Hilltop Director of Analytics & Research Morgan Henderson, Senior Director of Health Reform Studies Laura Spicer, and Interim Executive Director Alice Middleton discuss their re-examination of 2018-2019 enrollment data from Arkansas Works (the state’s Medicaid work requirement program) in this article published in the Health Affairs Forefront series.
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.
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).
The Hilltop Pre-HE Model™—which generates the rankings for the Hospice Eligibility and Advanced Care Planning (Pre-HE) scores—is designed to support proactive advanced care planning discussions by estimating a patient’s risk of eligibility for hospice. The Pre-HE Model provides risk scores and reasons for risk for all attributed beneficiaries of Maryland Primary Care Program (MDPCP) practices every month in order to identify patients that are potentially appropriate for hospice care and to provide care teams with information that can guide the sensitive and difficult conversations about end-of-life care with patients and their families.
The Hilltop Pre-DC Model™—which generates the rankings for the Severe Diabetes Complications (Pre-DC) scores—is designed to facilitate the active management of type 2 diabetes by estimating individuals’ risk of incurring inpatient admissions or emergency department (ED) visits for severe diabetes complications. The Pre-DC Model provides risk scores and reasons for risk for all attributed beneficiaries of Maryland Primary Care Program (MDPCP) practices every month to help care teams proactively identify high-risk individuals and allocate scarce care management resources.
The Hilltop Pre-AH Model™—which generates the rankings for the Avoidable Hospitalizations (Pre-AH) scores—is designed to assist providers by allowing them to easily identify patients at a high risk of incurring an avoidable inpatient hospitalization or emergency department (ED) visit. The Pre-AH Model provides risk scores and reasons for risk for all attributed beneficiaries of Maryland Primary Care Program (MDPCP) practices every month to help care teams make informed decisions about how to direct scarce care coordination resources to the individuals who will benefit from them the most.
This annual report, written for the UMBC community, provides an overview of key projects and staff accomplishments for FY 2024.