IezzoniLI.Risk adjustment for measuring health care outcomes, 4th edition. Chicago, IL: Health Administration Press, 2012.
2.
Minnesota Department of Health. Quality Reporting System Risk Adjustment Assessment: Report to the Minnesota Legislature. 2017. www.health.state.mn.us/healthreform/measurement/report/RiskAdjustmentAssessmentReportMar2017.pdf Accessed April3, 2019.
3.
KobewkaDM, van WalravenC, TurnbullJ, WorthingtonJ, CalderL, ForsterA. Quality gaps identified through mortality review. BMJ Qual Saf, 2017; 26:141–149.
4.
Marang-van de MheenPJ, ShojaniaKG. Simpson's paradox: how performance measurement can fail even with perfect risk adjustment. BMJ Qual Saf, 2014; 23:701–705.
5.
DimickJB, BirkmeyerJD. Ranking hospitals on surgical quality: does risk-adjustment always matter?. J Am Coll Surg, 2008; 207:347–351.
ChenJ, PaulD, ChunD, JamesJ.Variations and limitations in the risk adjustment of nationally-endorsed clinical quality measures. Presented at: American Public Health Association Annual Meeting and Expo, San Diego, CA, 2018.
8.
Centers for Medicare & Medicaid Services. Quality Measure Development: Supporting Efficiency and Innovation in the Process of Developing CMS Quality Measures. Technical Expert Panel Meeting Summary. 2018. www.health-hats.com/wp-content/uploads/2018/05/2018-01-18_Quality_Measure_Development_TEP_Summary_FINAL_ALL_508.pdf Accessed April3, 2019.
9.
National Qualtiy Forum. Evaluation of the NQF Trial Period for Risk Adjustment for Social Risk Factors (Final Report), Washington DC, 2017. www.qualityforum.org/Publications/2017/07/Social_Risk_Trial_Final_Report.aspx Accessed March20, 2019.
10.
AshA, MickE, EllisR, KiefeC, AllisonJ, ClarkM. Social determinants of health in managed care payment formulas. JAMA Intern Med, 2017; 177:1424–1430.
11.
Sharif-AskaryB, BittarPG, FarjatAE, LiuB, VissociJRN, AlloriAC. Geospatial analysis of risk factors contributing to loss to follow-up in cleft lip/palate care. Plast Reconstr Surg Glob Open, 2018; 6:e1910.
12.
OreskovicNM, ManiatesJ, WeilburgJ, ChoyG. Optimizing the use of electronic health records to identify high-risk psychosocial determinants of health. JMIR Med Inform, 2017; 5:e25.
13.
HaasA, ElliottMN, DemboskyJW, et al.Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Health Serv Res, 2019; 54:13–23.
WolffRF, MoonsKGM, RileyRD, et al.PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019; 170:51–58.
16.
MoonsKG, AltmanDG, ReitsmaJB, et al.Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015; 162:W1–W73.
17.
MoonsKG, de GrootJA, BouwmeesterW, et al.Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014; 11:e1001744.
18.
KronickR, WelchWP. Measuring coding intensity in the Medicare Advantage program. Medicare Medicaid Res Rev, 2014; 4:pii:mmrr2014.004.02.a06.
19.
BastaniH, GohJ, BayatiM.Evidence of upcoding in pay-for-performance programs. Manage Sci, 2018. DOI: 10.1287/mnsc.2017.2996.
