Subgroups of High-Cost Patients and Their Preventable Inpatient Cost in Rural China

Background: High -cost patients account for most healthcare costs and are highly heterogeneous. This study aims to classify high -cost patients into clinically homogeneous subgroups, describe healthcare utilization patterns of subgroups, and identify subgroups with relatively high preventable inpatient cost (PIC) in rural China. Methods: A population -based retrospective study was performed using claims data in Xi county, Henan province. 32 108 high -cost patients, representing the top 10% of individuals with the highest total spending, were identified. A densitybased clustering algorithm combined with expert opinions were used to group high -cost patients. Healthcare utilization (including admissions, length of stay, and outpatient visits) and spending characteristics (including total spending, and the proportion of PIC, inpatient and out-of-pocket spending on total spending) were described among subgroups. PIC was calculated based on potentially preventable hospitalizations (PPHs) which were identified according to the Agency for Healthcare Research and Quality Prevention Quality Indicators algorithm. Results: High -cost patients were more likely to be older (Mean = 51.87, SD = 22.28), male (49.03%) and from povertystricken families (37.67%) than non -high -cost patients, with 2.49 (SD = 2.47) admissions and 3.25 (SD = 4.52) outpatient visits annually. Fourteen subgroups of high -cost patients were identified: chronic disease, non -trauma diseases which need surgery, female disease, cancer, eye disease, respiratory infection/inflammation, skin disease, fracture, liver disease, vertigo syndrome and cerebral infarction, mental disease, arthritis, renal failure, and other neurological disorders. The annual admissions ranged from 1.83 (SD = 1.23, fracture) to 12.21 (SD = 9.26, renal failure), and the average length of stay ranged from 6.61 (SD = 10.00, eye disease) to 32.11 (SD = 28.78, mental disease) days among subgroups. The chronic disease subgroup showed the largest proportion of PIC on total spending (10.57%). Conclusion: High -cost patients were classified into 14 clinically distinct subgroups which had different healthcare utilization and spending characteristics. Different targeted strategies may be needed for subgroups to reduce preventable hospitalizations. Priority should be given to high -cost patients with chronic diseases.