Social Factors and Patient Perceptions Associated With Preventable Hospital Readmissions.
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ABSTRACT:
SUBMITTER: Carter J
PROVIDER: S-EPMC7036679 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
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Project description:Hospital readmission rates are increasingly used to assess quality. Little is known, however, about potential preventability of readmissions among children. Our objective was to evaluate potential preventability of 30-day readmissions using medical record review and interviews.A cross-sectional study in 305 children (<18 years old) readmitted within 30 days to a freestanding children's hospital between December 2012 and February 2013. Interviews (N = 1459) were conducted with parents/guardians, patients (if ≥13 years old), inpatient clinicians, and primary care providers. Reviewers evaluated medical records, interview summaries, and transcripts, and then rated potential preventability. Multivariate regression analysis was used to identify factors associated with potentially preventable readmission. Adjusted event curves were generated to model days to readmission.Of readmissions, 29.5% were potentially preventable. Potentially preventable readmissions occurred sooner after discharge than non-potentially preventable readmissions (5 vs 9 median days; P < .001). The odds of a readmission being potentially preventable were greatest when the index admission and readmission were causally related (adjusted odds ratio [AOR]: 2.6; 95% confidence interval [CI]: 1.0-6.8) and when hospital (AOR: 16.3; 95% CI: 5.9-44.8) or patient (AOR: 7.1; 95% CI: 2.5-20.5) factors were identified. Interviews provided new information about the readmission in 31.2% of cases.Nearly 30% of 30-day readmissions to a children's hospital may be potentially preventable. Hospital and patient factors are associated with potential preventability and may provide targets for quality improvement efforts. Interviews contribute important information and should be considered when evaluating readmissions.
| S-EPMC5557411 | biostudies-other
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.
| S-EPMC7704477 | biostudies-literature
Project description:New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown.Retrospective cohort study.Six geographically diverse medical centers.All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients.A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions.Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above.The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.
| S-EPMC5309170 | biostudies-other
Project description: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.
| S-EPMC3546583 | biostudies-other
Project description:OBJECTIVE:To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs). DESIGN:Systematic review. DATA SOURCE:CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017. ELIGIBILITY CRITERIA:Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included. DATA EXTRACTION AND SYNTHESIS:Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form. RESULTS:Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS. CONCLUSIONS:This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.
| S-EPMC6352831 | biostudies-other
Project description:BackgroundThe HOSPITAL Risk Score (HRS) predicts 30-day hospital readmissions and is internationally validated. Social determinants of health (SDOH) such as low socioeconomic status (SES) affect health outcomes and have been postulated to affect readmission rates. We hypothesized that adding SDOH to the HRS could improve its predictive accuracy.MethodsRecords of 37,105 inpatient admissions at the University of Chicago Medical Center were reviewed. HRS was calculated for each patient. Census tract-level SDOH then were combined with the HRS and the performance of the resultant "Social HRS" was compared against the HRS. Patients then were assigned to 1 of 7 typologies defined by their SDOH and a balanced dataset of 14,235 admissions was sampled from the larger dataset to avoid over-representation by any 1 sociodemographic group. Principal component analysis and multivariable linear regression then were performed to determine the effect of SDOH on the HRS.ResultsThe c-statistic for the HRS predicting 30-day readmission was 0.74, consistent with published values. However, the addition of SDOH to the HRS did not improve the c-statistic (0.71). Patients with unfavorable SDOH (no high-school, limited English, crowded housing, disabilities, and age?>?65?yrs) had significantly higher HRS (p?<?0.05 for all). Overall, SDOH explained 0.2% of the HRS.ConclusionAt an urban tertiary care center, the addition of census tract-level SDOH to the HRS did not improve its predictive power. Rather, the effects of SDOH are already reflected in the HRS.
| S-EPMC7780407 | biostudies-literature
Project description:BackgroundReadmissions after total joint arthroplasty have become a key quality measure in elective surgery in the United States. The Affordable Care Act includes the Hospital Readmission Reduction Program, which calls for reduced payments to hospitals with excessive readmissions. This policy uses a method to determine excess readmission ratios and calculate readmission payment adjustments to hospitals, however, it is unclear whether readmission rates are an effective quality metric. The reasons or conditions associated with readmission after elective THA have been well established but the extent to which readmissions can be prevented after THA remains unclear.Questions/purposes(1) Are unplanned readmissions after THA associated with orthopaedic or medical causes? (2) Are these readmissions preventable? (3) When during the course of aftercare are orthopaedic versus medical readmissions more likely to occur?MethodsWe retrospectively evaluated all 1096 elective THAs for osteoarthritis performed between January 1, 2011 and June 30, 2014 at a major academic medical center. Of those, 69 patients (6%) who met inclusion criteria were readmitted in our healthcare system within 90 days of discharge after the index procedure during the study period. Fifty patients were readmitted within 30 days of discharge after the index procedure (5%). We defined a readmission as any unplanned inpatient or observation status admission to the hospital spanning at least one midnight. A panel of physicians not involved in the care of these patients used available criteria and existing consensus guidelines to evaluate the medical records, radiographs, and operative reports to identify whether the underlying reason for readmission was orthopaedic versus medical. They subsequently were classified as either nonpreventable or potentially preventable readmissions, based on any care that may have occurred during the index hospitalization. To make such determinations, consensus specialty society guidelines were used whenever possible for each readmission diagnosis.ResultsA total of 50 of 1096 patients (5% of those who underwent THA during the period in question) were readmitted within 30 days and 69 of 1096 (6%) were readmitted within 90 days of their index procedures. Thirty-one patients were readmitted for orthopaedic reasons (31/69; 45%) and 38 of 69 were readmitted for medical reasons (55%). Three readmissions (three of 69; 4%) were identified as potentially preventable. Of these potentially preventable readmissions, one was orthopaedic (hip dislocation) and two were medical. Thirty-day readmissions were more likely to be orthopaedic than 90-day readmissions (odds ratio, 4.06; 95% CI, 1.18-13.96; p = 0.026).ConclusionsUsing a panel of expert reviewers, available existing criteria, and consensus methodology, it appears only a small percentage of readmissions after THA are potentially preventable. Orthopaedic readmissions occur earlier during the postoperative course. Currently, existing policies and readmission penalties may not serve as valuable external quality metrics. The readmission rates in our study may represent the threshold for expected readmission rates after THA. Future studies should enroll larger numbers of patients and have independent review panels in efforts to refine criteria for what constitutes preventable readmissions.Level of evidenceLevel III, therapeutic study.
| S-EPMC5384913 | biostudies-literature
Project description:BackgroundTo evaluate the association between social capital and 30-day readmission to the hospital among Medicare beneficiaries overall, beneficiaries with dementia and related memory disorders, and beneficiaries with dual eligibility for Medicaid.MethodsUsing Health and Retirement Study (HRS) data linked with 2008-2015 Medicare claims from traditional Medicare beneficiaries hospitalized during the study period (1246 unique respondents, 2212 total responses), we examined whether dementia and related memory disorders and dual eligibility were associated with social capital. We then estimated a multiple regression model to test whether social capital was associated with a reduced likelihood of readmission.ResultsDementia was associated with an -?0.241 standard deviation (sd) change in social capital (95% CI: -?0.378, -?0.103), dual eligibility with a?-?0.461 sd change (95% CI: -?0.611, -?0.310), and the occurrence of both was associated with an additional -?0.236 sd change (95% CI: -?0.525, -?0.053). 30-day readmission rates were 14.47% over the study period. In both adjusted and unadjusted models, social capital was associated with small and nonsignificant differences in 30-day readmissions. These effects did not vary across dementia status and socioeconomic status.ConclusionsDementia and dual eligibility were associated with lower social capital, but social capital was not associated with the risk of readmission for any population.
| S-EPMC7092426 | biostudies-literature
Project description:BACKGROUND:Reducing 30-day hospital readmissions has become a focus of the current national payment policies. Medicare requires that hospitals collect and report patients' experience with their care as a condition of payment. However, the extent to which patients' experience with hospital care is related to hospital readmission is unknown. METHODS:We established multivariate regression models in which 30-day risk-adjusted readmission rates were the dependent variables and patients' perceptions of the responsiveness of the hospital staff and communication (as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores) were the independent variables of interest. We selected six different clinical conditions for analyses, including acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), heart failure, hip/knee surgery, pneumonia, and stroke. Data included all acute care hospitals reporting in Hospital Compare in 2014. RESULTS:The number of hospitals with reported readmissions ranged from 2234 hospitals for AMI to 3758 hospitals for pneumonia. The average 30-day readmission rates ranged from 5.19% for knee/hip surgery to 22.7% for COPD. Patient experience of hospital-staff responsiveness as "top-box" ranged from 64% to 67% across the six clinical conditions, communication with nurses ranged from 77% to 79% and communication with doctors ranged from 80% to 81% (higher numbers are better). Our finding suggests that hospitals with better staff responsiveness were significantly more likely to have lower 30-day readmissions for all conditions. The effect size depended on the baseline readmission rates, with the largest effect on hospitals in the upper 75th quartile. A ten-percentage-point increase in staff responsiveness led to a 0.03-0.18 percentage point decrease in readmission rates. We found that neither communication with physicians nor communication with nurses was significantly associated with hospital readmissions. CONCLUSIONS:Our findings suggest that elements of care related to staff responsiveness during patients' stay may influence rehospitalization rates. Changes in staff responsiveness may offer an additional tool for hospitals to employ ongoing efforts to achieve reductions in readmissions, an important objective both financially and for patient health outcomes.
| S-EPMC5789545 | biostudies-literature
Project description:About 1 in 5 patients hospitalized for exacerbations of chronic obstructive pulmonary disease (COPD) in the United States are readmitted within 30 days. The U.S. Centers for Medicare and Medicaid Services has recently expanded its Hospital Readmissions Reduction Program to financially penalize hospitals with higher than expected all-cause 30-day readmission rates following a hospitalization for COPD exacerbation. In October 2013, the COPD Foundation convened a multi-stakeholder National COPD Readmissions Summit to summarize our understanding of how to reduce hospital readmissions in patients hospitalized for COPD exacerbations. Over 225 individuals participated in the Summit, including patients, clinicians, health service researchers, policy makers and representatives of academic health care centers, industry, and payers. Summit participants recommend that programs to reduce hospital readmissions: 1) Include specific recommendations about how to promote COPD self-management skills training for patients and their caregivers; 2) Adequately address co-existing disorders common to COPD in care plans during and after hospitalizations; 3) Include an evaluation of adverse events when implementing strategies to reduce hospital readmissions; and 4) Develop a strategy (e.g., a learning collaboratory) to connect groups who are engaged in developing, testing, and implementing programs to reduce hospital readmissions for COPD and other conditions.
| S-EPMC4410712 | biostudies-other