Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

Objective: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.

Design And Setting: This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database.

Background: High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study.

Background: As healthcare expenditure and utilization continue to rise, understanding key drivers of hospital expenditure and utilization is crucial in policy development and service planning. This study aims to investigate micro drivers of hospital expenditure and length of stay (LOS) in an Academic Medical Centre.

Methods: Data corresponding to 285,767 patients and 207,426 inpatient visits was extracted from electronic medical records of the National University of Hospital in Singapore between 2005 to 2013.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation.

Background: Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress.

Objective: The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas.