The following are some of the ongoing research projects being conducted by CHIDS Faculty and Fellows.
Public Health and Primary Care Integration through Enhanced Public Health Information Technology (PHIT) Maturity: A Case for Behavioral Health
The quality, cost and effectiveness of population health management in communities nationwide is critically dependent on effective coordination across primary care and public health organizations. Public health information technology (PHIT) provides unique opportunities for improved integration and coordination across public health systems and primary care providers. Limited evidence and understanding currently exists to aid communities in determining how effective their PHIT systems are for achieving care coordination. A primary objective of this project is to develop and validate an evidence-based PHIT Maturity Index to guide improvements in the usefulness, efficiency and outcomes of technology-mediated strategies for public health and clinical care program integration. The project will focus on behavioral health across a racially diverse underserved population and investigate integration across multiple PHIT systems including electronic health records (EHRs), public health reporting (PHR) data systems (such as surveillance systems); and ancillary systems (such as health information exchanges). The project is led by senior researchers and administrators from the University of Maryland, Montgomery County Department of Health and Human Services (MCDHHS), and the Primary Care Coalition of Montgomery County (PCC), and in coordination with the Chesapeake Regional Information System for Our Patients (CRISP) and Maryland’s State Health Improvement Process (SHIP). Funding provided by the Robert Wood Johnson Foundation.
Uma Ahluwali, Ritu Agarwal, Kenyon Crowley, Steve Galen, Bob Gold, Dushanka Kleinman, Tom Lewis, Stephen Manti, Karoline Mortensen, Colleen Ryan-Smith, Yang Yu
Evaluation and Innovation of Predictive Modeling for Healthcare Fraud, Waste and Abuse Prevention
CHIDS and CNSI of Gaithersburg, Md., have formed a partnership that marries innovation from industry with scientific research to reduce health care billing fraud, waste and abuse. This rampant problem creates excessive costs in the health care system and negatively impacts patient care, prompting the need for effective IT solutions to detect improper billing. CNSI’s ClaimsSure® solution can analyze sets of big data to detect fraud, waste and abuse and assess a health care claim’s risk, driving efficiency and cost savings through predictive probability analysis. Researchers at CHIDS will collaborate with CNSI engineers to conduct a series of technical, operational and economic assessments to explore ClaimsSure’s® modeling approach and effectiveness. The partnership aims to advance performance through scientific research for model technical excellence, system-wide impact assessment, and for the behavioral aspects of using the ClaimsSure® product. Through this partnership, CNSI and CHIDS hope to better understand the ability of analytical software to enable improved health care quality outcomes through IT innovation across the public sector.
Ritu Agarwal, Sean Barnes, Margret Bjarnadottir, Kenyon Crowley, Jennifer Jin, CK Kumar, Cecil Murthy, Kislaya Prasad, Nilakantan Rajaraman, Yang We
A Motivation Psychology-Based Smart Engagement System for Improved Older Adult Chronic Disease Management
Mobile technologies provide unprecedented opportunities for patients to engage with physicians and other peer patients and self-manage for disease management. However, there is no guarantee that the social and self-management produces desirable outcomes for all patients. Despite their increasing popularity, most behavioral intervention programs use a “one size fits all” approach, and individual patient’s psychological characteristics are seldom taken into account. As a result, social engagement and health self-management support, if not carefully designed, can be ineffective or even backfire for certain patients. This highlights the urgent need for personalized and adaptable smart social engagement and self-management support technologies for disease that are guided by a deep understanding of motivation psychology.
The objective of this project is to design, implement, and optimize a motivation psychology-based smart engagement system (MOSES) for older adults with diabetes. The MOSES tool, delivered via tablets, empowers the patients with two forms of social engagement, with healthcare providers, and with peer patients, as well as personalized self-engagement support. Our goal is to use MOSES as a research platform to advance knowledge on how the interventions should be optimized at the individual patient level by integrating motivational psychology theories. The project is being conducted in collaboration with the University of Maryland Medical School and the Fraunhofer Center for Experimental Software Engineering.
Ritu Agarwal, Kenyon Crowley, Michelle Dugas, Gordon Gao, Arie Kruglanski, Catherine Plaisant, Arnab Ray, Charles Song, Nanette Steinle, Bo Xie
Predicting Prostate Cancer Risk Using Magnetic Resonance Imaging Data
Prostate cancer is widely prevalent and hard to diagnose. The National Cancer Institute estimates that 16% of men born today will be diagnosed with prostate cancer in their lifetime (Howlader et al. 2012). In 2013, an estimated 238,590 new prostate cancer cases will be diagnosed and an estimated 29,720 men will die because of prostate cancer in United States (Siegel et al. 2013). Currently, the two main methods for diagnosing prostate cancer are a prostate specific antigen (PSA) test and a trans-rectal extended biopsy. Both of these methods have serious drawbacks. Although it has some predictive power, a PSA test can be unreliable with a high error rate (Hoffman et al. 2002). While a biopsy is more accurate, it is expensive and highly invasive with negative side effects (Cooper et al. 2004). Since a biopsy is conducted randomly within the prostate gland, it can result in a significant number of misses in cancer diagnosis, as well. Even when biopsy does detect cancer, the location can be uncertain and the localization of tumor within the prostate remains imprecise (Salomon et al 1999). We propose a non-invasive method for classifying patient risk using Magnetic Resonance Imaging (MRI) data. Logistic regression and nearest neighbor classification are combined to identify the risk of cancer. Our method performs well, having 79% predictive accuracy, and an area under the ROC curve of 0.85. It identifies the most aggressive cancers with 82% accuracy.
David Anderson, Bruce Golden, Edward Wasil, and Hao Howard Zhang
Understanding Sticker Prices: An Analysis of CMS Provider Charge Data
This year, the Centers for Medicare and Medicaid Services (CMS) released data containing information on provider charges (i.e., sticker prices) and CMS payments for the 100 most common inpatient services and 30 common outpatient services for the fiscal year 2011. Our objective is to evaluate how CMS payment models explain payments and sticker prices for both inpatient and outpatient services. We have found that payments for inpatient and outpatient services are explained extremely well by their respective models, but these payment models do not explain sticker prices well. We are currently applying advanced machine learning methods to an augmented data set to improve our understanding of sticker prices. Our analysis demonstrates that providers are using additional (and potentially diverse) information to determine their prices. Ongoing research investigates whether higher sticker prices are associated with higher quality providers, higher profits, and other potential provider-based outcomes.
Margret Bjarnadottir, Sean Barnes