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Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients.

ABSTRACT: Background:Many experts believe that hospitals with more frequent readmissions provide lower-quality care, but little is known about how the preventability of readmissions might change over the postdischarge time frame. Objective:To determine whether readmissions within 7 days of discharge differ from those between 8 and 30 days after discharge with respect to preventability. Design:Prospective cohort study. Setting:10 academic medical centers in the United States. Patients:822 adults readmitted to a general medicine service. Measurements:For each readmission, 2 site-specific physician adjudicators used a structured survey instrument to determine whether it was preventable and measured other characteristics. Results:Overall, 36.2% of early readmissions versus 23.0% of late readmissions were preventable (median risk difference, 13.0 percentage points [interquartile range, 5.5 to 26.4 percentage points]). Hospitals were identified as better locations for preventing early readmissions (47.2% vs. 25.5%; median risk difference, 22.8 percentage points [interquartile range, 17.9 to 31.8 percentage points]), whereas outpatient clinics (15.2% vs. 6.6%; median risk difference, 10.0 percentage points [interquartile range, 4.6 to 12.2 percentage points]) and home (19.4% vs. 14.0%; median risk difference, 5.6 percentage points [interquartile range, -6.1 to 17.1 percentage points]) were better for preventing late readmissions. Limitation:Physician adjudicators were not blinded to readmission timing, community hospitals were not included in the study, and readmissions to nonstudy hospitals were not included in the results. Conclusion:Early readmissions were more likely to be preventable and amenable to hospital-based interventions. Late readmissions were less likely to be preventable and were more amenable to ambulatory and home-based interventions. Primary Funding Source:Association of American Medical Colleges.

SUBMITTER: Graham KL

PROVIDER: S-EPMC6247894 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients.

Graham Kelly L KL Auerbach Andrew D AD Schnipper Jeffrey L JL Flanders Scott A SA Kim Christopher S CS Robinson Edmondo J EJ Ruhnke Gregory W GW Thomas Larissa R LR Kripalani Sunil S Vasilevskis Eduard E EE Fletcher Grant S GS Sehgal Neil J NJ Lindenauer Peter K PK Williams Mark V MV Metlay Joshua P JP Davis Roger B RB Yang Julius J Marcantonio Edward R ER Herzig Shoshana J SJ

Annals of internal medicine 20180501 11

<h4>Background</h4>Many experts believe that hospitals with more frequent readmissions provide lower-quality care, but little is known about how the preventability of readmissions might change over the postdischarge time frame.<h4>Objective</h4>To determine whether readmissions within 7 days of discharge differ from those between 8 and 30 days after discharge with respect to preventability.<h4>Design</h4>Prospective cohort study.<h4>Setting</h4>10 academic medical centers in the United States.<h ...[more]

PMID: 29710243

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Project description:Early and late readmissions may have different causal factors, requiring different prevention strategies.To determine whether predictors of readmission change within 30 days after discharge.Retrospective cohort study.Academic medical center.Patients admitted between 1 January 2009 and 31 December 2010.Factors related to the index hospitalization (acute illness burden, inpatient care process factors, and clinical indicators of instability at discharge) and unrelated factors (chronic illness burden and social determinants of health) and how they affect early readmissions (0 to 7 days after discharge) and late readmissions (8 to 30 days after discharge).13 334 admissions, representing 8078 patients, were included in the analysis. Early readmissions were associated with markers of acute illness burden, including length of hospital stay (odds ratio [OR], 1.02 [95% CI, 1.00 to 1.03]) and whether a rapid response team was called for assessment (OR, 1.48 [CI, 1.15 to 1.89]); markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.19 [CI, 1.02 to 1.40]); and social determinants of health, including barriers to learning (OR, 1.18 [CI, 1.01 to 1.38]). Early readmissions were less likely if a patient was discharged between 8:00 a.m. and 12:59 p.m. (OR, 0.76 [CI, 0.58 to 0.99]). Late readmissions were associated with markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.24 [CI, 1.08 to 1.41]) or hemodialysis (OR, 1.61 [CI, 1.12 to 2.17]), and social determinants of health, including barriers to learning (OR, 1.24 [CI, 1.09 to 1.42]) and having unsupplemented Medicare or Medicaid (OR, 1.16 [CI, 1.01 to 1.33]).Readmissions were ascertained at 1 institution.The time frame of 30 days after hospital discharge may not be homogeneous. Causal factors and readmission prevention strategies may differ for the early versus late periods.Health Resources and Services Administration, National Institute on Aging, National Institutes of Health, Harvard Catalyst, and Harvard University.

Project description:BACKGROUND:Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions. OBJECTIVE:Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication. DESIGN/SETTING/PARTICIPANTS:Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011-2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013-2014. Variables were identified at hospital discharge. RESULTS:The derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1-1.7)], admission in previous 6 months [2.3 (1.9-2.8)], heart failure [1.3 (1.0-1.7)], chronic ischemic heart disease [1.7 (1.2-2.3)], diabetes with organ damage [2.2 (1.3-3.8)], cancer [1.4 (1.0-1.9)], metastatic carcinoma [1.9 (1.3-3.0)], anemia [1.2 (1.0-1.5)], hypertension [1.3 (1.1-1.7)], arrhythmia [1.3 (1.0-1.6)], hyperkalemia [1.4 (1.0-1.7)], opioid drug prescription [1.3 (1.1-1.6)], and acute myocardial infarction [0.6 (0.4-0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort's operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064). CONCLUSION:This study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.

Project description:ObjectivesLimited studies have identified predictors of early and late hospital readmissions in Australian healthcare settings. Some of these predictors may be modifiable through targeted interventions. A recent study has identified malnutrition as a predictor of readmissions in older patients but this has not been verified in a larger population. This study investigated what predictors are associated with early and late readmissions and determined whether nutrition status during index hospitalisation can be used as a modifiable predictor of unplanned hospital readmissions.DesignA retrospective cohort study.SettingTwo tertiary-level hospitals in Australia.ParticipantsAll medical admissions ≥18 years over a period of 1 year.OutcomesPrimary objective was to determine predictors of early (0-7 days) and late (8-180 days) readmissions. Secondary objective was to determine whether nutrition status as determined by malnutrition universal screening tool (MUST) can be used to predict readmissions.ResultsThere were 11 750 (44.8%) readmissions within 6 months, with 2897 (11%) early and 8853 (33.8%) late readmissions. MUST was completed in 16.2% patients and prevalence of malnutrition during index admission was 31%. Malnourished patients had a higher risk of both early (OR 1.39, 95% CI 1.12 to 1.73) and late readmissions (OR 1.23, 95% CI 1.06 to 128). Weekend discharges were less likely to be associated with both early (OR 0.81, 95% CI 0.74 to 0.91) and late readmissions (OR 0.91, 95% CI 0.84 to 0.97). Indigenous Australians had a higher risk of early readmissions while those living alone had a higher risk of late readmissions. Patients ≥80 years had a lower risk of early readmissions while admission to intensive care unit was associated with a lower risk of late readmissions.ConclusionsMalnutrition is a strong predictor of unplanned readmissions while weekend discharges are less likely to be associated with readmissions. Targeted nutrition intervention may prevent unplanned hospital readmissions.Trial registrationANZCTRN 12617001362381; Results.

Project description:Background: Hospital readmission rates are increasingly used as a measure of healthcare quality. Medicines are the most common therapeutic intervention but estimating the contribution of adverse drug events as a cause of readmissions is difficult. Objectives: To assess the prevalence and preventability of medication-related readmissions within 30 days after hospital discharge and to describe the risk factors, type of medication errors and types of medication involved in these preventable readmissions. Design: A cross-sectional observational study. Setting: The study took place across the cardiology, gastroenterology, internal medicine, neurology, psychiatry, pulmonology and general surgery departments in the OLVG teaching hospital, Netherlands. Participants: Patients with an unplanned readmission within 30 days after discharge from an earlier hospitalization (index hospitalization: IH) were reviewed. Measurements: The prevalence and preventability of medication-related readmissions were assessed by residents in multidisciplinary meetings. A senior internist and hospital pharmacist reassessed the prevalence and preventability of identified cases. Generalized estimating equation with logistic regression was performed to identify risk factors of potentially preventable medication-related readmissions. Results: Of 1,111 included readmissions, 181 (16%) were medication-related, of which 72 (40%) were potentially preventable. The number of medication changes at IH (Adjusted odds ratio [ORadj]: 1.14; 95% CI: 1.05-1.24) and having ≥3 hospitalizations 6 months before IH (ORadj: 2.11; 95% CI: 1.12-3.98) were risk factors of a preventable medication-related readmission. Of these preventable readmissions, 35% were due to prescribing errors, 35% by non-adherence and 30% by transition errors. Medications most frequently involved were diuretics and antidiabetics. Conclusion: This study shows that 16% of readmissions are medication-related, of which 40% are potentially preventable. If the results are confirmed in larger multicentre studies, this may indicate that more attention should be paid to medication-related harm in order to lower the overall readmission rates.

Project description:BACKGROUND:The precise scope of hospital care delivered under general internal medicine services remains poorly quantified. The purpose of this study was to describe the demographic characteristics, medical conditions, health outcomes and resource use of patients admitted to general internal medicine at 7 hospital sites in the Greater Toronto Area. METHODS:This was a retrospective cohort study involving all patients who were admitted to or discharged from general internal medicine at the study sites between Apr. 1, 2010, and Mar. 31, 2015. Clinical data from hospital electronic information systems were linked to administrative data from each hospital. We examined trends in resource use and patient characteristics over the study period. RESULTS:There were 136?208 admissions to general internal medicine involving 88?121 unique patients over the study period. General internal medicine admissions accounted for 38.8% of all admissions from the emergency department and 23.7% of all hospital bed-days. Over the study period, the number of admissions to general internal medicine increased by 32.4%; there was no meaningful change in the median length of stay or cost per hospital stay. The median patient age was 73 (interquartile range [IQR] 57-84) years, and the median number of coexisting conditions was 6 (IQR 3-9). The median acute length of stay was 4.6 (IQR 2.5-8.6) days, and the median total cost per hospital stay was $5850 (IQR $3915-$10?061). Patients received at least 1 computed tomography scan in 52.2% of admissions. The most common primary discharge diagnoses were pneumonia (5.0% of admissions), heart failure (4.7%), chronic obstructive pulmonary disease (4.1%), urinary tract infection (4.0%) and stroke (3.6%). INTERPRETATION:Patients admitted to general internal medicine services represent a large, heterogeneous, resource-intensive and growing population. Understanding and improving general internal medicine care is essential to promote a high-quality, sustainable health care system.

Project description:IntroductionNew financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.Materials and methodsThis study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.ResultsPredictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.ConclusionsThis study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.

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Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients.

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Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients.

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