Focusing on the Medically Ready for Discharge Patient Using a Reliable Design Strategy: A Quality Improvement Project to Improve Length of Stay on a Medicine Service

A systematic literature review of predicting patient discharges using statistical methods and machine learning

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

Medically ready for discharge: A multisite "point-in-time" assessment of hospitalized patients

BACKGROUND

Time spent awaiting discharge after the acute need for hospitalization has resolved is an important potential contributor to hospital length of stay (LOS).

OBJECTIVE

To measure the prevalence, impact, and context of patients who remain hospitalized for prolonged periods after completion of acute care needs.

DESIGN, SETTING, AND PARTICIPANTS

We conducted a cross-sectional "point-in-time" survey at each of 15 academic US hospitals using a structured data collection tool with on-service acute care medicine attending physicians in fall 2022.

MAIN OUTCOMES AND MEASURES

Primary outcomes were number and percentage of patients considered "medically ready for discharge" with emphasis on those who had experienced a "major barrier to discharge" (medically ready for discharge for ≥1 week). Estimated LOS attributable to major discharge barriers, contributory discharge needs, and associated hospital characteristics were measured.

RESULTS

Of 1928 patients sampled, 35.0% (n = 674) were medically ready for discharge including 9.8% (n = 189) with major discharge barriers. Many patients with major discharge barriers (44.4%; 84/189) had spent a month or longer medically ready for discharge and commonly (84.1%; 159/189) required some form of skilled therapy or daily living support services for discharge. Higher proportions of patients experiencing major discharge barriers were found in public versus private, nonprofit hospitals (12.0% vs. 7.2%; p = .001) and county versus noncounty hospitals (14.5% vs. 8.8%; p = .002).

CONCLUSIONS

Patients experience major discharge barriers in many US hospitals and spend prolonged time awaiting discharge, often for support needs that may be outside of clinician control.

Multi-Disciplinary Discharge Coordination Team to Overcome Discharge Barriers and Address the Risk of Delayed Discharges

Background

Methods

Results

Conclusion