Dataset Information

Prescriptive analytics for reducing 30-day hospital readmissions after general surgery.

ABSTRACT: INTRODUCTION:New 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 METHODS:This 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. RESULTS:Predictions 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. CONCLUSIONS:This 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.

SUBMITTER: Bertsimas D

PROVIDER: S-EPMC7480861 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prescriptive analytics for reducing 30-day hospital readmissions after general surgery.

Bertsimas Dimitris D Li Michael Lingzhi ML Paschalidis Ioannis Ch IC Wang Taiyao T

PloS one 20200909 9

<h4>Introduction</h4>New 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 o ...[more]

PMID: 32903282

Similar Datasets

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.

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:ImportanceWith the approval of transcatheter aortic valve replacement (TAVR) for patients with severe symptomatic aortic stenosis at intermediate surgical risk, TAVR volume is projected to increase exponentially in the United States. The 30-day readmission rate for TAVR was recently reported at 17.9%. The association between institutional TAVR volume and the 30-day readmission metric has not been examined.ObjectiveTo assess the association between hospital TAVR volume and 30-day readmission.Design, setting, and participantsIn this observational study, we used the 2014 Nationwide Readmissions Database to identify hospitals with established TAVR programs (performing at least 5 TAVRs in the first quarter of 2014). Based on annual TAVR volume, hospitals were classified as low (<50), medium (≥50 to <100), and high (≥100) volume. Rates, causes, and costs of 30-day readmissions were compared between low-, medium-, and high-volume hospitals. Data were analyzed from November to December 2016.ExposureTranscatheter aortic valve replacement.Main outcomes and measuresThirty-day readmissions.ResultsOf 129 hospitals included in this study, 20 (15.5%) were categorized as low volume, 47 (36.4%) as medium volume, and 62 (48.1%) as high volume. Of 16 252 index TAVR procedures, 663 (4.1%), 3067 (18.9%), and 12 522 (77.0%) were performed at low-, medium-, and high-volume hospitals, respectively. Thirty-day readmission rates were significantly lower in high-volume compared with medium-volume (adjusted odds ratio, 0.76; 95% CI, 0.68-0.85; P < .001) and low-volume (adjusted odds ratio, 0.75; 95% CI, 0.60-0.92; P = .007) hospitals. Noncardiac readmissions were more common in low-volume hospitals (65.6% vs 60.6% in high-volume hospitals), whereas cardiac readmissions were more common in high-volume hospitals (39.4% vs 34.4% in low-volume hospitals). There were no significant differences in length of stay and costs per readmission among the 3 groups (mean [SD], 5.5 [5.0] days vs 5.9 [7.5] days vs 6.0 [5.8] days; P = .74, and $13 886 [18 333] vs $14 135 [17 939] vs $13 432 [15 725]; P = .63, respectively).Conclusions and relevanceWe report for the first time, to our knowledge, an inverse association between hospital TAVR volume and 30-day readmissions. Lower readmission at higher-volume hospitals was associated with significantly lower cost to the health care system.

Dataset Information

Prescriptive analytics for reducing 30-day hospital readmissions after general surgery.

Publications

Prescriptive analytics for reducing 30-day hospital readmissions after general surgery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets