A Machine Learning Approach for Predicting Inpatient Discharge at Central Maine Medical Center

Operating with a finite quantity of beds, medical resources, and physicians, hospitals are constantly allocating resources under conditions of scarcity. Misallocation of resources and operational inefficiencies are a substantial driver of the United States’ strikingly high healthcare costs. Accurately forecasting the duration which a specific patient will stay in a hospital, also known as a patient’s length of stay, could assist hospital decision makers in optimizing their workflow and allocating their resources efficiently. This paper demonstrates the superiority of a survival random forest approach over classical econometric techniques and current practice at the Central Maine Medical Center. Included in the discussion is an assessment of the strengths and weaknesses of the model, with the hope of informing the application of machine learning methods in the real world

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments

Simulation of 48-Hour Queue Dynamics for A Semi-Private Hospital Ward Considering Blocked Beds

This thesis study evaluates access to care at an internal medicine unit with solely semi-private rooms at Baystate Medical Center (BMC). Patients are divided into two types: Type I patient consumes one bed; Type II patient occupies two beds or an entire semi-private room as a private space for clinical reasons, resulting in one empty but unavailable (blocked) bed per Type II patient. Because little data is available on blocked beds and Type II patients, unit-level hospital bed planning studies that consider blocked beds have been lacking. This thesis study bridges that gap by building a single-stream and a two-stream discrete micro-simulation model in Excel VBA to describe unit-level bed queue dynamics at hourly granularity in the next 48-hour time horizon, using historical arrival rates and census-dependent discharge rates, supplemented with qualitative results on complexity of patient-level discharge prediction. Results showed that while we increase additional semiprivate beds, there was notable difference between the traditional single-stream model and the two-stream model concerning improvement in bed queue size. Possible directions for future research include patient-level discharge prediction considering both clinical and nonclinical milestones, and strategic redesign of hospital unit(s) considering overflows and internal transfers

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care.

AIM: We compared the abilities of two established clinical scores to predict emergency department (ED) disposition: the Glasgow Admission Prediction Score (GAPS) and the Ambulatory Score (Ambs). METHODS: The scores were compared in a prospective, multicentre cohort study. We recruited consecutive patients attending ED triage at two UK sites: Northern General Hospital in Sheffield and Glasgow Royal Infirmary, between February and May 2016. Each had a GAPS and Ambs calculated at the time of triage, with the triage nurses and treating clinicians blinded to the scores. Patients were followed up to hospital discharge. The ability of the scores to discriminate discharge from ED and from hospital at 12 and 48 hours after arrival was compared using the area under the curve (AUC) of their receiving-operator characteristics (ROC). RESULTS: 1424 triage attendances were suitable for analysis during the study period, of which 567 (39.8%) were admitted. The AUC for predicting admission was significantly higher for GAPS at 0.807 (95% CI 0.785 to 0.830), compared with 0.743 (95% CI 0.717 to 0.769) for Ambs, P12 hour and >48 hour. GAPS was also more accurate as a binary test, correctly predicting 1057 outcomes compared with 1004 for Ambs (74.2vs70.5%, P=0.012). CONCLUSION: The GAPS is a significantly better predictor of need for hospital admission than Ambs in an unselected ED population

Lessons Learned from the Quality Improvement Process in a Community Based Hospital: The Dissection of Implementation Failure of Use of the PRISM Mortality Risk Tool and Standardization of Case Management to Reduce Readmissions in High Risk Patients

Hospital readmission, particularly within 30 days of discharge, is a wicked problem. Effective case management is an imperative component of high quality healthcare for the successful transition of patients across acute and post-acute settings. Patients with complex care needs endure an increased risk for negative outcomes, mortality, and hospital readmission. A small body of evidence suggests that early, targeted interventions aimed at high risk patients can mitigate complications and poor transitions. Patient complexity is an important consideration when establishing a comprehensive care management plan. Risk prediction tools are valuable for ensuring that high risk patients receive appropriate resource allocation. Case management processes must promote identification of patients with the most complex needs for the timely delivery of services that are nurse-coordinated, collaborative, and patient-centered. The purpose of this scholarly project was to collaborate with the Case Management and Clinical Quality Management teams at an urban community-based hospital (CBH) to establish a standardized case management protocol for patients determined to be at high risk for mortality and readmission. Using the scores derived from a 30-day mortality risk prediction tool, PRISM, the project plan was to prioritize patients for case management services. The goal of this project was to augment current case management services to ensure that PRISM 1, 2, and 3 patients concurrently receive a standardized bundle of care and person-centered transition planning, beginning at the onset of the inpatient stay

Predicting length of stay across hospital departments

The length of hospital stay and its implications have a significant economic and human impact. As a consequence, the prediction of that key parameter has been subject to previous research in recent years. Most previous work has analysed length of stay in particular hospital departments within specific study groups, which has resulted in successful prediction rates, but only occasionally reporting predictive patterns. In this work we report a predictive model for length of stay (LOS) together with a study of trends and patterns that support a better understanding on how LOS varies across different hospital departments and specialties. We also analyse in which hospital departments the prediction of LOS from patient data is more insightful. After estimating predictions rates, several patterns were found; those patterns allowed, for instance, to determine how to increase prediction accuracy in women admitted to the emergency room for enteritis problems. Overall, concerning these recognised patterns, the results are up to 21.61% better than the results with baseline machine learning algorithms in terms of error rate calculation, and up to 23.83% in terms of success rate in the number of predicted which is useful to guide the decision on where to focus attention in predicting LOS