Below is a listing of select Hilltop publications and presentations. You can search by type, topic, date, and/or title. The search function searches for key words in both the title and the publication summary. Click on the publication’s title below to go to its summary.
Accessibility Notice: Publications released before April 24, 2026 have not been remediated for Section 508 compliance.
Hilltop Policy Analyst Advanced Roberto Millar, PhD, and Director of Aging and Disability Studies Christin Diehl coauthored this article published in Nursing Reports. This article discusses the results of a cross-sectional study that utilizes public data from 218 Medicare and Medicaid-certified nursing facilities in Maryland to examine the association between staffing requirements and quality of care ratings, as well as the role facility ownership plays.
Hilltop Policy Analyst Advanced Roberto Millar, PhD, and Director of Aging and Disability Studies Christin Diehl coauthored this article published in the Journal of Applied Gerontology. They discuss the importance of nursing facility structural characteristics in contributing to residents’ quality of care. The study used data from 220 Maryland nursing facilities to examine associations between two different quality-of-care metrics: family satisfaction and Care Compare five-star quality ratings.
In 2014, the state of Maryland partnered with the Centers for Medicare and Medicaid Services (CMS) to modernize its unique all-payer rate-setting system for hospital services to improve the overall health of Maryland residents by increasing health care quality and reducing the cost of care. In service of providing better care at lower costs, The Hilltop Institute at UMBC, in partnership with the Maryland Department of Health, has developed predictive risk stratification models to identify patients at high risk for potentially preventable health care utilization that can be used to help target care resources to the patients who need them most.
This document strives to explain the intended use, technical implementation, and model performance of the Hilltop Pre- Models as of December 2024. The Pre- Models are a suite of prediction tools spanning the Pre-AH Model, Pre-DC Model, and Pre-HE Model. This document will be updated as the models are updated or when new models become operational, and significant changes will be noted in the documentation edit history table and in the text when necessary.
This report describes the services The Hilltop Institute provided to the Maryland Department of Health under their Master Agreement. The report covers fiscal year (FY) 2024 (July 1, 2023, through June 30, 2024). Hilltop’s interdisciplinary staff provided a wide range of services, including Medicaid program development and policy analysis; HealthChoice program support, evaluation, and financial analysis; long-term services and supports program development, policy analysis, and financial analytics; and data management and web-accessible database development.
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.
The Hilltop Pre- Models are risk prediction models developed by The Hilltop Institute at UMBC that use a variety of risk factors derived from Medicare claims data to estimate the event risk that a given patient incurs a given outcome in the near future. As of November 2022, there are three such prediction models in production for the Maryland Primary Care Program (MDPCP) population: the Hilltop Pre-AH Model™, which generates the “Avoidable Hospitalizations (PreAH)” scores; the Hilltop Pre-DC Model™, which generates the “Severe Diabetes Complications (Pre-DC)” scores; and the Hilltop Pre-HE Model™, which generates the “Hospice Eligibility and Advanced Care Planning (Pre-HE)” scores. These risk scores are displayed in the MDPCP Prediction Tools area on Chesapeake Regional Information System for our Patients (CRISP).
This annual report, written for the UMBC community, provides an overview of key projects and staff accomplishments for FY 2024.
This is the third annual review of the integration requirements for Medicare Advantage dual eligible special needs plans (D-SNPs) completed for the Maryland Department of Health. The goal of D-SNP integration, which became effective in calendar year (CY) 2021, is to help improve coordination of care transitions for individuals who are dually eligible for Medicare and Medicaid. This review covers key findings from the third year of implementation.
The Maryland Primary Care Program (MDPCP) began in January 2019 as a key element of the state’s Total Cost of Care (TCOC) Model, an agreement between CMS and the state of Maryland. MDPCP is a voluntary program that provides funding and support for the delivery of advanced primary care throughout the state. It allows primary care providers to play an increased role in the prevention and management of chronic disease, as well as in the prevention of unnecessary hospital utilization, with the ultimate goal of improving quality of care while reducing Medicare TCOC growth.
This report documents the causal impact of the introduction of MDPCP on utilization and expenditure and is the state’s commissioned evaluation of the program to fulfill the 2024 Joint Chairmen’s Report (JCR) requirement of an independent evaluation of MDPCP.
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.