Hilltop researchers Leigh Goetschius, PhD, Ruichen Sun, Fei Han, PhD, and Morgan Henderson, PhD, co-authored this article published in Health Services Research.
The emergence of algorithm-based health care models boasted the promise of objectivity since algorithms are theoretically free from the types of biases and errors to which humans are prone. In practice, however, data are not neutral, and these approaches can perpetuate biases and reinforce existing health disparities.
This study evaluates whether a large predictive model of avoidable hospital (AH) events was biased based on patient race or sex. This model assigns monthly risk scores to all Medicare fee-for-service (FFS) beneficiaries attributed to primary care providers that participate in the Maryland Primary Care Program (MDPCP). The researchers found no evidence of meaningful race- or sex-based bias in the model.
Hilltop researchers Morgan Henderson, PhD, and Morgane Mouslim, DVM, SCM, published this article in the August 2024 issue of The American Journal of Managed Care. The first research of its kind, the piece compares the two different federally mandated sources of public, freely available health services price transparency data (insurer and hospital) for prices for maternity-related services negotiated between Blue Cross & Blue Shield of Mississippi and 26 Mississippi hospitals. Drs. Henderson and Mouslim examined the procedure code overlap for these pricing data sources, and then, for overlapping procedure codes, assessed price congruence. They found low levels of overlap: only 16.3% of hospital-billing code combinations appear in both data sources. However, for the overlapping observations, price concordance is high, with 77.4% of prices matching to the penny. The relatively low degree of overlap between the two pricing data sources indicates significant administrative misalignment between these pricing files; however, the strong concordance of overlapping prices suggests that these data sources are capturing pricing information from the same underlying contracts, as intended. This study is part of the ongoing research conducted by The Hilltop Institute on price transparency.
Hilltop researchers Morgan Henderson, PhD, Leigh Goetschius, PhD, and Fei Han, PhD, co-authored this article published in Medical Care.
Predictive models have proliferated in the health system in recent years and have been used to predict both health services utilization and medical outcomes. Less is known, however, on how these models function and how they might adapt to different contexts. The purpose of the current study is to shed light on the inner workings of a large-scale predictive model deployed in 2 distinct populations, with a particular emphasis on adaptability issues.
Senior Policy Analyst MaryAnn Mood, MA, gave a podium presentation at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.
This chart book explores utilization and expenditures for Medicaid-funded LTSS in Maryland for state fiscal year (FY) 2018 through FY 2022. The focus of this chart book is on Medicaid nursing facility services, with one chapter that illustrates Maryland’s efforts at providing home and community-based services (HCBS) to an increasing number of Medicaid recipients who may otherwise be served in nursing facilities.
Data Scientist Advanced Leigh Goetschius, PhD, gave this podium presentation at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.
Policy Analyst Advanced Morgane Mouslim, ScM, DVM, presented this poster at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.
Policy Analyst Advanced Morgane Mouslim, ScM, DVM, presented this poster at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.
Principal Policy Analyst Christine Gill, PhD, presented this poster at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.
Principal Data Scientist Fei Han, PhD, presented this poster at the 2024 AcademyHealth Annual Research Meeting (ARM) held June 29 – July 2 in Baltimore.