Using the Johns Hopkins ACG Case-Mix System for population segmentation in a hospital-based adult patient population in Singapore.

OBJECTIVE: Population health management involves risk characterisation and patient segmentation. Almost all population segmentation tools require comprehensive health information spanning the full care continuum. We assessed the utility of applying the ACG System as a population risk segmentation tool using only hospital data. DESIGN: Retrospective cohort study. SETTING: Tertiary hospital in central Singapore. PARTICIPANTS: 100 000 randomly selected adult patients from 1 January to 31 December 2017. INTERVENTION: Hospital encounters, diagnoses codes and medications prescribed to the participants were used as input data to the ACG System. PRIMARY AND SECONDARY OUTCOME MEASURES: Hospital costs, admission episodes and mortality of these patients in the subsequent year (2018) were used to assess the utility of ACG System outputs such as resource utilisation bands (RUBs) in stratifying patients and identifying high hospital care users. RESULTS: Patients placed in higher RUBs had higher prospective (2018) healthcare costs, and were more likely to have healthcare costs in the top five percentile, to have three or more hospital admissions, and to die in the subsequent year. A combination of RUBs and ACG System generated rank probability of high healthcare costs, age and gender that had good discriminatory ability for all three outcomes, with area under the receiver-operator characteristic curve (AUC) values of 0.827, 0.889 and 0.876, respectively. Application of machine learning methods improved AUCs marginally by about 0.02 in predicting the top five percentile of healthcare costs and death in the subsequent year. CONCLUSION: A population stratification and risk prediction tool can be used to appropriately segment populations in a hospital patient population even with incomplete clinical data.

Singapore's regional health systems-a data-driven perspective on frequent admitters and cross utilization of healthcare services in three systems.

INTRODUCTION: With population health management being a priority in the Singapore, this paper aims to provide a data-driven perspective of the population health management initiatives to aid program planning and serves as a baseline for evaluation of future implemented programs. METHODS: A database with information on patient demographics, health services utilization, cost, diagnoses and chronic disease information from 2008 to 2013 for three regional health systems in Singapore was used for analysis. Patients with three or more inpatient admissions were considered as "Frequent Admitters." Health service utilization was quantified, and cross utilization of services was studied. One-year readmission rate for inpatients was studied, and a predictive model for readmission or death was developed. RESULTS: There were a total of 2.8 M patients in the database. Frequent admitters accounted for 0.9% of all patients with an average cost per patient of S$29 547. Of these, 89% had chronic diseases. Cross utilization of health services showed that 8.2% of the patients utilized services from more than one hospital with 19.6% utilizing hospital and polyclinic services in 2013. The highest risk of readmission or death was for those patients who had five or more inpatient episodes in each of the preceding 2 years. CONCLUSION: By understanding the profile of the patients and their utilization patterns in the three regional health systems, our study will help clinicians and decision makers design appropriate integrated care programs for patients with the aim of covering the healthcare needs for the enitre population across the healthcare spectrum in Singapore. Copyright © 2015 John Wiley & Sons, Ltd.