Risk score-guided multidisciplinary team-based Care for Heart Failure Inpatients is associated with lower 30-day readmission and lower 30-day mortality.

OBJECTIVE: Using augmented intelligence clinical decision tools and a risk score-guided multidisciplinary team-based care process (MTCP), this study evaluated the MTCP for heart failure (HF) patients' 30-day readmission and 30-day mortality across 20 Intermountain Healthcare hospitals. BACKGROUND: HF inpatient care and 30-day post-discharge management require quality improvement to impact patient health, optimize utilization, and avoid readmissions. METHODS: HF inpatients (N = 6182) were studied from January 2013 to November 2016. In February 2014, patients began receiving care via the MTCP based on a phased implementation in which the 8 largest Intermountain hospitals (accounting for 89.8% of HF inpatients) were crossed over sequentially in a stepped manner from control to MTCP over 2.5 years. After implementation, patient risk scores were calculated within 24 hours of admission and delivered electronically to clinicians. High-risk patients received MTCP care (n = 1221), while lower-risk patients received standard HF care (n = 1220). Controls had their readmission and mortality scores calculated retrospectively (high risk: n = 1791; lower risk: n = 1950). RESULTS: High-risk MTCP recipients had 21% lower 30-day readmission compared to high-risk controls (adjusted P = .013, HR = 0.79, CI = 0.66, 0.95) and 52% lower 30-day mortality (adjusted P < .001, HR = 0.48, CI = 0.33, 0.69). Lower-risk patients did not experience increased readmission (adjusted HR = 0.88, P = .19) or mortality (adjusted HR = 0.88, P = .61). Some utilization was higher, such as prescription of home health, for MTCP recipients, with no changes in length of stay or overall costs. CONCLUSIONS: A risk score-guided MTCP was associated with lower 30-day readmission and 30-day mortality in high-risk HF inpatients. Further evaluation of this clinical management approach is required.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

OBJECTIVE: Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. METHODS: Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. RESULTS: The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. CONCLUSIONS: Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.