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.
01/02/2025
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.
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11/22/2024
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.
Read the article online.
11/01/2024
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).
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