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Telemetry Use for LOS and Cost Reduction
Inpatient hospital services are a major component of total US civilian noninstitutionalized healthcare expenses, accounting for 29.3% of spending in 2009[1] when the average cost per stay was $9700.[2] Telemetry monitoring, a widely used resource for the identification of life‐threatening arrhythmias, contributes to these costs. In 1998, Sivaram et al. estimated the cost per patient at $683; in 2010, Ivonye et al. published the cost difference between a telemetry bed and a nonmonitored bed in their inner‐city public teaching facility reached $800.[3, 4]
In 1991, the American College of Cardiology published guidelines for telemetry use, which were later revised by the American Heart Association in 2004.[5, 6] Notably, the guidelines are based on expert opinion and on research data in electrocardiography.[7] The guidelines divide patients into 3 classes based on clinical condition: recommending telemetry monitoring for almost all class I patients, stating possible benefit in class II patients, and discouraging cardiac monitoring for the low‐risk class III patients.[5, 6] The Choosing Wisely campaign, an initiative of the American Board of Internal Medicine and the Society of Hospital Medicine, highlights telemetry monitoring as 1 of the top 5 interventions that physicians and patients should question when determining tests and procedures.[8] Choosing Wisely suggests using a protocol to govern continuation of telemetry outside of the intensive care unit (ICU), as inappropriate monitoring increases care costs and may result in patient harm.[8] The Joint Commission 2014 National Patient Safety Goals notes that numerous alarm signals and the resulting noise and displayed information tends to desensitize staff and cause them to miss or ignore alarm signals or even disable them.[9]
Few studies have examined implementation methods for improved telemetry bed utilization. One study evaluated the impact of a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team, noting improved cardiac monitoring bed utilization and decreased academic hospital closure, which previously resulted in inability to accept new patients or procedure cancellation.[10] Another study provided an orientation handout discussed by the chief resident and telemetry indication reviews during multidisciplinary rounds 3 times a week.[11]
Our study is one the first to demonstrate a model for a hospitalist‐led approach to guide appropriate telemetry use. We investigated the impact of a multipronged approach to guide telemetry usage: (1) a hospitalist‐led, daily review of bed utilization during attending rounds, (2) a hospitalist attending‐driven, trainee‐focused education module on telemetry utilization, (3) quarterly feedback on telemetry bed utilization rates, and (4) financial incentives. We analyzed pre‐ and post‐evaluation results from the education module to measure impact on knowledge, skills, and attitudes. Additionally, we evaluated the effect of the intervention on length of stay (LOS) and bed utilization costs, while monitoring case mix index (CMI) and overall mortality.
METHODS
Setting
This study took place at Stanford Hospital and Clinics, a teaching academic center in Stanford, California. Stanford Hospital is a 444‐bed, urban medical center with 114 telemetry intermediate ICU beds, and 66 ICU beds. The 264 medicalsurgical beds lack telemetry monitoring, which can only be completed in the intermediate and full ICU. All patients on telemetry units receive both cardiac monitoring and increased nursing ratios. Transfer orders are placed in the electronic medical record to shift patients between care levels. Bed control attempts to transfer patients as soon as an open bed in the appropriate care level exists.
The study included all 5 housestaff inpatient general internal medicine wards teams (which excludes cardiology, pulmonary hypertension, hematology, oncology, and post‐transplant patients). Hospitalists and nonhospitalists attend on the wards for 1‐ to 2‐week blocks. Teaching teams are staffed by 1 to 2 medical students, 2 interns, 1 resident, and 1 attending. The university institutional review board notice of determination waived review for this study because it was classified as quality improvement.
Participants
Ten full‐ and part‐time hospitalist physicians participated in the standardized telemetry teaching. Fifty‐six of the approximately 80 medical students and housestaff on hospitalists' teams completed the educational evaluation. Both hospitalist and nonhospitalist teams participated in daily multidisciplinary rounds, focusing on barriers to discharge including telemetry use. Twelve nonhospitalists served on the wards during the intervention period. Hospitalists covered 72% of the internal medicine wards during the intervention period.
Study Design
We investigated the impact of a multipronged approach to guide telemetry usage from January 2013 to August 2013 (intervention period).
Hospitalist‐Led Daily Review of Bed Utilization
Hospitalists were encouraged to discuss the need of telemetry on daily attending rounds and review indications for telemetry while on service. Prior to starting a ward block, attendings were emailed the teaching module with a reminder to discuss the need for telemetry on attending rounds. Reminders to discuss telemetry utilization were also provided during every‐other‐week hospitalist meetings. Compliance of daily discussion was not tracked.
Hospitalist‐Driven, Trainee‐Focused, Education Module on Telemetry Utilization
The educational module was taught during teaching sessions only by the hospitalists. Trainees on nonhospitalist teams did not receive dedicated teaching about telemetry usage. The module was given to learners only once. The module was a 10‐slide, Microsoft PowerPoint (Microsoft Corp., Redmond, WA) presentation that reviewed the history of telemetry, the American College of Cardiology and the American Heart Association guidelines, the cost difference between telemetry and nonmonitored beds, and the perceived barriers to discontinuation. The presentation was accompanied by a pre‐ and post‐evaluation to elicit knowledge, skills, and attitudes of telemetry use (see Supporting Information, Appendix A, in the online version of this article). The pre‐ and post‐evaluations were created through consensus with a multidisciplinary, expert panel after reviewing the evidence‐based literature.
Quarterly Feedback on Telemetry Bed Utilization Rates
Hospital beduse and CMI data were obtained from the Stanford finance department for the intervention period and for the baseline period, which was the year prior to the study, January 1, 2012 to December 31, 2012. Hospital beduse data included the number of days patients were on telemetry units versus medicalsurgical units (nontelemetry units), differentiated by hospitalists and nonhospitalists. Cost savings were calculated by the Stanford finance department that used Stanford‐specific, internal cost accounting data to determine the impact of the intervention. These data were reviewed at hospitalist meetings on a quarterly basis. We also obtained the University Healthsystem Consortium mortality index (observed to expected) for the general internal medicine service during the baseline and intervention periods.
To measure sustainment of telemetry reduction in the postintervention period, we measured telemetry LOS from September 2014 to March 2015 (extension period).
Financial Incentives
Hospitalists were provided a $2000 bonus at the end of fiscal year 2013 if the group showed a decrease in telemetry bed use in comparison to the baseline period.
Statistical Analysis of Clinical Outcome Measures
Continuous outcomes were tested using 2‐tailed t tests. Comparison of continuous outcome included differences in telemetry and nontelemetry LOS and CMI. Pairwise comparisons were made for various time periods. A P value of <0.05 was considered statistically significant. Statistical analyses were performed using Stata 12.0 software (StataCorp, College Station, TX).
RESULTS
Clinical and Value Outcomes
Baseline (January 2012December 2012) Versus Intervention Period (January 2013August 2013)
LOS for telemetry beds was significantly reduced over the intervention period (2.75 days vs 2.13 days, P=0.005) for hospitalists. Notably, there was no significant difference in mean LOS between baseline and intervention periods for nontelemetry beds (2.84 days vs 2.72 days, P=0.32) for hospitalists. In comparison, for nonhospitalists, there was no difference in mean LOS for telemetry beds between baseline and intervention periods (2.75 days vs 2.46 days, P=0.33) and nontelemetry beds (2.64 days vs 2.89 days, P=0.26) (Table 1).
| Baseline Period | Intervention Period | P Value | Extension Period | P Value | |
|---|---|---|---|---|---|
| |||||
| Length of stay | |||||
| Hospitalists | |||||
| Telemetry beds | 2.75 | 2.13 | 0.005 | 1.93 | 0.09 |
| Nontelemetry beds | 2.84 | 2.72 | 0.324 | 2.44 | 0.21 |
| Nonhospitalists | |||||
| Telemetry beds | 2.75 | 2.46 | 0.331 | 2.22 | 0.43 |
| Nontelemetry beds | 2.64 | 2.89 | 0.261 | 2.26 | 0.05 |
| Case mix index | |||||
| Hospitalists | 1.44 | 1.45 | 0.68 | 1.40 | 0.21 |
| Nonhospitalists | 1.46 | 1.40 | 0.53 | 1.53 | 0.18 |
Costs of hospital stay were also reduced in the multipronged, hospitalist‐driven intervention group. Expenditures for telemetry beds were reduced by 22.5% over the intervention period for hospitalists (Table 2).
| Baseline to Intervention Period | Intervention to Extension Period | |
|---|---|---|
| ||
| Hospitalists | ||
| Telemetry beds | 22.55% | 9.55% |
| Nontelemetry beds | 4.23% | 10.14% |
| Nonhospitalists | ||
| Telemetry beds | 10.55% | 9.89% |
| Nontelemetry beds | 9.47% | 21.84% |
The mean CMI of the patient cohort managed by the hospitalists in the baseline and intervention periods was not significantly different (1.44 vs 1.45, P=0.68). The mean CMI of the patients managed by the nonhospitalists in the baseline and intervention periods was also not significantly different (1.46 vs 1.40, P=0.53) (Table 1). Mortality index during the baseline and intervention periods was not significantly different (0.770.22 vs 0.660.23, P=0.54), as during the intervention and extension periods (0.660.23 vs 0.650.15, P=0.95).
Intervention Period (January 2013August 2013) Versus Extension Period (September 2014‐March 2015)
The decreased telemetry LOS for hospitalists was sustained from the intervention period to the extension period, from 2.13 to 1.93 (P=0.09). There was no significant change in the nontelemetry LOS in the intervention period compared to the extension period (2.72 vs 2.44, P=0.21). There was no change in the telemetry LOS for nonhospitalists from the intervention period to the extension period (2.46 vs 2.22, P=0.43).
The mean CMI in the hospitalist group was not significantly different in the intervention period compared to the extension period (1.45 to 1.40, P=0.21). The mean CMI in the nonhospitalist group did not change from the intervention period to the extension period (1.40 vs 1.53, P=0.18) (Table 1).
Education Outcomes
Out of the 56 participants completing the education module and survey, 28.6% were medical students, 53.6% were interns, 12.5% were second‐year residents, and 5.4% were third‐year residents. Several findings were seen at baseline via pretest. In evaluating patterns of current telemetry use, 32.2% of participants reported evaluating the necessity of telemetry for patients on admission only, 26.3% during transitions of care, 5.1% after discharge plans were cemented, 33.1% on a daily basis, and 3.4% rarely. When asked which member of the care team was most likely to encourage use of appropriate telemetry, 20.8% identified another resident, 13.9% nursing, 37.5% attending physician, 20.8% self, 4.2% the team as a whole, and 2.8% as not any.
Figure 1 shows premodule results regarding the trainees perceived percentage of patient encounters during which a participant's team discussed their patient's need for telemetry.
In assessing perception of current telemetry utilization, 1.8% of participants thought 0% to 10% of patients were currently on telemetry, 19.6% thought 11% to 20%, 42.9% thought 21% to 31%, 30.4% thought 31% to 40%, and 3.6% thought 41% to 50%.
Two areas were assessed at both baseline and after the intervention: knowledge of indications of telemetry use and cost related to telemetry use. We saw increased awareness of cost‐saving actions. To assess current knowledge of the indications of proper telemetry use according to American Heart Association guidelines, participants were presented with a list of 5 patients with different clinical indications for telemetry use and asked which patient required telemetry the most. Of the participants, 54.5% identified the correct answer in the pretest and 61.8% identified the correct answer in the post‐test. To assess knowledge of the costs of telemetry relative to other patient care, participants were presented with a patient case and asked to identify the most and least cost‐saving actions to safely care for the patient. When asked to identify the most cost‐saving action, 20.3% identified the correct answer in the pretest and 61.0% identified the correct answer in the post‐test. Of those who answered incorrectly in the pretest, 51.1% answered correctly in the post‐test (P=0.002). When asked to identify the least cost‐saving action, 23.7% identified the correct answer in the pretest and 50.9% identified the correct answer in the posttest. Of those who answered incorrectly in the pretest, 60.0% answered correctly in the post‐test (P=0.003).
In the post‐test, when asked about the importance of appropriate telemetry usage in providing cost‐conscious care and assuring appropriate hospital resource management, 76.8% of participants found the need very important, 21.4% somewhat important, and 1.8% as not applicable. The most commonly perceived barriers impeding discontinuation of telemetry, as reported by participants via post‐test, were nursing desires and time. Figure 2 shows all perceived barriers.
DISCUSSION
Our study is one of the first to our knowledge to demonstrate reductions in telemetry LOS by a hospitalist intervention for telemetry utilization. Others[10, 11] have studied the impact of an orientation handout by chief residents or a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team. Dressler et al. later sustained a 70% reduction in telemetry use without adversely affecting patient safety, as assessed through numbers of rapid response activations, codes, and deaths, through integrating the AHA guidelines into their electronic ordering system.[12] However, our study has the advantage of the primary team, who knows the patient and clinical scenario best, driving the change during attending rounds. In an era where cost consciousness intersects the practice of medicine, any intervention in patient care that demonstrates cost savings without an adverse impact on patient care and resource utilization must be emphasized. This is particularly important in academic institutions, where residents and medical students are learning to integrate the principles of patient safety and quality improvement into their clinical practice.[13] We actually showed sustained telemetry LOS reductions into the extension period after our intervention. We believe this may be due to telemetry triage being integrated into our attending and resident rounding practices. Future work should include integration of telemetry triage into clinical decision support in the electronic medical record and multidisciplinary rounds to disseminate telemetry triage hospital‐wide in both the academic and community settings.
Our study also revealed that nearly half of participants were not aware of the criteria for appropriate utilization of telemetry before our intervention; in the preintervention period, there were many anecdotal and objective findings of inappropriate utilization of telemetry as well as prolonged continuation beyond the clinical needs in both the hospitalist and nonhospitalist group. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
We were able to show increased knowledge of cost‐saving actions among trainees with our educational module. We believe it is imperative to educate our providers (physicians, nurses, case managers, and students within these disciplines) on the appropriate indications for telemetry use, not only to help with cost savings and resource availability (ie, allowing telemetry beds to be available for patients who need them most), but also to instill consistent expectations among our patients. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
Additionally, we feel it is important to consider the impacts of inappropriate use of telemetry from a patient's perspective: it is physically restrictive/emnconvenient, alarms are disruptive, it can be a barrier for other treatments such as physical therapy, it may increase the time it takes for imaging studies, a nurse may be required to accompany patients on telemetry, and poses additional costs to their medical bill.
We believe our success is due to several strategies. First, at the start of the fiscal year when quality improvement metrics are established, this particular metric (improving the appropriate utilization and timely discontinuation of telemetry) was deemed important by all hospitalists, engendering group buy‐in prior to the intervention. Our hospitalists received a detailed and interactive tutorial session in person at the beginning of the study. This tutorial provided the hospitalists with a comprehensive understanding of the appropriate (and inappropriate) indications for telemetry monitoring, hence facilitating guideline‐directed utilization. Email reminders and the tutorial tool were provided each time a hospitalist attended on the wards, and hospitalists received a small financial incentive to comply with appropriate telemetry utilization.
Our study has several strengths. First, the time frame of our study was long enough (8 months) to allow consistent trends to emerge and to optimize exposure of housestaff and medical students to this quality‐improvement initiative. Second, our cost savings came from 2 factors, direct reduction of inappropriate telemetry use and reduction in length of stay, highlighting the dual impact of appropriate telemetry utilization on cost. The overall reductions in telemetry utilization for the intervention group were a result of both reductions in initial placement on telemetry for patients who did not meet criteria for such monitoring as well as timely discontinuation of telemetry during the patient's hospitalization. Third, our study demonstrates that physicians can be effective in driving appropriate telemetry usage by participating in the clinical decision making regarding necessity and educating providers, trainees/students, and patients on appropriate indications. Finally, we show sustainment of our intervention in the extension period, suggesting telemetry triage integration into rounding practice.
Our study has limitations as well. First, our sample size is relatively small at a single academic center. Second, due to complexities in our faculty scheduling, we were unable to completely randomize patients to a hospitalist versus nonhospitalist team. However, we believe that despite the inability to randomize, our study does show the benefit of a hospitalist attending to reduce telemetry LOS given there was no change in nonhospitalist telemetry LOS despite all of the other hospital‐wide interventions (multidisciplinary rounds, similar housestaff). Third, our study was limited in that the CMI was used as a proxy for patient complexity, and the mortality index was used as the overall marker of safety. Further studies should monitor frequency and outcomes of arrhythmic events of patients transferred from telemetry monitoring to medicalsurgical beds. Finally, as the intervention was multipronged, we are unable to determine which component led to the reductions in telemetry utilization. Each component, however, remains easily transferrable to outside institutions. We demonstrated both a reduction in initiation of telemetry as well as timely discontinuation; however, due to the complexity in capturing this accurately, we were unable to numerically quantify these individual outcomes.
Additionally, there were approximately 10 nonhospitalist attendings who also staffed the wards during the intervention time period of our study; these attendings did not undergo the telemetry tutorial/orientation. This difference, along with the Hawthorne effect for the hospitalist attendings, also likely contributed to the difference in outcomes between the 2 attending cohorts in the intervention period.
CONCLUSIONS
Our results demonstrate that a multipronged hospitalist‐driven intervention to improve appropriate use of telemetry reduces telemetry LOS and cost. Hence, we believe that targeted, education‐driven interventions with monitoring of progress can have demonstrable impacts on changing practice. Physicians will need to make trade‐offs in clinical practice to balance efficient resource utilization with the patient's evolving condition in the inpatient setting, the complexities of clinical workflow, and the patient's expectations.[14] Appropriate telemetry utilization is a prime example of what needs to be done well in the future for high‐value care.
Acknowledgements
The authors acknowledge the hospitalists who participated in the intervention: Jeffrey Chi, Willliam Daines, Sumbul Desai, Poonam Hosamani, John Kugler, Charles Liao, Errol Ozdalga, and Sang Hoon Woo. The authors also acknowledge Joan Hendershott in the Finance Department and Joseph Hopkins in the Quality Department.
Disclosures: All coauthors have seen and agree with the contents of the article; submission (aside from abstracts) was not under review by any other publication. The authors report no disclosures of financial support from, or equity positions in, manufacturers of drugs or products mentioned in the article.
- , National health care expenses in the U.S. civilian noninstitutionalized population, 2009. Statistical brief 355. 2012. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Costs for hospital stays in the United States, 2010. Statistical brief 146. 2013. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Telemetry outside critical care units: patterns of utilization and influence on management decisions. Clin Cardiol. 1998;21(7):503–505.
- , , , et al. Evaluation of telemetry utilization, policy, and outcomes in an inner‐city academic medical center. J Natl Med Assoc. 2010;102(7):598–604.
- , , Recommended guidelines for in‐hospital cardiac monitoring of adults for detection of arrhythmia. Emergency Cardiac Care Committee members. J Am Coll Cardiol. 1991;18(6):1431–1433.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , Is telemetry overused? Is it as helpful as thought? Cleve Clin J Med. 2009;76(6):368–372.
- Society of Hospital Medicine. Adult Hospital Medicine. Five things physicians and patients should question. Available at: http://www.choosingwisely.org/societies/society‐of‐hospital‐medicine‐adult. Published February 21, 2013. Accessed October 5, 2014.
- Joint Commission on Accreditation of Healthcare Organizations. The Joint Commission announces 2014 national patient safety goal. Jt Comm Perspect. 2013;33(7):1–4.
- , , , , Optimizing telemetry utilization in an academic medical center. J Clin Outcomes Manage. 2008;15(9):435–440.
- , Improving utilization of telemetry in a university hospital. J Clin Outcomes Manage. 2005;12(10):519–522.
- , , , , Altering overuse of cardiac telemetry in non‐intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174:1852–1854.
- , , "Innovation" institutes in academic health centers: enhancing value through leadership, education, engagement, and scholarship. Acad Med. 2014;89(9):1204–1206.
- , , , , , Controlling health costs: physician responses to patient expectations for medical care. J Gen Intern Med. 2014;29(9):1234–1241.
Inpatient hospital services are a major component of total US civilian noninstitutionalized healthcare expenses, accounting for 29.3% of spending in 2009[1] when the average cost per stay was $9700.[2] Telemetry monitoring, a widely used resource for the identification of life‐threatening arrhythmias, contributes to these costs. In 1998, Sivaram et al. estimated the cost per patient at $683; in 2010, Ivonye et al. published the cost difference between a telemetry bed and a nonmonitored bed in their inner‐city public teaching facility reached $800.[3, 4]
In 1991, the American College of Cardiology published guidelines for telemetry use, which were later revised by the American Heart Association in 2004.[5, 6] Notably, the guidelines are based on expert opinion and on research data in electrocardiography.[7] The guidelines divide patients into 3 classes based on clinical condition: recommending telemetry monitoring for almost all class I patients, stating possible benefit in class II patients, and discouraging cardiac monitoring for the low‐risk class III patients.[5, 6] The Choosing Wisely campaign, an initiative of the American Board of Internal Medicine and the Society of Hospital Medicine, highlights telemetry monitoring as 1 of the top 5 interventions that physicians and patients should question when determining tests and procedures.[8] Choosing Wisely suggests using a protocol to govern continuation of telemetry outside of the intensive care unit (ICU), as inappropriate monitoring increases care costs and may result in patient harm.[8] The Joint Commission 2014 National Patient Safety Goals notes that numerous alarm signals and the resulting noise and displayed information tends to desensitize staff and cause them to miss or ignore alarm signals or even disable them.[9]
Few studies have examined implementation methods for improved telemetry bed utilization. One study evaluated the impact of a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team, noting improved cardiac monitoring bed utilization and decreased academic hospital closure, which previously resulted in inability to accept new patients or procedure cancellation.[10] Another study provided an orientation handout discussed by the chief resident and telemetry indication reviews during multidisciplinary rounds 3 times a week.[11]
Our study is one the first to demonstrate a model for a hospitalist‐led approach to guide appropriate telemetry use. We investigated the impact of a multipronged approach to guide telemetry usage: (1) a hospitalist‐led, daily review of bed utilization during attending rounds, (2) a hospitalist attending‐driven, trainee‐focused education module on telemetry utilization, (3) quarterly feedback on telemetry bed utilization rates, and (4) financial incentives. We analyzed pre‐ and post‐evaluation results from the education module to measure impact on knowledge, skills, and attitudes. Additionally, we evaluated the effect of the intervention on length of stay (LOS) and bed utilization costs, while monitoring case mix index (CMI) and overall mortality.
METHODS
Setting
This study took place at Stanford Hospital and Clinics, a teaching academic center in Stanford, California. Stanford Hospital is a 444‐bed, urban medical center with 114 telemetry intermediate ICU beds, and 66 ICU beds. The 264 medicalsurgical beds lack telemetry monitoring, which can only be completed in the intermediate and full ICU. All patients on telemetry units receive both cardiac monitoring and increased nursing ratios. Transfer orders are placed in the electronic medical record to shift patients between care levels. Bed control attempts to transfer patients as soon as an open bed in the appropriate care level exists.
The study included all 5 housestaff inpatient general internal medicine wards teams (which excludes cardiology, pulmonary hypertension, hematology, oncology, and post‐transplant patients). Hospitalists and nonhospitalists attend on the wards for 1‐ to 2‐week blocks. Teaching teams are staffed by 1 to 2 medical students, 2 interns, 1 resident, and 1 attending. The university institutional review board notice of determination waived review for this study because it was classified as quality improvement.
Participants
Ten full‐ and part‐time hospitalist physicians participated in the standardized telemetry teaching. Fifty‐six of the approximately 80 medical students and housestaff on hospitalists' teams completed the educational evaluation. Both hospitalist and nonhospitalist teams participated in daily multidisciplinary rounds, focusing on barriers to discharge including telemetry use. Twelve nonhospitalists served on the wards during the intervention period. Hospitalists covered 72% of the internal medicine wards during the intervention period.
Study Design
We investigated the impact of a multipronged approach to guide telemetry usage from January 2013 to August 2013 (intervention period).
Hospitalist‐Led Daily Review of Bed Utilization
Hospitalists were encouraged to discuss the need of telemetry on daily attending rounds and review indications for telemetry while on service. Prior to starting a ward block, attendings were emailed the teaching module with a reminder to discuss the need for telemetry on attending rounds. Reminders to discuss telemetry utilization were also provided during every‐other‐week hospitalist meetings. Compliance of daily discussion was not tracked.
Hospitalist‐Driven, Trainee‐Focused, Education Module on Telemetry Utilization
The educational module was taught during teaching sessions only by the hospitalists. Trainees on nonhospitalist teams did not receive dedicated teaching about telemetry usage. The module was given to learners only once. The module was a 10‐slide, Microsoft PowerPoint (Microsoft Corp., Redmond, WA) presentation that reviewed the history of telemetry, the American College of Cardiology and the American Heart Association guidelines, the cost difference between telemetry and nonmonitored beds, and the perceived barriers to discontinuation. The presentation was accompanied by a pre‐ and post‐evaluation to elicit knowledge, skills, and attitudes of telemetry use (see Supporting Information, Appendix A, in the online version of this article). The pre‐ and post‐evaluations were created through consensus with a multidisciplinary, expert panel after reviewing the evidence‐based literature.
Quarterly Feedback on Telemetry Bed Utilization Rates
Hospital beduse and CMI data were obtained from the Stanford finance department for the intervention period and for the baseline period, which was the year prior to the study, January 1, 2012 to December 31, 2012. Hospital beduse data included the number of days patients were on telemetry units versus medicalsurgical units (nontelemetry units), differentiated by hospitalists and nonhospitalists. Cost savings were calculated by the Stanford finance department that used Stanford‐specific, internal cost accounting data to determine the impact of the intervention. These data were reviewed at hospitalist meetings on a quarterly basis. We also obtained the University Healthsystem Consortium mortality index (observed to expected) for the general internal medicine service during the baseline and intervention periods.
To measure sustainment of telemetry reduction in the postintervention period, we measured telemetry LOS from September 2014 to March 2015 (extension period).
Financial Incentives
Hospitalists were provided a $2000 bonus at the end of fiscal year 2013 if the group showed a decrease in telemetry bed use in comparison to the baseline period.
Statistical Analysis of Clinical Outcome Measures
Continuous outcomes were tested using 2‐tailed t tests. Comparison of continuous outcome included differences in telemetry and nontelemetry LOS and CMI. Pairwise comparisons were made for various time periods. A P value of <0.05 was considered statistically significant. Statistical analyses were performed using Stata 12.0 software (StataCorp, College Station, TX).
RESULTS
Clinical and Value Outcomes
Baseline (January 2012December 2012) Versus Intervention Period (January 2013August 2013)
LOS for telemetry beds was significantly reduced over the intervention period (2.75 days vs 2.13 days, P=0.005) for hospitalists. Notably, there was no significant difference in mean LOS between baseline and intervention periods for nontelemetry beds (2.84 days vs 2.72 days, P=0.32) for hospitalists. In comparison, for nonhospitalists, there was no difference in mean LOS for telemetry beds between baseline and intervention periods (2.75 days vs 2.46 days, P=0.33) and nontelemetry beds (2.64 days vs 2.89 days, P=0.26) (Table 1).
| Baseline Period | Intervention Period | P Value | Extension Period | P Value | |
|---|---|---|---|---|---|
| |||||
| Length of stay | |||||
| Hospitalists | |||||
| Telemetry beds | 2.75 | 2.13 | 0.005 | 1.93 | 0.09 |
| Nontelemetry beds | 2.84 | 2.72 | 0.324 | 2.44 | 0.21 |
| Nonhospitalists | |||||
| Telemetry beds | 2.75 | 2.46 | 0.331 | 2.22 | 0.43 |
| Nontelemetry beds | 2.64 | 2.89 | 0.261 | 2.26 | 0.05 |
| Case mix index | |||||
| Hospitalists | 1.44 | 1.45 | 0.68 | 1.40 | 0.21 |
| Nonhospitalists | 1.46 | 1.40 | 0.53 | 1.53 | 0.18 |
Costs of hospital stay were also reduced in the multipronged, hospitalist‐driven intervention group. Expenditures for telemetry beds were reduced by 22.5% over the intervention period for hospitalists (Table 2).
| Baseline to Intervention Period | Intervention to Extension Period | |
|---|---|---|
| ||
| Hospitalists | ||
| Telemetry beds | 22.55% | 9.55% |
| Nontelemetry beds | 4.23% | 10.14% |
| Nonhospitalists | ||
| Telemetry beds | 10.55% | 9.89% |
| Nontelemetry beds | 9.47% | 21.84% |
The mean CMI of the patient cohort managed by the hospitalists in the baseline and intervention periods was not significantly different (1.44 vs 1.45, P=0.68). The mean CMI of the patients managed by the nonhospitalists in the baseline and intervention periods was also not significantly different (1.46 vs 1.40, P=0.53) (Table 1). Mortality index during the baseline and intervention periods was not significantly different (0.770.22 vs 0.660.23, P=0.54), as during the intervention and extension periods (0.660.23 vs 0.650.15, P=0.95).
Intervention Period (January 2013August 2013) Versus Extension Period (September 2014‐March 2015)
The decreased telemetry LOS for hospitalists was sustained from the intervention period to the extension period, from 2.13 to 1.93 (P=0.09). There was no significant change in the nontelemetry LOS in the intervention period compared to the extension period (2.72 vs 2.44, P=0.21). There was no change in the telemetry LOS for nonhospitalists from the intervention period to the extension period (2.46 vs 2.22, P=0.43).
The mean CMI in the hospitalist group was not significantly different in the intervention period compared to the extension period (1.45 to 1.40, P=0.21). The mean CMI in the nonhospitalist group did not change from the intervention period to the extension period (1.40 vs 1.53, P=0.18) (Table 1).
Education Outcomes
Out of the 56 participants completing the education module and survey, 28.6% were medical students, 53.6% were interns, 12.5% were second‐year residents, and 5.4% were third‐year residents. Several findings were seen at baseline via pretest. In evaluating patterns of current telemetry use, 32.2% of participants reported evaluating the necessity of telemetry for patients on admission only, 26.3% during transitions of care, 5.1% after discharge plans were cemented, 33.1% on a daily basis, and 3.4% rarely. When asked which member of the care team was most likely to encourage use of appropriate telemetry, 20.8% identified another resident, 13.9% nursing, 37.5% attending physician, 20.8% self, 4.2% the team as a whole, and 2.8% as not any.
Figure 1 shows premodule results regarding the trainees perceived percentage of patient encounters during which a participant's team discussed their patient's need for telemetry.
In assessing perception of current telemetry utilization, 1.8% of participants thought 0% to 10% of patients were currently on telemetry, 19.6% thought 11% to 20%, 42.9% thought 21% to 31%, 30.4% thought 31% to 40%, and 3.6% thought 41% to 50%.
Two areas were assessed at both baseline and after the intervention: knowledge of indications of telemetry use and cost related to telemetry use. We saw increased awareness of cost‐saving actions. To assess current knowledge of the indications of proper telemetry use according to American Heart Association guidelines, participants were presented with a list of 5 patients with different clinical indications for telemetry use and asked which patient required telemetry the most. Of the participants, 54.5% identified the correct answer in the pretest and 61.8% identified the correct answer in the post‐test. To assess knowledge of the costs of telemetry relative to other patient care, participants were presented with a patient case and asked to identify the most and least cost‐saving actions to safely care for the patient. When asked to identify the most cost‐saving action, 20.3% identified the correct answer in the pretest and 61.0% identified the correct answer in the post‐test. Of those who answered incorrectly in the pretest, 51.1% answered correctly in the post‐test (P=0.002). When asked to identify the least cost‐saving action, 23.7% identified the correct answer in the pretest and 50.9% identified the correct answer in the posttest. Of those who answered incorrectly in the pretest, 60.0% answered correctly in the post‐test (P=0.003).
In the post‐test, when asked about the importance of appropriate telemetry usage in providing cost‐conscious care and assuring appropriate hospital resource management, 76.8% of participants found the need very important, 21.4% somewhat important, and 1.8% as not applicable. The most commonly perceived barriers impeding discontinuation of telemetry, as reported by participants via post‐test, were nursing desires and time. Figure 2 shows all perceived barriers.
DISCUSSION
Our study is one of the first to our knowledge to demonstrate reductions in telemetry LOS by a hospitalist intervention for telemetry utilization. Others[10, 11] have studied the impact of an orientation handout by chief residents or a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team. Dressler et al. later sustained a 70% reduction in telemetry use without adversely affecting patient safety, as assessed through numbers of rapid response activations, codes, and deaths, through integrating the AHA guidelines into their electronic ordering system.[12] However, our study has the advantage of the primary team, who knows the patient and clinical scenario best, driving the change during attending rounds. In an era where cost consciousness intersects the practice of medicine, any intervention in patient care that demonstrates cost savings without an adverse impact on patient care and resource utilization must be emphasized. This is particularly important in academic institutions, where residents and medical students are learning to integrate the principles of patient safety and quality improvement into their clinical practice.[13] We actually showed sustained telemetry LOS reductions into the extension period after our intervention. We believe this may be due to telemetry triage being integrated into our attending and resident rounding practices. Future work should include integration of telemetry triage into clinical decision support in the electronic medical record and multidisciplinary rounds to disseminate telemetry triage hospital‐wide in both the academic and community settings.
Our study also revealed that nearly half of participants were not aware of the criteria for appropriate utilization of telemetry before our intervention; in the preintervention period, there were many anecdotal and objective findings of inappropriate utilization of telemetry as well as prolonged continuation beyond the clinical needs in both the hospitalist and nonhospitalist group. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
We were able to show increased knowledge of cost‐saving actions among trainees with our educational module. We believe it is imperative to educate our providers (physicians, nurses, case managers, and students within these disciplines) on the appropriate indications for telemetry use, not only to help with cost savings and resource availability (ie, allowing telemetry beds to be available for patients who need them most), but also to instill consistent expectations among our patients. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
Additionally, we feel it is important to consider the impacts of inappropriate use of telemetry from a patient's perspective: it is physically restrictive/emnconvenient, alarms are disruptive, it can be a barrier for other treatments such as physical therapy, it may increase the time it takes for imaging studies, a nurse may be required to accompany patients on telemetry, and poses additional costs to their medical bill.
We believe our success is due to several strategies. First, at the start of the fiscal year when quality improvement metrics are established, this particular metric (improving the appropriate utilization and timely discontinuation of telemetry) was deemed important by all hospitalists, engendering group buy‐in prior to the intervention. Our hospitalists received a detailed and interactive tutorial session in person at the beginning of the study. This tutorial provided the hospitalists with a comprehensive understanding of the appropriate (and inappropriate) indications for telemetry monitoring, hence facilitating guideline‐directed utilization. Email reminders and the tutorial tool were provided each time a hospitalist attended on the wards, and hospitalists received a small financial incentive to comply with appropriate telemetry utilization.
Our study has several strengths. First, the time frame of our study was long enough (8 months) to allow consistent trends to emerge and to optimize exposure of housestaff and medical students to this quality‐improvement initiative. Second, our cost savings came from 2 factors, direct reduction of inappropriate telemetry use and reduction in length of stay, highlighting the dual impact of appropriate telemetry utilization on cost. The overall reductions in telemetry utilization for the intervention group were a result of both reductions in initial placement on telemetry for patients who did not meet criteria for such monitoring as well as timely discontinuation of telemetry during the patient's hospitalization. Third, our study demonstrates that physicians can be effective in driving appropriate telemetry usage by participating in the clinical decision making regarding necessity and educating providers, trainees/students, and patients on appropriate indications. Finally, we show sustainment of our intervention in the extension period, suggesting telemetry triage integration into rounding practice.
Our study has limitations as well. First, our sample size is relatively small at a single academic center. Second, due to complexities in our faculty scheduling, we were unable to completely randomize patients to a hospitalist versus nonhospitalist team. However, we believe that despite the inability to randomize, our study does show the benefit of a hospitalist attending to reduce telemetry LOS given there was no change in nonhospitalist telemetry LOS despite all of the other hospital‐wide interventions (multidisciplinary rounds, similar housestaff). Third, our study was limited in that the CMI was used as a proxy for patient complexity, and the mortality index was used as the overall marker of safety. Further studies should monitor frequency and outcomes of arrhythmic events of patients transferred from telemetry monitoring to medicalsurgical beds. Finally, as the intervention was multipronged, we are unable to determine which component led to the reductions in telemetry utilization. Each component, however, remains easily transferrable to outside institutions. We demonstrated both a reduction in initiation of telemetry as well as timely discontinuation; however, due to the complexity in capturing this accurately, we were unable to numerically quantify these individual outcomes.
Additionally, there were approximately 10 nonhospitalist attendings who also staffed the wards during the intervention time period of our study; these attendings did not undergo the telemetry tutorial/orientation. This difference, along with the Hawthorne effect for the hospitalist attendings, also likely contributed to the difference in outcomes between the 2 attending cohorts in the intervention period.
CONCLUSIONS
Our results demonstrate that a multipronged hospitalist‐driven intervention to improve appropriate use of telemetry reduces telemetry LOS and cost. Hence, we believe that targeted, education‐driven interventions with monitoring of progress can have demonstrable impacts on changing practice. Physicians will need to make trade‐offs in clinical practice to balance efficient resource utilization with the patient's evolving condition in the inpatient setting, the complexities of clinical workflow, and the patient's expectations.[14] Appropriate telemetry utilization is a prime example of what needs to be done well in the future for high‐value care.
Acknowledgements
The authors acknowledge the hospitalists who participated in the intervention: Jeffrey Chi, Willliam Daines, Sumbul Desai, Poonam Hosamani, John Kugler, Charles Liao, Errol Ozdalga, and Sang Hoon Woo. The authors also acknowledge Joan Hendershott in the Finance Department and Joseph Hopkins in the Quality Department.
Disclosures: All coauthors have seen and agree with the contents of the article; submission (aside from abstracts) was not under review by any other publication. The authors report no disclosures of financial support from, or equity positions in, manufacturers of drugs or products mentioned in the article.
Inpatient hospital services are a major component of total US civilian noninstitutionalized healthcare expenses, accounting for 29.3% of spending in 2009[1] when the average cost per stay was $9700.[2] Telemetry monitoring, a widely used resource for the identification of life‐threatening arrhythmias, contributes to these costs. In 1998, Sivaram et al. estimated the cost per patient at $683; in 2010, Ivonye et al. published the cost difference between a telemetry bed and a nonmonitored bed in their inner‐city public teaching facility reached $800.[3, 4]
In 1991, the American College of Cardiology published guidelines for telemetry use, which were later revised by the American Heart Association in 2004.[5, 6] Notably, the guidelines are based on expert opinion and on research data in electrocardiography.[7] The guidelines divide patients into 3 classes based on clinical condition: recommending telemetry monitoring for almost all class I patients, stating possible benefit in class II patients, and discouraging cardiac monitoring for the low‐risk class III patients.[5, 6] The Choosing Wisely campaign, an initiative of the American Board of Internal Medicine and the Society of Hospital Medicine, highlights telemetry monitoring as 1 of the top 5 interventions that physicians and patients should question when determining tests and procedures.[8] Choosing Wisely suggests using a protocol to govern continuation of telemetry outside of the intensive care unit (ICU), as inappropriate monitoring increases care costs and may result in patient harm.[8] The Joint Commission 2014 National Patient Safety Goals notes that numerous alarm signals and the resulting noise and displayed information tends to desensitize staff and cause them to miss or ignore alarm signals or even disable them.[9]
Few studies have examined implementation methods for improved telemetry bed utilization. One study evaluated the impact of a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team, noting improved cardiac monitoring bed utilization and decreased academic hospital closure, which previously resulted in inability to accept new patients or procedure cancellation.[10] Another study provided an orientation handout discussed by the chief resident and telemetry indication reviews during multidisciplinary rounds 3 times a week.[11]
Our study is one the first to demonstrate a model for a hospitalist‐led approach to guide appropriate telemetry use. We investigated the impact of a multipronged approach to guide telemetry usage: (1) a hospitalist‐led, daily review of bed utilization during attending rounds, (2) a hospitalist attending‐driven, trainee‐focused education module on telemetry utilization, (3) quarterly feedback on telemetry bed utilization rates, and (4) financial incentives. We analyzed pre‐ and post‐evaluation results from the education module to measure impact on knowledge, skills, and attitudes. Additionally, we evaluated the effect of the intervention on length of stay (LOS) and bed utilization costs, while monitoring case mix index (CMI) and overall mortality.
METHODS
Setting
This study took place at Stanford Hospital and Clinics, a teaching academic center in Stanford, California. Stanford Hospital is a 444‐bed, urban medical center with 114 telemetry intermediate ICU beds, and 66 ICU beds. The 264 medicalsurgical beds lack telemetry monitoring, which can only be completed in the intermediate and full ICU. All patients on telemetry units receive both cardiac monitoring and increased nursing ratios. Transfer orders are placed in the electronic medical record to shift patients between care levels. Bed control attempts to transfer patients as soon as an open bed in the appropriate care level exists.
The study included all 5 housestaff inpatient general internal medicine wards teams (which excludes cardiology, pulmonary hypertension, hematology, oncology, and post‐transplant patients). Hospitalists and nonhospitalists attend on the wards for 1‐ to 2‐week blocks. Teaching teams are staffed by 1 to 2 medical students, 2 interns, 1 resident, and 1 attending. The university institutional review board notice of determination waived review for this study because it was classified as quality improvement.
Participants
Ten full‐ and part‐time hospitalist physicians participated in the standardized telemetry teaching. Fifty‐six of the approximately 80 medical students and housestaff on hospitalists' teams completed the educational evaluation. Both hospitalist and nonhospitalist teams participated in daily multidisciplinary rounds, focusing on barriers to discharge including telemetry use. Twelve nonhospitalists served on the wards during the intervention period. Hospitalists covered 72% of the internal medicine wards during the intervention period.
Study Design
We investigated the impact of a multipronged approach to guide telemetry usage from January 2013 to August 2013 (intervention period).
Hospitalist‐Led Daily Review of Bed Utilization
Hospitalists were encouraged to discuss the need of telemetry on daily attending rounds and review indications for telemetry while on service. Prior to starting a ward block, attendings were emailed the teaching module with a reminder to discuss the need for telemetry on attending rounds. Reminders to discuss telemetry utilization were also provided during every‐other‐week hospitalist meetings. Compliance of daily discussion was not tracked.
Hospitalist‐Driven, Trainee‐Focused, Education Module on Telemetry Utilization
The educational module was taught during teaching sessions only by the hospitalists. Trainees on nonhospitalist teams did not receive dedicated teaching about telemetry usage. The module was given to learners only once. The module was a 10‐slide, Microsoft PowerPoint (Microsoft Corp., Redmond, WA) presentation that reviewed the history of telemetry, the American College of Cardiology and the American Heart Association guidelines, the cost difference between telemetry and nonmonitored beds, and the perceived barriers to discontinuation. The presentation was accompanied by a pre‐ and post‐evaluation to elicit knowledge, skills, and attitudes of telemetry use (see Supporting Information, Appendix A, in the online version of this article). The pre‐ and post‐evaluations were created through consensus with a multidisciplinary, expert panel after reviewing the evidence‐based literature.
Quarterly Feedback on Telemetry Bed Utilization Rates
Hospital beduse and CMI data were obtained from the Stanford finance department for the intervention period and for the baseline period, which was the year prior to the study, January 1, 2012 to December 31, 2012. Hospital beduse data included the number of days patients were on telemetry units versus medicalsurgical units (nontelemetry units), differentiated by hospitalists and nonhospitalists. Cost savings were calculated by the Stanford finance department that used Stanford‐specific, internal cost accounting data to determine the impact of the intervention. These data were reviewed at hospitalist meetings on a quarterly basis. We also obtained the University Healthsystem Consortium mortality index (observed to expected) for the general internal medicine service during the baseline and intervention periods.
To measure sustainment of telemetry reduction in the postintervention period, we measured telemetry LOS from September 2014 to March 2015 (extension period).
Financial Incentives
Hospitalists were provided a $2000 bonus at the end of fiscal year 2013 if the group showed a decrease in telemetry bed use in comparison to the baseline period.
Statistical Analysis of Clinical Outcome Measures
Continuous outcomes were tested using 2‐tailed t tests. Comparison of continuous outcome included differences in telemetry and nontelemetry LOS and CMI. Pairwise comparisons were made for various time periods. A P value of <0.05 was considered statistically significant. Statistical analyses were performed using Stata 12.0 software (StataCorp, College Station, TX).
RESULTS
Clinical and Value Outcomes
Baseline (January 2012December 2012) Versus Intervention Period (January 2013August 2013)
LOS for telemetry beds was significantly reduced over the intervention period (2.75 days vs 2.13 days, P=0.005) for hospitalists. Notably, there was no significant difference in mean LOS between baseline and intervention periods for nontelemetry beds (2.84 days vs 2.72 days, P=0.32) for hospitalists. In comparison, for nonhospitalists, there was no difference in mean LOS for telemetry beds between baseline and intervention periods (2.75 days vs 2.46 days, P=0.33) and nontelemetry beds (2.64 days vs 2.89 days, P=0.26) (Table 1).
| Baseline Period | Intervention Period | P Value | Extension Period | P Value | |
|---|---|---|---|---|---|
| |||||
| Length of stay | |||||
| Hospitalists | |||||
| Telemetry beds | 2.75 | 2.13 | 0.005 | 1.93 | 0.09 |
| Nontelemetry beds | 2.84 | 2.72 | 0.324 | 2.44 | 0.21 |
| Nonhospitalists | |||||
| Telemetry beds | 2.75 | 2.46 | 0.331 | 2.22 | 0.43 |
| Nontelemetry beds | 2.64 | 2.89 | 0.261 | 2.26 | 0.05 |
| Case mix index | |||||
| Hospitalists | 1.44 | 1.45 | 0.68 | 1.40 | 0.21 |
| Nonhospitalists | 1.46 | 1.40 | 0.53 | 1.53 | 0.18 |
Costs of hospital stay were also reduced in the multipronged, hospitalist‐driven intervention group. Expenditures for telemetry beds were reduced by 22.5% over the intervention period for hospitalists (Table 2).
| Baseline to Intervention Period | Intervention to Extension Period | |
|---|---|---|
| ||
| Hospitalists | ||
| Telemetry beds | 22.55% | 9.55% |
| Nontelemetry beds | 4.23% | 10.14% |
| Nonhospitalists | ||
| Telemetry beds | 10.55% | 9.89% |
| Nontelemetry beds | 9.47% | 21.84% |
The mean CMI of the patient cohort managed by the hospitalists in the baseline and intervention periods was not significantly different (1.44 vs 1.45, P=0.68). The mean CMI of the patients managed by the nonhospitalists in the baseline and intervention periods was also not significantly different (1.46 vs 1.40, P=0.53) (Table 1). Mortality index during the baseline and intervention periods was not significantly different (0.770.22 vs 0.660.23, P=0.54), as during the intervention and extension periods (0.660.23 vs 0.650.15, P=0.95).
Intervention Period (January 2013August 2013) Versus Extension Period (September 2014‐March 2015)
The decreased telemetry LOS for hospitalists was sustained from the intervention period to the extension period, from 2.13 to 1.93 (P=0.09). There was no significant change in the nontelemetry LOS in the intervention period compared to the extension period (2.72 vs 2.44, P=0.21). There was no change in the telemetry LOS for nonhospitalists from the intervention period to the extension period (2.46 vs 2.22, P=0.43).
The mean CMI in the hospitalist group was not significantly different in the intervention period compared to the extension period (1.45 to 1.40, P=0.21). The mean CMI in the nonhospitalist group did not change from the intervention period to the extension period (1.40 vs 1.53, P=0.18) (Table 1).
Education Outcomes
Out of the 56 participants completing the education module and survey, 28.6% were medical students, 53.6% were interns, 12.5% were second‐year residents, and 5.4% were third‐year residents. Several findings were seen at baseline via pretest. In evaluating patterns of current telemetry use, 32.2% of participants reported evaluating the necessity of telemetry for patients on admission only, 26.3% during transitions of care, 5.1% after discharge plans were cemented, 33.1% on a daily basis, and 3.4% rarely. When asked which member of the care team was most likely to encourage use of appropriate telemetry, 20.8% identified another resident, 13.9% nursing, 37.5% attending physician, 20.8% self, 4.2% the team as a whole, and 2.8% as not any.
Figure 1 shows premodule results regarding the trainees perceived percentage of patient encounters during which a participant's team discussed their patient's need for telemetry.
In assessing perception of current telemetry utilization, 1.8% of participants thought 0% to 10% of patients were currently on telemetry, 19.6% thought 11% to 20%, 42.9% thought 21% to 31%, 30.4% thought 31% to 40%, and 3.6% thought 41% to 50%.
Two areas were assessed at both baseline and after the intervention: knowledge of indications of telemetry use and cost related to telemetry use. We saw increased awareness of cost‐saving actions. To assess current knowledge of the indications of proper telemetry use according to American Heart Association guidelines, participants were presented with a list of 5 patients with different clinical indications for telemetry use and asked which patient required telemetry the most. Of the participants, 54.5% identified the correct answer in the pretest and 61.8% identified the correct answer in the post‐test. To assess knowledge of the costs of telemetry relative to other patient care, participants were presented with a patient case and asked to identify the most and least cost‐saving actions to safely care for the patient. When asked to identify the most cost‐saving action, 20.3% identified the correct answer in the pretest and 61.0% identified the correct answer in the post‐test. Of those who answered incorrectly in the pretest, 51.1% answered correctly in the post‐test (P=0.002). When asked to identify the least cost‐saving action, 23.7% identified the correct answer in the pretest and 50.9% identified the correct answer in the posttest. Of those who answered incorrectly in the pretest, 60.0% answered correctly in the post‐test (P=0.003).
In the post‐test, when asked about the importance of appropriate telemetry usage in providing cost‐conscious care and assuring appropriate hospital resource management, 76.8% of participants found the need very important, 21.4% somewhat important, and 1.8% as not applicable. The most commonly perceived barriers impeding discontinuation of telemetry, as reported by participants via post‐test, were nursing desires and time. Figure 2 shows all perceived barriers.
DISCUSSION
Our study is one of the first to our knowledge to demonstrate reductions in telemetry LOS by a hospitalist intervention for telemetry utilization. Others[10, 11] have studied the impact of an orientation handout by chief residents or a multispecialty telemetry policy with enforcement by an outside cardiologist and nurse team. Dressler et al. later sustained a 70% reduction in telemetry use without adversely affecting patient safety, as assessed through numbers of rapid response activations, codes, and deaths, through integrating the AHA guidelines into their electronic ordering system.[12] However, our study has the advantage of the primary team, who knows the patient and clinical scenario best, driving the change during attending rounds. In an era where cost consciousness intersects the practice of medicine, any intervention in patient care that demonstrates cost savings without an adverse impact on patient care and resource utilization must be emphasized. This is particularly important in academic institutions, where residents and medical students are learning to integrate the principles of patient safety and quality improvement into their clinical practice.[13] We actually showed sustained telemetry LOS reductions into the extension period after our intervention. We believe this may be due to telemetry triage being integrated into our attending and resident rounding practices. Future work should include integration of telemetry triage into clinical decision support in the electronic medical record and multidisciplinary rounds to disseminate telemetry triage hospital‐wide in both the academic and community settings.
Our study also revealed that nearly half of participants were not aware of the criteria for appropriate utilization of telemetry before our intervention; in the preintervention period, there were many anecdotal and objective findings of inappropriate utilization of telemetry as well as prolonged continuation beyond the clinical needs in both the hospitalist and nonhospitalist group. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
We were able to show increased knowledge of cost‐saving actions among trainees with our educational module. We believe it is imperative to educate our providers (physicians, nurses, case managers, and students within these disciplines) on the appropriate indications for telemetry use, not only to help with cost savings and resource availability (ie, allowing telemetry beds to be available for patients who need them most), but also to instill consistent expectations among our patients. For the hospitalist group (ie, the group receiving guideline‐based education on appropriate indications for telemetry utilization), there was an assessment of both appropriate usage and timely discontinuation of telemetry in the postintervention period, which we attribute in large part to adherence to the education provided to this group.
Additionally, we feel it is important to consider the impacts of inappropriate use of telemetry from a patient's perspective: it is physically restrictive/emnconvenient, alarms are disruptive, it can be a barrier for other treatments such as physical therapy, it may increase the time it takes for imaging studies, a nurse may be required to accompany patients on telemetry, and poses additional costs to their medical bill.
We believe our success is due to several strategies. First, at the start of the fiscal year when quality improvement metrics are established, this particular metric (improving the appropriate utilization and timely discontinuation of telemetry) was deemed important by all hospitalists, engendering group buy‐in prior to the intervention. Our hospitalists received a detailed and interactive tutorial session in person at the beginning of the study. This tutorial provided the hospitalists with a comprehensive understanding of the appropriate (and inappropriate) indications for telemetry monitoring, hence facilitating guideline‐directed utilization. Email reminders and the tutorial tool were provided each time a hospitalist attended on the wards, and hospitalists received a small financial incentive to comply with appropriate telemetry utilization.
Our study has several strengths. First, the time frame of our study was long enough (8 months) to allow consistent trends to emerge and to optimize exposure of housestaff and medical students to this quality‐improvement initiative. Second, our cost savings came from 2 factors, direct reduction of inappropriate telemetry use and reduction in length of stay, highlighting the dual impact of appropriate telemetry utilization on cost. The overall reductions in telemetry utilization for the intervention group were a result of both reductions in initial placement on telemetry for patients who did not meet criteria for such monitoring as well as timely discontinuation of telemetry during the patient's hospitalization. Third, our study demonstrates that physicians can be effective in driving appropriate telemetry usage by participating in the clinical decision making regarding necessity and educating providers, trainees/students, and patients on appropriate indications. Finally, we show sustainment of our intervention in the extension period, suggesting telemetry triage integration into rounding practice.
Our study has limitations as well. First, our sample size is relatively small at a single academic center. Second, due to complexities in our faculty scheduling, we were unable to completely randomize patients to a hospitalist versus nonhospitalist team. However, we believe that despite the inability to randomize, our study does show the benefit of a hospitalist attending to reduce telemetry LOS given there was no change in nonhospitalist telemetry LOS despite all of the other hospital‐wide interventions (multidisciplinary rounds, similar housestaff). Third, our study was limited in that the CMI was used as a proxy for patient complexity, and the mortality index was used as the overall marker of safety. Further studies should monitor frequency and outcomes of arrhythmic events of patients transferred from telemetry monitoring to medicalsurgical beds. Finally, as the intervention was multipronged, we are unable to determine which component led to the reductions in telemetry utilization. Each component, however, remains easily transferrable to outside institutions. We demonstrated both a reduction in initiation of telemetry as well as timely discontinuation; however, due to the complexity in capturing this accurately, we were unable to numerically quantify these individual outcomes.
Additionally, there were approximately 10 nonhospitalist attendings who also staffed the wards during the intervention time period of our study; these attendings did not undergo the telemetry tutorial/orientation. This difference, along with the Hawthorne effect for the hospitalist attendings, also likely contributed to the difference in outcomes between the 2 attending cohorts in the intervention period.
CONCLUSIONS
Our results demonstrate that a multipronged hospitalist‐driven intervention to improve appropriate use of telemetry reduces telemetry LOS and cost. Hence, we believe that targeted, education‐driven interventions with monitoring of progress can have demonstrable impacts on changing practice. Physicians will need to make trade‐offs in clinical practice to balance efficient resource utilization with the patient's evolving condition in the inpatient setting, the complexities of clinical workflow, and the patient's expectations.[14] Appropriate telemetry utilization is a prime example of what needs to be done well in the future for high‐value care.
Acknowledgements
The authors acknowledge the hospitalists who participated in the intervention: Jeffrey Chi, Willliam Daines, Sumbul Desai, Poonam Hosamani, John Kugler, Charles Liao, Errol Ozdalga, and Sang Hoon Woo. The authors also acknowledge Joan Hendershott in the Finance Department and Joseph Hopkins in the Quality Department.
Disclosures: All coauthors have seen and agree with the contents of the article; submission (aside from abstracts) was not under review by any other publication. The authors report no disclosures of financial support from, or equity positions in, manufacturers of drugs or products mentioned in the article.
- , National health care expenses in the U.S. civilian noninstitutionalized population, 2009. Statistical brief 355. 2012. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Costs for hospital stays in the United States, 2010. Statistical brief 146. 2013. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Telemetry outside critical care units: patterns of utilization and influence on management decisions. Clin Cardiol. 1998;21(7):503–505.
- , , , et al. Evaluation of telemetry utilization, policy, and outcomes in an inner‐city academic medical center. J Natl Med Assoc. 2010;102(7):598–604.
- , , Recommended guidelines for in‐hospital cardiac monitoring of adults for detection of arrhythmia. Emergency Cardiac Care Committee members. J Am Coll Cardiol. 1991;18(6):1431–1433.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , Is telemetry overused? Is it as helpful as thought? Cleve Clin J Med. 2009;76(6):368–372.
- Society of Hospital Medicine. Adult Hospital Medicine. Five things physicians and patients should question. Available at: http://www.choosingwisely.org/societies/society‐of‐hospital‐medicine‐adult. Published February 21, 2013. Accessed October 5, 2014.
- Joint Commission on Accreditation of Healthcare Organizations. The Joint Commission announces 2014 national patient safety goal. Jt Comm Perspect. 2013;33(7):1–4.
- , , , , Optimizing telemetry utilization in an academic medical center. J Clin Outcomes Manage. 2008;15(9):435–440.
- , Improving utilization of telemetry in a university hospital. J Clin Outcomes Manage. 2005;12(10):519–522.
- , , , , Altering overuse of cardiac telemetry in non‐intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174:1852–1854.
- , , "Innovation" institutes in academic health centers: enhancing value through leadership, education, engagement, and scholarship. Acad Med. 2014;89(9):1204–1206.
- , , , , , Controlling health costs: physician responses to patient expectations for medical care. J Gen Intern Med. 2014;29(9):1234–1241.
- , National health care expenses in the U.S. civilian noninstitutionalized population, 2009. Statistical brief 355. 2012. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Costs for hospital stays in the United States, 2010. Statistical brief 146. 2013. Agency for Healthcare Research and Quality, Rockville, MD.
- , , Telemetry outside critical care units: patterns of utilization and influence on management decisions. Clin Cardiol. 1998;21(7):503–505.
- , , , et al. Evaluation of telemetry utilization, policy, and outcomes in an inner‐city academic medical center. J Natl Med Assoc. 2010;102(7):598–604.
- , , Recommended guidelines for in‐hospital cardiac monitoring of adults for detection of arrhythmia. Emergency Cardiac Care Committee members. J Am Coll Cardiol. 1991;18(6):1431–1433.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , Is telemetry overused? Is it as helpful as thought? Cleve Clin J Med. 2009;76(6):368–372.
- Society of Hospital Medicine. Adult Hospital Medicine. Five things physicians and patients should question. Available at: http://www.choosingwisely.org/societies/society‐of‐hospital‐medicine‐adult. Published February 21, 2013. Accessed October 5, 2014.
- Joint Commission on Accreditation of Healthcare Organizations. The Joint Commission announces 2014 national patient safety goal. Jt Comm Perspect. 2013;33(7):1–4.
- , , , , Optimizing telemetry utilization in an academic medical center. J Clin Outcomes Manage. 2008;15(9):435–440.
- , Improving utilization of telemetry in a university hospital. J Clin Outcomes Manage. 2005;12(10):519–522.
- , , , , Altering overuse of cardiac telemetry in non‐intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174:1852–1854.
- , , "Innovation" institutes in academic health centers: enhancing value through leadership, education, engagement, and scholarship. Acad Med. 2014;89(9):1204–1206.
- , , , , , Controlling health costs: physician responses to patient expectations for medical care. J Gen Intern Med. 2014;29(9):1234–1241.
Secure Texting Improves Hospital Communication
Pagers, though reliable and familiar technology, can be suboptimal for facilitating healthcare team communication.[1, 2] Most paging systems utilize single‐function pagers and only allow one‐way communication, requiring recipients to disrupt workflow to respond to pages. Paging transmissions can also be intercepted, and the information presented on pager displays can be viewed by anyone in possession of the pager.
Smartphones allow for instantaneous two‐way and group communication through advanced technological features. Their use is widespread; over 81% of American physicians owned a smartphone in 2011.[3] Previous studies demonstrate that healthcare providers rate smartphone‐based email positively, and that team smartphones can facilitate communication between nurses and physicians.[4, 5] However, these studies specifically examined the utility of smartphone‐based email and voice calls, and did not include text messaging. Limitations of traditional smartphone‐based text messaging include Health Insurance Portability and Accountability Act (HIPAA) noncompliance and dependence on in‐hospital cellular reception, which can be unreliable. HIPAA is a 1996 US federal law that established a set of privacy and security rules governing not only what is considered protected health information (PHI), but also minimum standards for the protection of such information. HIPAA compliance is defined as meeting these minimum standards for physical, network, and process security.[6, 7] Though PHI is often transmitted via paging systems and commercial carrier‐based text messaging, these modalities are not secure and are thus not HIPAA‐compliant.
Text messaging applications that address these security and reliability issues have the potential to greatly enhance in‐hospital communication. We hypothesized that a smartphone‐based HIPAA‐compliant group messaging application could improve in‐hospital communication on the inpatient medicine service. To our knowledge, our study is the first to examine a HIPAA‐compliant text messaging system, and also the first to compare a combination paging/HIPAA‐compliant group messaging (HCGM) system with a paging‐only system in assessing healthcare provider perception of communication efficiency.
METHODS
Intervention
This study utilized Medigram (Medigram, Inc.,
Study Design
In an eight‐week prospective, cluster‐randomized, controlled trial conducted at Stanford Hospital (June 25, 2012August 17, 2012), three of five inpatient medicine teams were randomized to use Medigram in addition to the existing hospital paging system (HCGM teams); the remaining two teams were assigned to use hospital paging only (control teams). Each team included one attending physician, one resident, two interns, two medical students, and a case manager. According to prescheduled rotations, attendings rotated every two weeks, and residents, interns, and medical students rotated every four weeks. All rotations were either off‐service or off‐site, with the exception of two attendings who rotated between study teams but within their experimental designations. Case managers remained with the same team. Additionally, the satellite pharmacy was provided with an HCGM‐equipped smartphone to communicate with experimental teams.
Participation was voluntary, with a 96% participation rate (n=75). HCGM teams downloaded the free application onto their smartphones. Participants without smartphones were provided with one for the duration of the study. Proper application use was demonstrated by one researcher in a 10‐minute standardized presentation. HCGM teams were encouraged to use the application in lieu of paging, except when patient care could be compromised.
All participants completed linked baseline and post‐study surveys. Gift cards valued at $10 were provided on completion of each survey. Though participants were assigned to either HCGM or control groups based on the randomized assignment of their preset cluster (hospital team) to an HCGM or control group, analysis was performed on the individual level due to the hospital's set rotation schedule, which resulted in dynamic, frequently changing clusters. We also compared average length of stay and time of discharge for patients treated by control versus HCGM teams. Clinical outcome data were obtained from the hospital's database using Midas+ Statit Solutions (Midas+ Statit Solutions Group, Tucson, AZ). Survey and clinical outcome data were analyzed in Stata (StataCorp, College Station, TX) and R (R Foundation for Statistical Computing, Vienna, Austria).
Survey Design and Analysis
Identical, anonymous baseline surveys were administered to control and HCGM teams. These surveys assessed attitudes toward the hospital paging system using a 5‐point Likert scale (1=low, 5=high) to evaluate perceived measures of effectiveness, workflow integration, and overall satisfaction. Wilcoxon rank sum tests were used to compare control and HCGM group responses to these questions. Free response questions asked participants to list the most effective and ineffective aspects of the paging system.
Post‐study surveys included all baseline survey questions, as well as questions about personal texting behavior. Post‐study HCGM surveys also included a parallel set of questions rating the HCGM application on the same measures of perceived effectiveness, workflow integration, and overall satisfaction. Wilcoxon signed rank tests were used to compare HCGM participants' baseline evaluations of paging to their post‐study evaluations of the HCGM application. Baseline and post‐study surveys were linked by the last four digits of respondent cell phone numbers. To compare control and HCGM group perceptions of the hospital paging system at study completion, post‐study survey responses were evaluated using Wilcoxon rank sum tests. The family‐wise error rate was left unadjusted due to concerns around inflated type II errors, given the high degree of correlation between survey questions.
All free response questions were analyzed using thematic analysis and grounded theory. After reviewing responses to each question, a list of overarching themes was constructed. Two researchers then independently reviewed each free‐response entry to assign it to one or more of these themes (some responses included several ideas with distinct themes). Entries with concordant theme assignments (90%) were coded as such; nonconcordant entries required an additional round of review to reach concordance. Finally, objective outcome measures including length of stay and time of discharge were analyzed by two‐sample t test.
| Control Group | HCGM Group | |
|---|---|---|
| ||
| Paired surveys collected (completion rate) | 22 (85%) | 41 (84%) |
| Average age 95% CI | 30.10 1.71 | 30.95 2.94 |
| Gender | ||
| Male | 13 (59%) | 24 (59%) |
| Female | 9 (41%) | 17 (41%) |
| Role | ||
| Medical students | 6 (27%) | 11 (27%) |
| Interns (PGY 1) | 7 (32%) | 12 (29%) |
| Residents (PGY2 and 3) | 3 (14%) | 6 (15%) |
| Attending physicians | 5 (23%) | 5 (12%) |
| Case managers | 1 (5%) | 3 (7%) |
| Pharmacists | 0 (0%) | 4 (10%) |
Information Security
The HCGM application in this study features 256‐bit encryption technology and requires a six‐digit password to access texts. For added security, a study‐dedicated server (HP ProLiant DL 180 G6; Hewlett‐Packard Co., Palo Alto, CA) with 4‐TB hard drive capacity (4 Seagate Barracuda ST1000DM003 1 TB 7200 RPM internal hard drives; Seagate Technology PLC, Cupertino, CA) was installed in the Stanford School of Medicine Data Center to store encrypted text messages. Data stored on the phones/server were accessible only to study participants, not researchers. These security measures were approved by Stanford Hospital and Stanford School of Medicine's security and privacy review process.
Hospital Paging System
Stanford Hospital and Clinics is a quaternary care academic medical center with 613 beds, 49 operating rooms, and over 25,000 inpatient admissions per year.[8] The institution uses one‐way alphanumeric pagers (primary model: Daviscomm BR802 Flex Pager from USA Mobility, secondary model: Sun Telecom Titan 3 Plus from USA Mobility; USA Mobility Inc., Springfield, VA). USA Mobility operates the largest one‐ and two‐way paging networks in the United States.[9]
RESULTS
Of 26 control and 49 HCGM group members participating in the study, linked baseline and post‐study surveys were collected for 22 control and 41 HCGM participants (completion rates of 84.6% and 83.7%, respectively). To minimize recall bias, surveys not completed within a prespecified timeframe upon entering or leaving a team (two days attendings, four days others) were excluded.
Control and HCGM Group Characteristics
Control and HCGM groups were well matched demographically (Table 1). The average ages of control and HCGM group members were 30.10 and 30.95, respectively. Both groups were 59% male and 41% female.
| What do you find effective about the current hospital paging system? | What do you find ineffective about the current hospital paging system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Reliability of message transmission | 17 (30.4%) | Everyone is able to receive the pages I send, regardless of service | Time wasted waiting for a response | 17 (29.3%) | Inefficient use of time waiting for reply |
| Ability to text page | 14 (25.0%) | Text paging allows targeted questions | One‐way nature of communication | 14 (24.1%) | Cannot text back instantly |
| Ease of use | 8 (14.3%) | Easy to use | Needing to find a computer to send a text page | 12 (20.7%) | Have to find an available computer to send a page |
| Search function | 5 (8.9%) | Search function is pretty effective in finding the people you're looking for | Character limitation | 10 (17.2%) | Length of text allowed too short |
| Ubiquity | 5 (8.9%) | Everyone is on paging system | Search function | 6 (10.3%) | Delay in looking people up in the system |
| Speed | 4 (7.1%) | Fast | Finding a phone to return a page | 5 (8.6%) | When you receive a page you need to find a phone |
| Loud alerts | 4 (7.1%) | Pager loud enough to hear all the time | Receipt of page uncertain | 3 (5.2%) | Unknown if page received |
| Staff responsiveness to pages | 4 (7.1%) | I know MD has to be onsite or covering the pager so someone eventually will call back | Sender's pager number not always included in page | 3 (5.2%) | Not everyone puts their pager number when they page. Then it's impossible to get back to them. |
| Brevity of messages | 3 (5.4%) | Requires very brief messages (easier for recipient) | Needing to remain near a phone while waiting for a page response | 3 (5.2%) | Wait by a phone for someone to call back; sometimes they do not call back |
| Helpful page operators | 2 (3.6%) | Page operators very helpful | Reliability of message transmission | 3 (5.2%) | Sometimes messages don't go through |
| Other | 10 (17.9%) | It's online and allows paging from anywhere there's internet access | Other | 11 (19.0%) | You cannot text with patient info on it |
A similar distribution of team member roles was observed in both groups, with two exceptions. First, the proportion of attending respondents in the HCGM group was lower than in the control group. This was due to the fact that several HCGM attendings entered discrepant ID codes on their surveys, thus making it impossible to link baseline and post‐study responses; these data were excluded. Additionally, two HCGM attendings were on service for four, rather than the standard two weeks, meaning two additional data points from unique attendings could not be obtained. Second, the experimental group included four pharmacists, whereas the control group did not. As a sensitivity test, we analyzed the data excluding the pharmacists, and this did not change our results.
Baseline Evaluations of the Hospital Paging System
At baseline, there were no significant differences between control and HCGM participants' perceptions of paging effectiveness (see Supporting Table 1, in the online version of this article). On a 5‐point rating scale (1=low, 5=high), 63 subjects rated their overall satisfaction with the paging system an average of 2.79 (95% confidence interval: 2.55‐3.03).
In free response questions, components of the paging system most frequently cited as effective included: reliability of message transmission, alphanumeric text paging, and ease of use (30.4%, 25.0%, and 14.3% of 56 respondents, respectively) (Table 2). Ineffective aspects included: time wasted waiting for responses to pages, the unidirectional nature of pagers, and needing to find a computer to send a text page (29.3%, 24.1%, and 20.7% of 58 respondents, respectively) (Table 2).
| Question | Baseline Average Rating of Paging System* | Post‐Study Average Rating of HCGM Application | P Value |
|---|---|---|---|
| |||
| Rate the effectiveness of each in allowing you to | |||
| Communicate your thoughts clearly | 3.194 | 3.806 | 0.010 |
| Communicate your thoughts efficiently | 3.200 | 3.829 | 0.009 |
| Send messages to other hospital staff | 3.543 | 3.571 | 0.480 |
| Receive messages/stay informed in real time | 3.222 | 3.306 | 0.405 |
| Rate the effectiveness of each in integrating into your workflow during | |||
| Work rounds | 2.313 | 3.000 | 0.018 |
| Patient discharge | 2.448 | 3.276 | 0.012 |
| Patient admissions | 2.862 | 2.621 | 0.238 |
| Teaching sessions | 2.292 | 2.458 | 0.448 |
| Overall satisfaction | 2.811 | 3.459 | 0.003 |
Baseline Utilization of Text Messaging
The majority of participants were familiar with text messaging and regularly used it personally and professionally prior to the start of the study. 90.5% of participants (n = 63) reported sending an average of 1 personal text messages per day, with the largest proportion (39.7%) sending 1‐5 texts per day (see Supporting Figure 1A in the online version of this article). 58.1% of respondents (n=62) reported sending an average of 1 text messages per day related to patient care (see Supporting Figure 1B in the online version of this article), with the largest fraction (58.3%) sending 1‐5 texts per day.
HCGM Adoption and Usage Patterns
Active use of HCGM was defined as using the application to send or receive an average of 1 text messages per day. Of HCGM participants, 67% self‐reported 1 week of active use of the application, indicating a strong compliance rate. Among non‐attendings, 70% reported sending 1 or more texts to other team members per day; this percentage increased to 86% among those whose attendings texted them at least once per day (47% of non‐attendings). Respondents who text frequently in their personal lives (>5 texts/day) were more likely to use the application; 90% of these respondents sent 1 or more HCGM texts per day.
Among 12 subjects who did not report sending or receiving 1 HCGM text/day, the top three reasons were: other team members were not using it (67%), no need to use it given the close proximity of other team members (67%), and other (33%). A Wilcoxon rank sum test was used to compare the ages of active versus nonactive users; no significant age difference was found (P=0.200).
To provide an objective measure of application adoption, usage data for each HCGM participant were obtained from the application developers. Because much of the study's first week was spent onboarding and instructing participant, the first week was not included in the analysis. Of 43 individuals enrolled in the study for at least one of the seven remaining weeks, 56% sent a total of 5 texts, 44% sent 10 texts, and 28% sent 20 texts. HCGM users on three teams sent an aggregate mean of 123 texts/week. Data on number of messages received by each user were not available.
Perceived Effectiveness: Paging Versus HCGM
In post‐study surveys, HCGM participants rated HCGM significantly higher (P<0.05) than paging (Table 3) in terms of ability to communicate thoughts clearly (P=0.010) and efficiently (P=0.009). HCGM was also deemed more effective at integrating into workflow during rounds (P=0.018) and patient discharge (P=0.012). Overall satisfaction with HCGM was also significantly higher (P=0.003).
| Control (n=22) | HCGM (n=41) | |||||
|---|---|---|---|---|---|---|
| Baseline Mean | Post‐Study Mean | P Value* | Baseline Mean | Post‐Study Mean | P Value* | |
| ||||||
| Rate the effectiveness of each in allowing you to | ||||||
| Communicate your thoughts clearly | 2.905 | 2.619 | 0.103 | 3.250 | 2.850 | 0.004 |
| Communicate your thoughts efficiently | 2.952 | 2.762 | 0.106 | 3.250 | 2.825 | 0.018 |
| Send messages to other hospital staff | 3.762 | 3.190 | 0.019 | 3.550 | 3.450 | 0.253 |
| Receive messages/stay informed in real time | 3.667 | 2.857 | 0.002 | 3.300 | 2.900 | 0.031 |
| Rate the effectiveness of each in integrating into your workflow during | ||||||
| Work rounds | 2.429 | 2.476 | 0.303 | 2.410 | 2.718 | 0.078 |
| Patient discharge | 2.500 | 2.350 | 0.251 | 2.472 | 2.861 | 0.071 |
| Patient admissions | 2.905 | 2.524 | 0.020 | 2.889 | 3.000 | 0.384 |
| Teaching sessions | 2.143 | 2.200 | 0.386 | 2.367 | 2.400 | 0.418 |
Comparison of Pre‐ and Post‐study Perceived Effectiveness of the Hospital Paging System
In post‐study evaluations, both control and HCGM participants rated the paging system's effectiveness less favorably (P<0.05) compared to baseline in terms of ability to receive messages/stay informed in real time (control P=0.002, HCGM P=0.031) (Table 4). Controls also reported a decrease from baseline in perceived effectiveness of paging in terms of ability to send messages (P=0.019) and integrate into workflow during patient admissions (P=0.020). HCGM participants found paging less effective at communicating thoughts clearly (P=0.004) and efficiently (P=0.018). No significant differences existed between control and HCGM groups' average assessments of paging at the conclusion of the study (see Supporting Table 2, in the online version of this article).
| What do you find effective about the Medigram system? | What do you find ineffective about the Medigram system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Ease of use | 11 (32.4%) | Easy to use | Lack of ubiquity | 10 (30.3%) | Not enough people using it |
| Group texting feature | 11 (32.4%) | Ability to communicate with entire teameveryone seeing same message | Inconsistent usage | 8 (24.2%) | No one used it reliably |
| Speed | 8 (23.5%) | Faster than a page to send a message | Reliability of message transmission | 5 (15.2%) | Big negative is it requires Wi‐Fi |
| Accessibility | 5 (14.7%) | Able to get messages across quickly and anywhere without a computer | Missed message alerts | 4 (12.1%) | Unable to reliably know message was received if phone on silent |
| Efficiency | 4 (11.8%) | Very efficient way to communicate | Password login | 3 (9.1%) | Having to type a 6‐digit password in |
| Real‐time communication | 2 (5.9%) | Real‐time results | User interface | 2 (6.1%) | Interface is a little convoluted |
| No character limitation | 2 (5.9%) | No limit on words | Other | 10 (30.3%) | Not sure if all of the texts were relevant |
| Other | 4 (11.8%) | Great UI | |||
HCGM User Experience
When asked if they would recommend using an HCGM system to facilitate communication on the internal medicine wards, 85% of HCGM participants replied yes, 15% reported not sure, and 0% reported no. Based on free response entries, HCGM's most effective features (Table 5) included ease of use, group texting capacity, and speed (32.4%, 32.4%, and 23.5% of 34 respondents, respectively); its most ineffective aspects (Table 5) included lack of ubiquity, inconsistent usage by those with access to the application, and reliability of message transmission (30.3%, 24.2%, and 15.2% of 33 respondents, respectively).
DISCUSSION
We are the first to report that smartphone‐based, HIPAA‐compliant, group messaging applications improve provider perception of in‐hospital communication, while providing the information security that paging and commercial cellular networks do not. HCGM participants rated the application more favorably than paging in terms of clarity and efficiency of communication. These findings may be attributed to the expanded functionality offered by the application, including no character limit per HCGM text, the ability to use special characters such as slashes and ampersands, group texting, and the ability to reply immediately. HCGM may result in more efficient communication by facilitating direct two‐way communication via smartphones, whereas sending or returning pages requires a landline or computer.
HCGM participants rated the application higher than paging in terms of workflow integration during rounds and patient discharge, but not during patient admissions and teaching sessions. We had hypothesized that HCGM would integrate better into participants' workflows because HCGM texts could be replied to immediately. The reasons for the equivalence of HCGM and paging for workflow integration during patient admissions and teaching sessions may have been due to weak Wi‐Fi in certain areas of the hospital, and may warrant further investigation.
Analysis of HCGM utilization indicated that there were factors that made participants more or less likely to use the application. Individuals who reported that their attendings used HCGM regularly were more likely to use it themselves. Attending usage may legitimize use of HCGM for housestaff and medical students, who may otherwise feel that texting appears unprofessional. Participants who texted frequently in their personal lives were also more likely to utilize HCGM regularly, perhaps due to increased familiarity with/affinity for the platform.
HCGM participants who did not utilize the application regularly most often cited the fact that other team members did not use it. Among all users, the most frequently noted ineffective aspects of the application were its lack of ubiquity (HCGM was made available only to the small subset of individuals involved in the study) and inconsistent usage by those who did have access to the application. These findings suggest that HCGM effectiveness may be maximized with unrestricted access and mandated use; patchwork implementation, as in this study, detracts from perceived effectiveness.
Though objective outcome measures (average length of stay and average time of discharge) for patients of control attendings and HCGM attendings were examined, no significant differences were observed (P=0.089 and 0.494, respectively). These results may be due to the small size and short duration of the study.
Limitations
Our study had several limitations. HCGM was available only to individuals in the experimental arm of the study; most members of the internal medicine department and all other departments were not reachable through the application. This lack of ubiquity was a frequently cited frustration. Among individuals to whom HCGM was made available, barriers to adoption included: close proximity to would‐be message recipients, concern that smartphone usage in front of patients might appear unprofessional, and inconsistent or dropped service (weak or no Wi‐Fi signal in some areas). A technical problem with the Android platform midway through the study served as a potential frustration to several participants.
Due to the aforementioned issues, some participants used the HCGM application in a very limited way. We also did not replace hospital pagers (infeasible in this hospital setting); the HCGM application was added as a supplemental system. Future studies might explore the replacement of paging systems with HCGM‐type applications, as well as delve further into quantitative patient care outcomes.
It should be noted that the start of the study unintentionally coincided with the start of new interns and medical students in the hospital. Although it is possible that their relative unfamiliarity with the hospital may have made them more amenable to adopting a new technology, it is also possible that they may have been less likely to do so in the midst of such a major transitional period. Finally, this was a single‐site study, and as such, its findings may not be broadly generalizable. More research on such interventions is warranted, particularly in the context of current insecure communication methods such as paging that may make hospital‐wide adoption of new methods of secure communication, such as HCGM, mandatory.
CONCLUSION
Our study is the first to demonstrate that HCGM applications improve healthcare provider perception of multiple measures of in‐hospital communication, including efficiency of communication, workflow integration, and overall satisfaction. Notably, 85% of HCGM team respondents said they would recommend using an HCGM system on the wards. As smartphone use is expected to continue to increase among physicians and the general population, it is increasingly important to understand how to utilize these powerful communication tools to improve healthcare in an effective and secure manner.
Acknowledgements
The authors would like to thank the Stanford Medicine Residency Program and Stanford Hospital and Clinics for allowing them to perform the study, as well as Medigram, Inc., for supplying gift cards to incentivize survey completion and loaner iPhones to allow individuals without smartphones to participate in the study.
Disclosures: Stanford University School of Medicine's Medical Scholars Program provided financial support to the first author over the course of the study. Medigram, Inc. funded gift cards given to both control and HCGM participants upon survey completion and provided loaner iPhones to allow individuals without smartphones to participate in the study. Medigram, Inc. has also paid the fee for OnlineOpen to provide open access to this article. This study received an institutional review board exemption as quality improvement research. Participation was voluntary, and all participants signed a consent form. The authors report no conflicts of interest.
- . Communication systems in healthcare. Clin Biochem Rev. 2006;27:89–98.
- , , . Alphanumeric paging: a potential source of problems in patient care and communication. J Surg Educ. 2011;68:447–451.
- Manhattan Research. Taking the pulse: US market research report. Available at: http://manhattanresearch.com/Products‐and‐Services/Physician/Physician‐Research‐Modules. Accessed February 6, 2013.
- , , , et al. The use of wireless e‐mail to improve healthcare team communication. J Am Med Inform Assoc. 2009;16:705–713.
- , , , et al. The use of smartphones for clinical communication on internal medicine wards. J Hosp Med. 2010;5:553–559.
- US Department of Health and Human Services. Summary of the HIPAA privacy rule. Available at: http://www.hhs.gov/ocr/privacy/hipaa/understanding/summary/. Accessed May 11, 2013.
- United States Department of Health and Human Services. Summary of the HIPAA security rule. Available at: http://www.hhs.gov/ocr/privacy/hipaa/understanding/srsummary.html. Accessed May 11, 2013.
- Stanford University. Stanford facts: about Stanford. Available at: http://facts.stanford.edu/about/hospital. Accessed October 12, 2013.
- Wikinvest. USA mobility. Available at: http://www.wikinvest.com/stock/USA_Mobility_(USMO). Accessed February 6, 2013.
Pagers, though reliable and familiar technology, can be suboptimal for facilitating healthcare team communication.[1, 2] Most paging systems utilize single‐function pagers and only allow one‐way communication, requiring recipients to disrupt workflow to respond to pages. Paging transmissions can also be intercepted, and the information presented on pager displays can be viewed by anyone in possession of the pager.
Smartphones allow for instantaneous two‐way and group communication through advanced technological features. Their use is widespread; over 81% of American physicians owned a smartphone in 2011.[3] Previous studies demonstrate that healthcare providers rate smartphone‐based email positively, and that team smartphones can facilitate communication between nurses and physicians.[4, 5] However, these studies specifically examined the utility of smartphone‐based email and voice calls, and did not include text messaging. Limitations of traditional smartphone‐based text messaging include Health Insurance Portability and Accountability Act (HIPAA) noncompliance and dependence on in‐hospital cellular reception, which can be unreliable. HIPAA is a 1996 US federal law that established a set of privacy and security rules governing not only what is considered protected health information (PHI), but also minimum standards for the protection of such information. HIPAA compliance is defined as meeting these minimum standards for physical, network, and process security.[6, 7] Though PHI is often transmitted via paging systems and commercial carrier‐based text messaging, these modalities are not secure and are thus not HIPAA‐compliant.
Text messaging applications that address these security and reliability issues have the potential to greatly enhance in‐hospital communication. We hypothesized that a smartphone‐based HIPAA‐compliant group messaging application could improve in‐hospital communication on the inpatient medicine service. To our knowledge, our study is the first to examine a HIPAA‐compliant text messaging system, and also the first to compare a combination paging/HIPAA‐compliant group messaging (HCGM) system with a paging‐only system in assessing healthcare provider perception of communication efficiency.
METHODS
Intervention
This study utilized Medigram (Medigram, Inc.,
Study Design
In an eight‐week prospective, cluster‐randomized, controlled trial conducted at Stanford Hospital (June 25, 2012August 17, 2012), three of five inpatient medicine teams were randomized to use Medigram in addition to the existing hospital paging system (HCGM teams); the remaining two teams were assigned to use hospital paging only (control teams). Each team included one attending physician, one resident, two interns, two medical students, and a case manager. According to prescheduled rotations, attendings rotated every two weeks, and residents, interns, and medical students rotated every four weeks. All rotations were either off‐service or off‐site, with the exception of two attendings who rotated between study teams but within their experimental designations. Case managers remained with the same team. Additionally, the satellite pharmacy was provided with an HCGM‐equipped smartphone to communicate with experimental teams.
Participation was voluntary, with a 96% participation rate (n=75). HCGM teams downloaded the free application onto their smartphones. Participants without smartphones were provided with one for the duration of the study. Proper application use was demonstrated by one researcher in a 10‐minute standardized presentation. HCGM teams were encouraged to use the application in lieu of paging, except when patient care could be compromised.
All participants completed linked baseline and post‐study surveys. Gift cards valued at $10 were provided on completion of each survey. Though participants were assigned to either HCGM or control groups based on the randomized assignment of their preset cluster (hospital team) to an HCGM or control group, analysis was performed on the individual level due to the hospital's set rotation schedule, which resulted in dynamic, frequently changing clusters. We also compared average length of stay and time of discharge for patients treated by control versus HCGM teams. Clinical outcome data were obtained from the hospital's database using Midas+ Statit Solutions (Midas+ Statit Solutions Group, Tucson, AZ). Survey and clinical outcome data were analyzed in Stata (StataCorp, College Station, TX) and R (R Foundation for Statistical Computing, Vienna, Austria).
Survey Design and Analysis
Identical, anonymous baseline surveys were administered to control and HCGM teams. These surveys assessed attitudes toward the hospital paging system using a 5‐point Likert scale (1=low, 5=high) to evaluate perceived measures of effectiveness, workflow integration, and overall satisfaction. Wilcoxon rank sum tests were used to compare control and HCGM group responses to these questions. Free response questions asked participants to list the most effective and ineffective aspects of the paging system.
Post‐study surveys included all baseline survey questions, as well as questions about personal texting behavior. Post‐study HCGM surveys also included a parallel set of questions rating the HCGM application on the same measures of perceived effectiveness, workflow integration, and overall satisfaction. Wilcoxon signed rank tests were used to compare HCGM participants' baseline evaluations of paging to their post‐study evaluations of the HCGM application. Baseline and post‐study surveys were linked by the last four digits of respondent cell phone numbers. To compare control and HCGM group perceptions of the hospital paging system at study completion, post‐study survey responses were evaluated using Wilcoxon rank sum tests. The family‐wise error rate was left unadjusted due to concerns around inflated type II errors, given the high degree of correlation between survey questions.
All free response questions were analyzed using thematic analysis and grounded theory. After reviewing responses to each question, a list of overarching themes was constructed. Two researchers then independently reviewed each free‐response entry to assign it to one or more of these themes (some responses included several ideas with distinct themes). Entries with concordant theme assignments (90%) were coded as such; nonconcordant entries required an additional round of review to reach concordance. Finally, objective outcome measures including length of stay and time of discharge were analyzed by two‐sample t test.
| Control Group | HCGM Group | |
|---|---|---|
| ||
| Paired surveys collected (completion rate) | 22 (85%) | 41 (84%) |
| Average age 95% CI | 30.10 1.71 | 30.95 2.94 |
| Gender | ||
| Male | 13 (59%) | 24 (59%) |
| Female | 9 (41%) | 17 (41%) |
| Role | ||
| Medical students | 6 (27%) | 11 (27%) |
| Interns (PGY 1) | 7 (32%) | 12 (29%) |
| Residents (PGY2 and 3) | 3 (14%) | 6 (15%) |
| Attending physicians | 5 (23%) | 5 (12%) |
| Case managers | 1 (5%) | 3 (7%) |
| Pharmacists | 0 (0%) | 4 (10%) |
Information Security
The HCGM application in this study features 256‐bit encryption technology and requires a six‐digit password to access texts. For added security, a study‐dedicated server (HP ProLiant DL 180 G6; Hewlett‐Packard Co., Palo Alto, CA) with 4‐TB hard drive capacity (4 Seagate Barracuda ST1000DM003 1 TB 7200 RPM internal hard drives; Seagate Technology PLC, Cupertino, CA) was installed in the Stanford School of Medicine Data Center to store encrypted text messages. Data stored on the phones/server were accessible only to study participants, not researchers. These security measures were approved by Stanford Hospital and Stanford School of Medicine's security and privacy review process.
Hospital Paging System
Stanford Hospital and Clinics is a quaternary care academic medical center with 613 beds, 49 operating rooms, and over 25,000 inpatient admissions per year.[8] The institution uses one‐way alphanumeric pagers (primary model: Daviscomm BR802 Flex Pager from USA Mobility, secondary model: Sun Telecom Titan 3 Plus from USA Mobility; USA Mobility Inc., Springfield, VA). USA Mobility operates the largest one‐ and two‐way paging networks in the United States.[9]
RESULTS
Of 26 control and 49 HCGM group members participating in the study, linked baseline and post‐study surveys were collected for 22 control and 41 HCGM participants (completion rates of 84.6% and 83.7%, respectively). To minimize recall bias, surveys not completed within a prespecified timeframe upon entering or leaving a team (two days attendings, four days others) were excluded.
Control and HCGM Group Characteristics
Control and HCGM groups were well matched demographically (Table 1). The average ages of control and HCGM group members were 30.10 and 30.95, respectively. Both groups were 59% male and 41% female.
| What do you find effective about the current hospital paging system? | What do you find ineffective about the current hospital paging system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Reliability of message transmission | 17 (30.4%) | Everyone is able to receive the pages I send, regardless of service | Time wasted waiting for a response | 17 (29.3%) | Inefficient use of time waiting for reply |
| Ability to text page | 14 (25.0%) | Text paging allows targeted questions | One‐way nature of communication | 14 (24.1%) | Cannot text back instantly |
| Ease of use | 8 (14.3%) | Easy to use | Needing to find a computer to send a text page | 12 (20.7%) | Have to find an available computer to send a page |
| Search function | 5 (8.9%) | Search function is pretty effective in finding the people you're looking for | Character limitation | 10 (17.2%) | Length of text allowed too short |
| Ubiquity | 5 (8.9%) | Everyone is on paging system | Search function | 6 (10.3%) | Delay in looking people up in the system |
| Speed | 4 (7.1%) | Fast | Finding a phone to return a page | 5 (8.6%) | When you receive a page you need to find a phone |
| Loud alerts | 4 (7.1%) | Pager loud enough to hear all the time | Receipt of page uncertain | 3 (5.2%) | Unknown if page received |
| Staff responsiveness to pages | 4 (7.1%) | I know MD has to be onsite or covering the pager so someone eventually will call back | Sender's pager number not always included in page | 3 (5.2%) | Not everyone puts their pager number when they page. Then it's impossible to get back to them. |
| Brevity of messages | 3 (5.4%) | Requires very brief messages (easier for recipient) | Needing to remain near a phone while waiting for a page response | 3 (5.2%) | Wait by a phone for someone to call back; sometimes they do not call back |
| Helpful page operators | 2 (3.6%) | Page operators very helpful | Reliability of message transmission | 3 (5.2%) | Sometimes messages don't go through |
| Other | 10 (17.9%) | It's online and allows paging from anywhere there's internet access | Other | 11 (19.0%) | You cannot text with patient info on it |
A similar distribution of team member roles was observed in both groups, with two exceptions. First, the proportion of attending respondents in the HCGM group was lower than in the control group. This was due to the fact that several HCGM attendings entered discrepant ID codes on their surveys, thus making it impossible to link baseline and post‐study responses; these data were excluded. Additionally, two HCGM attendings were on service for four, rather than the standard two weeks, meaning two additional data points from unique attendings could not be obtained. Second, the experimental group included four pharmacists, whereas the control group did not. As a sensitivity test, we analyzed the data excluding the pharmacists, and this did not change our results.
Baseline Evaluations of the Hospital Paging System
At baseline, there were no significant differences between control and HCGM participants' perceptions of paging effectiveness (see Supporting Table 1, in the online version of this article). On a 5‐point rating scale (1=low, 5=high), 63 subjects rated their overall satisfaction with the paging system an average of 2.79 (95% confidence interval: 2.55‐3.03).
In free response questions, components of the paging system most frequently cited as effective included: reliability of message transmission, alphanumeric text paging, and ease of use (30.4%, 25.0%, and 14.3% of 56 respondents, respectively) (Table 2). Ineffective aspects included: time wasted waiting for responses to pages, the unidirectional nature of pagers, and needing to find a computer to send a text page (29.3%, 24.1%, and 20.7% of 58 respondents, respectively) (Table 2).
| Question | Baseline Average Rating of Paging System* | Post‐Study Average Rating of HCGM Application | P Value |
|---|---|---|---|
| |||
| Rate the effectiveness of each in allowing you to | |||
| Communicate your thoughts clearly | 3.194 | 3.806 | 0.010 |
| Communicate your thoughts efficiently | 3.200 | 3.829 | 0.009 |
| Send messages to other hospital staff | 3.543 | 3.571 | 0.480 |
| Receive messages/stay informed in real time | 3.222 | 3.306 | 0.405 |
| Rate the effectiveness of each in integrating into your workflow during | |||
| Work rounds | 2.313 | 3.000 | 0.018 |
| Patient discharge | 2.448 | 3.276 | 0.012 |
| Patient admissions | 2.862 | 2.621 | 0.238 |
| Teaching sessions | 2.292 | 2.458 | 0.448 |
| Overall satisfaction | 2.811 | 3.459 | 0.003 |
Baseline Utilization of Text Messaging
The majority of participants were familiar with text messaging and regularly used it personally and professionally prior to the start of the study. 90.5% of participants (n = 63) reported sending an average of 1 personal text messages per day, with the largest proportion (39.7%) sending 1‐5 texts per day (see Supporting Figure 1A in the online version of this article). 58.1% of respondents (n=62) reported sending an average of 1 text messages per day related to patient care (see Supporting Figure 1B in the online version of this article), with the largest fraction (58.3%) sending 1‐5 texts per day.
HCGM Adoption and Usage Patterns
Active use of HCGM was defined as using the application to send or receive an average of 1 text messages per day. Of HCGM participants, 67% self‐reported 1 week of active use of the application, indicating a strong compliance rate. Among non‐attendings, 70% reported sending 1 or more texts to other team members per day; this percentage increased to 86% among those whose attendings texted them at least once per day (47% of non‐attendings). Respondents who text frequently in their personal lives (>5 texts/day) were more likely to use the application; 90% of these respondents sent 1 or more HCGM texts per day.
Among 12 subjects who did not report sending or receiving 1 HCGM text/day, the top three reasons were: other team members were not using it (67%), no need to use it given the close proximity of other team members (67%), and other (33%). A Wilcoxon rank sum test was used to compare the ages of active versus nonactive users; no significant age difference was found (P=0.200).
To provide an objective measure of application adoption, usage data for each HCGM participant were obtained from the application developers. Because much of the study's first week was spent onboarding and instructing participant, the first week was not included in the analysis. Of 43 individuals enrolled in the study for at least one of the seven remaining weeks, 56% sent a total of 5 texts, 44% sent 10 texts, and 28% sent 20 texts. HCGM users on three teams sent an aggregate mean of 123 texts/week. Data on number of messages received by each user were not available.
Perceived Effectiveness: Paging Versus HCGM
In post‐study surveys, HCGM participants rated HCGM significantly higher (P<0.05) than paging (Table 3) in terms of ability to communicate thoughts clearly (P=0.010) and efficiently (P=0.009). HCGM was also deemed more effective at integrating into workflow during rounds (P=0.018) and patient discharge (P=0.012). Overall satisfaction with HCGM was also significantly higher (P=0.003).
| Control (n=22) | HCGM (n=41) | |||||
|---|---|---|---|---|---|---|
| Baseline Mean | Post‐Study Mean | P Value* | Baseline Mean | Post‐Study Mean | P Value* | |
| ||||||
| Rate the effectiveness of each in allowing you to | ||||||
| Communicate your thoughts clearly | 2.905 | 2.619 | 0.103 | 3.250 | 2.850 | 0.004 |
| Communicate your thoughts efficiently | 2.952 | 2.762 | 0.106 | 3.250 | 2.825 | 0.018 |
| Send messages to other hospital staff | 3.762 | 3.190 | 0.019 | 3.550 | 3.450 | 0.253 |
| Receive messages/stay informed in real time | 3.667 | 2.857 | 0.002 | 3.300 | 2.900 | 0.031 |
| Rate the effectiveness of each in integrating into your workflow during | ||||||
| Work rounds | 2.429 | 2.476 | 0.303 | 2.410 | 2.718 | 0.078 |
| Patient discharge | 2.500 | 2.350 | 0.251 | 2.472 | 2.861 | 0.071 |
| Patient admissions | 2.905 | 2.524 | 0.020 | 2.889 | 3.000 | 0.384 |
| Teaching sessions | 2.143 | 2.200 | 0.386 | 2.367 | 2.400 | 0.418 |
Comparison of Pre‐ and Post‐study Perceived Effectiveness of the Hospital Paging System
In post‐study evaluations, both control and HCGM participants rated the paging system's effectiveness less favorably (P<0.05) compared to baseline in terms of ability to receive messages/stay informed in real time (control P=0.002, HCGM P=0.031) (Table 4). Controls also reported a decrease from baseline in perceived effectiveness of paging in terms of ability to send messages (P=0.019) and integrate into workflow during patient admissions (P=0.020). HCGM participants found paging less effective at communicating thoughts clearly (P=0.004) and efficiently (P=0.018). No significant differences existed between control and HCGM groups' average assessments of paging at the conclusion of the study (see Supporting Table 2, in the online version of this article).
| What do you find effective about the Medigram system? | What do you find ineffective about the Medigram system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Ease of use | 11 (32.4%) | Easy to use | Lack of ubiquity | 10 (30.3%) | Not enough people using it |
| Group texting feature | 11 (32.4%) | Ability to communicate with entire teameveryone seeing same message | Inconsistent usage | 8 (24.2%) | No one used it reliably |
| Speed | 8 (23.5%) | Faster than a page to send a message | Reliability of message transmission | 5 (15.2%) | Big negative is it requires Wi‐Fi |
| Accessibility | 5 (14.7%) | Able to get messages across quickly and anywhere without a computer | Missed message alerts | 4 (12.1%) | Unable to reliably know message was received if phone on silent |
| Efficiency | 4 (11.8%) | Very efficient way to communicate | Password login | 3 (9.1%) | Having to type a 6‐digit password in |
| Real‐time communication | 2 (5.9%) | Real‐time results | User interface | 2 (6.1%) | Interface is a little convoluted |
| No character limitation | 2 (5.9%) | No limit on words | Other | 10 (30.3%) | Not sure if all of the texts were relevant |
| Other | 4 (11.8%) | Great UI | |||
HCGM User Experience
When asked if they would recommend using an HCGM system to facilitate communication on the internal medicine wards, 85% of HCGM participants replied yes, 15% reported not sure, and 0% reported no. Based on free response entries, HCGM's most effective features (Table 5) included ease of use, group texting capacity, and speed (32.4%, 32.4%, and 23.5% of 34 respondents, respectively); its most ineffective aspects (Table 5) included lack of ubiquity, inconsistent usage by those with access to the application, and reliability of message transmission (30.3%, 24.2%, and 15.2% of 33 respondents, respectively).
DISCUSSION
We are the first to report that smartphone‐based, HIPAA‐compliant, group messaging applications improve provider perception of in‐hospital communication, while providing the information security that paging and commercial cellular networks do not. HCGM participants rated the application more favorably than paging in terms of clarity and efficiency of communication. These findings may be attributed to the expanded functionality offered by the application, including no character limit per HCGM text, the ability to use special characters such as slashes and ampersands, group texting, and the ability to reply immediately. HCGM may result in more efficient communication by facilitating direct two‐way communication via smartphones, whereas sending or returning pages requires a landline or computer.
HCGM participants rated the application higher than paging in terms of workflow integration during rounds and patient discharge, but not during patient admissions and teaching sessions. We had hypothesized that HCGM would integrate better into participants' workflows because HCGM texts could be replied to immediately. The reasons for the equivalence of HCGM and paging for workflow integration during patient admissions and teaching sessions may have been due to weak Wi‐Fi in certain areas of the hospital, and may warrant further investigation.
Analysis of HCGM utilization indicated that there were factors that made participants more or less likely to use the application. Individuals who reported that their attendings used HCGM regularly were more likely to use it themselves. Attending usage may legitimize use of HCGM for housestaff and medical students, who may otherwise feel that texting appears unprofessional. Participants who texted frequently in their personal lives were also more likely to utilize HCGM regularly, perhaps due to increased familiarity with/affinity for the platform.
HCGM participants who did not utilize the application regularly most often cited the fact that other team members did not use it. Among all users, the most frequently noted ineffective aspects of the application were its lack of ubiquity (HCGM was made available only to the small subset of individuals involved in the study) and inconsistent usage by those who did have access to the application. These findings suggest that HCGM effectiveness may be maximized with unrestricted access and mandated use; patchwork implementation, as in this study, detracts from perceived effectiveness.
Though objective outcome measures (average length of stay and average time of discharge) for patients of control attendings and HCGM attendings were examined, no significant differences were observed (P=0.089 and 0.494, respectively). These results may be due to the small size and short duration of the study.
Limitations
Our study had several limitations. HCGM was available only to individuals in the experimental arm of the study; most members of the internal medicine department and all other departments were not reachable through the application. This lack of ubiquity was a frequently cited frustration. Among individuals to whom HCGM was made available, barriers to adoption included: close proximity to would‐be message recipients, concern that smartphone usage in front of patients might appear unprofessional, and inconsistent or dropped service (weak or no Wi‐Fi signal in some areas). A technical problem with the Android platform midway through the study served as a potential frustration to several participants.
Due to the aforementioned issues, some participants used the HCGM application in a very limited way. We also did not replace hospital pagers (infeasible in this hospital setting); the HCGM application was added as a supplemental system. Future studies might explore the replacement of paging systems with HCGM‐type applications, as well as delve further into quantitative patient care outcomes.
It should be noted that the start of the study unintentionally coincided with the start of new interns and medical students in the hospital. Although it is possible that their relative unfamiliarity with the hospital may have made them more amenable to adopting a new technology, it is also possible that they may have been less likely to do so in the midst of such a major transitional period. Finally, this was a single‐site study, and as such, its findings may not be broadly generalizable. More research on such interventions is warranted, particularly in the context of current insecure communication methods such as paging that may make hospital‐wide adoption of new methods of secure communication, such as HCGM, mandatory.
CONCLUSION
Our study is the first to demonstrate that HCGM applications improve healthcare provider perception of multiple measures of in‐hospital communication, including efficiency of communication, workflow integration, and overall satisfaction. Notably, 85% of HCGM team respondents said they would recommend using an HCGM system on the wards. As smartphone use is expected to continue to increase among physicians and the general population, it is increasingly important to understand how to utilize these powerful communication tools to improve healthcare in an effective and secure manner.
Acknowledgements
The authors would like to thank the Stanford Medicine Residency Program and Stanford Hospital and Clinics for allowing them to perform the study, as well as Medigram, Inc., for supplying gift cards to incentivize survey completion and loaner iPhones to allow individuals without smartphones to participate in the study.
Disclosures: Stanford University School of Medicine's Medical Scholars Program provided financial support to the first author over the course of the study. Medigram, Inc. funded gift cards given to both control and HCGM participants upon survey completion and provided loaner iPhones to allow individuals without smartphones to participate in the study. Medigram, Inc. has also paid the fee for OnlineOpen to provide open access to this article. This study received an institutional review board exemption as quality improvement research. Participation was voluntary, and all participants signed a consent form. The authors report no conflicts of interest.
Pagers, though reliable and familiar technology, can be suboptimal for facilitating healthcare team communication.[1, 2] Most paging systems utilize single‐function pagers and only allow one‐way communication, requiring recipients to disrupt workflow to respond to pages. Paging transmissions can also be intercepted, and the information presented on pager displays can be viewed by anyone in possession of the pager.
Smartphones allow for instantaneous two‐way and group communication through advanced technological features. Their use is widespread; over 81% of American physicians owned a smartphone in 2011.[3] Previous studies demonstrate that healthcare providers rate smartphone‐based email positively, and that team smartphones can facilitate communication between nurses and physicians.[4, 5] However, these studies specifically examined the utility of smartphone‐based email and voice calls, and did not include text messaging. Limitations of traditional smartphone‐based text messaging include Health Insurance Portability and Accountability Act (HIPAA) noncompliance and dependence on in‐hospital cellular reception, which can be unreliable. HIPAA is a 1996 US federal law that established a set of privacy and security rules governing not only what is considered protected health information (PHI), but also minimum standards for the protection of such information. HIPAA compliance is defined as meeting these minimum standards for physical, network, and process security.[6, 7] Though PHI is often transmitted via paging systems and commercial carrier‐based text messaging, these modalities are not secure and are thus not HIPAA‐compliant.
Text messaging applications that address these security and reliability issues have the potential to greatly enhance in‐hospital communication. We hypothesized that a smartphone‐based HIPAA‐compliant group messaging application could improve in‐hospital communication on the inpatient medicine service. To our knowledge, our study is the first to examine a HIPAA‐compliant text messaging system, and also the first to compare a combination paging/HIPAA‐compliant group messaging (HCGM) system with a paging‐only system in assessing healthcare provider perception of communication efficiency.
METHODS
Intervention
This study utilized Medigram (Medigram, Inc.,
Study Design
In an eight‐week prospective, cluster‐randomized, controlled trial conducted at Stanford Hospital (June 25, 2012August 17, 2012), three of five inpatient medicine teams were randomized to use Medigram in addition to the existing hospital paging system (HCGM teams); the remaining two teams were assigned to use hospital paging only (control teams). Each team included one attending physician, one resident, two interns, two medical students, and a case manager. According to prescheduled rotations, attendings rotated every two weeks, and residents, interns, and medical students rotated every four weeks. All rotations were either off‐service or off‐site, with the exception of two attendings who rotated between study teams but within their experimental designations. Case managers remained with the same team. Additionally, the satellite pharmacy was provided with an HCGM‐equipped smartphone to communicate with experimental teams.
Participation was voluntary, with a 96% participation rate (n=75). HCGM teams downloaded the free application onto their smartphones. Participants without smartphones were provided with one for the duration of the study. Proper application use was demonstrated by one researcher in a 10‐minute standardized presentation. HCGM teams were encouraged to use the application in lieu of paging, except when patient care could be compromised.
All participants completed linked baseline and post‐study surveys. Gift cards valued at $10 were provided on completion of each survey. Though participants were assigned to either HCGM or control groups based on the randomized assignment of their preset cluster (hospital team) to an HCGM or control group, analysis was performed on the individual level due to the hospital's set rotation schedule, which resulted in dynamic, frequently changing clusters. We also compared average length of stay and time of discharge for patients treated by control versus HCGM teams. Clinical outcome data were obtained from the hospital's database using Midas+ Statit Solutions (Midas+ Statit Solutions Group, Tucson, AZ). Survey and clinical outcome data were analyzed in Stata (StataCorp, College Station, TX) and R (R Foundation for Statistical Computing, Vienna, Austria).
Survey Design and Analysis
Identical, anonymous baseline surveys were administered to control and HCGM teams. These surveys assessed attitudes toward the hospital paging system using a 5‐point Likert scale (1=low, 5=high) to evaluate perceived measures of effectiveness, workflow integration, and overall satisfaction. Wilcoxon rank sum tests were used to compare control and HCGM group responses to these questions. Free response questions asked participants to list the most effective and ineffective aspects of the paging system.
Post‐study surveys included all baseline survey questions, as well as questions about personal texting behavior. Post‐study HCGM surveys also included a parallel set of questions rating the HCGM application on the same measures of perceived effectiveness, workflow integration, and overall satisfaction. Wilcoxon signed rank tests were used to compare HCGM participants' baseline evaluations of paging to their post‐study evaluations of the HCGM application. Baseline and post‐study surveys were linked by the last four digits of respondent cell phone numbers. To compare control and HCGM group perceptions of the hospital paging system at study completion, post‐study survey responses were evaluated using Wilcoxon rank sum tests. The family‐wise error rate was left unadjusted due to concerns around inflated type II errors, given the high degree of correlation between survey questions.
All free response questions were analyzed using thematic analysis and grounded theory. After reviewing responses to each question, a list of overarching themes was constructed. Two researchers then independently reviewed each free‐response entry to assign it to one or more of these themes (some responses included several ideas with distinct themes). Entries with concordant theme assignments (90%) were coded as such; nonconcordant entries required an additional round of review to reach concordance. Finally, objective outcome measures including length of stay and time of discharge were analyzed by two‐sample t test.
| Control Group | HCGM Group | |
|---|---|---|
| ||
| Paired surveys collected (completion rate) | 22 (85%) | 41 (84%) |
| Average age 95% CI | 30.10 1.71 | 30.95 2.94 |
| Gender | ||
| Male | 13 (59%) | 24 (59%) |
| Female | 9 (41%) | 17 (41%) |
| Role | ||
| Medical students | 6 (27%) | 11 (27%) |
| Interns (PGY 1) | 7 (32%) | 12 (29%) |
| Residents (PGY2 and 3) | 3 (14%) | 6 (15%) |
| Attending physicians | 5 (23%) | 5 (12%) |
| Case managers | 1 (5%) | 3 (7%) |
| Pharmacists | 0 (0%) | 4 (10%) |
Information Security
The HCGM application in this study features 256‐bit encryption technology and requires a six‐digit password to access texts. For added security, a study‐dedicated server (HP ProLiant DL 180 G6; Hewlett‐Packard Co., Palo Alto, CA) with 4‐TB hard drive capacity (4 Seagate Barracuda ST1000DM003 1 TB 7200 RPM internal hard drives; Seagate Technology PLC, Cupertino, CA) was installed in the Stanford School of Medicine Data Center to store encrypted text messages. Data stored on the phones/server were accessible only to study participants, not researchers. These security measures were approved by Stanford Hospital and Stanford School of Medicine's security and privacy review process.
Hospital Paging System
Stanford Hospital and Clinics is a quaternary care academic medical center with 613 beds, 49 operating rooms, and over 25,000 inpatient admissions per year.[8] The institution uses one‐way alphanumeric pagers (primary model: Daviscomm BR802 Flex Pager from USA Mobility, secondary model: Sun Telecom Titan 3 Plus from USA Mobility; USA Mobility Inc., Springfield, VA). USA Mobility operates the largest one‐ and two‐way paging networks in the United States.[9]
RESULTS
Of 26 control and 49 HCGM group members participating in the study, linked baseline and post‐study surveys were collected for 22 control and 41 HCGM participants (completion rates of 84.6% and 83.7%, respectively). To minimize recall bias, surveys not completed within a prespecified timeframe upon entering or leaving a team (two days attendings, four days others) were excluded.
Control and HCGM Group Characteristics
Control and HCGM groups were well matched demographically (Table 1). The average ages of control and HCGM group members were 30.10 and 30.95, respectively. Both groups were 59% male and 41% female.
| What do you find effective about the current hospital paging system? | What do you find ineffective about the current hospital paging system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Reliability of message transmission | 17 (30.4%) | Everyone is able to receive the pages I send, regardless of service | Time wasted waiting for a response | 17 (29.3%) | Inefficient use of time waiting for reply |
| Ability to text page | 14 (25.0%) | Text paging allows targeted questions | One‐way nature of communication | 14 (24.1%) | Cannot text back instantly |
| Ease of use | 8 (14.3%) | Easy to use | Needing to find a computer to send a text page | 12 (20.7%) | Have to find an available computer to send a page |
| Search function | 5 (8.9%) | Search function is pretty effective in finding the people you're looking for | Character limitation | 10 (17.2%) | Length of text allowed too short |
| Ubiquity | 5 (8.9%) | Everyone is on paging system | Search function | 6 (10.3%) | Delay in looking people up in the system |
| Speed | 4 (7.1%) | Fast | Finding a phone to return a page | 5 (8.6%) | When you receive a page you need to find a phone |
| Loud alerts | 4 (7.1%) | Pager loud enough to hear all the time | Receipt of page uncertain | 3 (5.2%) | Unknown if page received |
| Staff responsiveness to pages | 4 (7.1%) | I know MD has to be onsite or covering the pager so someone eventually will call back | Sender's pager number not always included in page | 3 (5.2%) | Not everyone puts their pager number when they page. Then it's impossible to get back to them. |
| Brevity of messages | 3 (5.4%) | Requires very brief messages (easier for recipient) | Needing to remain near a phone while waiting for a page response | 3 (5.2%) | Wait by a phone for someone to call back; sometimes they do not call back |
| Helpful page operators | 2 (3.6%) | Page operators very helpful | Reliability of message transmission | 3 (5.2%) | Sometimes messages don't go through |
| Other | 10 (17.9%) | It's online and allows paging from anywhere there's internet access | Other | 11 (19.0%) | You cannot text with patient info on it |
A similar distribution of team member roles was observed in both groups, with two exceptions. First, the proportion of attending respondents in the HCGM group was lower than in the control group. This was due to the fact that several HCGM attendings entered discrepant ID codes on their surveys, thus making it impossible to link baseline and post‐study responses; these data were excluded. Additionally, two HCGM attendings were on service for four, rather than the standard two weeks, meaning two additional data points from unique attendings could not be obtained. Second, the experimental group included four pharmacists, whereas the control group did not. As a sensitivity test, we analyzed the data excluding the pharmacists, and this did not change our results.
Baseline Evaluations of the Hospital Paging System
At baseline, there were no significant differences between control and HCGM participants' perceptions of paging effectiveness (see Supporting Table 1, in the online version of this article). On a 5‐point rating scale (1=low, 5=high), 63 subjects rated their overall satisfaction with the paging system an average of 2.79 (95% confidence interval: 2.55‐3.03).
In free response questions, components of the paging system most frequently cited as effective included: reliability of message transmission, alphanumeric text paging, and ease of use (30.4%, 25.0%, and 14.3% of 56 respondents, respectively) (Table 2). Ineffective aspects included: time wasted waiting for responses to pages, the unidirectional nature of pagers, and needing to find a computer to send a text page (29.3%, 24.1%, and 20.7% of 58 respondents, respectively) (Table 2).
| Question | Baseline Average Rating of Paging System* | Post‐Study Average Rating of HCGM Application | P Value |
|---|---|---|---|
| |||
| Rate the effectiveness of each in allowing you to | |||
| Communicate your thoughts clearly | 3.194 | 3.806 | 0.010 |
| Communicate your thoughts efficiently | 3.200 | 3.829 | 0.009 |
| Send messages to other hospital staff | 3.543 | 3.571 | 0.480 |
| Receive messages/stay informed in real time | 3.222 | 3.306 | 0.405 |
| Rate the effectiveness of each in integrating into your workflow during | |||
| Work rounds | 2.313 | 3.000 | 0.018 |
| Patient discharge | 2.448 | 3.276 | 0.012 |
| Patient admissions | 2.862 | 2.621 | 0.238 |
| Teaching sessions | 2.292 | 2.458 | 0.448 |
| Overall satisfaction | 2.811 | 3.459 | 0.003 |
Baseline Utilization of Text Messaging
The majority of participants were familiar with text messaging and regularly used it personally and professionally prior to the start of the study. 90.5% of participants (n = 63) reported sending an average of 1 personal text messages per day, with the largest proportion (39.7%) sending 1‐5 texts per day (see Supporting Figure 1A in the online version of this article). 58.1% of respondents (n=62) reported sending an average of 1 text messages per day related to patient care (see Supporting Figure 1B in the online version of this article), with the largest fraction (58.3%) sending 1‐5 texts per day.
HCGM Adoption and Usage Patterns
Active use of HCGM was defined as using the application to send or receive an average of 1 text messages per day. Of HCGM participants, 67% self‐reported 1 week of active use of the application, indicating a strong compliance rate. Among non‐attendings, 70% reported sending 1 or more texts to other team members per day; this percentage increased to 86% among those whose attendings texted them at least once per day (47% of non‐attendings). Respondents who text frequently in their personal lives (>5 texts/day) were more likely to use the application; 90% of these respondents sent 1 or more HCGM texts per day.
Among 12 subjects who did not report sending or receiving 1 HCGM text/day, the top three reasons were: other team members were not using it (67%), no need to use it given the close proximity of other team members (67%), and other (33%). A Wilcoxon rank sum test was used to compare the ages of active versus nonactive users; no significant age difference was found (P=0.200).
To provide an objective measure of application adoption, usage data for each HCGM participant were obtained from the application developers. Because much of the study's first week was spent onboarding and instructing participant, the first week was not included in the analysis. Of 43 individuals enrolled in the study for at least one of the seven remaining weeks, 56% sent a total of 5 texts, 44% sent 10 texts, and 28% sent 20 texts. HCGM users on three teams sent an aggregate mean of 123 texts/week. Data on number of messages received by each user were not available.
Perceived Effectiveness: Paging Versus HCGM
In post‐study surveys, HCGM participants rated HCGM significantly higher (P<0.05) than paging (Table 3) in terms of ability to communicate thoughts clearly (P=0.010) and efficiently (P=0.009). HCGM was also deemed more effective at integrating into workflow during rounds (P=0.018) and patient discharge (P=0.012). Overall satisfaction with HCGM was also significantly higher (P=0.003).
| Control (n=22) | HCGM (n=41) | |||||
|---|---|---|---|---|---|---|
| Baseline Mean | Post‐Study Mean | P Value* | Baseline Mean | Post‐Study Mean | P Value* | |
| ||||||
| Rate the effectiveness of each in allowing you to | ||||||
| Communicate your thoughts clearly | 2.905 | 2.619 | 0.103 | 3.250 | 2.850 | 0.004 |
| Communicate your thoughts efficiently | 2.952 | 2.762 | 0.106 | 3.250 | 2.825 | 0.018 |
| Send messages to other hospital staff | 3.762 | 3.190 | 0.019 | 3.550 | 3.450 | 0.253 |
| Receive messages/stay informed in real time | 3.667 | 2.857 | 0.002 | 3.300 | 2.900 | 0.031 |
| Rate the effectiveness of each in integrating into your workflow during | ||||||
| Work rounds | 2.429 | 2.476 | 0.303 | 2.410 | 2.718 | 0.078 |
| Patient discharge | 2.500 | 2.350 | 0.251 | 2.472 | 2.861 | 0.071 |
| Patient admissions | 2.905 | 2.524 | 0.020 | 2.889 | 3.000 | 0.384 |
| Teaching sessions | 2.143 | 2.200 | 0.386 | 2.367 | 2.400 | 0.418 |
Comparison of Pre‐ and Post‐study Perceived Effectiveness of the Hospital Paging System
In post‐study evaluations, both control and HCGM participants rated the paging system's effectiveness less favorably (P<0.05) compared to baseline in terms of ability to receive messages/stay informed in real time (control P=0.002, HCGM P=0.031) (Table 4). Controls also reported a decrease from baseline in perceived effectiveness of paging in terms of ability to send messages (P=0.019) and integrate into workflow during patient admissions (P=0.020). HCGM participants found paging less effective at communicating thoughts clearly (P=0.004) and efficiently (P=0.018). No significant differences existed between control and HCGM groups' average assessments of paging at the conclusion of the study (see Supporting Table 2, in the online version of this article).
| What do you find effective about the Medigram system? | What do you find ineffective about the Medigram system? | ||||
|---|---|---|---|---|---|
| Theme | No. of Respondents, (% of Total) | Response Example | Theme | No. of Respondents, (% of Total) | Response Example |
| |||||
| Ease of use | 11 (32.4%) | Easy to use | Lack of ubiquity | 10 (30.3%) | Not enough people using it |
| Group texting feature | 11 (32.4%) | Ability to communicate with entire teameveryone seeing same message | Inconsistent usage | 8 (24.2%) | No one used it reliably |
| Speed | 8 (23.5%) | Faster than a page to send a message | Reliability of message transmission | 5 (15.2%) | Big negative is it requires Wi‐Fi |
| Accessibility | 5 (14.7%) | Able to get messages across quickly and anywhere without a computer | Missed message alerts | 4 (12.1%) | Unable to reliably know message was received if phone on silent |
| Efficiency | 4 (11.8%) | Very efficient way to communicate | Password login | 3 (9.1%) | Having to type a 6‐digit password in |
| Real‐time communication | 2 (5.9%) | Real‐time results | User interface | 2 (6.1%) | Interface is a little convoluted |
| No character limitation | 2 (5.9%) | No limit on words | Other | 10 (30.3%) | Not sure if all of the texts were relevant |
| Other | 4 (11.8%) | Great UI | |||
HCGM User Experience
When asked if they would recommend using an HCGM system to facilitate communication on the internal medicine wards, 85% of HCGM participants replied yes, 15% reported not sure, and 0% reported no. Based on free response entries, HCGM's most effective features (Table 5) included ease of use, group texting capacity, and speed (32.4%, 32.4%, and 23.5% of 34 respondents, respectively); its most ineffective aspects (Table 5) included lack of ubiquity, inconsistent usage by those with access to the application, and reliability of message transmission (30.3%, 24.2%, and 15.2% of 33 respondents, respectively).
DISCUSSION
We are the first to report that smartphone‐based, HIPAA‐compliant, group messaging applications improve provider perception of in‐hospital communication, while providing the information security that paging and commercial cellular networks do not. HCGM participants rated the application more favorably than paging in terms of clarity and efficiency of communication. These findings may be attributed to the expanded functionality offered by the application, including no character limit per HCGM text, the ability to use special characters such as slashes and ampersands, group texting, and the ability to reply immediately. HCGM may result in more efficient communication by facilitating direct two‐way communication via smartphones, whereas sending or returning pages requires a landline or computer.
HCGM participants rated the application higher than paging in terms of workflow integration during rounds and patient discharge, but not during patient admissions and teaching sessions. We had hypothesized that HCGM would integrate better into participants' workflows because HCGM texts could be replied to immediately. The reasons for the equivalence of HCGM and paging for workflow integration during patient admissions and teaching sessions may have been due to weak Wi‐Fi in certain areas of the hospital, and may warrant further investigation.
Analysis of HCGM utilization indicated that there were factors that made participants more or less likely to use the application. Individuals who reported that their attendings used HCGM regularly were more likely to use it themselves. Attending usage may legitimize use of HCGM for housestaff and medical students, who may otherwise feel that texting appears unprofessional. Participants who texted frequently in their personal lives were also more likely to utilize HCGM regularly, perhaps due to increased familiarity with/affinity for the platform.
HCGM participants who did not utilize the application regularly most often cited the fact that other team members did not use it. Among all users, the most frequently noted ineffective aspects of the application were its lack of ubiquity (HCGM was made available only to the small subset of individuals involved in the study) and inconsistent usage by those who did have access to the application. These findings suggest that HCGM effectiveness may be maximized with unrestricted access and mandated use; patchwork implementation, as in this study, detracts from perceived effectiveness.
Though objective outcome measures (average length of stay and average time of discharge) for patients of control attendings and HCGM attendings were examined, no significant differences were observed (P=0.089 and 0.494, respectively). These results may be due to the small size and short duration of the study.
Limitations
Our study had several limitations. HCGM was available only to individuals in the experimental arm of the study; most members of the internal medicine department and all other departments were not reachable through the application. This lack of ubiquity was a frequently cited frustration. Among individuals to whom HCGM was made available, barriers to adoption included: close proximity to would‐be message recipients, concern that smartphone usage in front of patients might appear unprofessional, and inconsistent or dropped service (weak or no Wi‐Fi signal in some areas). A technical problem with the Android platform midway through the study served as a potential frustration to several participants.
Due to the aforementioned issues, some participants used the HCGM application in a very limited way. We also did not replace hospital pagers (infeasible in this hospital setting); the HCGM application was added as a supplemental system. Future studies might explore the replacement of paging systems with HCGM‐type applications, as well as delve further into quantitative patient care outcomes.
It should be noted that the start of the study unintentionally coincided with the start of new interns and medical students in the hospital. Although it is possible that their relative unfamiliarity with the hospital may have made them more amenable to adopting a new technology, it is also possible that they may have been less likely to do so in the midst of such a major transitional period. Finally, this was a single‐site study, and as such, its findings may not be broadly generalizable. More research on such interventions is warranted, particularly in the context of current insecure communication methods such as paging that may make hospital‐wide adoption of new methods of secure communication, such as HCGM, mandatory.
CONCLUSION
Our study is the first to demonstrate that HCGM applications improve healthcare provider perception of multiple measures of in‐hospital communication, including efficiency of communication, workflow integration, and overall satisfaction. Notably, 85% of HCGM team respondents said they would recommend using an HCGM system on the wards. As smartphone use is expected to continue to increase among physicians and the general population, it is increasingly important to understand how to utilize these powerful communication tools to improve healthcare in an effective and secure manner.
Acknowledgements
The authors would like to thank the Stanford Medicine Residency Program and Stanford Hospital and Clinics for allowing them to perform the study, as well as Medigram, Inc., for supplying gift cards to incentivize survey completion and loaner iPhones to allow individuals without smartphones to participate in the study.
Disclosures: Stanford University School of Medicine's Medical Scholars Program provided financial support to the first author over the course of the study. Medigram, Inc. funded gift cards given to both control and HCGM participants upon survey completion and provided loaner iPhones to allow individuals without smartphones to participate in the study. Medigram, Inc. has also paid the fee for OnlineOpen to provide open access to this article. This study received an institutional review board exemption as quality improvement research. Participation was voluntary, and all participants signed a consent form. The authors report no conflicts of interest.
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© 2014 The Authors Journal of Hospital Medicine published by Wiley Periodicals, Inc. on behalf of Society of Hospital Medicine