recommendations (247 vs 192, P<0.05) and higher implementation rates of consultant‐recommended orders in the intervention group vs control (78% vs 59%, P=0.01). High satisfaction scores on 5‐point Likert scale for the intervention system with good survey response rate (83%). |
| Dixon, 2010[52] | Observational study | Comparison of 2 extra‐EHR systems (NHS Choose and Book, Dutch ZorgDomein) for booking referrals. Patients choose doctor or hospital and the system transfers demographic and clinical information between PCP and specialist. | National data, patient and provider surveys, focus groups, observational studies. Focus was on patient choice, but evaluations included all aspects of the systems. | Resistance from PCPs during implementation; 78% of ZorgDomein PCPs felt referrals took more time; general displeasure on the part of specialists re: quality of referrals, although not quantified. |
| Patterson, 2010[51] | Observational study | E‐mail referral system to a neurologist in Northern Ireland. Referrals were template based and recorded as clinical episode in the patient administration system. Comparison of this system to conventional referrals to another neurologist. | Evaluated effectiveness, cost, safety for period 20022007. | Decreased referral wait times (4 vs 13 weeks) and 35% cost reduction per patient for the e‐mail referral vs conventional referrals. |
| No diminution in safety. Limitation: single neurologist participated. | ||||
| Singh, 2011[76] | Observational study | Chart review of electronic referrals to specialist practices in a Veterans Affairs outpatient system. | Follow‐up actions taken by subspecialists within 30 days of receiving referral. | An intra‐EHR referral system was still affected by communication breakdowns. Of 61,931 referrals, 36.4% were discontinued for inappropriate or incomplete referral requests. |
| Kim‐Hwang, 2010[77] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Follow‐up to Kim, 2009.[73] | Survey of medical and surgical subspecialty consultants. | Statistically significant differences in clarity of consult request in both medical and surgical clinics, in decreased inappropriate referrals in surgical clinics, in decreased use of follow‐up appointments by surgical specialists, and in decreased avoidable follow‐up surgical visits. |
| Warren, 2011[53] | Observational study | Electronic referrals from general medical practices to public referral network of Hutt Hospital in New Zealand (20072010). | Retrospective analysis of transactional data from messaging system and from general inpatient tracking system. Qualitative data collection via interviews. | Estimated 71% of 10,367 referrals were electronic referrals over 3 years. Statistically significant improvement in referral latency without change in staffing. Clinicians appreciate shared transparency of referrals but cite usability issues as barriers. |
| Need: Curbside consults (primary care physicians‐specialists) | ||||
| Bergus, 1998[54] | Observational study | Evaluation of the ECS for curbside consultations between family physicians and subspecialists. | Descriptive statistics of usage data; survey of users. | Median response time 16.1 hours; 92% of questions answered; almost 90% concerned specific patients. Both groups expressed satisfaction. |
| Abbott, 2002[55] | Observational study | Evaluation of Department of Defense Ask a Doc physician‐to‐physicians e‐mail consultation system over network of 21 states (19982000). | Descriptive statistics; qualitative assessment. | There were 3121 consultations. Average response time <12 hours. Minimal cost and effort to initiate and sustain. Felt to mirror clinical practice. Barriers were security and assignation of credit for consultation. |
| Need: Communication of results (primary care physicians ‐specialists) | ||||
| Singh, 2007[5][6] | Nonrandomized trial | Concurrent prospective evaluation of responses to 1017 critical imaging alert notifications in a Veterans Affairs outpatient system (2006). Radiologists generated alerts. Included receipt system. | Measured percentage of unacknowledged alerts and imaging lost to follow‐up. | There were 368 of 1017 transmitted alerts unacknowledged (36%); 45 were completely lost to follow‐up. There were 0.2% outpatient imaging results lost to follow‐up overall. |
| Singh, 2009[5][7] | Nonrandomized trial | Concurrent evaluation of responses to 1196 critical imaging alert notifications in a Veterans Affairs outpatient system (20072008). Similar coding system to Singh, 2007.[56] | Measured percentage of alerts acknowledged, timely follow‐up; compared electronic alerts alone to combination of alerts and phone calls or admission. | Percentage of alerts acknowledged did not differ by type of communication; combination of electronic alerts with phone follow‐up (OR: 0.12, P<0.001) or admission (OR: 0.22, P<0.001) decreased likelihood of delayed follow‐up. Alerts to 2 providers increased the likelihood of delayed follow‐up (OR: 1.99, P=0.03). |
| Abujudeh, 2009[5][8] | Observational study | Retrospective review of e‐mailbased alert system for abnormal imaging results at Massachusetts General Hospital 20052007. E‐mail alerting by radiologist to ordering physician of nonurgent findings. | Descriptive statistics; survey of referring physicians (12/26). | There were 56,691 out of 1,540,254 reports for important but not urgent findings; 93.3% generated e‐mail message (6.7% failure rate); 80% of alerts were viewed. Higher satisfaction for e‐mail alerts over conventional methods (eg, facsimile) for nonurgent but important findings. |
| Need: Communicate within 1 care setting (primary care physicians) | ||||
| Lanham, 2012[78] | Observational study | Comparison of practice‐level EHR use with communication patterns among physicians, nurses, medical assistants, practice managers, and nonclinical staff within individual practices in Texas. | Observation and semistructured interviews. Within‐practice communication patterns were categorized as fragmented or cohesive. Practice‐level EHR use was categorized as homogeneous or heterogeneous. | Clinical practices with cohesive within‐practice communication patterns were associated with homogeneous patterns of practice‐level EHR use. |
| Murphy, 2012[79] | Observational study | Review of note‐based messaging within the EHR in outpatient clinics of large tertiary Veterans Affairs facility. Clinic staff send additional signature request alerts linked to parent notes in the EHR to primary care physicians. | Reason for and origin of alerts. Parent note linked to alert was also reviewed for 3 value attributes: urgency; potential harm if alert was missed; subjective value to PCP of the alert. | Of the alerts reviewed, 53.7% of 525 were deemed of high value but required PCPs to review significant amounts of extraneous text (80.3% of words in parent notes) to get relevant information. Most alerts (40%) were medication, prescription, or refill related. |
Extra‐EHR IT
A review of electronic communication in 2000 examined electronic communication among primary care physicians but notably did not distinguish between communication and data exchange.[43] Of the thirty included publications in that review, seventeen publications dealt with electronically communicated information in general; the remaining studies focused on notifications of test results or transitions of care, reports from specialists, or electronic communication as replacement of traditional referral.[43] Although many studies of electronic communication described positive benefits, few included objective data, and most did not analyze provider‐to‐provider communication specifically. A survey of IT use outside of the EHR in 2006 documented that approximately 30% of clinicians used e‐mail to communicate with other clinicians, fewer than those who consulted on‐line journals (40.8%), but many more than those who communicated with patients by e‐mail at that time (3.6%).[44]
Intra‐EHR IT
A comparison of two physician surveys of EHR use in Massachusetts (the first in 2005 and the second in 2007) documented an increase in the percentage of practices with an EHR, from 23% to 35%; in those practices with EHRs, only the use of electronic prescribing increased over time. Use of secure electronic referrals or messaging including secure e‐mail remained unchanged; of note, referrals and messaging were considered a singular clinical function in that study. Between 2005 and 2007, referrals or clinical messaging were available in 62% and 63% of EHR systems, respectively, and they were used most or all of the time by 29% to 33% of the physicians who had an EHR.[45]
Electronic Referrals
Fourteen articles focused on electronic referrals. Two had a prepost or longitudinal study design,[46, 47] and five included a control group.[48, 49, 50, 51] The rest were descriptive. In most cases, electronic referral improved the transfer of information, especially when standardized message templates were created. Use of electronic referral appeared to result in reduced waiting time for appointments and enabled more efficient triage.
Barriers to integration of electronic referral in the EHR were also assessed. An intra‐EHR communication system requiring a primary care physician to integrate information e‐mailed by the consultant into the record showed the percentage of integrated notes decreasing over time.[47] Practitioners had mixed feelings about the system; although the majority (92% of respondents) felt that the system improved patient care and wanted to extend messaging to other patient groups, they also felt that electronic messaging decreased the ease of reviewing data (83%) and confused tasks and responsibilities (59%). A study of British and Dutch electronic referral systems described significant resistance on the part of practitioners to electronic referrals and concern on the part of specialists about the quality of referrals.[52] Another study demonstrated improvement in quality of demographic data but degradation in quality of clinical information when referrals were submitted electronically.[49] A recent transactional analysis of electronic referrals in New Zealand showed high uptake and reduced referral latency compared to conventional referral; clinicians cited usability concerns as the major barrier to use.[53]
Curbside Consultations via E‐mail
Two studies evaluated curbside consultations via e‐mail and documented high provider satisfaction and rapid turnaround.[54, 55] The preliminary nature of these studies raises questions of sustainability and long‐term implementation.
Results Notification
Three studies focused on test‐result reporting from radiologists. In these studies, a radiologist could designate a result as high priority and have an e‐mail notification sent to the ordering physicians.[56, 57, 58] Urgent results were relayed by telephone. Lack of acknowledgement of alerts impacted the results of every study, and in one of these studies, alerting two physicians, rather than just one, decreased the likelihood that the results would be followed up.[57] Providers did prefer e‐mail to fax notification.[58]
DISCUSSION
The principal findings of the literature review demonstrate the paucity of quantitative data surrounding provider‐to‐provider communication. The majority of studies focused on physicians as providers without emphasis on other provider types on the care team. Most of the quantitative studies investigated electronic referrals. Data collected largely represented measures of provider satisfaction and process measures. Few quantitative studies used established models or measures of team coordination or communication.
This study extends the work of others by compiling a comprehensive view of electronic provider‐to‐provider communication. A recent review of devices for clinical communication tells a part of the story,[28] and our review adds a comprehensive, device‐agnostic look at the systems physicians and other providers use every day.
Limitations of this review include the small number of eligible studies and a homogenous provider type (physicians). The latter is both an important finding and a limitation to generalizability of our results. Reviewed studies were in English only. The literature review by its nature is subject to publication bias.
Intra‐EHR communication cannot serve all purposes, and is it not a panacea for effective care coordination. One recent qualitative study warns about the pitfalls of electronic communication. Interviews with physicians from twenty‐six practices elicited some concerns about the resulting decrease in face‐to‐face communication that has resulted from the adoption of electronic communication tools.[32] This finding brings implications: (1) a false sense of security may reduce verbal communications when they are needed mostduring emergencies or when caring for complex patients who require detailed, nuanced discussion; and (2) fewer conversations within a practice can reduce both knowledge sharing and basic social interactions necessary for the maintenance of a collaboration. Last, privacy and confidentiality are top priorities. Common electronic communication tools are susceptible to security breaches,[47, 59] and innovations within this domain must conform to Health Insurance Portability and Accountability Act of 1996 and Health Information Technology for Economic and Clinical Health Act regulations.[60]
Although electronic communication is not a complete solution for clinical collaboration, it is difficult to use face‐to‐face communication and telephone communication to convey large amounts of patient information while simultaneously generating a record of the transaction. Moreover, paging functions, telephone calls, and face‐to‐face encounters can be highly interruptive, increasing cognitive load, burdening working memory, and shifting attention from the task at hand.[14] Interruptions contribute to inefficiency and to the potential for errors.[61]
Effective coordination of care for the chronically ill is one of the essential goals of the health system; it is an ongoing process that depends on constant, effective communication. Bates and Bitton have recognized this and described the crucial role that HIT will play in creating an effective medical home by enumerating seven domains of HIT especially in need of research.[62] In particular, they note that effective team care and care transitions will depend on an EHR that promotes both implicit and real‐time communication: it will be essential to develop communication tools that allow practices to record goals shared by providers and patients alike, and to track medical interventions and progress.[62]
Future research could investigate a number of open questions. Overall, an emphasis should be placed on rigorous qualitative and quantitative evaluation of electronic communication. Process measures, such as length of stay, hospital readmission rates, and measures of care coordination, should be framed ultimately with respect to patient health outcomes. Such data are beginning to be reported.[63]
It is unclear which types of communications would be best served within the EHR and which should remain external to it. Instant communication or chat has not been studied sufficiently to show a demonstrable impact on patient care. Cross‐coverage and team identification within the EHR can be further studied with respect to workflows and best practices. Studies using structured observation or time‐and‐motion analysis could provide insight into use cases and workflows that providers implement to discuss patients. Future research should incorporate established models of communication[5] and coordination.[64] Data on unintended consequences or harms of provider‐to‐provider electronic communication have been limited, and this area should be considered in subsequent work. Finally, although the scope of this review focused on communication between providers, transformative electronic communication systems should bridge communication gaps between providers and patients as well.
As adoption of EHRs in US hospitals has increased from 15.1% of US hospitals in 2010 to 26.6% in 2011 for any type of EHR and 3.6% to 8.7% for comprehensive EHRs,[65] it is worth noting that Meaningful Use, as it stands, incentivizes patient‐provider communication, but not communication between providers. Inclusion of certification criteria focused on provider‐to‐provider communication may spur additional innovation.
CONCLUSIONS
The optimal features to support electronic communication between providers remain under‐assessed, although there is preliminary evidence for the acceptability of electronic referrals. Without better understanding of electronic communication on workflow, provider satisfaction, and patient outcomes, the impact of such tools on coordination of complex medical care will be an open question, and it remains an important one to answer.
Acknowledgments
The authors would like to express their gratitude to Dr. Thomas Payne, Medical Director of IT Services at the University of Washington, for sharing his expertise, and to Marina Chilov, medical librarian at Columbia University, for her assistance with the literature search. The authors would like to thank Paul Sun, MA, for his assistance with the literature review.
Disclosures: This work was funded by 5K22LM8805 (PDS) and T15 LM007079 (CW, SC) grants. Dr. Stetson serves on the advisory board of the Allscripts Enterprise EHR.
INTRODUCTION
Coordination of care within a practice, during transitions of care, and between primary and specialty care teams requires more than data exchange; it requires effective communication among healthcare providers.[1, 2, 3] In clinical terms, data exchange, communication, and care coordination are related, but they represent distinct concepts.[4] Data exchange refers to transfer of information between settings, independent of the individuals involved, whereas communication is the multistep process that enables information exchange between two people.[5] Care coordination, as defined by O'Malley, is integration of care in consultation with patients, their families and caregivers across all of a patient's conditions, needs, clinicians and settings.[3]
Strong collaboration among providers has been associated with improved patient outcomes.[2, 6] Yet, despite the significant role of communication in healthcare, communication may not take place at all, even at high‐stakes events like transitions of care,[7, 8] or it may be done poorly at the risk of substantial clinical morbidity and mortality.[9, 10, 11, 12, 13, 14, 15, 16]
Proof of the global effectiveness of health information technology (HIT) to improve patient care is lacking, but data from some studies demonstrate real improvements in quality and safety in specific areas,[17, 18, 19] especially with computerized physician order entry[20] and electronic prescribing.[21]
The limited information about the effect of HIT on communication focuses largely on the anticipated improvements in patient‐physician communication[22, 23, 24, 25, 26, 27]; provider‐to‐provider communication within the electronic domain is not as well understood. A recent review of interventions involving communication devices such as pagers and mobile phones found limited high‐quality evidence in the literature.[28] Clinicians have described what they consider to be key characteristics of clinical electronic communications systems such as security/reliability, cross coverage, overall convenience, and message prioritization.[29] Although the electronic health record (EHR) is expected to assist with this communication,[30] it also has the potential to impede effective communication, leading physicians to resort to more traditional workarounds.[31, 32, 33]
Measuring and improving the use of EHRs nationally were driving forces behind the creation of the Meaningful Use incentive program in the United States.[34] To receive the incentive payments, providers must meet and report on a series of measures set in three stages over the course of five years.[35] In the current state, Meaningful Use does not reward provider‐to‐provider communication within the EHR.[36, 37] The main communication objectives for stages 1 and 2 concentrate on patient‐to‐provider communication, such as patient portals and patient‐to‐provider messaging.[36, 37]
Understanding the current evidence for provider‐to‐provider communication within EHRs, its reported effectiveness, and its shortcomings may help to develop a roadmap for identifying next‐generation solutions to support coordination of care.[38, 39] This review assesses the literature regarding provider‐to‐provider electronic communication tools (as supported within or external to an EHR). It is intended as a comprehensive view of studies reporting quantitative measures of the impact of electronic communication on providers and patients.
METHODS
Definitions and Conceptual Model of Provider‐to‐Provider Communication
We conducted a systematic review of studies of provider‐to‐provider electronic communication. This review included only formal clinical communication between providers and was informed by the Coiera communications paradigm.[5] This paradigm consists of four steps: (1) task identification, when a task is identified and associated with the appropriate individual; (2) connection, when an attempt is made to contact that person; (3) communication, when task‐specific information is exchanged between the parties; and (4) disconnection, when the task reaches some stage of completion.
Literature Review
We examined written electronic communication between providers including e‐mail, text messaging, and instant messaging. We did not review provider‐to‐provider telephone or telehealth communication, as these are not generally supported within EHR systems. Communication in all clinical contexts was included among providers within an individual clinic or hospital and among providers across specialties or practice settings.[40] We excluded physician handoff communication because it has been extensively reviewed elsewhere and because handoff occurs largely through verbal exchange not recorded in the EHR.[41, 42] Communication from clinical information systems to providers, such as automated notification of unacknowledged orders, was also excluded, as it is not within the scope of provider‐to‐provider interaction.
Data Sources and Searches
A comprehensive literature search was conducted in Ovid MEDLINE with the input of a medical librarian, and a parallel search was performed using PubMed. The Ovid MEDLINE query and parallel database search terms are documented in Table 1. Subsearches were conducted in Google Scholar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Academic Search Premier for peer‐reviewed journals. Subsequent studies citing the initially detected articles were found through citation maps.
| Database | Strategy | Items Reviewed |
|---|---|---|
| ||
| Ovid MEDLINE | Query terms: exp medicine/ or physicians or exp outpatient clinics/ or exp hospitals/ AND *communication/ or *computer communication networks/ or *interprofessional relations/ or *continuity of patient care/ AND electronic mail or referral and consultation or text messaging/ or reminder systems. | 1513 |
| PubMed | Healthcare, provider, communication, messaging, e‐mail, texting, text messaging, instant messaging, paging, coordination, referral, EHR, EMR, electronic health record, electronic medical record, electronic, and physician. Excluding patient‐provider and patient‐physician | 340 |
| Google Scholar | Physician‐physician electronic communication excluding physician‐patient | 940 |
| CINAHL | Medical records and communication; or computerized patient records and communication | None |
| Academic Search Premier (peer‐reviewed journals) | Electronic health record and communication | 54 |
| Communication and electronic health record | 80 | |
| Physician‐physician communication | 2 | |
| Physicians and electronic health records | 88 | |
Study Selection
Paper Inclusion Criteria
Requirements included publication in English‐language peer‐reviewed journals. Included studies provided quantitative provider‐to‐provider communication data, provider satisfaction statistics, or EHR communication data. Provider‐to‐staff communication was also included if it fell within the scope of studies of communication between providers.
Paper Exclusion Criteria
Studies excluded in this review were articles that reviewed EHR systems without any focus on communication between providers and those that discussed EHR models and strategies but did not include actual testing and quantitative results. Results that included nontraditional online documents or that were found on nonpeer‐reviewed websites were also discarded. Duplicate records or publications that covered the same study were also removed. The most common reason for exclusion was the lack of quantitative evaluation.
Data Extraction and Quality Assessment
Three authors (Walsh, Siegler, Stetson) reviewed titles and abstracts of resultant studies against inclusion and exclusion criteria (Figure 1). Studies were evaluated qualitatively and findings summarized. Given the heterogeneous nature of data reported, statistical analysis was not possible.
RESULTS
The primary and parallel searches produced 2946 results that were weaned through title review and exclusion of duplicates, nonEnglish‐language, and nonhuman studies to 820 articles for title and abstract review (Figure 1). After careful review of the articles' titles, abstracts, or full content (where appropriate), twenty‐five articles met inclusion criteria and presented data about provider‐to‐provider electronic communication, either within an EHR or through a system designed to promote provider‐to‐provider communication. All of the studies that met inclusion criteria focused on physicians as providers. Five studies (20%) described trial design, three (12%) were pilot studies, and seventeen (68%) were observational studies. Thirteen of twenty‐five articles (52%) described studies conducted in the United States and twelve in Europe.
Most of the studies (56%) focused on electronic referrals between primary care and subspecialty providers. The clinical need was to communicate information on a specific patient with a specialist who shared responsibility for the overall plan of care. Only two studies evaluated curbside consultation, where providers ask for clinical recommendations without formally engaging a specialist in the plan of care for a particular patient. Table 2 summarizes included studies and has been organized with respect to clinical need under evaluation. The major themes that emerged from this review included: studies of penetration of communication tools either within the EHR system (intra‐EHR IT) or external to the EHR (extra‐EHR IT); electronic referrals; curbside consultations; and test results reporting (results notification).
| Primary Author, Year | Design | Intervention | Measurement | Results |
|---|---|---|---|---|
| ||||
| Need: Communicate care across clinical settings (inpatient‐outpatient) | ||||
| Branger, 1992[4, 6] | Observational study | Introduction of electronic messaging system in the Netherlands between hospital and PCPs. | Satisfaction survey data using Likert scale of usefulness. | Free text messaging to exchange patient data was rated very useful or useful by 20 of 27 PCP respondents. |
| Reponen, 2004[66] | Observational study | Finnish study of electronic referrals XML messages between EHRs or secure web links. | User questionnaire. No description of respondents was provided. | Internists surveyed estimated that electronic referrals accelerate the referral process by 1 week. |
| Need: Communicate care across specialties (primary care physicians‐specialists) | ||||
| Kooijman, 1998[67] | Observational study | Survey of 45 PCPs who received notes from specialists via Electronic Data Interchange. | User questionnaire with 5‐point Likert scale of satisfaction, from 1 (much better) to 5 (much worse). | Highest satisfaction scores for speed (1.51.8) and efficiency (1.51.7) for electronic messages, with lower scores for reliability (2.52.7) and clarity (2.5). |
| Harno, 2000[4][8] | Nonrandomized trial | Eight‐month prospective comparative study in Finland of outpatient clinics in hospitals with and without intranet referral systems. | Comparison of numbers of electronic referrals, clinic visits, costs. | There were 43% of electronic referrals and 79% of outpatient referrals that resulted in outpatient visits. A 3‐fold increase in productivity overall and 7‐fold reduction in visit costs per patient using e‐mail consultation. |
| Moorman, 2001[4][7] | Observational study | Supersedes Branger, 1999.[68] Analyzes intra‐EHR communications between PCPs and consultant in Netherlands re: diabetes management of patients (19941998). | Descriptive statistics of number of messages, content, whether message had been copied into EMR; survey of PCPs (12 of 15 responded). | Decline in integration by PCPs of messages in the EHR from 75% to 51% over first 3 years. Despite this, most PCPs wanted to extend messaging to other patient groups. |
| Bergus, 2006[69] | Observational study | Follow‐up of Bergus, 1998[54]; evaluated formulation of clinical referrals to specialists at the University of Iowa by retrospective review of e‐mail transcripts. | Analyzed taxonomy of clinical questions; assessed need for clinical consultation of 1618 clinical questions. | Specialists less likely to recommend clinic consultation if referral specified the clinical task (OR: 0.36, P<0.001), intervention (OR: 0.62, P=0.004), or outcome (OR: 0.49, P<0.001). This effect was independent of clinical content (P>0.05). |
| Dennison, 2006[70] | Pilot study | Construction of an electronic referral pro forma to facilitate referral of patients to colorectal surgeons. | Descriptive statistics. Comparisons of patient attendance rate, delays to booking and to actual appointment between 54 electronic referrals and 189 paper referrals. | Compared to paper referrals, electronic referrals were booked more quickly (same day vs 1 week later on average) and patients had lower nonattendance rates (8.5% vs 22.5%). Both results stated as statistically significant, but P values were not provided. |
| Shaw, 2007[49] | Observational study | Dermatology electronic referral in England. | Content of 131 electronic vs 139 paper referrals to dermatologists(NHS Choose and Book).[71] | Paper superior to electronic for clinical data such as current treatments (included in 68% of paper vs 39% of electronic referrals, P<0.001); electronic superior for demographic data. |
| Gandhi, 2008[50] | Nonrandomized trial | Electronic referral tool in the Partners Healthcare System in Massachusetts that included a structured referral‐letter generator and referral status tracker. Assigned to 1 intervention site and 1 control site. | Survey assessment. Fifty‐four of 117 PCPs responded (46%), 235 of 430 specialists responded (55%), 143 out of 210 patients responded (69%). | Intervention group showed high voluntary adoption (99%), higher information transfer rates prior to subspecialty visit (62% vs 12%), and lower rates of conflicting information being given to patients (6% vs 20%). |
| John, 2008[72] | Pilot study | Validation study of the Lower Gastrointestinal e‐RP (through the Choose and Book System in the United Kingdom) intended to improve yield of colon cancers diagnosed and to reduce delays in diagnosis. | Comparison of actual to simulated referral patterns through e‐RP for 300 patients divided into colorectal cancer, 2‐week wait suspected cancer, and routine referral groups. | e‐RP was more accurate than traditional referral at upgrading patients who had cancer to the appropriate suspected cancer referral group (85% vs 43%, P=0.002). |
| Kim, 2009[73] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Included reply functionality and ability to forward messaging to a scheduler for calendaring. | Impact of electronic referral system as measured by questionnaire to referring providers. A total of 298/368 participated (24 clinics); 53.5% attending physicians. | Electronic referrals improved overall quality of care (reported by 72%), guidance of presubspecialty visit (73%), and the ability to track referrals (89%). Small change in access for urgent issues (35% better, 49% reported no change). |
| Scott, 2009[74] | Pilot study | Pilot of urgent electronic referral system from PCPs to oncologists at South West Wales Cancer Centre. | Satisfaction statistics (10‐point Likert scale) collected from PCPs via interview. | Over 6 months, 99 referrals submitted; 81% were processed within 1 hour with high satisfaction scores. |
| Were, 2009[75] | Nonrandomized trial | Geriatrics consultants were provided system to make electronic recommendations (consultant‐recommended orders) in the native CPOE system along with consult notes in the intervention vs consult notes alone in the control. | Rates of implementation of consultant recommendations. Qualitative survey of users of the new system. | Higher total number of recommendations (247 vs 192, P<0.05) and higher implementation rates of consultant‐recommended orders in the intervention group vs control (78% vs 59%, P=0.01). High satisfaction scores on 5‐point Likert scale for the intervention system with good survey response rate (83%). |
| Dixon, 2010[52] | Observational study | Comparison of 2 extra‐EHR systems (NHS Choose and Book, Dutch ZorgDomein) for booking referrals. Patients choose doctor or hospital and the system transfers demographic and clinical information between PCP and specialist. | National data, patient and provider surveys, focus groups, observational studies. Focus was on patient choice, but evaluations included all aspects of the systems. | Resistance from PCPs during implementation; 78% of ZorgDomein PCPs felt referrals took more time; general displeasure on the part of specialists re: quality of referrals, although not quantified. |
| Patterson, 2010[51] | Observational study | E‐mail referral system to a neurologist in Northern Ireland. Referrals were template based and recorded as clinical episode in the patient administration system. Comparison of this system to conventional referrals to another neurologist. | Evaluated effectiveness, cost, safety for period 20022007. | Decreased referral wait times (4 vs 13 weeks) and 35% cost reduction per patient for the e‐mail referral vs conventional referrals. |
| No diminution in safety. Limitation: single neurologist participated. | ||||
| Singh, 2011[76] | Observational study | Chart review of electronic referrals to specialist practices in a Veterans Affairs outpatient system. | Follow‐up actions taken by subspecialists within 30 days of receiving referral. | An intra‐EHR referral system was still affected by communication breakdowns. Of 61,931 referrals, 36.4% were discontinued for inappropriate or incomplete referral requests. |
| Kim‐Hwang, 2010[77] | Observational study | Electronic referrals via a portal to San Francisco General Hospital. Follow‐up to Kim, 2009.[73] | Survey of medical and surgical subspecialty consultants. | Statistically significant differences in clarity of consult request in both medical and surgical clinics, in decreased inappropriate referrals in surgical clinics, in decreased use of follow‐up appointments by surgical specialists, and in decreased avoidable follow‐up surgical visits. |
| Warren, 2011[53] | Observational study | Electronic referrals from general medical practices to public referral network of Hutt Hospital in New Zealand (20072010). | Retrospective analysis of transactional data from messaging system and from general inpatient tracking system. Qualitative data collection via interviews. | Estimated 71% of 10,367 referrals were electronic referrals over 3 years. Statistically significant improvement in referral latency without change in staffing. Clinicians appreciate shared transparency of referrals but cite usability issues as barriers. |
| Need: Curbside consults (primary care physicians‐specialists) | ||||
| Bergus, 1998[54] | Observational study | Evaluation of the ECS for curbside consultations between family physicians and subspecialists. | Descriptive statistics of usage data; survey of users. | Median response time 16.1 hours; 92% of questions answered; almost 90% concerned specific patients. Both groups expressed satisfaction. |
| Abbott, 2002[55] | Observational study | Evaluation of Department of Defense Ask a Doc physician‐to‐physicians e‐mail consultation system over network of 21 states (19982000). | Descriptive statistics; qualitative assessment. | There were 3121 consultations. Average response time <12 hours. Minimal cost and effort to initiate and sustain. Felt to mirror clinical practice. Barriers were security and assignation of credit for consultation. |
| Need: Communication of results (primary care physicians ‐specialists) | ||||
| Singh, 2007[5][6] | Nonrandomized trial | Concurrent prospective evaluation of responses to 1017 critical imaging alert notifications in a Veterans Affairs outpatient system (2006). Radiologists generated alerts. Included receipt system. | Measured percentage of unacknowledged alerts and imaging lost to follow‐up. | There were 368 of 1017 transmitted alerts unacknowledged (36%); 45 were completely lost to follow‐up. There were 0.2% outpatient imaging results lost to follow‐up overall. |
| Singh, 2009[5][7] | Nonrandomized trial | Concurrent evaluation of responses to 1196 critical imaging alert notifications in a Veterans Affairs outpatient system (20072008). Similar coding system to Singh, 2007.[56] | Measured percentage of alerts acknowledged, timely follow‐up; compared electronic alerts alone to combination of alerts and phone calls or admission. | Percentage of alerts acknowledged did not differ by type of communication; combination of electronic alerts with phone follow‐up (OR: 0.12, P<0.001) or admission (OR: 0.22, P<0.001) decreased likelihood of delayed follow‐up. Alerts to 2 providers increased the likelihood of delayed follow‐up (OR: 1.99, P=0.03). |
| Abujudeh, 2009[5][8] | Observational study | Retrospective review of e‐mailbased alert system for abnormal imaging results at Massachusetts General Hospital 20052007. E‐mail alerting by radiologist to ordering physician of nonurgent findings. | Descriptive statistics; survey of referring physicians (12/26). | There were 56,691 out of 1,540,254 reports for important but not urgent findings; 93.3% generated e‐mail message (6.7% failure rate); 80% of alerts were viewed. Higher satisfaction for e‐mail alerts over conventional methods (eg, facsimile) for nonurgent but important findings. |
| Need: Communicate within 1 care setting (primary care physicians) | ||||
| Lanham, 2012[78] | Observational study | Comparison of practice‐level EHR use with communication patterns among physicians, nurses, medical assistants, practice managers, and nonclinical staff within individual practices in Texas. | Observation and semistructured interviews. Within‐practice communication patterns were categorized as fragmented or cohesive. Practice‐level EHR use was categorized as homogeneous or heterogeneous. | Clinical practices with cohesive within‐practice communication patterns were associated with homogeneous patterns of practice‐level EHR use. |
| Murphy, 2012[79] | Observational study | Review of note‐based messaging within the EHR in outpatient clinics of large tertiary Veterans Affairs facility. Clinic staff send additional signature request alerts linked to parent notes in the EHR to primary care physicians. | Reason for and origin of alerts. Parent note linked to alert was also reviewed for 3 value attributes: urgency; potential harm if alert was missed; subjective value to PCP of the alert. | Of the alerts reviewed, 53.7% of 525 were deemed of high value but required PCPs to review significant amounts of extraneous text (80.3% of words in parent notes) to get relevant information. Most alerts (40%) were medication, prescription, or refill related. |
Extra‐EHR IT
A review of electronic communication in 2000 examined electronic communication among primary care physicians but notably did not distinguish between communication and data exchange.[43] Of the thirty included publications in that review, seventeen publications dealt with electronically communicated information in general; the remaining studies focused on notifications of test results or transitions of care, reports from specialists, or electronic communication as replacement of traditional referral.[43] Although many studies of electronic communication described positive benefits, few included objective data, and most did not analyze provider‐to‐provider communication specifically. A survey of IT use outside of the EHR in 2006 documented that approximately 30% of clinicians used e‐mail to communicate with other clinicians, fewer than those who consulted on‐line journals (40.8%), but many more than those who communicated with patients by e‐mail at that time (3.6%).[44]
Intra‐EHR IT
A comparison of two physician surveys of EHR use in Massachusetts (the first in 2005 and the second in 2007) documented an increase in the percentage of practices with an EHR, from 23% to 35%; in those practices with EHRs, only the use of electronic prescribing increased over time. Use of secure electronic referrals or messaging including secure e‐mail remained unchanged; of note, referrals and messaging were considered a singular clinical function in that study. Between 2005 and 2007, referrals or clinical messaging were available in 62% and 63% of EHR systems, respectively, and they were used most or all of the time by 29% to 33% of the physicians who had an EHR.[45]
Electronic Referrals
Fourteen articles focused on electronic referrals. Two had a prepost or longitudinal study design,[46, 47] and five included a control group.[48, 49, 50, 51] The rest were descriptive. In most cases, electronic referral improved the transfer of information, especially when standardized message templates were created. Use of electronic referral appeared to result in reduced waiting time for appointments and enabled more efficient triage.
Barriers to integration of electronic referral in the EHR were also assessed. An intra‐EHR communication system requiring a primary care physician to integrate information e‐mailed by the consultant into the record showed the percentage of integrated notes decreasing over time.[47] Practitioners had mixed feelings about the system; although the majority (92% of respondents) felt that the system improved patient care and wanted to extend messaging to other patient groups, they also felt that electronic messaging decreased the ease of reviewing data (83%) and confused tasks and responsibilities (59%). A study of British and Dutch electronic referral systems described significant resistance on the part of practitioners to electronic referrals and concern on the part of specialists about the quality of referrals.[52] Another study demonstrated improvement in quality of demographic data but degradation in quality of clinical information when referrals were submitted electronically.[49] A recent transactional analysis of electronic referrals in New Zealand showed high uptake and reduced referral latency compared to conventional referral; clinicians cited usability concerns as the major barrier to use.[53]
Curbside Consultations via E‐mail
Two studies evaluated curbside consultations via e‐mail and documented high provider satisfaction and rapid turnaround.[54, 55] The preliminary nature of these studies raises questions of sustainability and long‐term implementation.
Results Notification
Three studies focused on test‐result reporting from radiologists. In these studies, a radiologist could designate a result as high priority and have an e‐mail notification sent to the ordering physicians.[56, 57, 58] Urgent results were relayed by telephone. Lack of acknowledgement of alerts impacted the results of every study, and in one of these studies, alerting two physicians, rather than just one, decreased the likelihood that the results would be followed up.[57] Providers did prefer e‐mail to fax notification.[58]
DISCUSSION
The principal findings of the literature review demonstrate the paucity of quantitative data surrounding provider‐to‐provider communication. The majority of studies focused on physicians as providers without emphasis on other provider types on the care team. Most of the quantitative studies investigated electronic referrals. Data collected largely represented measures of provider satisfaction and process measures. Few quantitative studies used established models or measures of team coordination or communication.
This study extends the work of others by compiling a comprehensive view of electronic provider‐to‐provider communication. A recent review of devices for clinical communication tells a part of the story,[28] and our review adds a comprehensive, device‐agnostic look at the systems physicians and other providers use every day.
Limitations of this review include the small number of eligible studies and a homogenous provider type (physicians). The latter is both an important finding and a limitation to generalizability of our results. Reviewed studies were in English only. The literature review by its nature is subject to publication bias.
Intra‐EHR communication cannot serve all purposes, and is it not a panacea for effective care coordination. One recent qualitative study warns about the pitfalls of electronic communication. Interviews with physicians from twenty‐six practices elicited some concerns about the resulting decrease in face‐to‐face communication that has resulted from the adoption of electronic communication tools.[32] This finding brings implications: (1) a false sense of security may reduce verbal communications when they are needed mostduring emergencies or when caring for complex patients who require detailed, nuanced discussion; and (2) fewer conversations within a practice can reduce both knowledge sharing and basic social interactions necessary for the maintenance of a collaboration. Last, privacy and confidentiality are top priorities. Common electronic communication tools are susceptible to security breaches,[47, 59] and innovations within this domain must conform to Health Insurance Portability and Accountability Act of 1996 and Health Information Technology for Economic and Clinical Health Act regulations.[60]
Although electronic communication is not a complete solution for clinical collaboration, it is difficult to use face‐to‐face communication and telephone communication to convey large amounts of patient information while simultaneously generating a record of the transaction. Moreover, paging functions, telephone calls, and face‐to‐face encounters can be highly interruptive, increasing cognitive load, burdening working memory, and shifting attention from the task at hand.[14] Interruptions contribute to inefficiency and to the potential for errors.[61]
Effective coordination of care for the chronically ill is one of the essential goals of the health system; it is an ongoing process that depends on constant, effective communication. Bates and Bitton have recognized this and described the crucial role that HIT will play in creating an effective medical home by enumerating seven domains of HIT especially in need of research.[62] In particular, they note that effective team care and care transitions will depend on an EHR that promotes both implicit and real‐time communication: it will be essential to develop communication tools that allow practices to record goals shared by providers and patients alike, and to track medical interventions and progress.[62]
Future research could investigate a number of open questions. Overall, an emphasis should be placed on rigorous qualitative and quantitative evaluation of electronic communication. Process measures, such as length of stay, hospital readmission rates, and measures of care coordination, should be framed ultimately with respect to patient health outcomes. Such data are beginning to be reported.[63]
It is unclear which types of communications would be best served within the EHR and which should remain external to it. Instant communication or chat has not been studied sufficiently to show a demonstrable impact on patient care. Cross‐coverage and team identification within the EHR can be further studied with respect to workflows and best practices. Studies using structured observation or time‐and‐motion analysis could provide insight into use cases and workflows that providers implement to discuss patients. Future research should incorporate established models of communication[5] and coordination.[64] Data on unintended consequences or harms of provider‐to‐provider electronic communication have been limited, and this area should be considered in subsequent work. Finally, although the scope of this review focused on communication between providers, transformative electronic communication systems should bridge communication gaps between providers and patients as well.
As adoption of EHRs in US hospitals has increased from 15.1% of US hospitals in 2010 to 26.6% in 2011 for any type of EHR and 3.6% to 8.7% for comprehensive EHRs,[65] it is worth noting that Meaningful Use, as it stands, incentivizes patient‐provider communication, but not communication between providers. Inclusion of certification criteria focused on provider‐to‐provider communication may spur additional innovation.
CONCLUSIONS
The optimal features to support electronic communication between providers remain under‐assessed, although there is preliminary evidence for the acceptability of electronic referrals. Without better understanding of electronic communication on workflow, provider satisfaction, and patient outcomes, the impact of such tools on coordination of complex medical care will be an open question, and it remains an important one to answer.
Acknowledgments
The authors would like to express their gratitude to Dr. Thomas Payne, Medical Director of IT Services at the University of Washington, for sharing his expertise, and to Marina Chilov, medical librarian at Columbia University, for her assistance with the literature search. The authors would like to thank Paul Sun, MA, for his assistance with the literature review.
Disclosures: This work was funded by 5K22LM8805 (PDS) and T15 LM007079 (CW, SC) grants. Dr. Stetson serves on the advisory board of the Allscripts Enterprise EHR.
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- , , , , . Are electronic medical records helpful for care coordination? Experiences of physician practices. J Gen Intern Med. 2009;25:177–185.
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- , , , et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381–386.
- , , , et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–1316.
- , , , . Analysing potential harm in Australian general practice: an incident‐monitoring study. J Am Med Inform Assoc. 1998;169:73–76.
- . When conversation is better than computation. J Am Med Inform Assoc. 2000;7(3):277–286.
- , , , , . Communication loads on clinical staff in the emergency department. J Am Med Inform Assoc. 2002;176(9):415–418.
- , , , , , . Deficit in communicaiton and information transfer between hospital‐based and primary care physicians. JAMA. 2007;297:831–841.
- , . Improving clinical communication: a view from psychology. J Am Med Inform Assoc. 2000;7(5):453–461.
- , , . Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79(2):186–194.
- , , , , , . The Quality in Australian Health Care Study. Med J Aust. 1995;163:458–471.
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- , , , et al. Relationship between use of electronic health record features and health care quality: results of a statewide survey. Med Care. 2010;48:203–209.
- , , , et al. Decrease in hospital‐wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2010;126:e1–e8.
- , , , , . Electronic prescribing improves medication safety in community‐based office practices. J Gen Intern Med. 2010;25:530–536.
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- , , , . The utility of electronic mail as a medium for patient‐physician communication. Arch Fam Med. 1994;3(3):268–271.
- , , , et al. Effects of clinical communication interventions in hospitals: a systematic review of information and communication technology adoptions for improved communication between clinicians. Int J Med Inform. 2012;81(11):723–732.
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- , , , , . Using computerized provider order entry and clinical decision support to improve referring physicians' implementation of consultants' medical recommendations. J Am Med Inform Assoc. 2009;16(2):196–202.
- , , , et al. Follow‐up actions on electronic referral communication in a multispecialty outpatient setting. J Gen Intern Med. 2011;26(1):64–69.
- , , , , , . Evaluating electronic referrals for specialty care at a public hospital. J Gen Intern Med. 2010;25(10):1123–11028.
- , , . Same organization, same electronic health records (EHRs) system, different use: exploring the linkage between practice member communication patterns and EHR use patterns in an ambulatory care setting. J Am Med Inform Assoc. 2012;19(3):382–391.
- , , , et al. Electronic health record‐based messages to primary care providers: valuable information or just noise? Arch Intern Med. 2012;172(3):283–285.
- , . Physician‐physician communication: what's the hang‐up? J Gen Intern Med. 2009;24:437–439.
- , , , et al. Meta‐analysis: effect of interactive communication between collaborating primary care physicians and specialists. Ann Intern Med. 2010;152(4):247–258.
- , , , , . Are electronic medical records helpful for care coordination? Experiences of physician practices. J Gen Intern Med. 2009;25:177–185.
- , , , , , . Development of an ontology to model medical errors, information needs, and the clinical communication space. Proc AMIA Symp. 2001:672–676.
- . Clinical communication: a new informatics paradigm. Proc AMIA Annu Fall Symp. 1996:17–21.
- , , . Interprofessional collaboration: effects of practice‐based interventions on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2009;(3):CD000072.
- , , , . Primary care physician attitudes regarding communication with hospitalists. Dis Mon. 2002;48(4):218–229.
- , , , et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381–386.
- , , , et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–1316.
- , , , . Analysing potential harm in Australian general practice: an incident‐monitoring study. J Am Med Inform Assoc. 1998;169:73–76.
- . When conversation is better than computation. J Am Med Inform Assoc. 2000;7(3):277–286.
- , , , , . Communication loads on clinical staff in the emergency department. J Am Med Inform Assoc. 2002;176(9):415–418.
- , , , , , . Deficit in communicaiton and information transfer between hospital‐based and primary care physicians. JAMA. 2007;297:831–841.
- , . Improving clinical communication: a view from psychology. J Am Med Inform Assoc. 2000;7(5):453–461.
- , , . Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79(2):186–194.
- , , , , , . The Quality in Australian Health Care Study. Med J Aust. 1995;163:458–471.
- , , , et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742–752.
- , , . Costs and benefits of health information technology. Evid Rep Technol Assess. 2006;132:1–71.
- , , , et al. Relationship between use of electronic health record features and health care quality: results of a statewide survey. Med Care. 2010;48:203–209.
- , , , et al. Decrease in hospital‐wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2010;126:e1–e8.
- , , , , . Electronic prescribing improves medication safety in community‐based office practices. J Gen Intern Med. 2010;25:530–536.
- . Patient‐centered Care. Radiol Technol. 2009;81(2):133–147.
- , . The missing link: bridging the patient‐provider health information gap. Health Aff (Millwood). 2005;24(5):1290–1295.
- , , , , . Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006;13(2):121–126.
- , . Web messaging: a new tool for patient‐physician communication. J Am Med Inform Assoc. 2003;10(3):260–270.
- , , , . Expanding the guidelines for electronic communication with patients. J Am Med Inform Assoc. 2001;8(4):344–348.
- , , , . The utility of electronic mail as a medium for patient‐physician communication. Arch Fam Med. 1994;3(3):268–271.
- , , , et al. Effects of clinical communication interventions in hospitals: a systematic review of information and communication technology adoptions for improved communication between clinicians. Int J Med Inform. 2012;81(11):723–732.
- , . Desiderata for personal electronic communication in clinical systems. J Am Med Inform Assoc. 2002;9(3):209–216.
- . When conversation is better than computation. J Am Med Inform Assoc. 2000;7:277–286.
- . Getting in step: electronic health records and their role in care coordination. J Gen Intern Med. 2010;25:174–176.
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Study indicates potential for longer survival after radiosurgery for brain metastases
ATLANTA – Patients with non–small cell lung cancer and fewer than four brain metastases treated with stereotactic radiosurgery had better overall survival than did similar patients treated with whole-brain irradiation in a nonrandomized observational study.
The study of 413 patients who were eligible for either treatment showed that the median overall survival was 9.0 months for those treated with stereotactic radiosurgery (SRS) alone, versus 3.9 months for those treated with whole-brain radiation therapy (WBRT) alone, reported Dr. Lia M. Halasz, assistant professor of radiation oncology at the University of Washington in Seattle.
The findings suggest the need for a randomized clinical trial comparing the two treatment strategies in patients with non–small cell lung cancer and up to three brain metastases, she said at the annual meeting of the American Society for Radiation Oncology.
"This observational data may better reflect real-world practice; however, the caveat is that all of these patients were treated at large NCCN [National Comprehensive Cancer Network] institutions, and may not reflect practices all across the United States," she said.
Dr. James B. Yu, a therapeutic radiologist and cancer outcomes researcher at Yale University, New Haven, Conn., commented that the study shows "at the very least, NCCN sites are doing a very good job at selecting patients for radiosurgery." Dr. Yu, the invited discussant, was not involved in the study.
There have been no randomized clinical trials directly comparing SRS alone vs. WBRT alone in patients with newly diagnosed brain metastases, and the optimal treatment for such patients is unknown, Dr. Halasz said. The investigators therefore undertook an observational study to determine whether one strategy had a therapeutic advantage over the other.
They identified 413 patients diagnosed with brain metastases without leptomeningeal disease from an NCCN longitudinal database from November 2006 through January 2010. The patients had all received radiation therapy with no neurosurgical resection within 60 days of diagnosis.
Of this group, 118 (29%) underwent SRS, 295 (71%) had WBRT; and 13 patients (3%) had both as initial treatment.
Patients with three or fewer metastases were significantly more likely to receive SRS than WBRT, whereas those with four or more metastases were more likely to receive WBRT (P less than .001). Other factors associated with choice of SRS were smaller metastases (P = .036) and one or no sites of extracranial disease, compared with two or more (P = .013).
The authors analyzed a subset of 197 patients with fewer than four brain metastases and all metastatic sites smaller than 4 cm, all of whom were eligible for either treatment, and 48% of whom underwent SRS alone. As noted before, the unadjusted overall survival in this group was 9.0 months for the SRS-treated patients, and 3.9 months for those treated with WBRT.
To compensate for patient-selection biases, the authors then performed a propensity score analysis in which they stratified patients by their propensity to receive radiosurgery. In this analysis, the estimated treatment effect of SRS on overall survival was a hazard ratio (HR) of 0.62 (P = .018). Factors significantly associated with overall survival included SRS vs. WBRT, number of brain metastases, extent of extracranial disease, institution, and year of treatment.
In an analysis using a standardized mortality-ratio weighing method, they found that the estimated treatment effect of SRS on overall survival was an HR of 0.67 (P = .007).
Additionally, the authors performed a sensitivity analysis of potential unmeasured confounders, assuming that patients who underwent WBRT were three times more likely to have a Karnofsky performance score less than 70, and that the HR for that poor performance status was 2.13, based on recursive partitioning analysis (RPA) status. In this analysis, the HR favoring SRS was 0.64 (P = .037).
Finally, they performed a companion analysis with breast cancer data, and found a similar HR in favor of SRS (HR, 0.59; P = .036)
The funding source for the study was not reported. Dr. Halasz and Dr. Yu reported having no conflicts of interest to disclose.
ATLANTA – Patients with non–small cell lung cancer and fewer than four brain metastases treated with stereotactic radiosurgery had better overall survival than did similar patients treated with whole-brain irradiation in a nonrandomized observational study.
The study of 413 patients who were eligible for either treatment showed that the median overall survival was 9.0 months for those treated with stereotactic radiosurgery (SRS) alone, versus 3.9 months for those treated with whole-brain radiation therapy (WBRT) alone, reported Dr. Lia M. Halasz, assistant professor of radiation oncology at the University of Washington in Seattle.
The findings suggest the need for a randomized clinical trial comparing the two treatment strategies in patients with non–small cell lung cancer and up to three brain metastases, she said at the annual meeting of the American Society for Radiation Oncology.
"This observational data may better reflect real-world practice; however, the caveat is that all of these patients were treated at large NCCN [National Comprehensive Cancer Network] institutions, and may not reflect practices all across the United States," she said.
Dr. James B. Yu, a therapeutic radiologist and cancer outcomes researcher at Yale University, New Haven, Conn., commented that the study shows "at the very least, NCCN sites are doing a very good job at selecting patients for radiosurgery." Dr. Yu, the invited discussant, was not involved in the study.
There have been no randomized clinical trials directly comparing SRS alone vs. WBRT alone in patients with newly diagnosed brain metastases, and the optimal treatment for such patients is unknown, Dr. Halasz said. The investigators therefore undertook an observational study to determine whether one strategy had a therapeutic advantage over the other.
They identified 413 patients diagnosed with brain metastases without leptomeningeal disease from an NCCN longitudinal database from November 2006 through January 2010. The patients had all received radiation therapy with no neurosurgical resection within 60 days of diagnosis.
Of this group, 118 (29%) underwent SRS, 295 (71%) had WBRT; and 13 patients (3%) had both as initial treatment.
Patients with three or fewer metastases were significantly more likely to receive SRS than WBRT, whereas those with four or more metastases were more likely to receive WBRT (P less than .001). Other factors associated with choice of SRS were smaller metastases (P = .036) and one or no sites of extracranial disease, compared with two or more (P = .013).
The authors analyzed a subset of 197 patients with fewer than four brain metastases and all metastatic sites smaller than 4 cm, all of whom were eligible for either treatment, and 48% of whom underwent SRS alone. As noted before, the unadjusted overall survival in this group was 9.0 months for the SRS-treated patients, and 3.9 months for those treated with WBRT.
To compensate for patient-selection biases, the authors then performed a propensity score analysis in which they stratified patients by their propensity to receive radiosurgery. In this analysis, the estimated treatment effect of SRS on overall survival was a hazard ratio (HR) of 0.62 (P = .018). Factors significantly associated with overall survival included SRS vs. WBRT, number of brain metastases, extent of extracranial disease, institution, and year of treatment.
In an analysis using a standardized mortality-ratio weighing method, they found that the estimated treatment effect of SRS on overall survival was an HR of 0.67 (P = .007).
Additionally, the authors performed a sensitivity analysis of potential unmeasured confounders, assuming that patients who underwent WBRT were three times more likely to have a Karnofsky performance score less than 70, and that the HR for that poor performance status was 2.13, based on recursive partitioning analysis (RPA) status. In this analysis, the HR favoring SRS was 0.64 (P = .037).
Finally, they performed a companion analysis with breast cancer data, and found a similar HR in favor of SRS (HR, 0.59; P = .036)
The funding source for the study was not reported. Dr. Halasz and Dr. Yu reported having no conflicts of interest to disclose.
ATLANTA – Patients with non–small cell lung cancer and fewer than four brain metastases treated with stereotactic radiosurgery had better overall survival than did similar patients treated with whole-brain irradiation in a nonrandomized observational study.
The study of 413 patients who were eligible for either treatment showed that the median overall survival was 9.0 months for those treated with stereotactic radiosurgery (SRS) alone, versus 3.9 months for those treated with whole-brain radiation therapy (WBRT) alone, reported Dr. Lia M. Halasz, assistant professor of radiation oncology at the University of Washington in Seattle.
The findings suggest the need for a randomized clinical trial comparing the two treatment strategies in patients with non–small cell lung cancer and up to three brain metastases, she said at the annual meeting of the American Society for Radiation Oncology.
"This observational data may better reflect real-world practice; however, the caveat is that all of these patients were treated at large NCCN [National Comprehensive Cancer Network] institutions, and may not reflect practices all across the United States," she said.
Dr. James B. Yu, a therapeutic radiologist and cancer outcomes researcher at Yale University, New Haven, Conn., commented that the study shows "at the very least, NCCN sites are doing a very good job at selecting patients for radiosurgery." Dr. Yu, the invited discussant, was not involved in the study.
There have been no randomized clinical trials directly comparing SRS alone vs. WBRT alone in patients with newly diagnosed brain metastases, and the optimal treatment for such patients is unknown, Dr. Halasz said. The investigators therefore undertook an observational study to determine whether one strategy had a therapeutic advantage over the other.
They identified 413 patients diagnosed with brain metastases without leptomeningeal disease from an NCCN longitudinal database from November 2006 through January 2010. The patients had all received radiation therapy with no neurosurgical resection within 60 days of diagnosis.
Of this group, 118 (29%) underwent SRS, 295 (71%) had WBRT; and 13 patients (3%) had both as initial treatment.
Patients with three or fewer metastases were significantly more likely to receive SRS than WBRT, whereas those with four or more metastases were more likely to receive WBRT (P less than .001). Other factors associated with choice of SRS were smaller metastases (P = .036) and one or no sites of extracranial disease, compared with two or more (P = .013).
The authors analyzed a subset of 197 patients with fewer than four brain metastases and all metastatic sites smaller than 4 cm, all of whom were eligible for either treatment, and 48% of whom underwent SRS alone. As noted before, the unadjusted overall survival in this group was 9.0 months for the SRS-treated patients, and 3.9 months for those treated with WBRT.
To compensate for patient-selection biases, the authors then performed a propensity score analysis in which they stratified patients by their propensity to receive radiosurgery. In this analysis, the estimated treatment effect of SRS on overall survival was a hazard ratio (HR) of 0.62 (P = .018). Factors significantly associated with overall survival included SRS vs. WBRT, number of brain metastases, extent of extracranial disease, institution, and year of treatment.
In an analysis using a standardized mortality-ratio weighing method, they found that the estimated treatment effect of SRS on overall survival was an HR of 0.67 (P = .007).
Additionally, the authors performed a sensitivity analysis of potential unmeasured confounders, assuming that patients who underwent WBRT were three times more likely to have a Karnofsky performance score less than 70, and that the HR for that poor performance status was 2.13, based on recursive partitioning analysis (RPA) status. In this analysis, the HR favoring SRS was 0.64 (P = .037).
Finally, they performed a companion analysis with breast cancer data, and found a similar HR in favor of SRS (HR, 0.59; P = .036)
The funding source for the study was not reported. Dr. Halasz and Dr. Yu reported having no conflicts of interest to disclose.
AT THE ASTRO ANNUAL MEETING
Major finding: Median overall survival for patients with brain metastases from non–small cell lung cancer was 9.0 months for those treated with stereotactic radiosurgery, versus 3.9 months for those treated with whole-brain radiation therapy.
Data source: Observational study of 413 patients in a National Comprehensive Cancer Network database.
Disclosures: The funding source for the study was not reported. Dr. Halasz and Dr. Yu reported having no conflicts of interest to disclose.
When is too young for antiaging procedures?
DANA POINT, CALIF. – When is someone too young for antiaging procedures with cosmetic fillers or laser resurfacing?
Chronologic age "is somewhat irrelevant," in the opinion of Dr. Elizabeth L. Tanzi, codirector of the Washington (D.C.) Institute of Dermatologic Laser Surgery. "I’m looking at dermatologic age, with a critical evaluation of [a patient’s] need," she said at a meeting sponsored by SkinCare Physicians and Northwestern University.
Dr. Tanzi noted that genetics also plays a role in how each person’s skin ages over time. "Some people have inherited facial expressions," she explained. "They may get hyperdynamic movement in certain parts of their face and develop wrinkles much earlier than you would anticipate. Environmental exposure clearly plays a large role. Excessive ultraviolet exposure, growing up with outdoor sporting activities, tanning bed use, or poor habits such as smoking are going to lead to an accelerated aging process," she said.
The importance of establishing realistic patient expectations starts with the first office consultation, when clinicians emphasize that "we can slow down the signs of aging on your skin, but we cannot stop the process completely," said Dr. Tanzi, who is also an assistant professor of dermatology at George Washington University Medical Center, Washington. "I think it’s more important to talk about looking youthful, energetic, and vibrant, not necessarily looking young, because we may be inadvertently delivering the wrong message – that all aging is preventable if treatments are started early enough – and that sets the stage for unrealistic expectations."
Encouraging sun protection behaviors is sensible, and "most dermatologists realize that you can use neuromodulators and fillers strategically early on," Dr. Tanzi said. "But the idea of using fractionated laser resurfacing treatments to promote improved skin function is intriguing to me. We know we can improve the skin cosmetically through a series of fractional laser resurfacing treatments. But can we functionally improve the skin as it’s aging?" she questioned.
Cutting-edge research suggests that may be the case. In 2012, Dan F. Spandau, Ph.D., and his colleagues (J. Invest. Dermatol. 2012;132:1591-6) published data showing that dermal wounding procedures such as fractional resurfacing can "wake up senescent dermal fibroblasts to produce more insulin-like growth factor-1 (IGF-1), which helps the epidermis ward off the damaging effects of UVB on the skin," Dr. Tanzi said. In that case, she continued, "should we be recommending fractional resurfacing as part of a healthy antiaging routine? If so, at what age? These are exciting developments that need additional research to help guide new treatment protocols."
Although she is enthusiastic about preventing some signs of aging and helping patients maintain a youthful appearance, Dr. Tanzi expressed some concerns. "If we are not careful, we could be setting ourselves up for an expectation of being able to stop the aging process, and this can be a slippery slope, especially for women," she said. "Especially when it comes to fillers and neuromodulators, if not done judiciously they can lead to a very artificial look which, ironically, makes women look much older," Dr. Tanzi said. "As thoughtful physicians, it’s important to keep perspective and guide patients to know when enough is enough [in terms of procedures]," she added.
Dr. Tanzi disclosed that she is a consultant for Cynosure/Palomar, Lumenis, and other companies.
DANA POINT, CALIF. – When is someone too young for antiaging procedures with cosmetic fillers or laser resurfacing?
Chronologic age "is somewhat irrelevant," in the opinion of Dr. Elizabeth L. Tanzi, codirector of the Washington (D.C.) Institute of Dermatologic Laser Surgery. "I’m looking at dermatologic age, with a critical evaluation of [a patient’s] need," she said at a meeting sponsored by SkinCare Physicians and Northwestern University.
Dr. Tanzi noted that genetics also plays a role in how each person’s skin ages over time. "Some people have inherited facial expressions," she explained. "They may get hyperdynamic movement in certain parts of their face and develop wrinkles much earlier than you would anticipate. Environmental exposure clearly plays a large role. Excessive ultraviolet exposure, growing up with outdoor sporting activities, tanning bed use, or poor habits such as smoking are going to lead to an accelerated aging process," she said.
The importance of establishing realistic patient expectations starts with the first office consultation, when clinicians emphasize that "we can slow down the signs of aging on your skin, but we cannot stop the process completely," said Dr. Tanzi, who is also an assistant professor of dermatology at George Washington University Medical Center, Washington. "I think it’s more important to talk about looking youthful, energetic, and vibrant, not necessarily looking young, because we may be inadvertently delivering the wrong message – that all aging is preventable if treatments are started early enough – and that sets the stage for unrealistic expectations."
Encouraging sun protection behaviors is sensible, and "most dermatologists realize that you can use neuromodulators and fillers strategically early on," Dr. Tanzi said. "But the idea of using fractionated laser resurfacing treatments to promote improved skin function is intriguing to me. We know we can improve the skin cosmetically through a series of fractional laser resurfacing treatments. But can we functionally improve the skin as it’s aging?" she questioned.
Cutting-edge research suggests that may be the case. In 2012, Dan F. Spandau, Ph.D., and his colleagues (J. Invest. Dermatol. 2012;132:1591-6) published data showing that dermal wounding procedures such as fractional resurfacing can "wake up senescent dermal fibroblasts to produce more insulin-like growth factor-1 (IGF-1), which helps the epidermis ward off the damaging effects of UVB on the skin," Dr. Tanzi said. In that case, she continued, "should we be recommending fractional resurfacing as part of a healthy antiaging routine? If so, at what age? These are exciting developments that need additional research to help guide new treatment protocols."
Although she is enthusiastic about preventing some signs of aging and helping patients maintain a youthful appearance, Dr. Tanzi expressed some concerns. "If we are not careful, we could be setting ourselves up for an expectation of being able to stop the aging process, and this can be a slippery slope, especially for women," she said. "Especially when it comes to fillers and neuromodulators, if not done judiciously they can lead to a very artificial look which, ironically, makes women look much older," Dr. Tanzi said. "As thoughtful physicians, it’s important to keep perspective and guide patients to know when enough is enough [in terms of procedures]," she added.
Dr. Tanzi disclosed that she is a consultant for Cynosure/Palomar, Lumenis, and other companies.
DANA POINT, CALIF. – When is someone too young for antiaging procedures with cosmetic fillers or laser resurfacing?
Chronologic age "is somewhat irrelevant," in the opinion of Dr. Elizabeth L. Tanzi, codirector of the Washington (D.C.) Institute of Dermatologic Laser Surgery. "I’m looking at dermatologic age, with a critical evaluation of [a patient’s] need," she said at a meeting sponsored by SkinCare Physicians and Northwestern University.
Dr. Tanzi noted that genetics also plays a role in how each person’s skin ages over time. "Some people have inherited facial expressions," she explained. "They may get hyperdynamic movement in certain parts of their face and develop wrinkles much earlier than you would anticipate. Environmental exposure clearly plays a large role. Excessive ultraviolet exposure, growing up with outdoor sporting activities, tanning bed use, or poor habits such as smoking are going to lead to an accelerated aging process," she said.
The importance of establishing realistic patient expectations starts with the first office consultation, when clinicians emphasize that "we can slow down the signs of aging on your skin, but we cannot stop the process completely," said Dr. Tanzi, who is also an assistant professor of dermatology at George Washington University Medical Center, Washington. "I think it’s more important to talk about looking youthful, energetic, and vibrant, not necessarily looking young, because we may be inadvertently delivering the wrong message – that all aging is preventable if treatments are started early enough – and that sets the stage for unrealistic expectations."
Encouraging sun protection behaviors is sensible, and "most dermatologists realize that you can use neuromodulators and fillers strategically early on," Dr. Tanzi said. "But the idea of using fractionated laser resurfacing treatments to promote improved skin function is intriguing to me. We know we can improve the skin cosmetically through a series of fractional laser resurfacing treatments. But can we functionally improve the skin as it’s aging?" she questioned.
Cutting-edge research suggests that may be the case. In 2012, Dan F. Spandau, Ph.D., and his colleagues (J. Invest. Dermatol. 2012;132:1591-6) published data showing that dermal wounding procedures such as fractional resurfacing can "wake up senescent dermal fibroblasts to produce more insulin-like growth factor-1 (IGF-1), which helps the epidermis ward off the damaging effects of UVB on the skin," Dr. Tanzi said. In that case, she continued, "should we be recommending fractional resurfacing as part of a healthy antiaging routine? If so, at what age? These are exciting developments that need additional research to help guide new treatment protocols."
Although she is enthusiastic about preventing some signs of aging and helping patients maintain a youthful appearance, Dr. Tanzi expressed some concerns. "If we are not careful, we could be setting ourselves up for an expectation of being able to stop the aging process, and this can be a slippery slope, especially for women," she said. "Especially when it comes to fillers and neuromodulators, if not done judiciously they can lead to a very artificial look which, ironically, makes women look much older," Dr. Tanzi said. "As thoughtful physicians, it’s important to keep perspective and guide patients to know when enough is enough [in terms of procedures]," she added.
Dr. Tanzi disclosed that she is a consultant for Cynosure/Palomar, Lumenis, and other companies.
AT CONTROVERSIES AND CONVERSATIONS IN LASER AND COSMETIC SURGERY
