Measuring potentially avoidable hospital readmissions

The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.     

Readmission rate as an indicator of hospital performance: the case of Spain

OBJECTIVES: Hospital readmission rate is currently used as a quality of care indicator, although its validity has not been established. Our aims were to identify the frequency and characteristics of potential avoidable readmissions and to compare the assessment of quality of care derived from readmission rate with other measure of quality (judgment of experts). METHODS: Design: cross-sectional observational study; Setting: acute care hospital located in Marbella, South of Spain; Study participants: random sample of patients readmitted at the hospital within six months from discharge (n = 363); Interventions: review of clinical records by a pair of observers to assess the causes of readmissions and their potential avoidability; Main measures: logistic regression analysis to identify the variables from the databases of hospital discharges which are related to avoidability of readmissions. Determination of sensitivity and specificity of different definitions of readmission rate to detect avoidable situations. RESULTS: Nineteen percent of readmissions were considered potentially avoidable. Variables related to readmission avoidability were (i) time elapsed between index admission and readmission and (ii) difference in diagnoses of both episodes. None of the definitions of readmission rate used in this study provided adequate values of sensitivity and specificity in the identification of potentially avoidable readmissions. CONCLUSIONS: Most readmissions in our hospital were unavoidable. Thus, readmission rate might not be considered a valid indicator of quality of care.     

Is the emergency readmission rate a valid outcome indicator?

OBJECTIVES: The principal aim was to determine whether the emergency readmission rate varies between medical specialties, and to identify whether differences in emergency readmission rates between hospital trusts can be reduced by standardising for specialty. Possible factors influencing emergency readmission were also investigated, including frequency of previous admission and cause of readmission. DESIGN: Emergency readmission rates were obtained from the Scottish Morbidity Record scheme (SMR1) using record linkage, standardised for age and sex. Rates throughout Scotland were analysed by specialty, and rates for general medicine compared among teaching hospital trusts. Cause of emergency readmission was determined from hospital records in a random sample (177 patients). SETTING: Medical specialties throughout Scotland. SUBJECTS: All patients readmitted as an emergency within 28 days of discharge (October 1990 to September 1994). RESULTS: Emergency readmissions varied markedly between medical specialties, with highest rates in nephrology (24.2%, 95% CI 23.5 to 24.8) and haematology (20.4%, 95% CI 19.9 to 20.9), and the lowest in homeopathy (2.2%, 95% CI 1.6 to 2.7) and metabolic diseases (3.5%, 95% CI 2.4 to 4.5). The largest number of emergency readmissions was in general medicine, accounting for 63% of the total. Restricting emergency readmission rates to general medicine significantly altered previous rates. In the year preceding the emergency readmission, 59% of all patients had been admitted to hospital at least once, and most emergency readmissions (73.3%) resulted from a chronic underlying condition. CONCLUSIONS: Significant variations in emergency readmission rates occurred between medical specialties, suggesting that differences between hospital trusts are influenced by differences in specialties and thus case mix. The majority of emergency readmissions occurred in patients with an underlying chronic condition, and many had a history of multiple previous hospital admissions. The emergency readmission rate is therefore unlikely to be a valid outcome indicator reflecting quality of care until routine data are available for standardisation by case mix.     

When projecting required effectiveness of interventions for hospital readmission reduction,the percentage that is potentially avoidable must be considered

ObjectivesHospitals have strong incentives to decrease readmission rates. Not all hospital readmissions are potentially avoidable. Therefore, only a component of all hospital readmissions can be influenced by interventions designed to decrease them. In this study, we determined how effective interventions must be to attain specific reductions in hospital readmission rates.Study Design and SettingA conceptual model of all readmissions and potentially avoidable readmissions was used to derive a mathematical relationship between the relative reduction in the total number of readmissions, the relative reduction in potentially avoidable readmissions, and the proportion of readmissions that are potentially avoidable.ResultsWhen 22% of readmissions were potentially avoidable, achieving a 20% reduction in the total number of readmissions required a 91% reduction in potentially avoidable readmissions; decreasing potentially avoidable readmissions by 20% reduced total readmissions by 4.4%.ConclusionThese results highlight that relative reductions in the total number of readmissions are notably lower than that for potentially avoidable readmissions. This separation in relative reduction of all and potentially avoidable readmissions increases as the proportion of readmissions deemed potentially avoidable decreases. These results have important implications for health care planners and researchers.     

Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals

ObjectiveMost of the urgent readmissions are unavoidable. This study developed a method that used observed urgent readmission rates to compare the latent avoidable readmission rates between the two hospitals.Study Design and SettingTo compare two hospitals, we identified all proportions of urgent readmissions deemed avoidable at each hospital making their avoidable readmission rates significantly different. We then calculated the probability that any of these conditions occurred. We applied this method to 25 randomly selected Ontario acute-care hospitals in 2008.ResultsThe hospitals had a median 30-day urgent readmission rate of 10.8% (interquartile [IQR] 9.7–12.8%). The median P-value of the 300 hospital–hospital comparisons for 30-day urgent readmission rate was 0.05 (interquartile range [IQR] 0.0005–0.31). In contrast, the median probability that hospitals with the lower urgent 30-day readmission rate outperformed their comparator hospital with respect to avoidable readmissions was only 0.161 (IQR 0.079–0.274).ConclusionUrgent readmission rates can be used to estimate the probability that avoidable readmission rates differ significantly between the two hospitals. The probability that avoidable readmission rates differ significantly between hospitals is small even when significant differences in urgent 30-day readmission rates exist. Our results show that 30-day urgent readmission rates should be used very cautiously to compare hospital quality of care.     

72-hour hospital readmission of older people after hospital discharge with home care services

In this retrospective cohort study in Argentina, risk factors for hospital readmission of older adults, within 72 hours after hospital discharge with home care services, were analyzed. Fifty-three percent of unplanned emergency room visits within 72 hours after hospital discharge resulted in hospital readmissions, 65% of which were potentially avoidable. By multivariate logistic regression, low functionality, pressure ulcers, and age over 83 years predicted hospital readmission among emergency room attendees. It is important to identify and analyze barriers in current home care services and the high-risk population of hospital readmission to improve the strategies to avoid adverse outcomes.     

Comparison between planned and unplanned readmissions to a department of internal medicine

The objective of this study was to assess the respective frequency of planned and unplanned early readmissions after discharge from an internal medicine department, and to identify and compare risk factors for these two types of readmissions. Readmissions within 31 days of discharge were identified as planned or unplanned based on analysis of discharge summaries. Time-failure methods were used to describe the risk of readmissions over time and to assess relationships between patient and index stay characteristics and risk of readmission. Of 5828 patients discharged alive, 730 (12.5%) were readmitted within 31 days. There were slightly more planned than unplanned readmissions (393 vs. 337). The difference in time-to-event functions was significant (P=0.04). The risk of planned readmission was increased for men, younger patients, and for patients discharged with a diagnosis of coronary heart disease, cardiac arrhythmia, and neoplastic disease. Increased risk of unplanned readmission was associated with index length of stay longer than 3 days, an increased number of comorbidities, and with a diagnosis of neoplastic disease. Planned readmissions constitute more than half of early readmissions to our internal medicine department. Therefore, a crude readmission rate is unlikely to be a useful indicator of quality of care. Several patient characteristics influence the risk of unplanned readmission, suggesting that case-mix adjustments are necessary when readmission rates are compared between institutions or tracked over time.     

Is readmission to hospital an indicator of poor process of care for patients with heart failure?

Background: Controversy exists about the appropriateness of using readmission as an indicator of the quality of care. A study was undertaken to measure the validity and predictive ability of readmission in this context.

Methods: An evaluation study was performed in patients discharged alive with heart failure from three Swiss academic medical centres. Process quality indicators were derived from evidence based guidelines for the management and treatment of heart failure. Readmissions were calculated from hospital administrative data. The predictive ability of readmissions was evaluated using bivariate and multivariate analyses, and validity by calculating sensitivity, specificity, positive and negative predictive value, using process indicators as the "gold standard".

Results: Of 1055 eligible patients discharged alive, 139 (13.2%) were readmitted within 30 days. The adjusted odds ratio (OR) for absence of measurement of left ventricular function was 0.70 (95% CI 0.45 to 1.08) for readmissions. In patients with left ventricular systolic dysfunction, three dose categories of angiotensin converting enzyme inhibitor were examined using ordinal logistic regression. The adjusted OR for these categories was 1.07 (95% CI 0.56 to 2.06) for readmissions. When using process indicators as the gold standard to assess the validity of readmissions, sensitivity ranged from 0.08 to 0.17 and specificity from 0.86 to 0.93.

Conclusions: Readmission did not predict and was not a valid indicator of the quality of care for patients with heart failure admitted to three Swiss university hospitals.

     

Incidence,risk factors and adequation of early readmission among the elderly

BACKGROUND: The objectives of the study were to estimate the incidence of readmission one month after discharge, to determine the proportion of planned readmissions and of those avoidable, and to identify risk factors associated with early readmissions in elderly admitted to an acute geriatric unit. METHODS: A prospective study was conducted on a sample of 322 patients, 75 years of age or older, discharged from an acute geriatric service. A phone follow-up was realized one month after discharge. A multivariate logistic regression model was used to identify risk factors for readmission. RESULTS: Global incidence of early readmission was 16.2% (that is 50 rehospitalizations), 18.0% of which were planned. Among the 21 readmissions to the same service, five were avoidable according to the Appropriateness Evaluation Protocol. Logistic regression analysis identified three patient characteristics that were independent predictors of early readmission, which were: a need of help for locomotion (OR=4.38, p=0.002), a negative answer to the question "do you feel that your life is empty?" (OR=2.22, p=0.02) and a short length of stay (p<0.02). CONCLUSION: A better knowledge of risk factors should allow targeting patients at high risk of early hospital readmission, which should profit by preventive interventions during the first hospitalization. Two domains of possible action were identified in this study: a sufficient length of stay and a better attention to patients with reduced autonomy, especially for those who go back home after discharge.     

Impact of physician training level on emergency readmission within internal medicine

OBJECTIVES: The research question was whether training level of admitting physicians and referrals from practitioners in primary health care (PHC) are risk factors for emergency readmission within 30 days to internal medicine. METHODS: This report is a prospective multicenter study carried out during 1 month in 1997 in seven departments of internal medicine in the County of Stockholm, Sweden. Two of the units were at university hospitals, three at county hospitals and two in district hospitals. The study area is metropolitan-suburban with 1,762,924 residents. Data were analyzed by multiple logistic regression. RESULTS: A total of 5,131 admissions, thereby 408 unplanned readmissions (8 percent) were registered (69.8 percent of 7348 true inpatient episodes). The risk of emergency readmission increased with patient's age and independently 1.40 times (95 percent confidence interval [CI], 1.13-1.74) when residents decided on hospitalization. Congestive heart failure as primary or comorbid condition was the main reason for unplanned readmission. Referrals from PHC were associated with risk decrease (odds ratio, 0.53; 95 percent CI, 0.38-0.73). CONCLUSION: The causes of unplanned hospital readmissions are mixed. Patient contact with primary health care appears to reduce the recurrence. In addition to the diagnoses of cardiac failure, training level of admitting physicians in emergency departments was an independent risk factor for early readmission. Our conclusion is that it is cost-effective to have all decisions on admission to hospital care confirmed by senior doctors. Inappropriate selection of patients to inpatient care contributes to poor patient outcomes and reduces cost-effectiveness and quality of care.     

Emergency hospital admissions and readmissions of patients aged over 75 years and the effects of a community-based discharge scheme

This paper reports the results of a retrospective review which analysed emergency admissions and readmissions of elderly patients to a district general hospital. All patients received standard after-care allocated by the community health and social services departments following referral by hospital staff. In addition, half of the cohort was randomly allocated to receive care attendant support for a maximum of 12 hours a week for two weeks following the first and any subsequent discharge from hospital. The effect of this additional community support on emergency readmissions was also reviewed. The findings show that the patients randomly allocated to receive the modest domiciliary after-care service were less likely to have another emergency readmission or multiple readmissions. The results suggest that patients over 75 years-of-age, living alone, or having two or more emergency admissions within six months, should have a domiciliary assessment and follow-up after hospital discharge.     

Evaluation of prediction strategy and care coordination for COPD readmissions

Objectives: Factors that influence the likelihood of readmission for chronic obstructive pulmonary disease (COPD) patients and the impact of posthospital care coordination remain uncertain. LACE index (L = length of stay, A = Acuity of admission; C = Charlson comorbidity index; E = No. of emergency department (ED) visits in last 6 months) is a validated tool for predicting 30-days readmissions for general medicine patients. We aimed to identify variables predictive of COPD readmissions including LACE index and determine the impact of a novel care management process on 30-day all-cause readmission rate.

Methods: In a case-control design, potential readmission predictors including LACE index were analyzed using multivariable logistic regression for 461 COPD patients between January-October 2013. Patients with a high LACE index at discharge began receiving care coordination in July 2013. We tested for association between readmission and receipt of care coordination between July-October 2013. Care coordination consists of a telephone call from the care manager who: 1) reviews discharge instructions and medication reconciliation; 2) emphasizes importance of medication adherence; 3) makes a follow-up appointment with primary care physician within 1–2 weeks and; 4) makes an emergency back-up plan.

Results: COPD readmission rate was 16.5%. An adjusted LACE index of ≥ 13 was not associated with readmission (p = 0.186). Significant predictors included female gender (odds ratio [OR] 0.51, 95% confidence interval [CI] 0.29–0.91, p = 0.021); discharge to skilled nursing facility (OR 3.03, 95% CI 1.36–6.75, p = 0.007); 4–6 comorbid illnesses (OR 9.21, 95% CI 1.17–76.62, p = 0.035) and ≥ 4 ED visits in previous 6 months (OR 6.40, 95% CI 1.25–32.87, p = 0.026). Out of 119 patients discharged between July-October 2013, 41% received the care coordination. The readmission rate in the intervention group was 14.3% compared to 18.6% in controls (p = 0.62).

Conclusions: Factors influencing COPD readmissions are complex and poorly understood. LACE index did not predict 30-days all-cause COPD readmissions. Posthospital care coordination for transition of care from hospital to the community showed a 4.3% reduction in the 30-days all-cause readmission rate which did not reach statistical significance (p = 0.62).     

The Effect of a Medicaid Managed Care Program on Patterns of Psychiatric Readmission Among Adolescents: Evidence from Maryland

This study evaluates the effect of Maryland’s Medicaid managed care program on patterns of psychiatric readmission for adolescents. Rates and frequency of readmissions are compared before (FY 1997) and after (FY 1998) the implementation of Maryland’s Medicaid managed care program. Medicaid claims files were reviewed for 881 adolescents consecutively admitted to three major Maryland psychiatric hospitals between July 1, 1996 and June 30, 1998. Adolescents admitted after the managed care reforms were more likely to experience multiple readmissions. The 1-year cumulative rate of readmission pre- and postmanaged care was 33% and 38%, respectively; the highest risk period fell within the first 15–30 days postdischarge. The high rate of early readmissions raises concern about the quality of care and the adequacy of community-based services. Findings also suggest that youths with serious emotional disturbances who are high users of inpatient care may be adversely affected by the managed care reforms. Cynthia A. Fontanella, PhD, is a postdoctoral fellow at Institute for Health, Health Care Policy, and Aging Research (IHHCPAR), Rutgers University, 30 College Avenue, New Brunswick, NJ 08901 USA. Susan J. Zuravin, PhD, is a professor at University of Maryland School of Social Work, 525 West Redwood Street, Baltimore, MD 21201 USA. Caroline L. Burry, PhD, is a associate professor at University of Maryland School of Social Work, 525 West Redwood Street, Baltimore, MD 21201 USA.     

Readmission data for outcomes measurement: identifying and strengthening the empirical base

Hospital readmission is an important indicator of patient care outcomes and is widely used in evaluating the quality of health care provided. However, few definitions of the term "readmission" are listed in the literature, and exact measurement parameters regarding readmission are seldom stated. The term readmission is indiscriminately used and defined, making accurate comparisons of results difficult across studies if not impossible. This article analyzes the concept readmission, creates a criteria-based definition for the term, and proposes a measurement instrument on the basis of these criteria to consistently quantify readmission in quality management programs and research studies. Multiple databases were searched using various key terms to locate literature covering adult and pediatric populations and all clinical conditions or diagnoses. Articles were selected on the basis of the specified inclusion/exclusion criteria.     

Predicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions

OBJECTIVE: To determine clinical and patient-centered factors predicting non-elective hospital readmissions. DESIGN: Secondary analysis from a randomized clinical trial. CLINICAL SETTING: Nine VA medical centers. PARTICIPANTS: Patients discharged from the medical service with diabetes mellitus, congestive heart failure, and/or chronic obstructive pulmonary disease (COPD). MAIN OUTCOME MEASUREMENT: Non-elective readmission within 90 days. RESULTS: Of 1378 patients discharged, 23.3% were readmitted. After controlling for hospital and intervention status, risk of readmission was increased if the patient had more hospitalizations and emergency room visits in the prior 6 months, higher blood urea nitrogen, lower mental health function, a diagnosis of COPD, and increased satisfaction with access to emergency care assessed on the index hospitalization. CONCLUSIONS: Both clinical and patient-centered factors identifiable at discharge are related to non-elective readmission. These factors identify high-risk patients and provide guidance for future interventions. The relationship of patient satisfaction measures to readmission deserves further study.     

Readmissions to hospital: the contribution of morbidity data to the evaluation of asthma management

Abstract: We evaluated hospital readmission as an indicator of the quality of management of asthma patients, between July 1989 and June 1990. Using hospital separation data, we constructed a matched data set to identify early (within two weeks of discharge) readmissions. Of over 14 000 admissions for asthma in the 1-to-44-year age group, 2.8 per cent were classified as early readmissions. Admissions and readmissions were more common in rural than metropolitan areas. Admissions were most common during autumn, but early readmissions occurred most often during spring. Patients staving more than one day were 0.5 times (95 per cent confidence interval (CI) 0.37 to 0.68) as likely to have an early readmission than patients staving less than one day. Using the same data set, we identified patients who had the potential for readmission within a six-month period. Of the 5052 patients, 17.8 per cent were readmitted at least once during the period; 3.7 per cent had at least one early readmission, and 15.8 per cent had at least one late readmission (more than two weeks following discharge). A length of stay of more than one day was associated with 0.41 times (CI 0.24 to 0.70) the risk of early readmission in this cohort A length of stay of more than one day was associated with a higher risk of late readmission (1.52, CI 1.09 to 2.12), which was less likely to occur in rural than metropolitan areas (0.45, CI 0.37 to 0.55). This study showed that hospital data can identify factors associated with readmission for asthma; such readmission may be an indicator of asthma morbidity and/or management in the population.     

Does case mix matter for substance abuse treatment? A comparison of observed and case mix-adjusted readmission rates for inpatient substance abuse treatment in the Department of Veterans Affairs.

OBJECTIVE: To develop a case mix model for inpatient substance abuse treatment to assess the effect of case mix on readmission across Veterans Affairs Medical Centers (VAMCs). DATA SOURCES/STUDY SETTING: The computerized patient records from the 116 VAMCs with inpatient substance abuse treatment programs between 1987 and 1992. STUDY DESIGN: Logistic regression was used on patient data to model the effect of demographic, psychiatric, medical, and substance abuse factors on readmission to VAMCs for substance abuse treatment within six months of discharge. The model predictions were aggregated for each VAMC to produce an expected number of readmissions. The observed number of readmissions for each VAMC was divided by its expected number to create a measure of facility performance. Confidence intervals and rankings were used to examine how case mix adjustment changed relative performance among VAMCs. DATA COLLECTION/EXTRACTION METHODS: Ward where care was provided and ICD-9-CM diagnosis codes were used to identify patients receiving treatment for substance abuse (N = 313,886). PRINCIPAL FINDINGS: The case mix model explains 36 percent of the observed facility level variation in readmission. Over half of the VAMCs had numbers of readmissions that were significantly different than expected. There were also noticeable differences between the rankings based on actual and case mix-adjusted readmissions. CONCLUSIONS: Secondary data can be used to build a reasonably stable case mix model for substance abuse treatment that will identify meaningful variation across facilities. Further, case mix has a large effect on facility level readmission rates for substance abuse treatment. Uncontrolled facility comparisons can be misleading. Case mix models are potentially useful for quality assurance efforts.     

Studies of avoidable factors influencing death: a call for explicit criteria.

OBJECTIVE: To analyse studies evaluating cases of potentially "avoidable" death. DESIGN: The definitions, sources of information, and methods were reviewed with a structured protocol. The different types of avoidable factors,--that is, deficiencies in medical care that may have contributed to death--were categorised. The presence of explicit classifications and standards was examined. basic criteria for quality of the studies were defined and the numbers of studies fulfilling these criteria were assessed. SETTING AND PARTICIPANTS: 65 studies, published during 1988-93 in peer reviewed medical journal for which the title, or abstract, or both indicated that they had analysed potentially avoidable factors influencing death. Studies analysing aggregated data only, were not included. RESULTS: Only one third of the studies fulfilled basic quality criteria,--namely, that the avoidable factors examined should be defined and the sources of information and people responsible for the judgements presented. The definitions used comprised two levels, one stating that there had been errors in management (process) and the other that the errors may have contributed to the deaths (outcome). Only 15% of the studies explicitly defined what type of factors they had looked for and 8% referred to specified standards of care. CONCLUSIONS: Studies of avoidable factors influencing death may have considerable potential as part of a system of improving medical care and reducing avoidable mortality. At present, however, the results from different studies are not comparable, due to differences in materials and methods. There is a need to improve the quality of the studies and to define standardised explicit definitions and classifications.     

Confidential enquiry into avoidable vehicle accident deaths in the province of Modena,Italy

The authors describe the methods and results of a kind of study – confidential enquiries into avoidable deaths – very rarely performed in the Mediterranean area. After assessing some quali/quantitative evaluation criteria, an independent expert panel investigated the quality of each step in emergency health care. Information was collected by clinical and forensic reports (clinical method). Of 102 cases, 4 were avoidable deaths and 18 probably avoidable. These results, which are comparable with other similar ones found in Italy (autoptic method) and abroad, have been useful in highlighting some health care errors: in particular, in on-site care and in emergency department diagnosis and treatment. Other avoidable factors emerging were the inappropriateness of transporting severe trauma cases to a small hospital lacking proper equipment and trained staff, and the importance of staff training in first emergency care of severe trauma on ambulance. This situation had been highlighted previously and led to implementation of trauma centres. The methods implemented turned out to be quite statistically reproducible and have been used in local health care planning, especially in the rearrangement of ambulance deployment and emergency staff training.