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Patient vs provider perspectives of 30-day hospital readmissions.

ABSTRACT: Objective:To compare patients' and providers' views on contributors to 30-day hospital readmissions. Design:Analysis of a qualitative interview survey between 18 May-30 June 2015. Setting:Interviews were conducted during the 30-day readmission hospitalisation at a single tertiary care academic hospital. Participants:We conducted 178 interviews of readmitted patients. Measures:We queried opinions of what factors patients believed contributed to their rehospitalisation and compared this with the perspective of the index admission provider. The primary outcome was the view that the readmission was preventable. A review by a RN (nurse) case manager also provided an assessment based on patient report, provider report and chart review. Results:Patients were more likely to view a readmission as preventable compared with physicians (p<0.0001). Patients identified system issues (defined as factors controlled by the hospital discharge process) as contributors to their readmission in 58% (103/178) of cases while providers identified system issues as the contributor to a patients' readmission in 2% (2/101) of cases. Patients with poor functional status were more likely to feel the cause of their readmission was due to system issues than patients with better functional status (p=0.03). A RN case manager review determined that in 48% (86/178) of cases the system had some amount of contribution to a patient's readmission. There was no significant difference in belief that the readmission was preventable between the RN case manager and the patient (p=0.47). Conclusions:Readmitted patients often feel that the hospital system contributed to their readmission. Providers did not recognise patient and RN case manager identified issues as contributors to hospital readmissions.

SUBMITTER: Smeraglio A

PROVIDER: S-EPMC6327873 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Patient vs provider perspectives of 30-day hospital readmissions.

Smeraglio Andrea A Heidenreich Paul A PA Krishnan Gomathi G Hopkins Joseph J Chen Jonathan J Shieh Lisa L

BMJ open quality 20190107 1

<h4>Objective</h4>To compare patients' and providers' views on contributors to 30-day hospital readmissions.<h4>Design</h4>Analysis of a qualitative interview survey between 18 May-30 June 2015.<h4>Setting</h4>Interviews were conducted during the 30-day readmission hospitalisation at a single tertiary care academic hospital.<h4>Participants</h4>We conducted 178 interviews of readmitted patients.<h4>Measures</h4>We queried opinions of what factors patients believed contributed to their rehospital ...[more]

PMID: 30687798

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Project description:BackgroundPredictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized.ObjectiveWe aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM).DesignThis cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach.ParticipantsNurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night.Main measuresWe performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP.Key resultsWe collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home.ConclusionsOur study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.

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.

Project description:Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.International retrospective cohort study of 117?065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.Overall, 117?065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16?992 of 117?065 (14.5%) were followed by a 30-day readmission, and 11?307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n?=?73?031 [62.4%]), intermediate (n?=?27?612 [23.6%]), and high risk (n?=?16?422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson ?2 test P?=?.89).The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

Project description:This study sought to determine the range and prevalence of practices being implemented by hospitals to reduce 30-day readmissions of patients with heart failure or acute myocardial infarction (AMI).Readmissions of patients with heart failure or AMI are both common and costly; however, evidence on strategies adopted by hospitals to reduce readmission rates is limited.We used a Web-based survey to conduct a cross-sectional study of hospitals' reported use of specific practices to reduce readmissions for patients with heart failure or AMI. We contacted all hospitals enrolled in the Hospital to Home (H2H) quality improvement initiative as of July 2010. Of 594 hospitals, 537 completed the survey (response rate of 90.4%). We used standard frequency analysis to describe the prevalence of key hospital practices in the areas of: 1) quality improvement resources and performance monitoring; 2) medication management efforts; and 3) discharge and follow-up processes.Nearly 90% of hospitals agreed or strongly agreed that they had a written objective of reducing preventable readmission for patients with heart failure or AMI. More hospitals reported having quality improvement teams to reduce preventable readmissions for patients with heart failure (87%) than for patients with AMI (54%). Less than one-half (49.3%) of hospitals had partnered with community physicians and only 23.5% had partnered with local hospitals to manage patients at high risk for readmissions. Inpatient and outpatient prescription records were electronically linked usually or always in 28.9% of hospitals, and the discharge summary was always sent directly to the patient's primary medical doctor in only 25.5% of hospitals. On average, hospitals used 4.8 of 10 key practices; <3% of hospitals utilized all 10 practices.Although most hospitals have a written objective of reducing preventable readmissions of patients with heart failure or AMI, the implementation of recommended practices varied widely. More evidence establishing the effectiveness of various practices is needed.

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Patient vs provider perspectives of 30-day hospital readmissions.

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Patient vs provider perspectives of 30-day hospital readmissions.

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