Patients with acute coronary syndrome (ACS) are a challenge for the hospitalist, as they require substantial coordination of care and support during hospitalization and in the transition back into primary care. ACS accounted for 733,000 discharge diagnoses in 2006 (inclusive of unstable angina or acute myocardial infarction [MI]), and the American Heart Association considers this a conservative estimate.1 Readmission of these patients occurs with some frequency and expends healthcare resources. A multiemployer claims database showed a 20% rehospitalization rate for patients with ischemic heart disease within 1 year of discharge after ACS.2

Implementation of evidenced‐based care for patients with ACS and an emphasis on preventive measures for coronary heart disease (CHD) have improved CHD‐related outcomes. A 2007 study revealed that almost one‐half of the 40% decrease in CHD‐related mortality between 1988 and 2000 was directly attributable to therapeutic interventions and prevention and treatment of recognized risk factors for CHD. However, this news was tempered by the effect of 2 risk factors, increased body mass index and diabetes, which accounted for additional CHD‐related deaths in 2000.3

The Society of Hospital Medicine (SHM) has established core competencies for hospitalists who manage patients with ACS, defining the scope of interactions between the hospitalist, patient, and other clinicians such as specialists and primary care providers (PCP).4, 5 Several competencies focus on ACS and transitions in care, requiring the hospitalist to demonstrate the skills and attitudes outlined in Table 1. Of concern is the risk for adverse events associated with medication errors and lack of follow‐up related to diagnostic tests during the postdischarge period, which in turn raises the risk of readmission.6 The hospitalist is in a unique position to have a positive impact on the transition from inpatient to primary care by proactively addressing patient‐specific issues through careful planning and coordination with the patient, the PCP, and other stakeholders. The hospitalist cannot be accountable for all elements of the transition process, but can be proactive, working with hospital leadership and allied health professionals to promote systems that support safe transitions in care.

Case Study

Jose is a 66‐year‐old retiree recovering on the medical floor after diagnosis of an ST‐segment elevation myocardial infarction (STEMI) and implantation of a drug‐eluting stent (DES). Jose and his family were poor historians on admission and it is unclear whether his medical and medication history are accurate. Jose is anxious to get out of here and thinks this is no big deal. Upon admission Jose was hypertensive and mildly obese. He denies smoking and exercises once in awhile by working in his yard. His lipid profile on admission indicated elevated low‐density lipoprotein (LDL) and total cholesterol.

You decide to address several issues during Jose's hospital stay. These include contacting Jose's PCP to obtain a complete medical and medication history, and educating Jose about his cardiac disease, his risk of future events, and strategies for risk reduction.

Conclusion

Timely and accurate communication between the hospitalist and the PCP is a vital component of a safe transition from inpatient to primary care. This communication directly impacts the continuity of care, patient outcomes, patient and caregiver satisfaction, and use of healthcare resources. The role of the hospitalist is still evolving. Hospitalists will continue to have a pivotal role in transitions of care, and have a direct impact on the quality of the transition at discharge and patient outcomes after ACS. Hospitalists should be cognizant of gaps in care related to how information is generated, recorded, and shared between the inpatient setting and primary care, and should be proactive in identifying barriers and facilitating solutions. The hospitalist's responsibility for the patient does not end at the time of discharge but extends until the PCP assumes responsibility for patient care following hospitalization. We must make every reasonable effort to assure that our patients and their outpatient providers and caregivers are given all the tools necessary to complete and maintain the patient's therapy.

Patients with acute coronary syndrome (ACS) are a challenge for the hospitalist, as they require substantial coordination of care and support during hospitalization and in the transition back into primary care. ACS accounted for 733,000 discharge diagnoses in 2006 (inclusive of unstable angina or acute myocardial infarction [MI]), and the American Heart Association considers this a conservative estimate.1 Readmission of these patients occurs with some frequency and expends healthcare resources. A multiemployer claims database showed a 20% rehospitalization rate for patients with ischemic heart disease within 1 year of discharge after ACS.2

Implementation of evidenced‐based care for patients with ACS and an emphasis on preventive measures for coronary heart disease (CHD) have improved CHD‐related outcomes. A 2007 study revealed that almost one‐half of the 40% decrease in CHD‐related mortality between 1988 and 2000 was directly attributable to therapeutic interventions and prevention and treatment of recognized risk factors for CHD. However, this news was tempered by the effect of 2 risk factors, increased body mass index and diabetes, which accounted for additional CHD‐related deaths in 2000.3

The Society of Hospital Medicine (SHM) has established core competencies for hospitalists who manage patients with ACS, defining the scope of interactions between the hospitalist, patient, and other clinicians such as specialists and primary care providers (PCP).4, 5 Several competencies focus on ACS and transitions in care, requiring the hospitalist to demonstrate the skills and attitudes outlined in Table 1. Of concern is the risk for adverse events associated with medication errors and lack of follow‐up related to diagnostic tests during the postdischarge period, which in turn raises the risk of readmission.6 The hospitalist is in a unique position to have a positive impact on the transition from inpatient to primary care by proactively addressing patient‐specific issues through careful planning and coordination with the patient, the PCP, and other stakeholders. The hospitalist cannot be accountable for all elements of the transition process, but can be proactive, working with hospital leadership and allied health professionals to promote systems that support safe transitions in care.

Case Study

Jose is a 66‐year‐old retiree recovering on the medical floor after diagnosis of an ST‐segment elevation myocardial infarction (STEMI) and implantation of a drug‐eluting stent (DES). Jose and his family were poor historians on admission and it is unclear whether his medical and medication history are accurate. Jose is anxious to get out of here and thinks this is no big deal. Upon admission Jose was hypertensive and mildly obese. He denies smoking and exercises once in awhile by working in his yard. His lipid profile on admission indicated elevated low‐density lipoprotein (LDL) and total cholesterol.

You decide to address several issues during Jose's hospital stay. These include contacting Jose's PCP to obtain a complete medical and medication history, and educating Jose about his cardiac disease, his risk of future events, and strategies for risk reduction.

Conclusion

Timely and accurate communication between the hospitalist and the PCP is a vital component of a safe transition from inpatient to primary care. This communication directly impacts the continuity of care, patient outcomes, patient and caregiver satisfaction, and use of healthcare resources. The role of the hospitalist is still evolving. Hospitalists will continue to have a pivotal role in transitions of care, and have a direct impact on the quality of the transition at discharge and patient outcomes after ACS. Hospitalists should be cognizant of gaps in care related to how information is generated, recorded, and shared between the inpatient setting and primary care, and should be proactive in identifying barriers and facilitating solutions. The hospitalist's responsibility for the patient does not end at the time of discharge but extends until the PCP assumes responsibility for patient care following hospitalization. We must make every reasonable effort to assure that our patients and their outpatient providers and caregivers are given all the tools necessary to complete and maintain the patient's therapy.

Patients with acute coronary syndrome (ACS) are a challenge for the hospitalist, as they require substantial coordination of care and support during hospitalization and in the transition back into primary care. ACS accounted for 733,000 discharge diagnoses in 2006 (inclusive of unstable angina or acute myocardial infarction [MI]), and the American Heart Association considers this a conservative estimate.1 Readmission of these patients occurs with some frequency and expends healthcare resources. A multiemployer claims database showed a 20% rehospitalization rate for patients with ischemic heart disease within 1 year of discharge after ACS.2

Implementation of evidenced‐based care for patients with ACS and an emphasis on preventive measures for coronary heart disease (CHD) have improved CHD‐related outcomes. A 2007 study revealed that almost one‐half of the 40% decrease in CHD‐related mortality between 1988 and 2000 was directly attributable to therapeutic interventions and prevention and treatment of recognized risk factors for CHD. However, this news was tempered by the effect of 2 risk factors, increased body mass index and diabetes, which accounted for additional CHD‐related deaths in 2000.3

The Society of Hospital Medicine (SHM) has established core competencies for hospitalists who manage patients with ACS, defining the scope of interactions between the hospitalist, patient, and other clinicians such as specialists and primary care providers (PCP).4, 5 Several competencies focus on ACS and transitions in care, requiring the hospitalist to demonstrate the skills and attitudes outlined in Table 1. Of concern is the risk for adverse events associated with medication errors and lack of follow‐up related to diagnostic tests during the postdischarge period, which in turn raises the risk of readmission.6 The hospitalist is in a unique position to have a positive impact on the transition from inpatient to primary care by proactively addressing patient‐specific issues through careful planning and coordination with the patient, the PCP, and other stakeholders. The hospitalist cannot be accountable for all elements of the transition process, but can be proactive, working with hospital leadership and allied health professionals to promote systems that support safe transitions in care.

Case Study

Jose is a 66‐year‐old retiree recovering on the medical floor after diagnosis of an ST‐segment elevation myocardial infarction (STEMI) and implantation of a drug‐eluting stent (DES). Jose and his family were poor historians on admission and it is unclear whether his medical and medication history are accurate. Jose is anxious to get out of here and thinks this is no big deal. Upon admission Jose was hypertensive and mildly obese. He denies smoking and exercises once in awhile by working in his yard. His lipid profile on admission indicated elevated low‐density lipoprotein (LDL) and total cholesterol.

You decide to address several issues during Jose's hospital stay. These include contacting Jose's PCP to obtain a complete medical and medication history, and educating Jose about his cardiac disease, his risk of future events, and strategies for risk reduction.

Conclusion

Timely and accurate communication between the hospitalist and the PCP is a vital component of a safe transition from inpatient to primary care. This communication directly impacts the continuity of care, patient outcomes, patient and caregiver satisfaction, and use of healthcare resources. The role of the hospitalist is still evolving. Hospitalists will continue to have a pivotal role in transitions of care, and have a direct impact on the quality of the transition at discharge and patient outcomes after ACS. Hospitalists should be cognizant of gaps in care related to how information is generated, recorded, and shared between the inpatient setting and primary care, and should be proactive in identifying barriers and facilitating solutions. The hospitalist's responsibility for the patient does not end at the time of discharge but extends until the PCP assumes responsibility for patient care following hospitalization. We must make every reasonable effort to assure that our patients and their outpatient providers and caregivers are given all the tools necessary to complete and maintain the patient's therapy.

Two national surveys indicate that 14% to 28% of patients admitted to intensive care units (ICU's) are unplanned transfers (i.e., moving a patient to the ICU from other areas in the hospital providing lower intensity care due to an unanticipated change in the patient's clinical status), and that the most common reason for unplanned transfers is respiratory insufficiency/failure.1, 2 Patients suffering adverse events during a hospitalization are more likely to have an unplanned ICU transfer and patients requiring unplanned transfers have a higher mortality.35 Accordingly, the Joint Commission has identified improved recognition and response to changes in a patient's condition as a national patient safety goal,6 and Rapid Response Teams (RRTs) have been advocated to deal with these changes,7 although recent studies question the effectiveness of RRTs.811

We sought to classify the causes of unplanned, in‐hospital transfers to a medical ICU (MICU) with the idea of identifying common problems in care that might be addressed by process improvement activities. We also sought to determine the fraction of patients requiring an unplanned MICU transfer that had evidence of clinical deterioration prior to the time of transfer and whether, in retrospect, different or earlier interventions might have prevented the transfer. Our hypotheses were that (1) most unplanned MICU transfers occurred as a result of errors in care, (2) most were preceded by clinical deterioration within 12 hours prior to the transfer, and (3) most were preventable.

Methods

We conducted a retrospective cohort study of patients transferring to the MICU from non‐ICU Medicine units at Denver Health, a university‐affiliated, public safety net hospital. All adult patients between 18 to 89 years of age, who were admitted to the Medicine service between June, 2005 and May, 2006 were included in the study. Exclusion criteria included patients who (1) transferred from outside hospitals, (2) transferred from nonMedicine units within Denver Health, (3) were admitted directly to the MICU from the emergency department (ED), (4) were prisoners, (5) were readmitted to the MICU during the same hospitalization, (6) were known to be pregnant, or (7) were planned MICU transfers following invasive procedures (eg, elective cardiac catheterization, defibrillator placement, ablations). Patients readmitted to the MICU were excluded because of the difficulty distinguishing between premature transfer from the MICU or potential problems in care that might have occurred prior to the time of transfer from those occurring during follow‐up care on the Medicine floor services.

Computerized medical records of eligible patients were searched for demographic information and for admitting and transfer diagnoses (with the latter being categorized using a taxonomy we developed for classifying unplanned transfers, Table 1). Three independent observers (all of whom were board certified in Internal Medicine and had been practicing as Hospitalists at our institution for a minimum of three years) retrospectively reviewed each patient's hospital record to determine the cause of the unplanned transfer using this taxonomy. All three also made a judgment as to whether deterioration was evident at any time within the 12 hours preceding the unplanned transfer on the basis of clinical criteria used as our hospital's rapid response triggers (Table 2). When clinical triggers were found, each of the reviewers independently judged whether the unplanned transfer might have been prevented had different or earlier interventions been instituted. Each reviewer was blinded to the results of the other two.

All analyses were done using SAS Enterprise Guide 4.1, SAS Institute, Cary, NC. Data are presented as mean (standard deviation [SD]). Interobserver agreement was measured by calculating a statistic. values were interpreted by using the guidelines suggested by Landis and colleagues.12 A chi‐square test was used to seek associations between baseline characteristics, reasons for MICU transfer and mortality. P < 0.05 was considered to be statistically significant. The Colorado Multiple Institutional Review Board approved the research protocol.

Results

Over the period of the study the Medicine floor services had 4468 admissions of which 152 met the inclusion criteria for having an unplanned MICU transfer (Table 3). The most common admitting diagnoses were heart failure (12%) and community acquired pneumonia (9%). The most common diagnoses to which the unplanned MICU transfers were attributed were respiratory failure (27%) and sepsis (9%) (Table 4). Seven cardiopulmonary arrests were successfully resuscitated and transferred to the MICU. Throughout the period of the study, no patients were admitted to non‐MICU units because the MICU was at full capacity. Additionally the investigators did not find any inordinate delays in transfer to the ICU while waiting for a bed.

A total of 51 patients (34%) were transferred within the first 24 hours of admission. The most common diagnoses resulting in transfer in this group were respiratory failure, hypertensive emergency, hypotension, gastrointestinal bleed, and acute coronary syndrome. The remaining 101 patients (66%) were transferred from two to 15 days following admission for a variety of problems but respiratory failure was most common (34 patients, 22%).

Worsening of the problem for which the patients were initially admitted accounted for the unplanned transfers of 73 patients (48%) (Table 5). Development of a new problem unrelated to the admitting diagnosis accounted for the transfer in 59 patients (39%). Five patients were transferred to the ICU for a critical laboratory value that required a closer monitoring of the patient or needed more frequent lab draws that could not be achieved on the floor.

Errors in care were thought to be present in 29 patients (19% of the unplanned transfers). For 15 of these (52%) the error involved incorrect triage from the ED as 14 of the 15 patients met MICU admission criteria at the time they were triaged to non‐MICU units (Table 6). The remaining patient had a dissecting aortic aneurysm that was not considered while he was being evaluated for acute chest pain. All these patients were transferred to the ICU within 24 hours of their admission and the reviewers agreed that all could have been prevented if existing diagnostic and admission algorithms were followed.

Of the remaining 14 patients thought to have errors in care, nine were classified as the development of a new, iatrogenic problem (ie, opiate or benzodiazepine overdose occurring during treatment for pain and/or anxiety in 3, volume overload in 2, insulin‐induced hypoglycemia, antibiotic associated reaction, ‐blocker overdose and acute renal failure from over‐diuresis in one each) and five occurred because the patient's admitting problem worsened because treatment was thought to be either delayed, incorrect, or inadequate (Table 5). The reviewers all agreed that the unplanned transfers could have been prevented in eight of the 14 patients who developed iatrogenic problems if existing algorithms were followed or if an earlier or different intervention had occurred. The reviewers did not agree about whether the unplanned transfer could have been prevented in one patient who developed an iatrogenic problem and in all five patients whose underlying condition worsened. Accordingly, in sum, the reviewers felt that 23 of the 152 unplanned transfers (15%) could have been prevented.

In addition to trying to determine how many of the unplanned MICU transfers could have been prevented, we also investigated the utility of rapid response triggers in alerting the physicians and nurses of impending deteriorations in status and whether earlier recognition of this deterioration might have prevented the transfers. Of the 152 unplanned transfers, 106 (70%) had one or more rapid response triggers within the preceding 12 hours. All three reviewers agreed and concluded that in 94 (89%) of these, the unplanned transfer could not have been prevented, even with different or earlier interventions. For five patients (5% of the 106) all reviewers agreed and concluded that earlier intervention might have averted the subsequent transfer. For the other seven patients (6%), no consensus was reached. If we assume that, for all of these latter seven, earlier or different intervention might have averted the unplanned transfer, a maximum of 12 unplanned transfers (11% of the 106) might have been prevented by having a system of care that employed regularly assessing rapid response triggers and acting on them when recognized.

The interobserver reliability for the three reviewers was moderate to almost perfect with = 0.60, 95% confidence interval (CI) (0.31, 0.88); = 0.90, 95% CI (0.71, 1); = 0.55, 95% CI (0.26, 0.84).

A total of 27 (18%) of the patients with unplanned transfers died in the MICU. During this same time period 91 of 1511 patients (6%) admitted directly from the ED to the MICU died (P < 0.05). Mortality was lower for patients transferred within 24 hours of admission compared to those transferred > 24 hours after admission (4% vs. 22% mortality, respectively, P < 0.05; 95% CI, 0.09‐0.89). We found no difference in mortality as a function of time of admission or time of transfer implying that differences in staffing, or the availability of various services, did not contribute to the unplanned transfers.

Discussion

The important findings of this study were that (1) 19% of unplanned, in‐hospital transfers from Medicine floor services to the MICU seemed to result from apparent errors in care, (2) 15% of the unplanned transfers were potentially preventable, (3) the majority of the errors in care involved inappropriate triage of patients from the ED to the non‐MICU units, (4) 106 (70%) of the patients requiring unplanned transfers developed rapid response criteria within 12 hours prior to the transfer, but on review of these (5) the transfer was thought to be preventable in only a maximum of 12 (11%).

We designed our study in part to find specific errors that commonly resulted in unplanned MICU transfers with the idea that, if these could be identified, they might be corrected, thereby improving care. Contrary to our hypothesis we found that only 29 (19%) of the unplanned transfers seemed to result from errors in care. Of these, however, half were attributable to overlooking that patients met our own institution's MICU admission criteria at the time they were triaged to non‐MICU units. This result is consistent with Walter et al.13 finding that while 88% of MICUs in academic health centers had written MICU admission criteria, only 25% used these criteria on a regular basis. Hospital mortality is likely lower for patients meeting MICU admission criteria when they are appropriately and expeditiously triaged.1418 Accordingly, developing mechanisms by which patients are routinely screened for meeting MICU admission criteria could and should reduce this source of error and improve patient outcomes.

Nine of the remaining 14 errors in care resulted from what the chart reviewers concluded was overly aggressive treatment; either excess fluid resuscitation or excess treatment of pain or anxiety. It is not clear that these represent correctable errors in care, however, as hypotensive patients require fluid resuscitation, and patients with pain or anxiety should receive analgesics or anxiolytics and it is not reasonable to expect that these interventions will be appropriately titrated in every instance. Nonetheless, our reviewers all agreed that, in eight of these patients, different interventions could have prevented the unplanned transfer.

Since 41 (27%) of the unplanned transfers were for respiratory failure, we reviewed each of these patients' records seeking evidence suggesting that the problem might have resulted from excessive use of fluids, narcotics, or anxiolytics. By retrospective analysis only six such cases could be identified. Most were due to worsening of the problem for which the patient was admitted.

Consistent with our hypothesis the majority of patients requiring unplanned MICU transfers (106/152, 70%) developed rapid response clinical triggers within the 12 hours preceding transfer, as has been previously demonstrated by Hillman et al.7 and others.8‐10, 19 Our reviewers tried to determine whether earlier or different interventions might have prevented the deterioration and the resulting unplanned transfer. Interestingly, in the large majority (94/106, 89%) they concluded that nothing different could have been done and that the transfer could not have been avoided. While this observation contrasts with our hypothesis, it is consistent with two studies questioning the utility of RRTs in preventing unplanned ICU transfers.9, 10 In addition some patients may ultimately need an ICU transfer despite receiving appropriate interventions as it is impossible to prevent an ICU transfer in every patient. Conversely, just because a patient meets a rapid response criteria does not mean that the patient needs a higher level of care or an ICU transfer as some can be safely managed on the floor.

Our study has a number of potential limitations. The data came from a single teaching hospital and we only assessed patients admitted to General Internal Medicine units and transferred to a MICU. Accordingly, our results might not generalize to other hospitals (teaching or nonteaching), to other services or to other types of ICUs. We found, however, that (1) unplanned transfers accounted for 10% of the total admissions to our MICU, a similar fraction as reported by Angus et al.1 in 2006; (2) respiratory failure/emnsufficiency and sepsis were the most common diagnoses leading to unplanned transfers as previously reported by Groeger et al.2 and Hillman et al.5; (3) mortality was increased in patients requiring unplanned transfer, as noted by Escarce and Kelley3 and Hillman et al.5; and (4) patients who were transferred to the MICU within 24 hours of admission had better outcomes than those who were transferred later, as reported by Goldhill et al.4 Accordingly, our patient population seems quite similar to others in the literature.

Since we did not use objective criteria to assign patients to each of the categories itemized in Table 5 we could have misclassified patients with respect to the cause for their unplanned MICU transfer. Despite this shortcoming, however, the scores among our independent reviewers were moderate to almost perfect suggesting misclassification did not occur commonly.

Our retrospective study design may have underestimated the utility of RRTs as we had no way of knowing the outcomes of patients who met rapid response criteria and had interventions that prevented unplanned MICU transfers.

In summary, approximately 15% of unplanned MICU transfers seem to be preventable and approximately one‐fifth seem to result from errors in care, the majority of which are errors in triage from the ED. While the large majority of unplanned transfers were preceded by clinical deterioration within the preceding 12 hours, manifested by the presence of rapid response triggers, the large majority of these do not seem to be preventable. From these findings we suggest that unplanned transfers could be reduced by more closely screening patients for the presence of defined MICU admission criteria at the time of admission from the ED, by recognizing that fluid resuscitation and control of pain and/or anxiety can have adverse effects and by monitoring patients receiving these interventions more closely.

Two national surveys indicate that 14% to 28% of patients admitted to intensive care units (ICU's) are unplanned transfers (i.e., moving a patient to the ICU from other areas in the hospital providing lower intensity care due to an unanticipated change in the patient's clinical status), and that the most common reason for unplanned transfers is respiratory insufficiency/failure.1, 2 Patients suffering adverse events during a hospitalization are more likely to have an unplanned ICU transfer and patients requiring unplanned transfers have a higher mortality.35 Accordingly, the Joint Commission has identified improved recognition and response to changes in a patient's condition as a national patient safety goal,6 and Rapid Response Teams (RRTs) have been advocated to deal with these changes,7 although recent studies question the effectiveness of RRTs.811

We sought to classify the causes of unplanned, in‐hospital transfers to a medical ICU (MICU) with the idea of identifying common problems in care that might be addressed by process improvement activities. We also sought to determine the fraction of patients requiring an unplanned MICU transfer that had evidence of clinical deterioration prior to the time of transfer and whether, in retrospect, different or earlier interventions might have prevented the transfer. Our hypotheses were that (1) most unplanned MICU transfers occurred as a result of errors in care, (2) most were preceded by clinical deterioration within 12 hours prior to the transfer, and (3) most were preventable.

Methods

We conducted a retrospective cohort study of patients transferring to the MICU from non‐ICU Medicine units at Denver Health, a university‐affiliated, public safety net hospital. All adult patients between 18 to 89 years of age, who were admitted to the Medicine service between June, 2005 and May, 2006 were included in the study. Exclusion criteria included patients who (1) transferred from outside hospitals, (2) transferred from nonMedicine units within Denver Health, (3) were admitted directly to the MICU from the emergency department (ED), (4) were prisoners, (5) were readmitted to the MICU during the same hospitalization, (6) were known to be pregnant, or (7) were planned MICU transfers following invasive procedures (eg, elective cardiac catheterization, defibrillator placement, ablations). Patients readmitted to the MICU were excluded because of the difficulty distinguishing between premature transfer from the MICU or potential problems in care that might have occurred prior to the time of transfer from those occurring during follow‐up care on the Medicine floor services.

Computerized medical records of eligible patients were searched for demographic information and for admitting and transfer diagnoses (with the latter being categorized using a taxonomy we developed for classifying unplanned transfers, Table 1). Three independent observers (all of whom were board certified in Internal Medicine and had been practicing as Hospitalists at our institution for a minimum of three years) retrospectively reviewed each patient's hospital record to determine the cause of the unplanned transfer using this taxonomy. All three also made a judgment as to whether deterioration was evident at any time within the 12 hours preceding the unplanned transfer on the basis of clinical criteria used as our hospital's rapid response triggers (Table 2). When clinical triggers were found, each of the reviewers independently judged whether the unplanned transfer might have been prevented had different or earlier interventions been instituted. Each reviewer was blinded to the results of the other two.

All analyses were done using SAS Enterprise Guide 4.1, SAS Institute, Cary, NC. Data are presented as mean (standard deviation [SD]). Interobserver agreement was measured by calculating a statistic. values were interpreted by using the guidelines suggested by Landis and colleagues.12 A chi‐square test was used to seek associations between baseline characteristics, reasons for MICU transfer and mortality. P < 0.05 was considered to be statistically significant. The Colorado Multiple Institutional Review Board approved the research protocol.

Results

Over the period of the study the Medicine floor services had 4468 admissions of which 152 met the inclusion criteria for having an unplanned MICU transfer (Table 3). The most common admitting diagnoses were heart failure (12%) and community acquired pneumonia (9%). The most common diagnoses to which the unplanned MICU transfers were attributed were respiratory failure (27%) and sepsis (9%) (Table 4). Seven cardiopulmonary arrests were successfully resuscitated and transferred to the MICU. Throughout the period of the study, no patients were admitted to non‐MICU units because the MICU was at full capacity. Additionally the investigators did not find any inordinate delays in transfer to the ICU while waiting for a bed.

A total of 51 patients (34%) were transferred within the first 24 hours of admission. The most common diagnoses resulting in transfer in this group were respiratory failure, hypertensive emergency, hypotension, gastrointestinal bleed, and acute coronary syndrome. The remaining 101 patients (66%) were transferred from two to 15 days following admission for a variety of problems but respiratory failure was most common (34 patients, 22%).

Worsening of the problem for which the patients were initially admitted accounted for the unplanned transfers of 73 patients (48%) (Table 5). Development of a new problem unrelated to the admitting diagnosis accounted for the transfer in 59 patients (39%). Five patients were transferred to the ICU for a critical laboratory value that required a closer monitoring of the patient or needed more frequent lab draws that could not be achieved on the floor.

Errors in care were thought to be present in 29 patients (19% of the unplanned transfers). For 15 of these (52%) the error involved incorrect triage from the ED as 14 of the 15 patients met MICU admission criteria at the time they were triaged to non‐MICU units (Table 6). The remaining patient had a dissecting aortic aneurysm that was not considered while he was being evaluated for acute chest pain. All these patients were transferred to the ICU within 24 hours of their admission and the reviewers agreed that all could have been prevented if existing diagnostic and admission algorithms were followed.

Of the remaining 14 patients thought to have errors in care, nine were classified as the development of a new, iatrogenic problem (ie, opiate or benzodiazepine overdose occurring during treatment for pain and/or anxiety in 3, volume overload in 2, insulin‐induced hypoglycemia, antibiotic associated reaction, ‐blocker overdose and acute renal failure from over‐diuresis in one each) and five occurred because the patient's admitting problem worsened because treatment was thought to be either delayed, incorrect, or inadequate (Table 5). The reviewers all agreed that the unplanned transfers could have been prevented in eight of the 14 patients who developed iatrogenic problems if existing algorithms were followed or if an earlier or different intervention had occurred. The reviewers did not agree about whether the unplanned transfer could have been prevented in one patient who developed an iatrogenic problem and in all five patients whose underlying condition worsened. Accordingly, in sum, the reviewers felt that 23 of the 152 unplanned transfers (15%) could have been prevented.

In addition to trying to determine how many of the unplanned MICU transfers could have been prevented, we also investigated the utility of rapid response triggers in alerting the physicians and nurses of impending deteriorations in status and whether earlier recognition of this deterioration might have prevented the transfers. Of the 152 unplanned transfers, 106 (70%) had one or more rapid response triggers within the preceding 12 hours. All three reviewers agreed and concluded that in 94 (89%) of these, the unplanned transfer could not have been prevented, even with different or earlier interventions. For five patients (5% of the 106) all reviewers agreed and concluded that earlier intervention might have averted the subsequent transfer. For the other seven patients (6%), no consensus was reached. If we assume that, for all of these latter seven, earlier or different intervention might have averted the unplanned transfer, a maximum of 12 unplanned transfers (11% of the 106) might have been prevented by having a system of care that employed regularly assessing rapid response triggers and acting on them when recognized.

The interobserver reliability for the three reviewers was moderate to almost perfect with = 0.60, 95% confidence interval (CI) (0.31, 0.88); = 0.90, 95% CI (0.71, 1); = 0.55, 95% CI (0.26, 0.84).

A total of 27 (18%) of the patients with unplanned transfers died in the MICU. During this same time period 91 of 1511 patients (6%) admitted directly from the ED to the MICU died (P < 0.05). Mortality was lower for patients transferred within 24 hours of admission compared to those transferred > 24 hours after admission (4% vs. 22% mortality, respectively, P < 0.05; 95% CI, 0.09‐0.89). We found no difference in mortality as a function of time of admission or time of transfer implying that differences in staffing, or the availability of various services, did not contribute to the unplanned transfers.

Discussion

The important findings of this study were that (1) 19% of unplanned, in‐hospital transfers from Medicine floor services to the MICU seemed to result from apparent errors in care, (2) 15% of the unplanned transfers were potentially preventable, (3) the majority of the errors in care involved inappropriate triage of patients from the ED to the non‐MICU units, (4) 106 (70%) of the patients requiring unplanned transfers developed rapid response criteria within 12 hours prior to the transfer, but on review of these (5) the transfer was thought to be preventable in only a maximum of 12 (11%).

We designed our study in part to find specific errors that commonly resulted in unplanned MICU transfers with the idea that, if these could be identified, they might be corrected, thereby improving care. Contrary to our hypothesis we found that only 29 (19%) of the unplanned transfers seemed to result from errors in care. Of these, however, half were attributable to overlooking that patients met our own institution's MICU admission criteria at the time they were triaged to non‐MICU units. This result is consistent with Walter et al.13 finding that while 88% of MICUs in academic health centers had written MICU admission criteria, only 25% used these criteria on a regular basis. Hospital mortality is likely lower for patients meeting MICU admission criteria when they are appropriately and expeditiously triaged.1418 Accordingly, developing mechanisms by which patients are routinely screened for meeting MICU admission criteria could and should reduce this source of error and improve patient outcomes.

Nine of the remaining 14 errors in care resulted from what the chart reviewers concluded was overly aggressive treatment; either excess fluid resuscitation or excess treatment of pain or anxiety. It is not clear that these represent correctable errors in care, however, as hypotensive patients require fluid resuscitation, and patients with pain or anxiety should receive analgesics or anxiolytics and it is not reasonable to expect that these interventions will be appropriately titrated in every instance. Nonetheless, our reviewers all agreed that, in eight of these patients, different interventions could have prevented the unplanned transfer.

Since 41 (27%) of the unplanned transfers were for respiratory failure, we reviewed each of these patients' records seeking evidence suggesting that the problem might have resulted from excessive use of fluids, narcotics, or anxiolytics. By retrospective analysis only six such cases could be identified. Most were due to worsening of the problem for which the patient was admitted.

Consistent with our hypothesis the majority of patients requiring unplanned MICU transfers (106/152, 70%) developed rapid response clinical triggers within the 12 hours preceding transfer, as has been previously demonstrated by Hillman et al.7 and others.8‐10, 19 Our reviewers tried to determine whether earlier or different interventions might have prevented the deterioration and the resulting unplanned transfer. Interestingly, in the large majority (94/106, 89%) they concluded that nothing different could have been done and that the transfer could not have been avoided. While this observation contrasts with our hypothesis, it is consistent with two studies questioning the utility of RRTs in preventing unplanned ICU transfers.9, 10 In addition some patients may ultimately need an ICU transfer despite receiving appropriate interventions as it is impossible to prevent an ICU transfer in every patient. Conversely, just because a patient meets a rapid response criteria does not mean that the patient needs a higher level of care or an ICU transfer as some can be safely managed on the floor.

Our study has a number of potential limitations. The data came from a single teaching hospital and we only assessed patients admitted to General Internal Medicine units and transferred to a MICU. Accordingly, our results might not generalize to other hospitals (teaching or nonteaching), to other services or to other types of ICUs. We found, however, that (1) unplanned transfers accounted for 10% of the total admissions to our MICU, a similar fraction as reported by Angus et al.1 in 2006; (2) respiratory failure/emnsufficiency and sepsis were the most common diagnoses leading to unplanned transfers as previously reported by Groeger et al.2 and Hillman et al.5; (3) mortality was increased in patients requiring unplanned transfer, as noted by Escarce and Kelley3 and Hillman et al.5; and (4) patients who were transferred to the MICU within 24 hours of admission had better outcomes than those who were transferred later, as reported by Goldhill et al.4 Accordingly, our patient population seems quite similar to others in the literature.

Since we did not use objective criteria to assign patients to each of the categories itemized in Table 5 we could have misclassified patients with respect to the cause for their unplanned MICU transfer. Despite this shortcoming, however, the scores among our independent reviewers were moderate to almost perfect suggesting misclassification did not occur commonly.

Our retrospective study design may have underestimated the utility of RRTs as we had no way of knowing the outcomes of patients who met rapid response criteria and had interventions that prevented unplanned MICU transfers.

In summary, approximately 15% of unplanned MICU transfers seem to be preventable and approximately one‐fifth seem to result from errors in care, the majority of which are errors in triage from the ED. While the large majority of unplanned transfers were preceded by clinical deterioration within the preceding 12 hours, manifested by the presence of rapid response triggers, the large majority of these do not seem to be preventable. From these findings we suggest that unplanned transfers could be reduced by more closely screening patients for the presence of defined MICU admission criteria at the time of admission from the ED, by recognizing that fluid resuscitation and control of pain and/or anxiety can have adverse effects and by monitoring patients receiving these interventions more closely.

Two national surveys indicate that 14% to 28% of patients admitted to intensive care units (ICU's) are unplanned transfers (i.e., moving a patient to the ICU from other areas in the hospital providing lower intensity care due to an unanticipated change in the patient's clinical status), and that the most common reason for unplanned transfers is respiratory insufficiency/failure.1, 2 Patients suffering adverse events during a hospitalization are more likely to have an unplanned ICU transfer and patients requiring unplanned transfers have a higher mortality.35 Accordingly, the Joint Commission has identified improved recognition and response to changes in a patient's condition as a national patient safety goal,6 and Rapid Response Teams (RRTs) have been advocated to deal with these changes,7 although recent studies question the effectiveness of RRTs.811

We sought to classify the causes of unplanned, in‐hospital transfers to a medical ICU (MICU) with the idea of identifying common problems in care that might be addressed by process improvement activities. We also sought to determine the fraction of patients requiring an unplanned MICU transfer that had evidence of clinical deterioration prior to the time of transfer and whether, in retrospect, different or earlier interventions might have prevented the transfer. Our hypotheses were that (1) most unplanned MICU transfers occurred as a result of errors in care, (2) most were preceded by clinical deterioration within 12 hours prior to the transfer, and (3) most were preventable.

Methods

We conducted a retrospective cohort study of patients transferring to the MICU from non‐ICU Medicine units at Denver Health, a university‐affiliated, public safety net hospital. All adult patients between 18 to 89 years of age, who were admitted to the Medicine service between June, 2005 and May, 2006 were included in the study. Exclusion criteria included patients who (1) transferred from outside hospitals, (2) transferred from nonMedicine units within Denver Health, (3) were admitted directly to the MICU from the emergency department (ED), (4) were prisoners, (5) were readmitted to the MICU during the same hospitalization, (6) were known to be pregnant, or (7) were planned MICU transfers following invasive procedures (eg, elective cardiac catheterization, defibrillator placement, ablations). Patients readmitted to the MICU were excluded because of the difficulty distinguishing between premature transfer from the MICU or potential problems in care that might have occurred prior to the time of transfer from those occurring during follow‐up care on the Medicine floor services.

Computerized medical records of eligible patients were searched for demographic information and for admitting and transfer diagnoses (with the latter being categorized using a taxonomy we developed for classifying unplanned transfers, Table 1). Three independent observers (all of whom were board certified in Internal Medicine and had been practicing as Hospitalists at our institution for a minimum of three years) retrospectively reviewed each patient's hospital record to determine the cause of the unplanned transfer using this taxonomy. All three also made a judgment as to whether deterioration was evident at any time within the 12 hours preceding the unplanned transfer on the basis of clinical criteria used as our hospital's rapid response triggers (Table 2). When clinical triggers were found, each of the reviewers independently judged whether the unplanned transfer might have been prevented had different or earlier interventions been instituted. Each reviewer was blinded to the results of the other two.

All analyses were done using SAS Enterprise Guide 4.1, SAS Institute, Cary, NC. Data are presented as mean (standard deviation [SD]). Interobserver agreement was measured by calculating a statistic. values were interpreted by using the guidelines suggested by Landis and colleagues.12 A chi‐square test was used to seek associations between baseline characteristics, reasons for MICU transfer and mortality. P < 0.05 was considered to be statistically significant. The Colorado Multiple Institutional Review Board approved the research protocol.

Results

Over the period of the study the Medicine floor services had 4468 admissions of which 152 met the inclusion criteria for having an unplanned MICU transfer (Table 3). The most common admitting diagnoses were heart failure (12%) and community acquired pneumonia (9%). The most common diagnoses to which the unplanned MICU transfers were attributed were respiratory failure (27%) and sepsis (9%) (Table 4). Seven cardiopulmonary arrests were successfully resuscitated and transferred to the MICU. Throughout the period of the study, no patients were admitted to non‐MICU units because the MICU was at full capacity. Additionally the investigators did not find any inordinate delays in transfer to the ICU while waiting for a bed.

A total of 51 patients (34%) were transferred within the first 24 hours of admission. The most common diagnoses resulting in transfer in this group were respiratory failure, hypertensive emergency, hypotension, gastrointestinal bleed, and acute coronary syndrome. The remaining 101 patients (66%) were transferred from two to 15 days following admission for a variety of problems but respiratory failure was most common (34 patients, 22%).

Worsening of the problem for which the patients were initially admitted accounted for the unplanned transfers of 73 patients (48%) (Table 5). Development of a new problem unrelated to the admitting diagnosis accounted for the transfer in 59 patients (39%). Five patients were transferred to the ICU for a critical laboratory value that required a closer monitoring of the patient or needed more frequent lab draws that could not be achieved on the floor.

Errors in care were thought to be present in 29 patients (19% of the unplanned transfers). For 15 of these (52%) the error involved incorrect triage from the ED as 14 of the 15 patients met MICU admission criteria at the time they were triaged to non‐MICU units (Table 6). The remaining patient had a dissecting aortic aneurysm that was not considered while he was being evaluated for acute chest pain. All these patients were transferred to the ICU within 24 hours of their admission and the reviewers agreed that all could have been prevented if existing diagnostic and admission algorithms were followed.

Of the remaining 14 patients thought to have errors in care, nine were classified as the development of a new, iatrogenic problem (ie, opiate or benzodiazepine overdose occurring during treatment for pain and/or anxiety in 3, volume overload in 2, insulin‐induced hypoglycemia, antibiotic associated reaction, ‐blocker overdose and acute renal failure from over‐diuresis in one each) and five occurred because the patient's admitting problem worsened because treatment was thought to be either delayed, incorrect, or inadequate (Table 5). The reviewers all agreed that the unplanned transfers could have been prevented in eight of the 14 patients who developed iatrogenic problems if existing algorithms were followed or if an earlier or different intervention had occurred. The reviewers did not agree about whether the unplanned transfer could have been prevented in one patient who developed an iatrogenic problem and in all five patients whose underlying condition worsened. Accordingly, in sum, the reviewers felt that 23 of the 152 unplanned transfers (15%) could have been prevented.

In addition to trying to determine how many of the unplanned MICU transfers could have been prevented, we also investigated the utility of rapid response triggers in alerting the physicians and nurses of impending deteriorations in status and whether earlier recognition of this deterioration might have prevented the transfers. Of the 152 unplanned transfers, 106 (70%) had one or more rapid response triggers within the preceding 12 hours. All three reviewers agreed and concluded that in 94 (89%) of these, the unplanned transfer could not have been prevented, even with different or earlier interventions. For five patients (5% of the 106) all reviewers agreed and concluded that earlier intervention might have averted the subsequent transfer. For the other seven patients (6%), no consensus was reached. If we assume that, for all of these latter seven, earlier or different intervention might have averted the unplanned transfer, a maximum of 12 unplanned transfers (11% of the 106) might have been prevented by having a system of care that employed regularly assessing rapid response triggers and acting on them when recognized.

The interobserver reliability for the three reviewers was moderate to almost perfect with = 0.60, 95% confidence interval (CI) (0.31, 0.88); = 0.90, 95% CI (0.71, 1); = 0.55, 95% CI (0.26, 0.84).

A total of 27 (18%) of the patients with unplanned transfers died in the MICU. During this same time period 91 of 1511 patients (6%) admitted directly from the ED to the MICU died (P < 0.05). Mortality was lower for patients transferred within 24 hours of admission compared to those transferred > 24 hours after admission (4% vs. 22% mortality, respectively, P < 0.05; 95% CI, 0.09‐0.89). We found no difference in mortality as a function of time of admission or time of transfer implying that differences in staffing, or the availability of various services, did not contribute to the unplanned transfers.

Discussion

The important findings of this study were that (1) 19% of unplanned, in‐hospital transfers from Medicine floor services to the MICU seemed to result from apparent errors in care, (2) 15% of the unplanned transfers were potentially preventable, (3) the majority of the errors in care involved inappropriate triage of patients from the ED to the non‐MICU units, (4) 106 (70%) of the patients requiring unplanned transfers developed rapid response criteria within 12 hours prior to the transfer, but on review of these (5) the transfer was thought to be preventable in only a maximum of 12 (11%).

We designed our study in part to find specific errors that commonly resulted in unplanned MICU transfers with the idea that, if these could be identified, they might be corrected, thereby improving care. Contrary to our hypothesis we found that only 29 (19%) of the unplanned transfers seemed to result from errors in care. Of these, however, half were attributable to overlooking that patients met our own institution's MICU admission criteria at the time they were triaged to non‐MICU units. This result is consistent with Walter et al.13 finding that while 88% of MICUs in academic health centers had written MICU admission criteria, only 25% used these criteria on a regular basis. Hospital mortality is likely lower for patients meeting MICU admission criteria when they are appropriately and expeditiously triaged.1418 Accordingly, developing mechanisms by which patients are routinely screened for meeting MICU admission criteria could and should reduce this source of error and improve patient outcomes.

Nine of the remaining 14 errors in care resulted from what the chart reviewers concluded was overly aggressive treatment; either excess fluid resuscitation or excess treatment of pain or anxiety. It is not clear that these represent correctable errors in care, however, as hypotensive patients require fluid resuscitation, and patients with pain or anxiety should receive analgesics or anxiolytics and it is not reasonable to expect that these interventions will be appropriately titrated in every instance. Nonetheless, our reviewers all agreed that, in eight of these patients, different interventions could have prevented the unplanned transfer.

Since 41 (27%) of the unplanned transfers were for respiratory failure, we reviewed each of these patients' records seeking evidence suggesting that the problem might have resulted from excessive use of fluids, narcotics, or anxiolytics. By retrospective analysis only six such cases could be identified. Most were due to worsening of the problem for which the patient was admitted.

Consistent with our hypothesis the majority of patients requiring unplanned MICU transfers (106/152, 70%) developed rapid response clinical triggers within the 12 hours preceding transfer, as has been previously demonstrated by Hillman et al.7 and others.8‐10, 19 Our reviewers tried to determine whether earlier or different interventions might have prevented the deterioration and the resulting unplanned transfer. Interestingly, in the large majority (94/106, 89%) they concluded that nothing different could have been done and that the transfer could not have been avoided. While this observation contrasts with our hypothesis, it is consistent with two studies questioning the utility of RRTs in preventing unplanned ICU transfers.9, 10 In addition some patients may ultimately need an ICU transfer despite receiving appropriate interventions as it is impossible to prevent an ICU transfer in every patient. Conversely, just because a patient meets a rapid response criteria does not mean that the patient needs a higher level of care or an ICU transfer as some can be safely managed on the floor.

Our study has a number of potential limitations. The data came from a single teaching hospital and we only assessed patients admitted to General Internal Medicine units and transferred to a MICU. Accordingly, our results might not generalize to other hospitals (teaching or nonteaching), to other services or to other types of ICUs. We found, however, that (1) unplanned transfers accounted for 10% of the total admissions to our MICU, a similar fraction as reported by Angus et al.1 in 2006; (2) respiratory failure/emnsufficiency and sepsis were the most common diagnoses leading to unplanned transfers as previously reported by Groeger et al.2 and Hillman et al.5; (3) mortality was increased in patients requiring unplanned transfer, as noted by Escarce and Kelley3 and Hillman et al.5; and (4) patients who were transferred to the MICU within 24 hours of admission had better outcomes than those who were transferred later, as reported by Goldhill et al.4 Accordingly, our patient population seems quite similar to others in the literature.

Since we did not use objective criteria to assign patients to each of the categories itemized in Table 5 we could have misclassified patients with respect to the cause for their unplanned MICU transfer. Despite this shortcoming, however, the scores among our independent reviewers were moderate to almost perfect suggesting misclassification did not occur commonly.

Our retrospective study design may have underestimated the utility of RRTs as we had no way of knowing the outcomes of patients who met rapid response criteria and had interventions that prevented unplanned MICU transfers.

In summary, approximately 15% of unplanned MICU transfers seem to be preventable and approximately one‐fifth seem to result from errors in care, the majority of which are errors in triage from the ED. While the large majority of unplanned transfers were preceded by clinical deterioration within the preceding 12 hours, manifested by the presence of rapid response triggers, the large majority of these do not seem to be preventable. From these findings we suggest that unplanned transfers could be reduced by more closely screening patients for the presence of defined MICU admission criteria at the time of admission from the ED, by recognizing that fluid resuscitation and control of pain and/or anxiety can have adverse effects and by monitoring patients receiving these interventions more closely.

Patient flow in a hospital refers to the management and movement of patients through the facility. Optimizing patient flow is considered of great importance to improvement of quality (including safety, efficiency, timeliness, equity, effectiveness, and patient‐centeredness), as well as finance, staff satisfaction, education and overall healthcare value.18 Central to concerns about patient flow at hospitals is occupancy, which is the census (number of patients at a point in time) divided by the bed capacity. Occupancy that is too high is associated with challenges to quality and access,913 while occupancy that is too low may underutilize resources and be costly.14, 15 Occupancy is determined by the pattern of admission and discharge, thus including length of stay (LOS) as a factor. While all related, admissions, census, occupancy, and LOS convey different aspects of hospital operations and may point to different opportunities to improve patient flow.

Variability in patient flow over time has been noted as a common occurrence in adult hospitals, due to uneven patterns of scheduled (elective) admissions, as well as uncontrollable variability of emergent admissions.2, 45, 16 Typically very few patients are scheduled to enter hospitals over weekends. In addition, when the admission is expected to be 5 days or less, clinical and operational staff may schedule those admissions early in the week to avoid patients staying the weekend. This artificial variability has been shown to lead to uneven levels of occupancy, with crowding on some days of the week more than others.2, 45, 16 As hospital crowding adversely affects access to emergent and elective care, quality and safety of care, and patient and staff satisfaction, many groups are focusing attention on patient flow and strategies to avoid high occupancy.19, 17 This is true for children's hospitals, as well, particularly as these scarce resources have ever increasing demand placed on them.1820

Patient flow improvements can be made by increasing efficiency of throughput, primarily measured by decreased LOS, or by addressing artificial variability in how hospital beds are used. As children's hospitals have short LOSs and are relatively efficient (as measured by standardized LOS ratios), we sought to evaluate how much artificial variability was active at 1 large children's hospital. We did this to both evaluate flow at 1 institution and to create methodology for other hospitals to use in order to better understand and improve their flow.

Our specific aims were to describe daily and monthly variability in admission, discharge, LOS, and occupancy patterns at a large children's hospital and assess the relationship between scheduled admissions and occupancy.

Methods

This retrospective administrative data analysis was performed with admission‐discharge‐transfer (ADT) data for inpatient admissions from one urban, tertiary‐care children's hospital for the period July 1, 2007 to June 30, 2008. The dataset included the date and time of all arrivals and departures from all inpatient units (including observation‐status patients), as entered by the unit clerks into the electronic ADT system. The dataset also included categorization of the admission as emergent, urgent, or elective (hereafter referred to as scheduled.) Registration staff entered these codes at or prior to admission. Using the timestamps, LOS was calculated by subtracting admission date and time from discharge date and time. An SAS macro was applied to the timestamps to calculate a hospital census for every hour of each calendar day. Peak census figures were extracted for each day. Occupancy was calculated as census over number of beds in use (monthly average). Data for the hospital's peak daily census and occupancy were utilized to analyze patterns of occupancy by day of week and month of year. To express variability, coefficient of variation (CV) (standard deviation [SD] divided by its mean) was used, as it is helpful when samples sizes are different.21

Analysis of number of admissions per day of week and month by type was performed with descriptive statistics and t‐tests for significant differences across seasons. We calculated a measure of patient hours generated by day of admission based on the LOS generated by each admission, in which the average number of admissions for each day of the week was multiplied by the average LOS (in hours) for those admissions. In order to remove outliers and focus on patients whose occupancy would affect weekly variation, we analyzed in detail the admissions with LOS 30 days and 7 days, respectively.

Statistical analyses were performed with SAS 9.2 (SAS Institute, Cary, NC), Stata 10.0 (StataCorp, College Station, TX) and Microsoft Excel (Microsoft, Redmond, WA). The study was approved by the Human Subjects Committee of the hospital's Institutional Review Board.

Results

A total of 22,310 patients were admitted over the period July 1, 2007 to June 30, 2008, including 4957 (22%) coded as scheduled and 17,353 (78%) coded as emergent. (Only 200 patients were registered as urgent and these were recoded as emergent for this analysis). Details on admission types and discharging departments are provided in Table 1. Overall, mean LOS was 5.6 days (median 2.29 days). For patients with LOS 30 days, mean LOS was 3.88 days (median 2.22 days). For patients staying 7 days, mean LOS was 2.4 days (median 1.98 days). Among patients with LOS 7 days, mean LOS for scheduled patients was longer for those admitted on Monday than on any other weekday (2.49 vs. 2.08 days, P < 0.0001). In contrast, mean LOS for emergent patients was longer for patients admitted on Friday and Saturday than the rest of the week (2.57 vs. 2.44 days, P < 0.0001).

Total admissions per month (Figure 1) averaged 1937 in October through April and 1751 in May through September (P = 0.03). Variation in the number of emergent and scheduled patients over months of the year were similar (CV 10% for each), but emergent admissions did decrease in summer (mean 1299 for June‐September vs. 1520 for the rest of the year, P = 0.003). Conversely, scheduled admissions remained relatively stable all year‐long: mean 423 per month for May through September vs. mean 413 per month for October through April (P = 0.48). Even just the summer months of June‐August, when school‐age children are on vacation, were not significantly different from other months (440 vs. 404, P = 0.2).

Figure 1

Variation in volume of admissions was large over days of the week, driven primarily by the pattern of scheduled admissions (CV 65.3%), which dropped off completely on weekends (Table 2, Figure 2). In contrast, there was much less variation in the number of emergent admissions across days of the week (CV 12%). For both emergent and scheduled admissions, more patients came in on Mondays than any other day of the week, but even more so for scheduled patients. While emergent admissions did decline on weekends, it was driven primarily by a decrease in physician referrals (ie, direct admission) from clinics (mean 7.48 per weekday vs. 0.73 per weekend day for the entire year, P < 0.001), while emergency department (ED) admissions remained relatively stable (mean 35.8 per weekday vs. 32.7 per weekend day, P = 0.08). Emergency transports were also stable (mean 7.15 per weekday vs. 6.49 per weekend day, P = 0.10).

Figure 2

Although scheduled patients contributed less to the hospital's overall occupancy, they conferred most of the variability by day of week. Over the days of the week, variation for scheduled occupancy was nearly twice that for emergent occupancy (CV 19% vs. 10%). Within the higher‐volume period of October to April, the differential was even more evident (CV 19% for scheduled occupancy versus 6% for emergent).

For scheduled patients with LOS 30 days (98% of scheduled patients), Mondays and Tuesdays together accounted for 42.5% of admission volume and 44.7% of the patient‐hours generated. For scheduled patients with LOS 7 days (89% of scheduled patients), Mondays and Tuesdays together accounted for 42% of admission volume and 45.2% of the patient‐hours generated. This combined impact of volume and LOS from admissions earlier in the week (restricted to patients with LOS 7days) is displayed graphically in Figure 3, which depicts the unevenness of scheduled admissions and their time in the hospital, with many patients overlapping in the middle of the week. Together with the more steady flow of emergent patients, this variability in scheduled occupancy contributed to mid‐week crowding, with higher risk of the hospital being >90% and >95% occupied on Wednesday through Friday (Figure 4). Detailed hourly analysis (not displayed) showed this risk to be highest from Wednesday afternoon to Friday afternoon. Due to higher emergent census, certain months also had a higher risk of high occupancy at daily peak. For example, while the entire year had 50% to 60% of Wednesdays and Thursdays with occupancy >90%, during the months of November through February, 70% to 85% of those days had occupancy at that level or higher (all these patterns were seen for both stays with LOS 30 days and 7days).

Figure 3

Figure 4

Discussion

In this study, we found that a large children's hospital was frequently at high occupancy in certain months and on certain days more than others, driven largely by predictable seasonal increases in emergent admissions and variability in scheduled admissions by day of week, respectively. Patient‐hours generated by day of admission varied as a result of both volume and LOS, both of which were larger in the early part of the week and diminished as the week progressed for scheduled admissions. But, the cumulative effect of many admissions with relatively‐longer LOS on Monday through Wednesday contributed to high occupancy on Wednesday afternoon to Friday morning, underscoring the importance of admission patterns on census later in the week. Our finding that the occupancy of scheduled patientsthe result of both the admission pattern and their LOSis also highly variable suggests that managing the inflow of scheduled patients could decrease crowding on weekdays, assure a consistent supply of capacity for regular admissions and surges, and improve the value of the delivery system.18 This inflow management would ideally consider both admissions and associated LOS, since rescheduling patients with a longer LOS (eg, 34 days) would have a greater impact on occupancy than rescheduling patients with a shorter LOS (eg, 12 days).

Not surprisingly, total admissions decreased in summer months, especially in July and August, due primarily to fewer emergent admissions. In fact, scheduled admissions per month remained relatively stable over the entire year. The decrease in summer emergent admissions may present an opportunity to stepwise shift a proportion of scheduled admissions from the spring and fall into the summer months, and winter into spring and fall, to alleviate crowding in the winter (Figure 1). Assuming clinical conditions, families and staff members were amenable to this change, hospitals with similar patterns could use this method to reduce the crowding (eg, days over 90% or 95% occupancy) that occurs in the winter.

Using patient‐hours (or days) generated by day of admission, it is evident that admission of more and longer‐stay patients at the start of the week contributes to higher occupancy later in the week (Figure 4). Mid‐week crowding could potentially contribute to a number of operational issues, including delays of new admissions, decreases in physician referrals and patient satisfaction, and an increased use of nontraditional beds (eg, treatment rooms, playrooms, doubling up single rooms) that lead to excessive patient to staff ratios and burnout for clinical staff.

The reasons for these patterns of admissions may include clinician or patient preference to avoid weekend admissions, lack of availability of particular services or resources on weekends, or concerns about safety and efficiency (due to relatively lower staffing on weekends in many hospitals).2230 While clinicians may prefer to avoid additional work on weekends, there are benefits to smoothing occupancy, including less risk of excessive work mid‐week and potential revenue opportunities. In addition, when contrasted with the estimated $1 million to $2 million cost per bed of construction, the marginal cost of staffing to provide safe, high‐quality care on weekends is dramatically lower than that of adding more beds (when faced with mid‐week crowding and unused weekend capacity). In addition, empty beds also do not generate revenue to cover fixed or variable costs, meaning that hospitals are not matching revenue to cost when there is unused capacity due to artificial variability.15, 31 Hospitals looking to make greater use of weekends, however, must be sensitive to staff concerns and the organizational dynamics of 7‐day operations, including the risk for burn‐out and attrition. Such factors should not be perceived as insurmountable barriers, particularly in light of opportunities for flexible scheduling and gain‐sharing.

Patients' and parents' preferences may partially drive admitting patterns, but a reasonable proportion of them may prefer to minimize the number of work and school days missed by being admitted near or on weekends. For example, an expected 3‐day admission could start on Friday and end on Sunday or Monday, rather than the current practice which appears to be to admit on Monday and discharge before the weekend. This may not only meet preferences among some parents to avoid missing work or school, but also by consideration of educational outcomes for hospitalized children.32

In addition, higher mean LOS for emergent patients on the weekends suggests that some services are currently unavailable on weekends to treat patients who are admitted on Fridays through Sundays.2, 25, 29, 33 More even staffing and provision of diagnostic and therapeutic services on weekends (eg, advanced radiology, consult, and laboratory services) would not only remove the barrier to weekend occupancy, it would also improve efficiency, timeliness, patient‐centeredness, and potentially effectiveness and safety for emergent patients. Running hospitals at full functionality on only 5 days of the week means that 2 out of 7 days puts patients at risk for disparate care, which may be appearing in this analysis as prolonged LOS due to lack of services over the weekenda pattern reported in the literature for adult hospitals.

Operations management and queuing theory suggest that systems function well up to 85% to 90% of capacity.34 Hospitals that plan ahead and ensure a buffer for unscheduled admissions during months or days when that demand is known to rise are less likely to cross into high occupancy. On the other hand, hospitals that do not anticipate increases in unscheduled admissions are more likely to encounter excess capacity problems.35 Aligning incentives with all staff can assist in this planning and maximize control of capacity.

Adopting the use of CV in health care operations would also be of value as a way to better express and track variation in admissions, occupancy, and discharges. Since different patient populations, different units, different hospitals, and different months have different scales, SD is not easily comparable across these settings. CV allows for comparison of variation by normalizing on the mean. In this study, it clearly differentiated the variation in elective admissions (CV 65%) over days of the week from the relative stability of emergent admissions (CV 12%). As variability and its management are important to operations, quality control, and quality improvement, use of CV can play an important role in hospital management and health services research. As days with high levels of activity may put more stress on the system, tracking this variation could lead to improvements in quality and value.

This study has several limitations. Data were analyzed for 1 children's hospital, so the analysis may or may not generally apply to other hospitals. However, in a separate study, we analyzed data from the Pediatric Health Information System database, and observed similar patterns.18 In addition, the proportion of elective patients shown in this study was similar to the national data in Kids Inpatient Database (KID, about 15% of all admissions elective).36 Moreover, the methods are reproducible for other settings, which would be useful to clinical and hospital leadership. Second, the trends depicted in the data only reflected data for one year. Third, coding of the admission as emergent or elective was done by registrars at or before arrival and may not reflect actual clinical need. In addition, those admissions coded as elective included a heterogeneous population (eg, chemotherapy to research studies).

Further studies should analyze trends for other hospitals and evaluate the effect of high peak census and high levels of variation with quality, safety, efficiency, patient satisfaction, financial, and educational outcomes for those receiving care, working, or learning at hospitals. In addition, a qualitative study that develops insights into clinician and patient/parent preferences would help answer questions in regard to usage of weekends for scheduled patients.

Conclusions

Scheduled admissions drive most variability in day‐to‐day occupancy despite the fact that they are a smaller proportion of the inpatient population. Variation in scheduled admissions by day of week provides hospitals with an opportunity to address crowding without adding beds or delaying admissions. Rather, fully utilizing capacity by smoothing occupancy over all days of the week can reduce the risk of high occupancy and thereby improve accessibility and patient/parent satisfaction. While family and staff preferences need to be considered, some combination of within‐week smoothing and shifting admissions towards summer are likely to achieve dramatic improvements in patient flow without large expenditures of capital. The key, then, is to ensure that organizational dynamic factors support these changes, so that staff members do not become stressed working at a 7‐day facility. Taken together, these strategies would better match revenue to capacity, and ultimately increase the quality and value of healthcare operations.

Patient flow in a hospital refers to the management and movement of patients through the facility. Optimizing patient flow is considered of great importance to improvement of quality (including safety, efficiency, timeliness, equity, effectiveness, and patient‐centeredness), as well as finance, staff satisfaction, education and overall healthcare value.18 Central to concerns about patient flow at hospitals is occupancy, which is the census (number of patients at a point in time) divided by the bed capacity. Occupancy that is too high is associated with challenges to quality and access,913 while occupancy that is too low may underutilize resources and be costly.14, 15 Occupancy is determined by the pattern of admission and discharge, thus including length of stay (LOS) as a factor. While all related, admissions, census, occupancy, and LOS convey different aspects of hospital operations and may point to different opportunities to improve patient flow.

Variability in patient flow over time has been noted as a common occurrence in adult hospitals, due to uneven patterns of scheduled (elective) admissions, as well as uncontrollable variability of emergent admissions.2, 45, 16 Typically very few patients are scheduled to enter hospitals over weekends. In addition, when the admission is expected to be 5 days or less, clinical and operational staff may schedule those admissions early in the week to avoid patients staying the weekend. This artificial variability has been shown to lead to uneven levels of occupancy, with crowding on some days of the week more than others.2, 45, 16 As hospital crowding adversely affects access to emergent and elective care, quality and safety of care, and patient and staff satisfaction, many groups are focusing attention on patient flow and strategies to avoid high occupancy.19, 17 This is true for children's hospitals, as well, particularly as these scarce resources have ever increasing demand placed on them.1820

Patient flow improvements can be made by increasing efficiency of throughput, primarily measured by decreased LOS, or by addressing artificial variability in how hospital beds are used. As children's hospitals have short LOSs and are relatively efficient (as measured by standardized LOS ratios), we sought to evaluate how much artificial variability was active at 1 large children's hospital. We did this to both evaluate flow at 1 institution and to create methodology for other hospitals to use in order to better understand and improve their flow.

Our specific aims were to describe daily and monthly variability in admission, discharge, LOS, and occupancy patterns at a large children's hospital and assess the relationship between scheduled admissions and occupancy.

Methods

This retrospective administrative data analysis was performed with admission‐discharge‐transfer (ADT) data for inpatient admissions from one urban, tertiary‐care children's hospital for the period July 1, 2007 to June 30, 2008. The dataset included the date and time of all arrivals and departures from all inpatient units (including observation‐status patients), as entered by the unit clerks into the electronic ADT system. The dataset also included categorization of the admission as emergent, urgent, or elective (hereafter referred to as scheduled.) Registration staff entered these codes at or prior to admission. Using the timestamps, LOS was calculated by subtracting admission date and time from discharge date and time. An SAS macro was applied to the timestamps to calculate a hospital census for every hour of each calendar day. Peak census figures were extracted for each day. Occupancy was calculated as census over number of beds in use (monthly average). Data for the hospital's peak daily census and occupancy were utilized to analyze patterns of occupancy by day of week and month of year. To express variability, coefficient of variation (CV) (standard deviation [SD] divided by its mean) was used, as it is helpful when samples sizes are different.21

Analysis of number of admissions per day of week and month by type was performed with descriptive statistics and t‐tests for significant differences across seasons. We calculated a measure of patient hours generated by day of admission based on the LOS generated by each admission, in which the average number of admissions for each day of the week was multiplied by the average LOS (in hours) for those admissions. In order to remove outliers and focus on patients whose occupancy would affect weekly variation, we analyzed in detail the admissions with LOS 30 days and 7 days, respectively.

Statistical analyses were performed with SAS 9.2 (SAS Institute, Cary, NC), Stata 10.0 (StataCorp, College Station, TX) and Microsoft Excel (Microsoft, Redmond, WA). The study was approved by the Human Subjects Committee of the hospital's Institutional Review Board.

Results

A total of 22,310 patients were admitted over the period July 1, 2007 to June 30, 2008, including 4957 (22%) coded as scheduled and 17,353 (78%) coded as emergent. (Only 200 patients were registered as urgent and these were recoded as emergent for this analysis). Details on admission types and discharging departments are provided in Table 1. Overall, mean LOS was 5.6 days (median 2.29 days). For patients with LOS 30 days, mean LOS was 3.88 days (median 2.22 days). For patients staying 7 days, mean LOS was 2.4 days (median 1.98 days). Among patients with LOS 7 days, mean LOS for scheduled patients was longer for those admitted on Monday than on any other weekday (2.49 vs. 2.08 days, P < 0.0001). In contrast, mean LOS for emergent patients was longer for patients admitted on Friday and Saturday than the rest of the week (2.57 vs. 2.44 days, P < 0.0001).

Total admissions per month (Figure 1) averaged 1937 in October through April and 1751 in May through September (P = 0.03). Variation in the number of emergent and scheduled patients over months of the year were similar (CV 10% for each), but emergent admissions did decrease in summer (mean 1299 for June‐September vs. 1520 for the rest of the year, P = 0.003). Conversely, scheduled admissions remained relatively stable all year‐long: mean 423 per month for May through September vs. mean 413 per month for October through April (P = 0.48). Even just the summer months of June‐August, when school‐age children are on vacation, were not significantly different from other months (440 vs. 404, P = 0.2).

Figure 1

Variation in volume of admissions was large over days of the week, driven primarily by the pattern of scheduled admissions (CV 65.3%), which dropped off completely on weekends (Table 2, Figure 2). In contrast, there was much less variation in the number of emergent admissions across days of the week (CV 12%). For both emergent and scheduled admissions, more patients came in on Mondays than any other day of the week, but even more so for scheduled patients. While emergent admissions did decline on weekends, it was driven primarily by a decrease in physician referrals (ie, direct admission) from clinics (mean 7.48 per weekday vs. 0.73 per weekend day for the entire year, P < 0.001), while emergency department (ED) admissions remained relatively stable (mean 35.8 per weekday vs. 32.7 per weekend day, P = 0.08). Emergency transports were also stable (mean 7.15 per weekday vs. 6.49 per weekend day, P = 0.10).

Figure 2

Although scheduled patients contributed less to the hospital's overall occupancy, they conferred most of the variability by day of week. Over the days of the week, variation for scheduled occupancy was nearly twice that for emergent occupancy (CV 19% vs. 10%). Within the higher‐volume period of October to April, the differential was even more evident (CV 19% for scheduled occupancy versus 6% for emergent).

For scheduled patients with LOS 30 days (98% of scheduled patients), Mondays and Tuesdays together accounted for 42.5% of admission volume and 44.7% of the patient‐hours generated. For scheduled patients with LOS 7 days (89% of scheduled patients), Mondays and Tuesdays together accounted for 42% of admission volume and 45.2% of the patient‐hours generated. This combined impact of volume and LOS from admissions earlier in the week (restricted to patients with LOS 7days) is displayed graphically in Figure 3, which depicts the unevenness of scheduled admissions and their time in the hospital, with many patients overlapping in the middle of the week. Together with the more steady flow of emergent patients, this variability in scheduled occupancy contributed to mid‐week crowding, with higher risk of the hospital being >90% and >95% occupied on Wednesday through Friday (Figure 4). Detailed hourly analysis (not displayed) showed this risk to be highest from Wednesday afternoon to Friday afternoon. Due to higher emergent census, certain months also had a higher risk of high occupancy at daily peak. For example, while the entire year had 50% to 60% of Wednesdays and Thursdays with occupancy >90%, during the months of November through February, 70% to 85% of those days had occupancy at that level or higher (all these patterns were seen for both stays with LOS 30 days and 7days).

Figure 3

Figure 4

Discussion

In this study, we found that a large children's hospital was frequently at high occupancy in certain months and on certain days more than others, driven largely by predictable seasonal increases in emergent admissions and variability in scheduled admissions by day of week, respectively. Patient‐hours generated by day of admission varied as a result of both volume and LOS, both of which were larger in the early part of the week and diminished as the week progressed for scheduled admissions. But, the cumulative effect of many admissions with relatively‐longer LOS on Monday through Wednesday contributed to high occupancy on Wednesday afternoon to Friday morning, underscoring the importance of admission patterns on census later in the week. Our finding that the occupancy of scheduled patientsthe result of both the admission pattern and their LOSis also highly variable suggests that managing the inflow of scheduled patients could decrease crowding on weekdays, assure a consistent supply of capacity for regular admissions and surges, and improve the value of the delivery system.18 This inflow management would ideally consider both admissions and associated LOS, since rescheduling patients with a longer LOS (eg, 34 days) would have a greater impact on occupancy than rescheduling patients with a shorter LOS (eg, 12 days).

Not surprisingly, total admissions decreased in summer months, especially in July and August, due primarily to fewer emergent admissions. In fact, scheduled admissions per month remained relatively stable over the entire year. The decrease in summer emergent admissions may present an opportunity to stepwise shift a proportion of scheduled admissions from the spring and fall into the summer months, and winter into spring and fall, to alleviate crowding in the winter (Figure 1). Assuming clinical conditions, families and staff members were amenable to this change, hospitals with similar patterns could use this method to reduce the crowding (eg, days over 90% or 95% occupancy) that occurs in the winter.

Using patient‐hours (or days) generated by day of admission, it is evident that admission of more and longer‐stay patients at the start of the week contributes to higher occupancy later in the week (Figure 4). Mid‐week crowding could potentially contribute to a number of operational issues, including delays of new admissions, decreases in physician referrals and patient satisfaction, and an increased use of nontraditional beds (eg, treatment rooms, playrooms, doubling up single rooms) that lead to excessive patient to staff ratios and burnout for clinical staff.

The reasons for these patterns of admissions may include clinician or patient preference to avoid weekend admissions, lack of availability of particular services or resources on weekends, or concerns about safety and efficiency (due to relatively lower staffing on weekends in many hospitals).2230 While clinicians may prefer to avoid additional work on weekends, there are benefits to smoothing occupancy, including less risk of excessive work mid‐week and potential revenue opportunities. In addition, when contrasted with the estimated $1 million to $2 million cost per bed of construction, the marginal cost of staffing to provide safe, high‐quality care on weekends is dramatically lower than that of adding more beds (when faced with mid‐week crowding and unused weekend capacity). In addition, empty beds also do not generate revenue to cover fixed or variable costs, meaning that hospitals are not matching revenue to cost when there is unused capacity due to artificial variability.15, 31 Hospitals looking to make greater use of weekends, however, must be sensitive to staff concerns and the organizational dynamics of 7‐day operations, including the risk for burn‐out and attrition. Such factors should not be perceived as insurmountable barriers, particularly in light of opportunities for flexible scheduling and gain‐sharing.

Patients' and parents' preferences may partially drive admitting patterns, but a reasonable proportion of them may prefer to minimize the number of work and school days missed by being admitted near or on weekends. For example, an expected 3‐day admission could start on Friday and end on Sunday or Monday, rather than the current practice which appears to be to admit on Monday and discharge before the weekend. This may not only meet preferences among some parents to avoid missing work or school, but also by consideration of educational outcomes for hospitalized children.32

In addition, higher mean LOS for emergent patients on the weekends suggests that some services are currently unavailable on weekends to treat patients who are admitted on Fridays through Sundays.2, 25, 29, 33 More even staffing and provision of diagnostic and therapeutic services on weekends (eg, advanced radiology, consult, and laboratory services) would not only remove the barrier to weekend occupancy, it would also improve efficiency, timeliness, patient‐centeredness, and potentially effectiveness and safety for emergent patients. Running hospitals at full functionality on only 5 days of the week means that 2 out of 7 days puts patients at risk for disparate care, which may be appearing in this analysis as prolonged LOS due to lack of services over the weekenda pattern reported in the literature for adult hospitals.

Operations management and queuing theory suggest that systems function well up to 85% to 90% of capacity.34 Hospitals that plan ahead and ensure a buffer for unscheduled admissions during months or days when that demand is known to rise are less likely to cross into high occupancy. On the other hand, hospitals that do not anticipate increases in unscheduled admissions are more likely to encounter excess capacity problems.35 Aligning incentives with all staff can assist in this planning and maximize control of capacity.

Adopting the use of CV in health care operations would also be of value as a way to better express and track variation in admissions, occupancy, and discharges. Since different patient populations, different units, different hospitals, and different months have different scales, SD is not easily comparable across these settings. CV allows for comparison of variation by normalizing on the mean. In this study, it clearly differentiated the variation in elective admissions (CV 65%) over days of the week from the relative stability of emergent admissions (CV 12%). As variability and its management are important to operations, quality control, and quality improvement, use of CV can play an important role in hospital management and health services research. As days with high levels of activity may put more stress on the system, tracking this variation could lead to improvements in quality and value.

This study has several limitations. Data were analyzed for 1 children's hospital, so the analysis may or may not generally apply to other hospitals. However, in a separate study, we analyzed data from the Pediatric Health Information System database, and observed similar patterns.18 In addition, the proportion of elective patients shown in this study was similar to the national data in Kids Inpatient Database (KID, about 15% of all admissions elective).36 Moreover, the methods are reproducible for other settings, which would be useful to clinical and hospital leadership. Second, the trends depicted in the data only reflected data for one year. Third, coding of the admission as emergent or elective was done by registrars at or before arrival and may not reflect actual clinical need. In addition, those admissions coded as elective included a heterogeneous population (eg, chemotherapy to research studies).

Further studies should analyze trends for other hospitals and evaluate the effect of high peak census and high levels of variation with quality, safety, efficiency, patient satisfaction, financial, and educational outcomes for those receiving care, working, or learning at hospitals. In addition, a qualitative study that develops insights into clinician and patient/parent preferences would help answer questions in regard to usage of weekends for scheduled patients.

Conclusions

Scheduled admissions drive most variability in day‐to‐day occupancy despite the fact that they are a smaller proportion of the inpatient population. Variation in scheduled admissions by day of week provides hospitals with an opportunity to address crowding without adding beds or delaying admissions. Rather, fully utilizing capacity by smoothing occupancy over all days of the week can reduce the risk of high occupancy and thereby improve accessibility and patient/parent satisfaction. While family and staff preferences need to be considered, some combination of within‐week smoothing and shifting admissions towards summer are likely to achieve dramatic improvements in patient flow without large expenditures of capital. The key, then, is to ensure that organizational dynamic factors support these changes, so that staff members do not become stressed working at a 7‐day facility. Taken together, these strategies would better match revenue to capacity, and ultimately increase the quality and value of healthcare operations.

Patient flow in a hospital refers to the management and movement of patients through the facility. Optimizing patient flow is considered of great importance to improvement of quality (including safety, efficiency, timeliness, equity, effectiveness, and patient‐centeredness), as well as finance, staff satisfaction, education and overall healthcare value.18 Central to concerns about patient flow at hospitals is occupancy, which is the census (number of patients at a point in time) divided by the bed capacity. Occupancy that is too high is associated with challenges to quality and access,913 while occupancy that is too low may underutilize resources and be costly.14, 15 Occupancy is determined by the pattern of admission and discharge, thus including length of stay (LOS) as a factor. While all related, admissions, census, occupancy, and LOS convey different aspects of hospital operations and may point to different opportunities to improve patient flow.

Variability in patient flow over time has been noted as a common occurrence in adult hospitals, due to uneven patterns of scheduled (elective) admissions, as well as uncontrollable variability of emergent admissions.2, 45, 16 Typically very few patients are scheduled to enter hospitals over weekends. In addition, when the admission is expected to be 5 days or less, clinical and operational staff may schedule those admissions early in the week to avoid patients staying the weekend. This artificial variability has been shown to lead to uneven levels of occupancy, with crowding on some days of the week more than others.2, 45, 16 As hospital crowding adversely affects access to emergent and elective care, quality and safety of care, and patient and staff satisfaction, many groups are focusing attention on patient flow and strategies to avoid high occupancy.19, 17 This is true for children's hospitals, as well, particularly as these scarce resources have ever increasing demand placed on them.1820

Patient flow improvements can be made by increasing efficiency of throughput, primarily measured by decreased LOS, or by addressing artificial variability in how hospital beds are used. As children's hospitals have short LOSs and are relatively efficient (as measured by standardized LOS ratios), we sought to evaluate how much artificial variability was active at 1 large children's hospital. We did this to both evaluate flow at 1 institution and to create methodology for other hospitals to use in order to better understand and improve their flow.

Our specific aims were to describe daily and monthly variability in admission, discharge, LOS, and occupancy patterns at a large children's hospital and assess the relationship between scheduled admissions and occupancy.

Methods

This retrospective administrative data analysis was performed with admission‐discharge‐transfer (ADT) data for inpatient admissions from one urban, tertiary‐care children's hospital for the period July 1, 2007 to June 30, 2008. The dataset included the date and time of all arrivals and departures from all inpatient units (including observation‐status patients), as entered by the unit clerks into the electronic ADT system. The dataset also included categorization of the admission as emergent, urgent, or elective (hereafter referred to as scheduled.) Registration staff entered these codes at or prior to admission. Using the timestamps, LOS was calculated by subtracting admission date and time from discharge date and time. An SAS macro was applied to the timestamps to calculate a hospital census for every hour of each calendar day. Peak census figures were extracted for each day. Occupancy was calculated as census over number of beds in use (monthly average). Data for the hospital's peak daily census and occupancy were utilized to analyze patterns of occupancy by day of week and month of year. To express variability, coefficient of variation (CV) (standard deviation [SD] divided by its mean) was used, as it is helpful when samples sizes are different.21

Analysis of number of admissions per day of week and month by type was performed with descriptive statistics and t‐tests for significant differences across seasons. We calculated a measure of patient hours generated by day of admission based on the LOS generated by each admission, in which the average number of admissions for each day of the week was multiplied by the average LOS (in hours) for those admissions. In order to remove outliers and focus on patients whose occupancy would affect weekly variation, we analyzed in detail the admissions with LOS 30 days and 7 days, respectively.

Statistical analyses were performed with SAS 9.2 (SAS Institute, Cary, NC), Stata 10.0 (StataCorp, College Station, TX) and Microsoft Excel (Microsoft, Redmond, WA). The study was approved by the Human Subjects Committee of the hospital's Institutional Review Board.

Results

A total of 22,310 patients were admitted over the period July 1, 2007 to June 30, 2008, including 4957 (22%) coded as scheduled and 17,353 (78%) coded as emergent. (Only 200 patients were registered as urgent and these were recoded as emergent for this analysis). Details on admission types and discharging departments are provided in Table 1. Overall, mean LOS was 5.6 days (median 2.29 days). For patients with LOS 30 days, mean LOS was 3.88 days (median 2.22 days). For patients staying 7 days, mean LOS was 2.4 days (median 1.98 days). Among patients with LOS 7 days, mean LOS for scheduled patients was longer for those admitted on Monday than on any other weekday (2.49 vs. 2.08 days, P < 0.0001). In contrast, mean LOS for emergent patients was longer for patients admitted on Friday and Saturday than the rest of the week (2.57 vs. 2.44 days, P < 0.0001).

Total admissions per month (Figure 1) averaged 1937 in October through April and 1751 in May through September (P = 0.03). Variation in the number of emergent and scheduled patients over months of the year were similar (CV 10% for each), but emergent admissions did decrease in summer (mean 1299 for June‐September vs. 1520 for the rest of the year, P = 0.003). Conversely, scheduled admissions remained relatively stable all year‐long: mean 423 per month for May through September vs. mean 413 per month for October through April (P = 0.48). Even just the summer months of June‐August, when school‐age children are on vacation, were not significantly different from other months (440 vs. 404, P = 0.2).

Figure 1

Variation in volume of admissions was large over days of the week, driven primarily by the pattern of scheduled admissions (CV 65.3%), which dropped off completely on weekends (Table 2, Figure 2). In contrast, there was much less variation in the number of emergent admissions across days of the week (CV 12%). For both emergent and scheduled admissions, more patients came in on Mondays than any other day of the week, but even more so for scheduled patients. While emergent admissions did decline on weekends, it was driven primarily by a decrease in physician referrals (ie, direct admission) from clinics (mean 7.48 per weekday vs. 0.73 per weekend day for the entire year, P < 0.001), while emergency department (ED) admissions remained relatively stable (mean 35.8 per weekday vs. 32.7 per weekend day, P = 0.08). Emergency transports were also stable (mean 7.15 per weekday vs. 6.49 per weekend day, P = 0.10).

Figure 2

Although scheduled patients contributed less to the hospital's overall occupancy, they conferred most of the variability by day of week. Over the days of the week, variation for scheduled occupancy was nearly twice that for emergent occupancy (CV 19% vs. 10%). Within the higher‐volume period of October to April, the differential was even more evident (CV 19% for scheduled occupancy versus 6% for emergent).

For scheduled patients with LOS 30 days (98% of scheduled patients), Mondays and Tuesdays together accounted for 42.5% of admission volume and 44.7% of the patient‐hours generated. For scheduled patients with LOS 7 days (89% of scheduled patients), Mondays and Tuesdays together accounted for 42% of admission volume and 45.2% of the patient‐hours generated. This combined impact of volume and LOS from admissions earlier in the week (restricted to patients with LOS 7days) is displayed graphically in Figure 3, which depicts the unevenness of scheduled admissions and their time in the hospital, with many patients overlapping in the middle of the week. Together with the more steady flow of emergent patients, this variability in scheduled occupancy contributed to mid‐week crowding, with higher risk of the hospital being >90% and >95% occupied on Wednesday through Friday (Figure 4). Detailed hourly analysis (not displayed) showed this risk to be highest from Wednesday afternoon to Friday afternoon. Due to higher emergent census, certain months also had a higher risk of high occupancy at daily peak. For example, while the entire year had 50% to 60% of Wednesdays and Thursdays with occupancy >90%, during the months of November through February, 70% to 85% of those days had occupancy at that level or higher (all these patterns were seen for both stays with LOS 30 days and 7days).

Figure 3

Figure 4

Discussion

In this study, we found that a large children's hospital was frequently at high occupancy in certain months and on certain days more than others, driven largely by predictable seasonal increases in emergent admissions and variability in scheduled admissions by day of week, respectively. Patient‐hours generated by day of admission varied as a result of both volume and LOS, both of which were larger in the early part of the week and diminished as the week progressed for scheduled admissions. But, the cumulative effect of many admissions with relatively‐longer LOS on Monday through Wednesday contributed to high occupancy on Wednesday afternoon to Friday morning, underscoring the importance of admission patterns on census later in the week. Our finding that the occupancy of scheduled patientsthe result of both the admission pattern and their LOSis also highly variable suggests that managing the inflow of scheduled patients could decrease crowding on weekdays, assure a consistent supply of capacity for regular admissions and surges, and improve the value of the delivery system.18 This inflow management would ideally consider both admissions and associated LOS, since rescheduling patients with a longer LOS (eg, 34 days) would have a greater impact on occupancy than rescheduling patients with a shorter LOS (eg, 12 days).

Not surprisingly, total admissions decreased in summer months, especially in July and August, due primarily to fewer emergent admissions. In fact, scheduled admissions per month remained relatively stable over the entire year. The decrease in summer emergent admissions may present an opportunity to stepwise shift a proportion of scheduled admissions from the spring and fall into the summer months, and winter into spring and fall, to alleviate crowding in the winter (Figure 1). Assuming clinical conditions, families and staff members were amenable to this change, hospitals with similar patterns could use this method to reduce the crowding (eg, days over 90% or 95% occupancy) that occurs in the winter.

Using patient‐hours (or days) generated by day of admission, it is evident that admission of more and longer‐stay patients at the start of the week contributes to higher occupancy later in the week (Figure 4). Mid‐week crowding could potentially contribute to a number of operational issues, including delays of new admissions, decreases in physician referrals and patient satisfaction, and an increased use of nontraditional beds (eg, treatment rooms, playrooms, doubling up single rooms) that lead to excessive patient to staff ratios and burnout for clinical staff.

The reasons for these patterns of admissions may include clinician or patient preference to avoid weekend admissions, lack of availability of particular services or resources on weekends, or concerns about safety and efficiency (due to relatively lower staffing on weekends in many hospitals).2230 While clinicians may prefer to avoid additional work on weekends, there are benefits to smoothing occupancy, including less risk of excessive work mid‐week and potential revenue opportunities. In addition, when contrasted with the estimated $1 million to $2 million cost per bed of construction, the marginal cost of staffing to provide safe, high‐quality care on weekends is dramatically lower than that of adding more beds (when faced with mid‐week crowding and unused weekend capacity). In addition, empty beds also do not generate revenue to cover fixed or variable costs, meaning that hospitals are not matching revenue to cost when there is unused capacity due to artificial variability.15, 31 Hospitals looking to make greater use of weekends, however, must be sensitive to staff concerns and the organizational dynamics of 7‐day operations, including the risk for burn‐out and attrition. Such factors should not be perceived as insurmountable barriers, particularly in light of opportunities for flexible scheduling and gain‐sharing.

Patients' and parents' preferences may partially drive admitting patterns, but a reasonable proportion of them may prefer to minimize the number of work and school days missed by being admitted near or on weekends. For example, an expected 3‐day admission could start on Friday and end on Sunday or Monday, rather than the current practice which appears to be to admit on Monday and discharge before the weekend. This may not only meet preferences among some parents to avoid missing work or school, but also by consideration of educational outcomes for hospitalized children.32

In addition, higher mean LOS for emergent patients on the weekends suggests that some services are currently unavailable on weekends to treat patients who are admitted on Fridays through Sundays.2, 25, 29, 33 More even staffing and provision of diagnostic and therapeutic services on weekends (eg, advanced radiology, consult, and laboratory services) would not only remove the barrier to weekend occupancy, it would also improve efficiency, timeliness, patient‐centeredness, and potentially effectiveness and safety for emergent patients. Running hospitals at full functionality on only 5 days of the week means that 2 out of 7 days puts patients at risk for disparate care, which may be appearing in this analysis as prolonged LOS due to lack of services over the weekenda pattern reported in the literature for adult hospitals.

Operations management and queuing theory suggest that systems function well up to 85% to 90% of capacity.34 Hospitals that plan ahead and ensure a buffer for unscheduled admissions during months or days when that demand is known to rise are less likely to cross into high occupancy. On the other hand, hospitals that do not anticipate increases in unscheduled admissions are more likely to encounter excess capacity problems.35 Aligning incentives with all staff can assist in this planning and maximize control of capacity.

Adopting the use of CV in health care operations would also be of value as a way to better express and track variation in admissions, occupancy, and discharges. Since different patient populations, different units, different hospitals, and different months have different scales, SD is not easily comparable across these settings. CV allows for comparison of variation by normalizing on the mean. In this study, it clearly differentiated the variation in elective admissions (CV 65%) over days of the week from the relative stability of emergent admissions (CV 12%). As variability and its management are important to operations, quality control, and quality improvement, use of CV can play an important role in hospital management and health services research. As days with high levels of activity may put more stress on the system, tracking this variation could lead to improvements in quality and value.

This study has several limitations. Data were analyzed for 1 children's hospital, so the analysis may or may not generally apply to other hospitals. However, in a separate study, we analyzed data from the Pediatric Health Information System database, and observed similar patterns.18 In addition, the proportion of elective patients shown in this study was similar to the national data in Kids Inpatient Database (KID, about 15% of all admissions elective).36 Moreover, the methods are reproducible for other settings, which would be useful to clinical and hospital leadership. Second, the trends depicted in the data only reflected data for one year. Third, coding of the admission as emergent or elective was done by registrars at or before arrival and may not reflect actual clinical need. In addition, those admissions coded as elective included a heterogeneous population (eg, chemotherapy to research studies).

Further studies should analyze trends for other hospitals and evaluate the effect of high peak census and high levels of variation with quality, safety, efficiency, patient satisfaction, financial, and educational outcomes for those receiving care, working, or learning at hospitals. In addition, a qualitative study that develops insights into clinician and patient/parent preferences would help answer questions in regard to usage of weekends for scheduled patients.

Conclusions

Scheduled admissions drive most variability in day‐to‐day occupancy despite the fact that they are a smaller proportion of the inpatient population. Variation in scheduled admissions by day of week provides hospitals with an opportunity to address crowding without adding beds or delaying admissions. Rather, fully utilizing capacity by smoothing occupancy over all days of the week can reduce the risk of high occupancy and thereby improve accessibility and patient/parent satisfaction. While family and staff preferences need to be considered, some combination of within‐week smoothing and shifting admissions towards summer are likely to achieve dramatic improvements in patient flow without large expenditures of capital. The key, then, is to ensure that organizational dynamic factors support these changes, so that staff members do not become stressed working at a 7‐day facility. Taken together, these strategies would better match revenue to capacity, and ultimately increase the quality and value of healthcare operations.

A 92‐year old man presented with a 5‐day history of obstipation, nausea, and vomiting. A computed tomography (CT) scan of the abdomen revealed a 4.1‐cm gallstone impacted in the sigmoid colon (Figure 1). The proximal colon was diffusely dilated in caliber consistent with obstruction (Figure 1B). The CT also showed a cholecystocolic fistula at the hepatic flexure of the colon (Figure 2) with an edematous gallbladder wall and a residual 3.8‐cm gallstone. Under colonoscopic guidance the stone was fragmented using intraluminal shock wave lithotripsy and other endoscopic techniques. The pieces were retrieved (Figure 3, shown reassembled). Cholecystectomy, common hepatic duct repair, and fistula takedown were electively performed to prevent recurrence.

Figure 1

Figure 2

Figure 3

Gallstone ileus is the mechanical impaction of gallstones within the gastrointestinal (GI) tract. It requires the formation of either a biliary‐enteric fistula or less often a choledocho‐enteric fistula. Usually the stone must be 2 cm or greater to cause obstruction.1 The site of obstruction is typically the terminal ileum or ileocecal valve because of the smaller diameter lumen and less active peristalsis. Although mortality rates approach 15%,2 this patient did remarkably well with early recognition, use of complex endoscopic removal, and avoidance of urgent laparotomy.

A 92‐year old man presented with a 5‐day history of obstipation, nausea, and vomiting. A computed tomography (CT) scan of the abdomen revealed a 4.1‐cm gallstone impacted in the sigmoid colon (Figure 1). The proximal colon was diffusely dilated in caliber consistent with obstruction (Figure 1B). The CT also showed a cholecystocolic fistula at the hepatic flexure of the colon (Figure 2) with an edematous gallbladder wall and a residual 3.8‐cm gallstone. Under colonoscopic guidance the stone was fragmented using intraluminal shock wave lithotripsy and other endoscopic techniques. The pieces were retrieved (Figure 3, shown reassembled). Cholecystectomy, common hepatic duct repair, and fistula takedown were electively performed to prevent recurrence.

Figure 1

Figure 2

Figure 3

Gallstone ileus is the mechanical impaction of gallstones within the gastrointestinal (GI) tract. It requires the formation of either a biliary‐enteric fistula or less often a choledocho‐enteric fistula. Usually the stone must be 2 cm or greater to cause obstruction.1 The site of obstruction is typically the terminal ileum or ileocecal valve because of the smaller diameter lumen and less active peristalsis. Although mortality rates approach 15%,2 this patient did remarkably well with early recognition, use of complex endoscopic removal, and avoidance of urgent laparotomy.

A 92‐year old man presented with a 5‐day history of obstipation, nausea, and vomiting. A computed tomography (CT) scan of the abdomen revealed a 4.1‐cm gallstone impacted in the sigmoid colon (Figure 1). The proximal colon was diffusely dilated in caliber consistent with obstruction (Figure 1B). The CT also showed a cholecystocolic fistula at the hepatic flexure of the colon (Figure 2) with an edematous gallbladder wall and a residual 3.8‐cm gallstone. Under colonoscopic guidance the stone was fragmented using intraluminal shock wave lithotripsy and other endoscopic techniques. The pieces were retrieved (Figure 3, shown reassembled). Cholecystectomy, common hepatic duct repair, and fistula takedown were electively performed to prevent recurrence.

Figure 1

Figure 2

Figure 3

Gallstone ileus is the mechanical impaction of gallstones within the gastrointestinal (GI) tract. It requires the formation of either a biliary‐enteric fistula or less often a choledocho‐enteric fistula. Usually the stone must be 2 cm or greater to cause obstruction.1 The site of obstruction is typically the terminal ileum or ileocecal valve because of the smaller diameter lumen and less active peristalsis. Although mortality rates approach 15%,2 this patient did remarkably well with early recognition, use of complex endoscopic removal, and avoidance of urgent laparotomy.