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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.
Retrospective cohort study.
Tertiary hospital in central Singapore.
100 000 randomly selected adult patients from 1 January to 31 December 2017.
Hospital encounters, diagnoses codes and medications prescribed to the participants were used as input data to the ACG System.
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.
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.
A population stratification and risk prediction tool can be used to appropriately segment populations in a hospital patient population even with incomplete clinical data.
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