Dataset Information

System-based approach to educating internal medicine residents on preventable hospital readmissions.

ABSTRACT: BACKGROUND: A literature gap exists in educating internal medicine residents about hospital readmissions and how to prevent them. INTERVENTION: The study aimed to implement a readmissions education initiative for general internal medicine inpatient resident teams in 3 general practice units at an urban, tertiary hospital. METHODS: Senior residents were given access to a daily list of readmissions, used a readmission assessment tool to investigate causes and to assess whether each readmission was preventable, led a monthly general practice unit team meeting to discuss each case, and presented their findings at the monthly multidisciplinary readmissions meeting for additional feedback. For program evaluation, we hypothesized that the "preventable" readmissions count tracked via the readmissions assessment tool would increase as residents became better educated on the root causes of readmissions. We also conducted a survey to assess perception of the readmissions education initiative. RESULTS: "Preventable" readmissions increased from 21% for the first 3 months of the intervention (September-November 2010) to 46% for the most recent 3 months (January-March 2011). The survey showed that 98% (41 of 42) of respondents who had attended a multidisciplinary readmissions meeting felt involved in an effort to review or improve the rate of hospital readmissions, whereas only 40% (21 of 53) of the group that never attended a session shared the same answer. CONCLUSIONS: This initiative required few resources, and it appeared to help residents identify "preventable" reasons for readmissions, as well as increased their perceptions of being actively involved in reducing hospital readmissions. The intervention was not associated with a statistically significant reduction in readmissions, which may be influenced primarily by multiple factors outside residents' control.

SUBMITTER: Njeim M

PROVIDER: S-EPMC3546583 | biostudies-other | 2012 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:ObjectiveTo assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).Data sourcesElectronic health records maintained by Kaiser Permanente Northern California (KPNC).Study designRetrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies in terms of the number of readmissions prevented.Data collection1 539 285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2018 at 21 KPNC hospitals.Principal findingsThere appears to be substantial heterogeneity in patients' responses to the intervention (omnibus test for heterogeneity p = 2.23 × 10-7 ), particularly across levels of predicted risk. Notably, predicted treatment effects become more positive as predicted risk increases; patients at somewhat lower risk appear to have the largest predicted effects. Moreover, these estimates appear to be well calibrated, yielding the same estimate of annual readmissions prevented in the actual treatment subgroup (1246, 95% confidence interval [CI] 1110-1381) as did a formal evaluation of the Transitions Program (1210, 95% CI 990-1430). Estimates of the impacts of alternative targeting strategies suggest that as many as 4458 (95% CI 3925-4990) readmissions could be prevented annually, while decreasing the number needed to treat from 33 to 23, by targeting patients with the largest predicted effects rather than those at highest risk.ConclusionsCausal machine learning can be used to identify preventable hospital readmissions, if the requisite interventional data are available. Moreover, our results suggest a mismatch between risk and treatment effects.

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.

Dataset Information

System-based approach to educating internal medicine residents on preventable hospital readmissions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets