TY - JOUR
T1 - Implementation of resource use measures in Medicare Advantage
AU - Jung, Jeah
AU - Carlin, Caroline
AU - Feldman, Roger
AU - Tran, Linh
N1 - Publisher Copyright:
© 2022 Health Research and Educational Trust.
PY - 2022/8
Y1 - 2022/8
N2 - Objective: To complement the previously illustrated method to measure resource use in Medicare Advantage (MA) using Encounter data and provide technical details and SAS code to validate Encounter data and implement resource use measures in MA. Data Sources: 2015–2018 MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and Traditional Medicare (TM) claims data. Study Design: Secondary data analysis. Data Collection/Extraction Methods: We select MA contracts with high data completeness (≤10% missing hospital stays in Encounter data and ≤±10% difference in ambulatory and emergency department visits between Encounter and HEDIS data). We randomly sample TM beneficiaries with a similar geographic distribution as MA enrollees in the selected contracts. We develop standardized prices of services using TM payments, and we measure MA resource use for inpatient, outpatient, Part D, and hospice services. Principal Findings: We report identifiers/names of contracts with high data completeness. We provide SAS code to manage Encounter data, develop standardized prices, and measure MA resource use. Conclusions: Greater use and validation of Encounter data can help improve data quality. Our results can be used to inform studies using Encounter data to learn about MA performance.
AB - Objective: To complement the previously illustrated method to measure resource use in Medicare Advantage (MA) using Encounter data and provide technical details and SAS code to validate Encounter data and implement resource use measures in MA. Data Sources: 2015–2018 MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and Traditional Medicare (TM) claims data. Study Design: Secondary data analysis. Data Collection/Extraction Methods: We select MA contracts with high data completeness (≤10% missing hospital stays in Encounter data and ≤±10% difference in ambulatory and emergency department visits between Encounter and HEDIS data). We randomly sample TM beneficiaries with a similar geographic distribution as MA enrollees in the selected contracts. We develop standardized prices of services using TM payments, and we measure MA resource use for inpatient, outpatient, Part D, and hospice services. Principal Findings: We report identifiers/names of contracts with high data completeness. We provide SAS code to manage Encounter data, develop standardized prices, and measure MA resource use. Conclusions: Greater use and validation of Encounter data can help improve data quality. Our results can be used to inform studies using Encounter data to learn about MA performance.
UR - http://www.scopus.com/inward/record.url?scp=85128506349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128506349&partnerID=8YFLogxK
U2 - 10.1111/1475-6773.13970
DO - 10.1111/1475-6773.13970
M3 - Article
C2 - 35411550
AN - SCOPUS:85128506349
SN - 0017-9124
VL - 57
SP - 957
EP - 962
JO - Health Services Research
JF - Health Services Research
IS - 4
ER -