An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient's admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

Comparison of Direct Patient Care Costs and Quality Outcomes of the Teaching and Nonteaching Hospitalist Services at a Large Academic Medical Center.

PURPOSE: To compare costs of care and quality outcomes between teaching and nonteaching hospitalist services, while testing the assumption that resident-driven care is more expensive. METHOD: Records of inpatients with the top 20 Medicare Severity Diagnosis-Related Groups admitted to the University Teaching Service (UTS) and nonteaching hospitalist service (NTHS) at Houston Methodist Hospital from 2014-2015 were analyzed retrospectively. Direct costs of care, length of stay (LOS), in-hospital mortality (IHM), 30-day readmission rate (30DRR), and consultant utilization were compared between the UTS and NTHS. Propensity score matching and case mix index (CMI) were used to mitigate differences in baseline characteristics. To compare outcomes between matched groups, the Wilcoxon rank sum test and chi-square test were used. A sensitivity analysis was conducted using multivariable regression analysis. RESULTS: From the overall study population of 8,457 patients, 1,041 UTS and 3,123 NTHS patients were matched. CMI was 1.07 for each group. The UTS had lower direct costs of care per case ($5,028 vs. $5,502, P = .006), lower LOS (4.7 vs. 5.2 days, P = .0002), and lower consultant utilization (1.0 vs. 1.6, P ≤ .0001) versus the NTHS. The UTS and NTHS 30DRR (17.2% vs. 19.3%, P = .110) and IHM (2.9% vs. 3.7%, P = .206) were comparable. The multivariable regression analysis validated the matched data and identified an incremental cost savings of $333/UTS patient. CONCLUSIONS: Patients of an academic hospitalist service had significantly shorter LOS, fewer consultants, and lower direct care costs than comparable patients of a nonteaching service.