Lakshmi K. Halasyamani, MD

The systematic deployment of prediction rules within health systems remains a challenge, although such decision aids have been available for decades.[1, 2] We previously developed and validated a prediction rule for 30‐day mortality in a retrospective cohort, noting that the mortality risk is associated with a number of other clinical events.[3] These relationships suggest risk strata, defined by the predicted probability of 30‐day mortality, and could trigger a number of coordinated care processes proportional to the level of risk.[4] For example, patients within the higher‐risk strata could be considered for placement into an intermediate or intensive care unit (ICU), be monitored more closely by physician and nurse team members for clinical deterioration, be seen by a physician within a few days of hospital discharge, and be considered for advance care planning discussions.[3, 4, 5, 6, 7] Patients within the lower‐risk strata might not need the same intensity of these processes routinely unless some other indication were present.

However attractive this conceptual framework may be, its realization is dependent on the willingness of clinical staff to generate predictions consistently on a substantial portion of the patient population, and on the accuracy of the predictions when the risk factors are determined with some level of uncertainty at the beginning of the hospitalization.[2, 8] Skepticism is justified, because the work involved in completing the prediction rule might be incompatible with existing workflow. A patient might not be scored if the emergency physician lacks time or if technical issues arise with the information system and computation process.[9] There is also a generic concern that the predictions will prove to be less accurate outside of the original study population.[8, 9, 10] A more specific concern for our rule is how well present on admission diagnoses can be determined during the relatively short emergency department or presurgery evaluation period. For example, a final diagnosis of heart failure might not be established until later in the hospitalization, after the results of diagnostic testing and clinical response to treatment are known. Moreover, our retrospective prediction rule requires an assessment of the presence or absence of sepsis and respiratory failure. These diagnoses appear to be susceptible to secular trends in medical record coding practices, suggesting the rule's accuracy might not be stable over time.[11]

We report the feasibility of having emergency physicians and the surgical preparation center team generate mortality predictions before an inpatient bed is assigned. We evaluate and report the accuracy of these prospective predictions.

METHODS

The study population consisted of all patients 18 years of age or less than 100 years who were admitted from the emergency department or assigned an inpatient bed following elective surgery at a tertiary, community teaching hospital in the Midwestern United States from September 1, 2012 through February 15, 2013. Although patients entering the hospital from these 2 pathways would be expected to have different levels of mortality risk, we used the original prediction rule for both because such distinctions were not made in its derivation and validation. Patients were not considered if they were admitted for childbirth or other obstetrical reasons, admitted directly from physician offices, the cardiac catheterization laboratory, hemodialysis unit, or from another hospital. The site institutional review board approved this study.

The implementation process began with presentations to the administrative and medical staff leadership on the accuracy of the retrospectively generated mortality predictions and risk of other adverse events.[3] The chief medical and nursing officers became project champions, secured internal funding for the technical components, and arranged to have 2 project comanagers available. A multidisciplinary task force endorsed the implementation details at biweekly meetings throughout the planning year. The leadership of the emergency department and surgical preparation center committed their colleagues to generate the predictions. The support of the emergency leadership was contingent on the completion of the entire prediction generating process in a very short time (within the time a physician could hold his/her breath). The chief medical officer, with the support of the leadership of the hospitalists and emergency physicians, made the administrative decision that a prediction must be generated prior to the assignment of a hospital room.

During the consensus‐building phase, a Web‐based application was developed to generate the predictions. Emergency physicians and surgical preparation staff were trained on the definitions of the risk factors (see Supporting Information, Appendix, in the online version of this article) and how to use the Web application. Three supporting databases were created. Each midnight, a past medical history database was updated, identifying those who had been discharged from the study hospital in the previous 365 days, and whether or not their diagnoses included atrial fibrillation, leukemia/lymphoma, metastatic cancer, cancer other than leukemia, lymphoma, cognitive disorder, or other neurological conditions (eg, Parkinson's, multiple sclerosis, epilepsy, coma, and stupor). Similarly, a clinical laboratory results database was created and updated real time through an HL7 (Health Level Seven, a standard data exchange format[12]) interface with the laboratory information system for the following tests performed in the preceding 30 days at a hospital‐affiliated facility: hemoglobin, platelet count, white blood count, serum troponin, blood urea nitrogen, serum albumin, serum lactate, arterial pH, arterial partial pressure of oxygen values. The third database, admission‐discharge‐transfer, was created and updated every 15 minutes to identify patients currently in the emergency room or scheduled for surgery. When a patient registration event was added to this database, the Web application created a record, retrieved all relevant data, and displayed the patient name for scoring. When the decision for hospitalization was made, the clinician selected the patient's name and reviewed the pre‐populated medical diagnoses of interest, which could be overwritten based on his/her own assessment (Figure 1A,B). The clinician then indicated (yes, no, or unknown) if the patient currently had or was being treated for each of the following: injury, heart failure, sepsis, respiratory failure, and whether or not the admitting service would be medicine (ie, nonsurgical, nonobstetrical). We considered unknown status to indicate the patient did not have the condition. When laboratory values were not available, a normal value was imputed using a previously developed algorithm.[3] Two additional questions, not used in the current prediction process, were answered to provide data for a future analysis: 1 concerning the change in the patient's condition while in the emergency department and the other concerning the presence of abnormal vital signs. The probability of 30‐day mortality was calculated via the Web application using the risk information supplied and the scoring weights (ie, parameter estimates) provided in the Appendices of our original publication.[3] Predictions were updated every minute as new laboratory values became available, and flagged with an alert if a more severe score resulted.

Figure 1

For the analyses of this study, the last prospective prediction viewed by emergency department personnel, a hospital bed manager, or surgical suite staff prior to arrival on the nursing unit is the one referenced as prospective. Once the patient had been discharged from the hospital, we generated a second mortality prediction based on previously published parameter estimates applied to risk factor data ascertained retrospectively as was done in the original article[3]; we subsequently refer to this prediction as retrospective. We will report on the group of patients who had both prospective and retrospective scores (1 patient had a prospective but not retrospective score available).

The prediction scores were made available to the clinical teams gradually during the study period. All scores were viewable by the midpoint of the study for emergency department admissions and near the end of the study for elective‐surgery patients. Only 2 changes in care processes based on level of risk were introduced during the study period. The first required initial placement of patients having a probability of dying of 0.3 or greater into an intensive or intermediate care unit unless the patient or family requested a less aggressive approach. The second occurred in the final 2 months of the study when a large multispecialty practice began routinely arranging for high‐risk patients to be seen within 3 or 7 days of hospital discharge.

RESULTS

Mortality predictions were generated on demand for 7291 out of 7777 (93.8%) eligible patients admitted from the emergency department, and for 2021 out of 2250 (89.8%) eligible elective surgical cases, for a total of 9312 predictions generated out of a possible 10,027 hospitalizations (92.9%). Table 1 displays the characteristics of the study population. The mean age was 65.2 years and 53.8% were women. The most common risk factors were atrial fibrillation (16.4%) and cancer (14.6%). Orders for a comfort care approach (rather than curative) were entered within 4 hours of admission for 32/9312 patients (0.34%), and 9/9312 (0.1%) were hospice patients on admission.

Evaluation of Prediction Accuracy

The AROC for 30‐day mortality was 0.850 (95% confidence interval [CI]: 0.833‐0.866) for prospectively collected covariates, and 0.870 (95% CI: 0.855‐0.885) for retrospectively determined risk factors. These AROCs are not substantively different from each other, demonstrating comparable prediction performance. Calibration was excellent, as indicated in Figure 2, in which the predicted level of risk lay within the 95% confidence limits of the actual 30‐day mortality for 19 out of 20 intervals of 5 percentile increments.

Figure 2

DISCUSSION

Emergency physicians and surgical preparation center nurses generated predictions by the time of hospital admission for over 90% of the target population during usual workflow, without the addition of staff or resources. The discrimination of the prospectively generated predictions was very good to excellent, with an AROC of 0.850 (95% CI: 0.833‐0.866), similar to that obtained from the retrospective version. Calibration was excellent. The prospectively calculated mortality risk was associated with a number of other events. As shown in Table 3, the differing frequency of events within the risk strata support the development of differing intensities of multidisciplinary strategies according to the level of risk.[5] Our study provides useful experience for others who anticipate generating real‐time predictions. We consider the key reasons for success to be the considerable time spent achieving consensus, the technical development of the Web application, the brief clinician time required for the scoring process, the leadership of the chief medical and nursing officers, and the requirement that a prediction be generated before assignment of a hospital room.

Our study has a number of limitations, some of which were noted in our original publication, and although still relevant, will not be repeated here for space considerations. This is a single‐site study that used a prediction rule developed by the same site, albeit on a patient population 4 to 5 years earlier. It is not known how well the specific rule might perform in other hospital populations; any such use should therefore be accompanied by independent validation studies prior to implementation. Our successful experience should motivate future validation studies. Second, because the prognoses of patients scored from the emergency department are likely to be worse than those of elective surgery patients, our rule should be recalibrated for each subgroup separately. We plan to do this in the near future, as well as consider additional risk factors. Third, the other events of interest might be predicted more accurately if rules specifically developed for each were deployed. The mortality risk by itself is unlikely to be a sufficiently accurate predictor, particularly for complications and intensive care use, for reasons outlined in our original publication.[3] However, the varying levels of events within the higher versus lower strata should be noted by the clinical team as they design their team‐based processes. A follow‐up visit with a physician within a few days of discharge could address the concurrent risk of dying as well as readmission, for example. Finally, it is too early to determine if the availability of mortality predictions from this rule will benefit patients.[2, 8, 10] During the study period, we implemented only 2 new care processes based on the level of risk. This lack of interventions allowed us to evaluate the prediction accuracy with minimal additional confounding, but at the expense of not yet knowing the clinical impact of this work. After the study period, we implemented a number of other interventions and plan on evaluating their effectiveness in the future. We are also considering an evaluation of the potential information gained by updating the predictions throughout the course of the hospitalization.[14]

In conclusion, it is feasible to have a reasonably accurate prediction of mortality risk for most adult patients at the beginning of their hospitalizations. The availability of this prognostic information provides an opportunity to develop proactive care plans for high‐ and low‐risk subsets of patients.

The systematic deployment of prediction rules within health systems remains a challenge, although such decision aids have been available for decades.[1, 2] We previously developed and validated a prediction rule for 30‐day mortality in a retrospective cohort, noting that the mortality risk is associated with a number of other clinical events.[3] These relationships suggest risk strata, defined by the predicted probability of 30‐day mortality, and could trigger a number of coordinated care processes proportional to the level of risk.[4] For example, patients within the higher‐risk strata could be considered for placement into an intermediate or intensive care unit (ICU), be monitored more closely by physician and nurse team members for clinical deterioration, be seen by a physician within a few days of hospital discharge, and be considered for advance care planning discussions.[3, 4, 5, 6, 7] Patients within the lower‐risk strata might not need the same intensity of these processes routinely unless some other indication were present.

However attractive this conceptual framework may be, its realization is dependent on the willingness of clinical staff to generate predictions consistently on a substantial portion of the patient population, and on the accuracy of the predictions when the risk factors are determined with some level of uncertainty at the beginning of the hospitalization.[2, 8] Skepticism is justified, because the work involved in completing the prediction rule might be incompatible with existing workflow. A patient might not be scored if the emergency physician lacks time or if technical issues arise with the information system and computation process.[9] There is also a generic concern that the predictions will prove to be less accurate outside of the original study population.[8, 9, 10] A more specific concern for our rule is how well present on admission diagnoses can be determined during the relatively short emergency department or presurgery evaluation period. For example, a final diagnosis of heart failure might not be established until later in the hospitalization, after the results of diagnostic testing and clinical response to treatment are known. Moreover, our retrospective prediction rule requires an assessment of the presence or absence of sepsis and respiratory failure. These diagnoses appear to be susceptible to secular trends in medical record coding practices, suggesting the rule's accuracy might not be stable over time.[11]

We report the feasibility of having emergency physicians and the surgical preparation center team generate mortality predictions before an inpatient bed is assigned. We evaluate and report the accuracy of these prospective predictions.

METHODS

The study population consisted of all patients 18 years of age or less than 100 years who were admitted from the emergency department or assigned an inpatient bed following elective surgery at a tertiary, community teaching hospital in the Midwestern United States from September 1, 2012 through February 15, 2013. Although patients entering the hospital from these 2 pathways would be expected to have different levels of mortality risk, we used the original prediction rule for both because such distinctions were not made in its derivation and validation. Patients were not considered if they were admitted for childbirth or other obstetrical reasons, admitted directly from physician offices, the cardiac catheterization laboratory, hemodialysis unit, or from another hospital. The site institutional review board approved this study.

The implementation process began with presentations to the administrative and medical staff leadership on the accuracy of the retrospectively generated mortality predictions and risk of other adverse events.[3] The chief medical and nursing officers became project champions, secured internal funding for the technical components, and arranged to have 2 project comanagers available. A multidisciplinary task force endorsed the implementation details at biweekly meetings throughout the planning year. The leadership of the emergency department and surgical preparation center committed their colleagues to generate the predictions. The support of the emergency leadership was contingent on the completion of the entire prediction generating process in a very short time (within the time a physician could hold his/her breath). The chief medical officer, with the support of the leadership of the hospitalists and emergency physicians, made the administrative decision that a prediction must be generated prior to the assignment of a hospital room.

During the consensus‐building phase, a Web‐based application was developed to generate the predictions. Emergency physicians and surgical preparation staff were trained on the definitions of the risk factors (see Supporting Information, Appendix, in the online version of this article) and how to use the Web application. Three supporting databases were created. Each midnight, a past medical history database was updated, identifying those who had been discharged from the study hospital in the previous 365 days, and whether or not their diagnoses included atrial fibrillation, leukemia/lymphoma, metastatic cancer, cancer other than leukemia, lymphoma, cognitive disorder, or other neurological conditions (eg, Parkinson's, multiple sclerosis, epilepsy, coma, and stupor). Similarly, a clinical laboratory results database was created and updated real time through an HL7 (Health Level Seven, a standard data exchange format[12]) interface with the laboratory information system for the following tests performed in the preceding 30 days at a hospital‐affiliated facility: hemoglobin, platelet count, white blood count, serum troponin, blood urea nitrogen, serum albumin, serum lactate, arterial pH, arterial partial pressure of oxygen values. The third database, admission‐discharge‐transfer, was created and updated every 15 minutes to identify patients currently in the emergency room or scheduled for surgery. When a patient registration event was added to this database, the Web application created a record, retrieved all relevant data, and displayed the patient name for scoring. When the decision for hospitalization was made, the clinician selected the patient's name and reviewed the pre‐populated medical diagnoses of interest, which could be overwritten based on his/her own assessment (Figure 1A,B). The clinician then indicated (yes, no, or unknown) if the patient currently had or was being treated for each of the following: injury, heart failure, sepsis, respiratory failure, and whether or not the admitting service would be medicine (ie, nonsurgical, nonobstetrical). We considered unknown status to indicate the patient did not have the condition. When laboratory values were not available, a normal value was imputed using a previously developed algorithm.[3] Two additional questions, not used in the current prediction process, were answered to provide data for a future analysis: 1 concerning the change in the patient's condition while in the emergency department and the other concerning the presence of abnormal vital signs. The probability of 30‐day mortality was calculated via the Web application using the risk information supplied and the scoring weights (ie, parameter estimates) provided in the Appendices of our original publication.[3] Predictions were updated every minute as new laboratory values became available, and flagged with an alert if a more severe score resulted.

Figure 1

For the analyses of this study, the last prospective prediction viewed by emergency department personnel, a hospital bed manager, or surgical suite staff prior to arrival on the nursing unit is the one referenced as prospective. Once the patient had been discharged from the hospital, we generated a second mortality prediction based on previously published parameter estimates applied to risk factor data ascertained retrospectively as was done in the original article[3]; we subsequently refer to this prediction as retrospective. We will report on the group of patients who had both prospective and retrospective scores (1 patient had a prospective but not retrospective score available).

The prediction scores were made available to the clinical teams gradually during the study period. All scores were viewable by the midpoint of the study for emergency department admissions and near the end of the study for elective‐surgery patients. Only 2 changes in care processes based on level of risk were introduced during the study period. The first required initial placement of patients having a probability of dying of 0.3 or greater into an intensive or intermediate care unit unless the patient or family requested a less aggressive approach. The second occurred in the final 2 months of the study when a large multispecialty practice began routinely arranging for high‐risk patients to be seen within 3 or 7 days of hospital discharge.

RESULTS

Mortality predictions were generated on demand for 7291 out of 7777 (93.8%) eligible patients admitted from the emergency department, and for 2021 out of 2250 (89.8%) eligible elective surgical cases, for a total of 9312 predictions generated out of a possible 10,027 hospitalizations (92.9%). Table 1 displays the characteristics of the study population. The mean age was 65.2 years and 53.8% were women. The most common risk factors were atrial fibrillation (16.4%) and cancer (14.6%). Orders for a comfort care approach (rather than curative) were entered within 4 hours of admission for 32/9312 patients (0.34%), and 9/9312 (0.1%) were hospice patients on admission.

Evaluation of Prediction Accuracy

The AROC for 30‐day mortality was 0.850 (95% confidence interval [CI]: 0.833‐0.866) for prospectively collected covariates, and 0.870 (95% CI: 0.855‐0.885) for retrospectively determined risk factors. These AROCs are not substantively different from each other, demonstrating comparable prediction performance. Calibration was excellent, as indicated in Figure 2, in which the predicted level of risk lay within the 95% confidence limits of the actual 30‐day mortality for 19 out of 20 intervals of 5 percentile increments.

Figure 2

DISCUSSION

Emergency physicians and surgical preparation center nurses generated predictions by the time of hospital admission for over 90% of the target population during usual workflow, without the addition of staff or resources. The discrimination of the prospectively generated predictions was very good to excellent, with an AROC of 0.850 (95% CI: 0.833‐0.866), similar to that obtained from the retrospective version. Calibration was excellent. The prospectively calculated mortality risk was associated with a number of other events. As shown in Table 3, the differing frequency of events within the risk strata support the development of differing intensities of multidisciplinary strategies according to the level of risk.[5] Our study provides useful experience for others who anticipate generating real‐time predictions. We consider the key reasons for success to be the considerable time spent achieving consensus, the technical development of the Web application, the brief clinician time required for the scoring process, the leadership of the chief medical and nursing officers, and the requirement that a prediction be generated before assignment of a hospital room.

Our study has a number of limitations, some of which were noted in our original publication, and although still relevant, will not be repeated here for space considerations. This is a single‐site study that used a prediction rule developed by the same site, albeit on a patient population 4 to 5 years earlier. It is not known how well the specific rule might perform in other hospital populations; any such use should therefore be accompanied by independent validation studies prior to implementation. Our successful experience should motivate future validation studies. Second, because the prognoses of patients scored from the emergency department are likely to be worse than those of elective surgery patients, our rule should be recalibrated for each subgroup separately. We plan to do this in the near future, as well as consider additional risk factors. Third, the other events of interest might be predicted more accurately if rules specifically developed for each were deployed. The mortality risk by itself is unlikely to be a sufficiently accurate predictor, particularly for complications and intensive care use, for reasons outlined in our original publication.[3] However, the varying levels of events within the higher versus lower strata should be noted by the clinical team as they design their team‐based processes. A follow‐up visit with a physician within a few days of discharge could address the concurrent risk of dying as well as readmission, for example. Finally, it is too early to determine if the availability of mortality predictions from this rule will benefit patients.[2, 8, 10] During the study period, we implemented only 2 new care processes based on the level of risk. This lack of interventions allowed us to evaluate the prediction accuracy with minimal additional confounding, but at the expense of not yet knowing the clinical impact of this work. After the study period, we implemented a number of other interventions and plan on evaluating their effectiveness in the future. We are also considering an evaluation of the potential information gained by updating the predictions throughout the course of the hospitalization.[14]

In conclusion, it is feasible to have a reasonably accurate prediction of mortality risk for most adult patients at the beginning of their hospitalizations. The availability of this prognostic information provides an opportunity to develop proactive care plans for high‐ and low‐risk subsets of patients.

The systematic deployment of prediction rules within health systems remains a challenge, although such decision aids have been available for decades.[1, 2] We previously developed and validated a prediction rule for 30‐day mortality in a retrospective cohort, noting that the mortality risk is associated with a number of other clinical events.[3] These relationships suggest risk strata, defined by the predicted probability of 30‐day mortality, and could trigger a number of coordinated care processes proportional to the level of risk.[4] For example, patients within the higher‐risk strata could be considered for placement into an intermediate or intensive care unit (ICU), be monitored more closely by physician and nurse team members for clinical deterioration, be seen by a physician within a few days of hospital discharge, and be considered for advance care planning discussions.[3, 4, 5, 6, 7] Patients within the lower‐risk strata might not need the same intensity of these processes routinely unless some other indication were present.

However attractive this conceptual framework may be, its realization is dependent on the willingness of clinical staff to generate predictions consistently on a substantial portion of the patient population, and on the accuracy of the predictions when the risk factors are determined with some level of uncertainty at the beginning of the hospitalization.[2, 8] Skepticism is justified, because the work involved in completing the prediction rule might be incompatible with existing workflow. A patient might not be scored if the emergency physician lacks time or if technical issues arise with the information system and computation process.[9] There is also a generic concern that the predictions will prove to be less accurate outside of the original study population.[8, 9, 10] A more specific concern for our rule is how well present on admission diagnoses can be determined during the relatively short emergency department or presurgery evaluation period. For example, a final diagnosis of heart failure might not be established until later in the hospitalization, after the results of diagnostic testing and clinical response to treatment are known. Moreover, our retrospective prediction rule requires an assessment of the presence or absence of sepsis and respiratory failure. These diagnoses appear to be susceptible to secular trends in medical record coding practices, suggesting the rule's accuracy might not be stable over time.[11]

We report the feasibility of having emergency physicians and the surgical preparation center team generate mortality predictions before an inpatient bed is assigned. We evaluate and report the accuracy of these prospective predictions.

METHODS

The study population consisted of all patients 18 years of age or less than 100 years who were admitted from the emergency department or assigned an inpatient bed following elective surgery at a tertiary, community teaching hospital in the Midwestern United States from September 1, 2012 through February 15, 2013. Although patients entering the hospital from these 2 pathways would be expected to have different levels of mortality risk, we used the original prediction rule for both because such distinctions were not made in its derivation and validation. Patients were not considered if they were admitted for childbirth or other obstetrical reasons, admitted directly from physician offices, the cardiac catheterization laboratory, hemodialysis unit, or from another hospital. The site institutional review board approved this study.

The implementation process began with presentations to the administrative and medical staff leadership on the accuracy of the retrospectively generated mortality predictions and risk of other adverse events.[3] The chief medical and nursing officers became project champions, secured internal funding for the technical components, and arranged to have 2 project comanagers available. A multidisciplinary task force endorsed the implementation details at biweekly meetings throughout the planning year. The leadership of the emergency department and surgical preparation center committed their colleagues to generate the predictions. The support of the emergency leadership was contingent on the completion of the entire prediction generating process in a very short time (within the time a physician could hold his/her breath). The chief medical officer, with the support of the leadership of the hospitalists and emergency physicians, made the administrative decision that a prediction must be generated prior to the assignment of a hospital room.

During the consensus‐building phase, a Web‐based application was developed to generate the predictions. Emergency physicians and surgical preparation staff were trained on the definitions of the risk factors (see Supporting Information, Appendix, in the online version of this article) and how to use the Web application. Three supporting databases were created. Each midnight, a past medical history database was updated, identifying those who had been discharged from the study hospital in the previous 365 days, and whether or not their diagnoses included atrial fibrillation, leukemia/lymphoma, metastatic cancer, cancer other than leukemia, lymphoma, cognitive disorder, or other neurological conditions (eg, Parkinson's, multiple sclerosis, epilepsy, coma, and stupor). Similarly, a clinical laboratory results database was created and updated real time through an HL7 (Health Level Seven, a standard data exchange format[12]) interface with the laboratory information system for the following tests performed in the preceding 30 days at a hospital‐affiliated facility: hemoglobin, platelet count, white blood count, serum troponin, blood urea nitrogen, serum albumin, serum lactate, arterial pH, arterial partial pressure of oxygen values. The third database, admission‐discharge‐transfer, was created and updated every 15 minutes to identify patients currently in the emergency room or scheduled for surgery. When a patient registration event was added to this database, the Web application created a record, retrieved all relevant data, and displayed the patient name for scoring. When the decision for hospitalization was made, the clinician selected the patient's name and reviewed the pre‐populated medical diagnoses of interest, which could be overwritten based on his/her own assessment (Figure 1A,B). The clinician then indicated (yes, no, or unknown) if the patient currently had or was being treated for each of the following: injury, heart failure, sepsis, respiratory failure, and whether or not the admitting service would be medicine (ie, nonsurgical, nonobstetrical). We considered unknown status to indicate the patient did not have the condition. When laboratory values were not available, a normal value was imputed using a previously developed algorithm.[3] Two additional questions, not used in the current prediction process, were answered to provide data for a future analysis: 1 concerning the change in the patient's condition while in the emergency department and the other concerning the presence of abnormal vital signs. The probability of 30‐day mortality was calculated via the Web application using the risk information supplied and the scoring weights (ie, parameter estimates) provided in the Appendices of our original publication.[3] Predictions were updated every minute as new laboratory values became available, and flagged with an alert if a more severe score resulted.

Figure 1

For the analyses of this study, the last prospective prediction viewed by emergency department personnel, a hospital bed manager, or surgical suite staff prior to arrival on the nursing unit is the one referenced as prospective. Once the patient had been discharged from the hospital, we generated a second mortality prediction based on previously published parameter estimates applied to risk factor data ascertained retrospectively as was done in the original article[3]; we subsequently refer to this prediction as retrospective. We will report on the group of patients who had both prospective and retrospective scores (1 patient had a prospective but not retrospective score available).

The prediction scores were made available to the clinical teams gradually during the study period. All scores were viewable by the midpoint of the study for emergency department admissions and near the end of the study for elective‐surgery patients. Only 2 changes in care processes based on level of risk were introduced during the study period. The first required initial placement of patients having a probability of dying of 0.3 or greater into an intensive or intermediate care unit unless the patient or family requested a less aggressive approach. The second occurred in the final 2 months of the study when a large multispecialty practice began routinely arranging for high‐risk patients to be seen within 3 or 7 days of hospital discharge.

RESULTS

Mortality predictions were generated on demand for 7291 out of 7777 (93.8%) eligible patients admitted from the emergency department, and for 2021 out of 2250 (89.8%) eligible elective surgical cases, for a total of 9312 predictions generated out of a possible 10,027 hospitalizations (92.9%). Table 1 displays the characteristics of the study population. The mean age was 65.2 years and 53.8% were women. The most common risk factors were atrial fibrillation (16.4%) and cancer (14.6%). Orders for a comfort care approach (rather than curative) were entered within 4 hours of admission for 32/9312 patients (0.34%), and 9/9312 (0.1%) were hospice patients on admission.

Evaluation of Prediction Accuracy

The AROC for 30‐day mortality was 0.850 (95% confidence interval [CI]: 0.833‐0.866) for prospectively collected covariates, and 0.870 (95% CI: 0.855‐0.885) for retrospectively determined risk factors. These AROCs are not substantively different from each other, demonstrating comparable prediction performance. Calibration was excellent, as indicated in Figure 2, in which the predicted level of risk lay within the 95% confidence limits of the actual 30‐day mortality for 19 out of 20 intervals of 5 percentile increments.

Figure 2

DISCUSSION

Emergency physicians and surgical preparation center nurses generated predictions by the time of hospital admission for over 90% of the target population during usual workflow, without the addition of staff or resources. The discrimination of the prospectively generated predictions was very good to excellent, with an AROC of 0.850 (95% CI: 0.833‐0.866), similar to that obtained from the retrospective version. Calibration was excellent. The prospectively calculated mortality risk was associated with a number of other events. As shown in Table 3, the differing frequency of events within the risk strata support the development of differing intensities of multidisciplinary strategies according to the level of risk.[5] Our study provides useful experience for others who anticipate generating real‐time predictions. We consider the key reasons for success to be the considerable time spent achieving consensus, the technical development of the Web application, the brief clinician time required for the scoring process, the leadership of the chief medical and nursing officers, and the requirement that a prediction be generated before assignment of a hospital room.

Our study has a number of limitations, some of which were noted in our original publication, and although still relevant, will not be repeated here for space considerations. This is a single‐site study that used a prediction rule developed by the same site, albeit on a patient population 4 to 5 years earlier. It is not known how well the specific rule might perform in other hospital populations; any such use should therefore be accompanied by independent validation studies prior to implementation. Our successful experience should motivate future validation studies. Second, because the prognoses of patients scored from the emergency department are likely to be worse than those of elective surgery patients, our rule should be recalibrated for each subgroup separately. We plan to do this in the near future, as well as consider additional risk factors. Third, the other events of interest might be predicted more accurately if rules specifically developed for each were deployed. The mortality risk by itself is unlikely to be a sufficiently accurate predictor, particularly for complications and intensive care use, for reasons outlined in our original publication.[3] However, the varying levels of events within the higher versus lower strata should be noted by the clinical team as they design their team‐based processes. A follow‐up visit with a physician within a few days of discharge could address the concurrent risk of dying as well as readmission, for example. Finally, it is too early to determine if the availability of mortality predictions from this rule will benefit patients.[2, 8, 10] During the study period, we implemented only 2 new care processes based on the level of risk. This lack of interventions allowed us to evaluate the prediction accuracy with minimal additional confounding, but at the expense of not yet knowing the clinical impact of this work. After the study period, we implemented a number of other interventions and plan on evaluating their effectiveness in the future. We are also considering an evaluation of the potential information gained by updating the predictions throughout the course of the hospitalization.[14]

In conclusion, it is feasible to have a reasonably accurate prediction of mortality risk for most adult patients at the beginning of their hospitalizations. The availability of this prognostic information provides an opportunity to develop proactive care plans for high‐ and low‐risk subsets of patients.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.

With the United States mired in its most severe recession in decades, stories of hospital struggles have emerged. Beaumont Hospital, located near the headquarters of major automakers and several assembly plants outside Detroit, recently cut hundreds of jobs and put major construction on indefinite hold.1 The CEO of Boston's Beth Israel Deaconess Medical Center made an agreement with employees to take large cuts in pay and vacation time to prevent laying off 10% of the staff.2 The University of Chicago Medical Center made plans to limit the number of emergency room beds, thereby decreasing low‐reimbursing emergency admissions while making beds available for higher‐paying elective hospitalizations.3

What is surprising about these stories is that hospitals have long been considered recession‐proof. Yet, with one‐half of US hospitals having reduced their staff to balance their budgets4 and with hospitals' financial margins falling dramatically,5 economic struggles are now a widespread problem.

Furthermore, it is difficult to determine if hospitals' clinical care has been damaged by the recession. The measurement of hospital quality is new and still under‐developed: there is virtually no reliable information on hospital quality from previous recessions, and even now it will be difficult to assess quality in real time.

Critics of waste and excess in the US health care system may see tough economic times as a Darwinian proving ground for hospitals, through which efficiency will improve and poor performers will close their doors. But more likely, hospital cutbacks will risk the quality and safety of health care delivery. For reasons of both public health and fiscal impact on communities, state and federal leaders may need to watch these trends closely to design and to be ready to implement potential government remedies for hospitals' fiscal woes.

In this commentary, we describe how hospitals have fared historically during recessions, how this recession could have different effectsfirst fiscally, then clinically, and we examine policy options to mitigate these untoward effects.

Decades of Recession‐Proof Hospitals

During the Great Depression, hospital insolvency was a national problem that prompted federal and state aid. Keeping hospitals alive was a critical policy goal and proved central to the early development of health insurance that focused on payment for hospital care.6

Since WWII, growth in America's hospitals has been only loosely related to national macroeconomic trends, with other changes like technological innovations and the advent of managed care far more influential to hospital finances. In fact, during recessions, hospital care spending growth often escalates in tandem with worsening unemployment (Figure 1). One explanation for this phenomenon is that economic pressures lead to declining primary care utilization, with adverse consequences for individuals' health.7

Figure 1

Hospitals' Current Fiscal Vulnerability

However, the current recession is the worst in 70 years. Every method of income generation available to hospitals appears at risk, including reimbursement per discharge (70% of hospitals report moderate or significant increases in uncompensated care), number of inpatient admissions (over one‐half report a moderate or significant decrease), difficulty obtaining bonds (60% report at least significant problems), and charitable donations.4 Over 50% of US hospitals had negative margins in the fourth quarter of 2008, though there has been some improvement since that time.8

Future hospital stability concerns remain. Growth in revenue per discharge is still below the norm.5 Because employment lags a recovering economy, further reimbursement decreases are possible from increasing proportions of patients with low‐reimbursing insurers or no coverage at all, decreasing payment rates from all payers, and decreasing elective care. The lower‐reimbursing payers, like state Medicaid programs, are experiencing increased enrollment as Americans lose their jobs and their better‐paying, employer‐sponsored private insurance.9 There's also evidence that reimbursement rates are declining from both Medicare and private insurers,10 which threatens the fragile cost‐shift through which hospitals have long used private insurance reimbursement to subsidize government reimbursements.11

Hospitals' specific financial challenges will likely vary across markets. The authors' state of Michigan has been hit particularly long and hard by the current recession. Unemployment rates exceeding 11% are expected to cause dramatic losses in private health insurance.9 Patients' increasing need with decreasing ability to pay will make markets in the deepest recession particularly vulnerable.

A Possible Stimulus: Investing in Quality Initiatives at Fiscally Vulnerable Hospitals

It is not enough to keep hospitals' doors open in a recession. Hospitals must continue to improve the quality and safety of the care they delivervital for their future patients and also for their communities who depend on them as anchors of health systems. We believe there is a need for a new, federally supported alignment of hospital finance and hospital quality that can limit damage to hospitals, help community employment, and improve patient safety.

Timely, structural quality measures could speed the introduction of functional value‐based purchasing, promote hospital safety, and help local economies at the same time. There are many simple structural measures that could be examined, such as development of discharge coordinators, promoting effective nurse‐to‐patient ratios, and encouraging health information technology (IT). Importantly, this would not duplicate efforts already underway to promote quality with process measures. With effective financial monitoring in real time, these measures could focus on high‐risk, fiscally disadvantaged hospitals.

To its credit, the Obama administration has already reached out to support hospitals, although aid has not been targeted specifically to hospitals in the most dire financial circumstances. Along with support for Medicaid and community health centers to improve primary care during the recession, the administration has provided a $268 million increase in Disproportionate Share Hospital payments towards hospitals that care for vulnerable patients, an increase of about 3%.17 Concurrently, the Centers for Medicare and Medicaid Services are implementing a value‐based purchasing program that starts with a 5% withhold in reimbursement that institutions need to earn back through a combination of mortality, process, and patient satisfaction metrics.18 The administration also reserved $19 billion to promote improvement of health IT for American medicine.19

Using health IT investment to help hospitals is an appealing concept, but for many institutions the infrastructure required to make that transition directly competes with other patient needs, including bedside patient care. IT investments have large initial costs, at a time when bank loans are difficult to acquire and few organizations can make expensive capital improvements. In fact, one‐quarter of hospitals report scaling back health IT investments that they had already started, in spite of the stimulus funds available.4

Instead, the administration may have more influence on improving care delivery by focusing on connecting hospital safety with hospital financial stability, by appropriating stimulus funds to center on quality and safety programs like those described above. Here is how: a hospital that would receive stimulus money for employing nurse discharge advocates would preserve employment while advancing patient safety, as would a hospital that retains a nurse‐to‐patient ratio above a specified threshold. By focusing on measures of structural quality, the government could improve care in ways that are easy to measure and maximize local economic stimulus without difficult outcomes assessment, insurance reform, or duplicating process measure efforts. There could even be an innovation differential (ie, payment/reward) for hospitals that improve quality while holding flat or lowering overall costs.

Equally important is to use this national financial crisis as an opportunity to improve monitoring of hospital quality. While quality assessment of hospitals is difficult, increased federal awareness of local medical need, hospital financial stability, and government awareness of emergency services overcrowding, nurse‐to‐patient ratios, and IT utilization are all valuable and easy to measure.

None of these quality‐focused fiscal interventions would be guaranteed to prevent hospital closure. Especially in small population centers, hospital closures can affect an entire community's financial growth and clinical safety net,20 while leaving hundreds or even thousands unemployed. Hospital closure should be assessed by state and federal government officials in these larger terms, perhaps even encouraging closure when appropriate, and helping prevent it when necessary.

Conclusion

Hospitals, as complex pieces of America's health care system, are central to communities' safety and economic growth. While national health coverage reform, as currently being discussed in Washington, would make hospital infrastructure less sensitive to macroeconomic changes, major reform would not come fast enough if hospitals start closing. While the worst of the recession may be over, recovery and the continuing rise in unemployment is a tenuous lifeline for hospitals on the financial brink.

We are not arguing against all hospital layoffs, or even closures. Indeed, this recession is a lean time for most industries and is likely to lead to closures for hospitals that cannot compete on efficiency or quality. But a hospital closure is a major event for a community and should not be permitted to occur without thorough consideration of alternatives. Current data on hospitals' financial status and clinical safety are limited, potentially biased, and not timely enough for this rapidly changing economic crisis. Therefore, state and federal government officials should assess whether hospitals would be eligible not just for possible emergency loans, but for linking loans to quality of care and community need. In so doing, this difficult time could be an opportunity to help hospitals improve their care, rather than watching it diminish.

With the United States mired in its most severe recession in decades, stories of hospital struggles have emerged. Beaumont Hospital, located near the headquarters of major automakers and several assembly plants outside Detroit, recently cut hundreds of jobs and put major construction on indefinite hold.1 The CEO of Boston's Beth Israel Deaconess Medical Center made an agreement with employees to take large cuts in pay and vacation time to prevent laying off 10% of the staff.2 The University of Chicago Medical Center made plans to limit the number of emergency room beds, thereby decreasing low‐reimbursing emergency admissions while making beds available for higher‐paying elective hospitalizations.3

What is surprising about these stories is that hospitals have long been considered recession‐proof. Yet, with one‐half of US hospitals having reduced their staff to balance their budgets4 and with hospitals' financial margins falling dramatically,5 economic struggles are now a widespread problem.

Furthermore, it is difficult to determine if hospitals' clinical care has been damaged by the recession. The measurement of hospital quality is new and still under‐developed: there is virtually no reliable information on hospital quality from previous recessions, and even now it will be difficult to assess quality in real time.

Critics of waste and excess in the US health care system may see tough economic times as a Darwinian proving ground for hospitals, through which efficiency will improve and poor performers will close their doors. But more likely, hospital cutbacks will risk the quality and safety of health care delivery. For reasons of both public health and fiscal impact on communities, state and federal leaders may need to watch these trends closely to design and to be ready to implement potential government remedies for hospitals' fiscal woes.

In this commentary, we describe how hospitals have fared historically during recessions, how this recession could have different effectsfirst fiscally, then clinically, and we examine policy options to mitigate these untoward effects.

Decades of Recession‐Proof Hospitals

During the Great Depression, hospital insolvency was a national problem that prompted federal and state aid. Keeping hospitals alive was a critical policy goal and proved central to the early development of health insurance that focused on payment for hospital care.6

Since WWII, growth in America's hospitals has been only loosely related to national macroeconomic trends, with other changes like technological innovations and the advent of managed care far more influential to hospital finances. In fact, during recessions, hospital care spending growth often escalates in tandem with worsening unemployment (Figure 1). One explanation for this phenomenon is that economic pressures lead to declining primary care utilization, with adverse consequences for individuals' health.7

Figure 1

Hospitals' Current Fiscal Vulnerability

However, the current recession is the worst in 70 years. Every method of income generation available to hospitals appears at risk, including reimbursement per discharge (70% of hospitals report moderate or significant increases in uncompensated care), number of inpatient admissions (over one‐half report a moderate or significant decrease), difficulty obtaining bonds (60% report at least significant problems), and charitable donations.4 Over 50% of US hospitals had negative margins in the fourth quarter of 2008, though there has been some improvement since that time.8

Future hospital stability concerns remain. Growth in revenue per discharge is still below the norm.5 Because employment lags a recovering economy, further reimbursement decreases are possible from increasing proportions of patients with low‐reimbursing insurers or no coverage at all, decreasing payment rates from all payers, and decreasing elective care. The lower‐reimbursing payers, like state Medicaid programs, are experiencing increased enrollment as Americans lose their jobs and their better‐paying, employer‐sponsored private insurance.9 There's also evidence that reimbursement rates are declining from both Medicare and private insurers,10 which threatens the fragile cost‐shift through which hospitals have long used private insurance reimbursement to subsidize government reimbursements.11

Hospitals' specific financial challenges will likely vary across markets. The authors' state of Michigan has been hit particularly long and hard by the current recession. Unemployment rates exceeding 11% are expected to cause dramatic losses in private health insurance.9 Patients' increasing need with decreasing ability to pay will make markets in the deepest recession particularly vulnerable.

A Possible Stimulus: Investing in Quality Initiatives at Fiscally Vulnerable Hospitals

It is not enough to keep hospitals' doors open in a recession. Hospitals must continue to improve the quality and safety of the care they delivervital for their future patients and also for their communities who depend on them as anchors of health systems. We believe there is a need for a new, federally supported alignment of hospital finance and hospital quality that can limit damage to hospitals, help community employment, and improve patient safety.

Timely, structural quality measures could speed the introduction of functional value‐based purchasing, promote hospital safety, and help local economies at the same time. There are many simple structural measures that could be examined, such as development of discharge coordinators, promoting effective nurse‐to‐patient ratios, and encouraging health information technology (IT). Importantly, this would not duplicate efforts already underway to promote quality with process measures. With effective financial monitoring in real time, these measures could focus on high‐risk, fiscally disadvantaged hospitals.

To its credit, the Obama administration has already reached out to support hospitals, although aid has not been targeted specifically to hospitals in the most dire financial circumstances. Along with support for Medicaid and community health centers to improve primary care during the recession, the administration has provided a $268 million increase in Disproportionate Share Hospital payments towards hospitals that care for vulnerable patients, an increase of about 3%.17 Concurrently, the Centers for Medicare and Medicaid Services are implementing a value‐based purchasing program that starts with a 5% withhold in reimbursement that institutions need to earn back through a combination of mortality, process, and patient satisfaction metrics.18 The administration also reserved $19 billion to promote improvement of health IT for American medicine.19

Using health IT investment to help hospitals is an appealing concept, but for many institutions the infrastructure required to make that transition directly competes with other patient needs, including bedside patient care. IT investments have large initial costs, at a time when bank loans are difficult to acquire and few organizations can make expensive capital improvements. In fact, one‐quarter of hospitals report scaling back health IT investments that they had already started, in spite of the stimulus funds available.4

Instead, the administration may have more influence on improving care delivery by focusing on connecting hospital safety with hospital financial stability, by appropriating stimulus funds to center on quality and safety programs like those described above. Here is how: a hospital that would receive stimulus money for employing nurse discharge advocates would preserve employment while advancing patient safety, as would a hospital that retains a nurse‐to‐patient ratio above a specified threshold. By focusing on measures of structural quality, the government could improve care in ways that are easy to measure and maximize local economic stimulus without difficult outcomes assessment, insurance reform, or duplicating process measure efforts. There could even be an innovation differential (ie, payment/reward) for hospitals that improve quality while holding flat or lowering overall costs.

Equally important is to use this national financial crisis as an opportunity to improve monitoring of hospital quality. While quality assessment of hospitals is difficult, increased federal awareness of local medical need, hospital financial stability, and government awareness of emergency services overcrowding, nurse‐to‐patient ratios, and IT utilization are all valuable and easy to measure.

None of these quality‐focused fiscal interventions would be guaranteed to prevent hospital closure. Especially in small population centers, hospital closures can affect an entire community's financial growth and clinical safety net,20 while leaving hundreds or even thousands unemployed. Hospital closure should be assessed by state and federal government officials in these larger terms, perhaps even encouraging closure when appropriate, and helping prevent it when necessary.

Conclusion

Hospitals, as complex pieces of America's health care system, are central to communities' safety and economic growth. While national health coverage reform, as currently being discussed in Washington, would make hospital infrastructure less sensitive to macroeconomic changes, major reform would not come fast enough if hospitals start closing. While the worst of the recession may be over, recovery and the continuing rise in unemployment is a tenuous lifeline for hospitals on the financial brink.

We are not arguing against all hospital layoffs, or even closures. Indeed, this recession is a lean time for most industries and is likely to lead to closures for hospitals that cannot compete on efficiency or quality. But a hospital closure is a major event for a community and should not be permitted to occur without thorough consideration of alternatives. Current data on hospitals' financial status and clinical safety are limited, potentially biased, and not timely enough for this rapidly changing economic crisis. Therefore, state and federal government officials should assess whether hospitals would be eligible not just for possible emergency loans, but for linking loans to quality of care and community need. In so doing, this difficult time could be an opportunity to help hospitals improve their care, rather than watching it diminish.

With the United States mired in its most severe recession in decades, stories of hospital struggles have emerged. Beaumont Hospital, located near the headquarters of major automakers and several assembly plants outside Detroit, recently cut hundreds of jobs and put major construction on indefinite hold.1 The CEO of Boston's Beth Israel Deaconess Medical Center made an agreement with employees to take large cuts in pay and vacation time to prevent laying off 10% of the staff.2 The University of Chicago Medical Center made plans to limit the number of emergency room beds, thereby decreasing low‐reimbursing emergency admissions while making beds available for higher‐paying elective hospitalizations.3

What is surprising about these stories is that hospitals have long been considered recession‐proof. Yet, with one‐half of US hospitals having reduced their staff to balance their budgets4 and with hospitals' financial margins falling dramatically,5 economic struggles are now a widespread problem.

Furthermore, it is difficult to determine if hospitals' clinical care has been damaged by the recession. The measurement of hospital quality is new and still under‐developed: there is virtually no reliable information on hospital quality from previous recessions, and even now it will be difficult to assess quality in real time.

Critics of waste and excess in the US health care system may see tough economic times as a Darwinian proving ground for hospitals, through which efficiency will improve and poor performers will close their doors. But more likely, hospital cutbacks will risk the quality and safety of health care delivery. For reasons of both public health and fiscal impact on communities, state and federal leaders may need to watch these trends closely to design and to be ready to implement potential government remedies for hospitals' fiscal woes.

In this commentary, we describe how hospitals have fared historically during recessions, how this recession could have different effectsfirst fiscally, then clinically, and we examine policy options to mitigate these untoward effects.

Decades of Recession‐Proof Hospitals

During the Great Depression, hospital insolvency was a national problem that prompted federal and state aid. Keeping hospitals alive was a critical policy goal and proved central to the early development of health insurance that focused on payment for hospital care.6

Since WWII, growth in America's hospitals has been only loosely related to national macroeconomic trends, with other changes like technological innovations and the advent of managed care far more influential to hospital finances. In fact, during recessions, hospital care spending growth often escalates in tandem with worsening unemployment (Figure 1). One explanation for this phenomenon is that economic pressures lead to declining primary care utilization, with adverse consequences for individuals' health.7

Figure 1

Hospitals' Current Fiscal Vulnerability

However, the current recession is the worst in 70 years. Every method of income generation available to hospitals appears at risk, including reimbursement per discharge (70% of hospitals report moderate or significant increases in uncompensated care), number of inpatient admissions (over one‐half report a moderate or significant decrease), difficulty obtaining bonds (60% report at least significant problems), and charitable donations.4 Over 50% of US hospitals had negative margins in the fourth quarter of 2008, though there has been some improvement since that time.8

Future hospital stability concerns remain. Growth in revenue per discharge is still below the norm.5 Because employment lags a recovering economy, further reimbursement decreases are possible from increasing proportions of patients with low‐reimbursing insurers or no coverage at all, decreasing payment rates from all payers, and decreasing elective care. The lower‐reimbursing payers, like state Medicaid programs, are experiencing increased enrollment as Americans lose their jobs and their better‐paying, employer‐sponsored private insurance.9 There's also evidence that reimbursement rates are declining from both Medicare and private insurers,10 which threatens the fragile cost‐shift through which hospitals have long used private insurance reimbursement to subsidize government reimbursements.11

Hospitals' specific financial challenges will likely vary across markets. The authors' state of Michigan has been hit particularly long and hard by the current recession. Unemployment rates exceeding 11% are expected to cause dramatic losses in private health insurance.9 Patients' increasing need with decreasing ability to pay will make markets in the deepest recession particularly vulnerable.

A Possible Stimulus: Investing in Quality Initiatives at Fiscally Vulnerable Hospitals

It is not enough to keep hospitals' doors open in a recession. Hospitals must continue to improve the quality and safety of the care they delivervital for their future patients and also for their communities who depend on them as anchors of health systems. We believe there is a need for a new, federally supported alignment of hospital finance and hospital quality that can limit damage to hospitals, help community employment, and improve patient safety.

Timely, structural quality measures could speed the introduction of functional value‐based purchasing, promote hospital safety, and help local economies at the same time. There are many simple structural measures that could be examined, such as development of discharge coordinators, promoting effective nurse‐to‐patient ratios, and encouraging health information technology (IT). Importantly, this would not duplicate efforts already underway to promote quality with process measures. With effective financial monitoring in real time, these measures could focus on high‐risk, fiscally disadvantaged hospitals.

To its credit, the Obama administration has already reached out to support hospitals, although aid has not been targeted specifically to hospitals in the most dire financial circumstances. Along with support for Medicaid and community health centers to improve primary care during the recession, the administration has provided a $268 million increase in Disproportionate Share Hospital payments towards hospitals that care for vulnerable patients, an increase of about 3%.17 Concurrently, the Centers for Medicare and Medicaid Services are implementing a value‐based purchasing program that starts with a 5% withhold in reimbursement that institutions need to earn back through a combination of mortality, process, and patient satisfaction metrics.18 The administration also reserved $19 billion to promote improvement of health IT for American medicine.19

Using health IT investment to help hospitals is an appealing concept, but for many institutions the infrastructure required to make that transition directly competes with other patient needs, including bedside patient care. IT investments have large initial costs, at a time when bank loans are difficult to acquire and few organizations can make expensive capital improvements. In fact, one‐quarter of hospitals report scaling back health IT investments that they had already started, in spite of the stimulus funds available.4

Instead, the administration may have more influence on improving care delivery by focusing on connecting hospital safety with hospital financial stability, by appropriating stimulus funds to center on quality and safety programs like those described above. Here is how: a hospital that would receive stimulus money for employing nurse discharge advocates would preserve employment while advancing patient safety, as would a hospital that retains a nurse‐to‐patient ratio above a specified threshold. By focusing on measures of structural quality, the government could improve care in ways that are easy to measure and maximize local economic stimulus without difficult outcomes assessment, insurance reform, or duplicating process measure efforts. There could even be an innovation differential (ie, payment/reward) for hospitals that improve quality while holding flat or lowering overall costs.

Equally important is to use this national financial crisis as an opportunity to improve monitoring of hospital quality. While quality assessment of hospitals is difficult, increased federal awareness of local medical need, hospital financial stability, and government awareness of emergency services overcrowding, nurse‐to‐patient ratios, and IT utilization are all valuable and easy to measure.

None of these quality‐focused fiscal interventions would be guaranteed to prevent hospital closure. Especially in small population centers, hospital closures can affect an entire community's financial growth and clinical safety net,20 while leaving hundreds or even thousands unemployed. Hospital closure should be assessed by state and federal government officials in these larger terms, perhaps even encouraging closure when appropriate, and helping prevent it when necessary.

Conclusion

Hospitals, as complex pieces of America's health care system, are central to communities' safety and economic growth. While national health coverage reform, as currently being discussed in Washington, would make hospital infrastructure less sensitive to macroeconomic changes, major reform would not come fast enough if hospitals start closing. While the worst of the recession may be over, recovery and the continuing rise in unemployment is a tenuous lifeline for hospitals on the financial brink.

We are not arguing against all hospital layoffs, or even closures. Indeed, this recession is a lean time for most industries and is likely to lead to closures for hospitals that cannot compete on efficiency or quality. But a hospital closure is a major event for a community and should not be permitted to occur without thorough consideration of alternatives. Current data on hospitals' financial status and clinical safety are limited, potentially biased, and not timely enough for this rapidly changing economic crisis. Therefore, state and federal government officials should assess whether hospitals would be eligible not just for possible emergency loans, but for linking loans to quality of care and community need. In so doing, this difficult time could be an opportunity to help hospitals improve their care, rather than watching it diminish.

National concerns about the quality of health care in the United States have prompted calls for transparent efforts to measure and report hospital performance to the public. Consumer groups, payers, and credentialing organizations now rate the quality of hospitals and health care through a variety of mechanisms, yielding a kaleidoscope of quality measurement scorecards. However, health care consumers have minimal information about how hospital quality rating systems compare with each other or which rating system might best address their information needs.

The Hospital Compare Web site was launched in April 2005 by the Hospital Quality Alliance (HQA), a public‐private collaboration among organizations, including the Centers for Medicare and Medicaid Services (CMS). The CMS describes Hospital Compare as information [that] measures how well hospitals care for their patients.1 A limited set of Hospital Compare data from 2004 were posted online in 2005 for more than 4200 hospitals, permitting community‐specific comparisons of hospitals' self‐reported standardized core measures that reflect quality of care for acute myocardial infarction (AMI), congestive heart failure (CHF), and community‐acquired pneumonia (CAP) in adult patients.

Other current hospital quality evaluation tools target payers and purchasers of health care. However, many of these evaluations require that institutions pay a fee for submitting their data to be benchmarked against other participating institutions or require that the requesting individual or organization pay a fee to examine a hospital's performance on a specific condition or procedure.

We examined Hospital Compare data alongside that of another hospital rating system that has existed for a longer period of time and is likely better known to the lay publicthe Best Hospitals lists published annually by U.S. News and World Report.2, 3 Together, Hospital Compare and Best Hospitals are hospital quality scorecards that offer consumers assessments of hospital performance on a national scale. However, their measures of hospital quality differ, and we investigated whether they would provide consumers with concordant assessments of hospital quality.

METHODS

RESULTS

DISCUSSION

With the public release of Hospital Compare data for more than 4200 hospitals in April 2005, national efforts to report hospital quality to the public passed a major milestone. Our findings indicate that the separate Hospital Compare measures for AMI, CHF, and CAP care have moderate to strong internal consistency, which suggests they are capturing similar hospital‐level care behaviors across institutions for these 3 common conditions.

However, Hospital Compare scores are largely discordant with the Best Hospital rank lists for cardiac and respiratory disorders care. Several institutions listed as Best Hospitals nationally scored below the national median on disease‐specific Hospital Compare core measures, perhaps leaving data‐conscious consumers to wonder how to synthesize rating systems that employ different indicators and measure different aspects of health care delivery.

CONCLUSIONS

The Best Hospitals lists and Hospital Compare core measure scores agree only a minority of the time on the best institutions for the care of cardiac and respiratory conditions in the United States. Prominent, publicly reported hospital quality scorecards that paint discordant pictures of institutional performance potentially present a conundrum for physicians, patients, and payers with growing incentives to compare institutional quality.

If the movement to improve health care quality is to succeed, the challenge will be to harness the growing professional and lay interest in quality measurement to create rating scales that reflect the best aspects of Hospital Compare and the Best Hospitals lists, with the broadest inclusion of institutions and scope of conditions. For example, it would be more helpful to the public if the Best Hospitals lists included available Hospital Compare measures. It would also benefit consumers if Hospital Compare included more metrics about preventive and elective procedures, domains in which consumers can maximally exercise their choice of health care institutions. Moreover, voluntary reporting may constrain the quality effort. Only with mandatory reporting on quality measures will consistent and sufficient institutional accountability be achieved.

National concerns about the quality of health care in the United States have prompted calls for transparent efforts to measure and report hospital performance to the public. Consumer groups, payers, and credentialing organizations now rate the quality of hospitals and health care through a variety of mechanisms, yielding a kaleidoscope of quality measurement scorecards. However, health care consumers have minimal information about how hospital quality rating systems compare with each other or which rating system might best address their information needs.

The Hospital Compare Web site was launched in April 2005 by the Hospital Quality Alliance (HQA), a public‐private collaboration among organizations, including the Centers for Medicare and Medicaid Services (CMS). The CMS describes Hospital Compare as information [that] measures how well hospitals care for their patients.1 A limited set of Hospital Compare data from 2004 were posted online in 2005 for more than 4200 hospitals, permitting community‐specific comparisons of hospitals' self‐reported standardized core measures that reflect quality of care for acute myocardial infarction (AMI), congestive heart failure (CHF), and community‐acquired pneumonia (CAP) in adult patients.

Other current hospital quality evaluation tools target payers and purchasers of health care. However, many of these evaluations require that institutions pay a fee for submitting their data to be benchmarked against other participating institutions or require that the requesting individual or organization pay a fee to examine a hospital's performance on a specific condition or procedure.

We examined Hospital Compare data alongside that of another hospital rating system that has existed for a longer period of time and is likely better known to the lay publicthe Best Hospitals lists published annually by U.S. News and World Report.2, 3 Together, Hospital Compare and Best Hospitals are hospital quality scorecards that offer consumers assessments of hospital performance on a national scale. However, their measures of hospital quality differ, and we investigated whether they would provide consumers with concordant assessments of hospital quality.

METHODS

RESULTS

DISCUSSION

With the public release of Hospital Compare data for more than 4200 hospitals in April 2005, national efforts to report hospital quality to the public passed a major milestone. Our findings indicate that the separate Hospital Compare measures for AMI, CHF, and CAP care have moderate to strong internal consistency, which suggests they are capturing similar hospital‐level care behaviors across institutions for these 3 common conditions.

However, Hospital Compare scores are largely discordant with the Best Hospital rank lists for cardiac and respiratory disorders care. Several institutions listed as Best Hospitals nationally scored below the national median on disease‐specific Hospital Compare core measures, perhaps leaving data‐conscious consumers to wonder how to synthesize rating systems that employ different indicators and measure different aspects of health care delivery.

CONCLUSIONS

The Best Hospitals lists and Hospital Compare core measure scores agree only a minority of the time on the best institutions for the care of cardiac and respiratory conditions in the United States. Prominent, publicly reported hospital quality scorecards that paint discordant pictures of institutional performance potentially present a conundrum for physicians, patients, and payers with growing incentives to compare institutional quality.

If the movement to improve health care quality is to succeed, the challenge will be to harness the growing professional and lay interest in quality measurement to create rating scales that reflect the best aspects of Hospital Compare and the Best Hospitals lists, with the broadest inclusion of institutions and scope of conditions. For example, it would be more helpful to the public if the Best Hospitals lists included available Hospital Compare measures. It would also benefit consumers if Hospital Compare included more metrics about preventive and elective procedures, domains in which consumers can maximally exercise their choice of health care institutions. Moreover, voluntary reporting may constrain the quality effort. Only with mandatory reporting on quality measures will consistent and sufficient institutional accountability be achieved.

National concerns about the quality of health care in the United States have prompted calls for transparent efforts to measure and report hospital performance to the public. Consumer groups, payers, and credentialing organizations now rate the quality of hospitals and health care through a variety of mechanisms, yielding a kaleidoscope of quality measurement scorecards. However, health care consumers have minimal information about how hospital quality rating systems compare with each other or which rating system might best address their information needs.

The Hospital Compare Web site was launched in April 2005 by the Hospital Quality Alliance (HQA), a public‐private collaboration among organizations, including the Centers for Medicare and Medicaid Services (CMS). The CMS describes Hospital Compare as information [that] measures how well hospitals care for their patients.1 A limited set of Hospital Compare data from 2004 were posted online in 2005 for more than 4200 hospitals, permitting community‐specific comparisons of hospitals' self‐reported standardized core measures that reflect quality of care for acute myocardial infarction (AMI), congestive heart failure (CHF), and community‐acquired pneumonia (CAP) in adult patients.

Other current hospital quality evaluation tools target payers and purchasers of health care. However, many of these evaluations require that institutions pay a fee for submitting their data to be benchmarked against other participating institutions or require that the requesting individual or organization pay a fee to examine a hospital's performance on a specific condition or procedure.

We examined Hospital Compare data alongside that of another hospital rating system that has existed for a longer period of time and is likely better known to the lay publicthe Best Hospitals lists published annually by U.S. News and World Report.2, 3 Together, Hospital Compare and Best Hospitals are hospital quality scorecards that offer consumers assessments of hospital performance on a national scale. However, their measures of hospital quality differ, and we investigated whether they would provide consumers with concordant assessments of hospital quality.

METHODS

RESULTS

DISCUSSION

With the public release of Hospital Compare data for more than 4200 hospitals in April 2005, national efforts to report hospital quality to the public passed a major milestone. Our findings indicate that the separate Hospital Compare measures for AMI, CHF, and CAP care have moderate to strong internal consistency, which suggests they are capturing similar hospital‐level care behaviors across institutions for these 3 common conditions.

However, Hospital Compare scores are largely discordant with the Best Hospital rank lists for cardiac and respiratory disorders care. Several institutions listed as Best Hospitals nationally scored below the national median on disease‐specific Hospital Compare core measures, perhaps leaving data‐conscious consumers to wonder how to synthesize rating systems that employ different indicators and measure different aspects of health care delivery.

CONCLUSIONS

The Best Hospitals lists and Hospital Compare core measure scores agree only a minority of the time on the best institutions for the care of cardiac and respiratory conditions in the United States. Prominent, publicly reported hospital quality scorecards that paint discordant pictures of institutional performance potentially present a conundrum for physicians, patients, and payers with growing incentives to compare institutional quality.

If the movement to improve health care quality is to succeed, the challenge will be to harness the growing professional and lay interest in quality measurement to create rating scales that reflect the best aspects of Hospital Compare and the Best Hospitals lists, with the broadest inclusion of institutions and scope of conditions. For example, it would be more helpful to the public if the Best Hospitals lists included available Hospital Compare measures. It would also benefit consumers if Hospital Compare included more metrics about preventive and elective procedures, domains in which consumers can maximally exercise their choice of health care institutions. Moreover, voluntary reporting may constrain the quality effort. Only with mandatory reporting on quality measures will consistent and sufficient institutional accountability be achieved.