The American journal of managed care · 2017
Statewide data infrastructure supports population health management: diabetes case study.
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Jones Craig, Mohlman Mary Kate, Jorgenson David, Finison Karl, McGee Katie, Kastensmith Hans
Аннотация
OBJECTIVES: To understand how a statewide data infrastructure, including clinical and multipayer claims data, can inform preventive care and reduce medical expenditures for patients with diabetes.
STUDY DESIGN: A retrospective 1-year cross-sectional analysis of claims linked to clinical data for 6719 patients with diabetes in 2014 to evaluate impacts of comorbidities on the total cost of care.
METHODS: Initially, variation in healthcare expenditures was examined versus a measure of disease control (most recent glycated hemoglobin [A1C] test results). Multivariable linear regression calculated the relative impact of a series of risk factors on medical expenditures. Poisson regression estimated the relative impact on inpatient hospital admissions. Possible savings were estimated with a reduction in potentially avoidable hospital admissions.
RESULTS: No linear relationship was found between A1C and same-year medical expenditures. Comorbidities in the population with diabetes with the largest relative impact on expenditures and inpatient hospital admissions were renal failure, congestive heart failure, chronic obstructive pulmonary disease, and discordant blood pressure. Diabetes plus congestive heart failure had the highest cost per inpatient admission; diabetes plus body mass index (BMI) ≥35 had the highest aggregate costs and potential savings.
CONCLUSIONS: A statewide data infrastructure can be used to identify criteria for outreach and population management of diabetes. The selection criteria are applicable across a statewide population and are associated with a higher relative impact on near-term expenditures than recent A1C test results. Whole-population data aggregation can be used to develop actionable information that is particularly relevant as independent organizations work together under alternative payment model arrangements.
STUDY DESIGN: A retrospective 1-year cross-sectional analysis of claims linked to clinical data for 6719 patients with diabetes in 2014 to evaluate impacts of comorbidities on the total cost of care.
METHODS: Initially, variation in healthcare expenditures was examined versus a measure of disease control (most recent glycated hemoglobin [A1C] test results). Multivariable linear regression calculated the relative impact of a series of risk factors on medical expenditures. Poisson regression estimated the relative impact on inpatient hospital admissions. Possible savings were estimated with a reduction in potentially avoidable hospital admissions.
RESULTS: No linear relationship was found between A1C and same-year medical expenditures. Comorbidities in the population with diabetes with the largest relative impact on expenditures and inpatient hospital admissions were renal failure, congestive heart failure, chronic obstructive pulmonary disease, and discordant blood pressure. Diabetes plus congestive heart failure had the highest cost per inpatient admission; diabetes plus body mass index (BMI) ≥35 had the highest aggregate costs and potential savings.
CONCLUSIONS: A statewide data infrastructure can be used to identify criteria for outreach and population management of diabetes. The selection criteria are applicable across a statewide population and are associated with a higher relative impact on near-term expenditures than recent A1C test results. Whole-population data aggregation can be used to develop actionable information that is particularly relevant as independent organizations work together under alternative payment model arrangements.