HBHI announced six pilot grant awardees for our 2022 cohort.  We sought pilots which advance HBHI’s mission to incubate, accelerate, and disseminate impactful, world-class, collaborative research across the Divisions of Johns Hopkins University that aims to improve the productivity of the nation’s health system.  We requested pilot proposals that incubate or accelerate collaborative research within the domains of at least one of the HBHI strategic pillars:

Market competition and healthcare policy

Health delivery performance

Health of populations and health equity

Innovation – health technology and systems

Read about the winning pilots and teams below.  Congratulations on such excellent proposals!

Bad Medical News and the Aversion of Generic Drugs

PI: Manuel Hermosilla, PhD, Carey Business School; and Andrew Ching, PhD, Carey Business School

Project Description: Many factors may be involved in medical decision-making. This proposal tests the hypothesis if a recent adverse diagnosis, such as the diagnosis of an elevated LDL level, results in greater risk aversion and resulting in selection of brand name drugs over generic drugs for treatment. This proposal will use regression discontinuity and MarketScan data to evaluate this research question. This project will help us better understand variables that may influence drug choices and pharmaceutical costs.

Do Hospital Mergers Make Patients Less Safe?

PI: Zenon Zabinski, PhD, Bates White Economic Consulting; and Matthew Zahn, PhD candidate, JHU Department of Economics

Project Description: As hospitals consolidate into health systems throughout the country, do these mergers impact patient safety? This proposal plans to investigate the impact of healthcare mergers on 17 AHRQ- defined and nationally used Patient Safety Indicators (PSIs) using a MedPAR data and state discharge data through a difference-in-difference approach. This analysis will help inform the impact of hospital mergers on patient care.

Precision Intraoperative Teaming: Evidence-based Strategies for Surgical Team Composition

PI: Anna Mayo, PhD, Carey Business School; and Chris Myers, PhD, Carey Business School.  Co-Investigators from the School of Medicine include Michael Rosen, Kathleen Sutcliffe (and Carey), Rahul Koka, Jake Abernathy, Giancarlo Suffredini, Peter Najjar, and Emily Boss

Project Description: The composition and familiarity of intra-operative teams have both positive and negative associations with outcomes. This proposal seeks to develop a data set linking team composition metrics with surgical outcomes and to use these data to develop multi-level models to evaluate surgical outcomes. These results will guide optimal team intra-operative team structure for the best surgical outcomes.

Waste-Free Formularies: Developing a tool to identify savings opportunities from substituting high-cost drugs with less expensive but clinically equivalent alternatives

PI: Mariana Socal, PhD, MD, Bloomberg School of Public Health; Celia Proctor, PharmD, MBA, Bloomberg School of Public Health; and Ge Bai, PhD, CPA, Carey Business School

Project Description: Efficient drug management helps reduce extraneous healthcare costs. This proposal seeks to develop an automated tool that will quickly allow pharmacies to identify savings opportunities from substituting expensive medications with less expensive and equally effective therapeutic options using data from Medi-Span and then test this algorithm using Marketscan data. Tools such as this will help pharmacies identify opportunities to become more waste-free.

Elective Orthopaedic Surgery as a Model to Compare Medicare Advantage and Traditional Medicare

PI: Amit Jain, MD, School of Medicine; Michael Darden, PhD, Carey Business School, and Kelly Anderson PhD, School of Pharmacy, University of Colorado

Project Description: Medicare Advantage is growing across the US and there is limited understanding of patient outcomes between these groups due to low volumes and patient heterogeneity. This proposal seeks to compare differences in utilization and outcomes of inpatient orthopedic surgery, which are increasingly common high-cost procedures, using the HBHI data enclave and the 2018 Master Beneficiary Summary File (MBSF) as well as other data sources. This project will also investigate other analytic approaches to control for confounding factors and establish causality as the regression discontinuity approach is no longer appropriate. This project will help evaluate patient-related outcomes between two different payment models.

Medical Demography of Clinical Populations

PI: Jodi Segal, MD, MPH, School of Medicine

Project Description: Advancing health systems to address the health of communities will require extracting data from the electronic medical record (EHR) that can rigorously characterize populations of patients in a way that is reproducible. Population surveys developed by demographers are the standard method for characterizing populations, but these data are not readily available to the hospitals and response planners who need this information. The goal of this pilot is to advance the work of medical demographics to characterize populations of patients, so that this information is readily available to varied stakeholders working to advance health care and health. Through this pilot, HBHI is investing in engagement with the Johns Hopkins InHealth and the recently launched the (Adult) Primary Care Center of Excellence (PCCOE). The PCCOE’s mission is to accelerate discovery and translation of research into primary care practice. The PCCOE can access real-time data (updated nightly) from the electronic health record of all patients who receive care at any of the 32 sites across Johns Hopkins Medicine that provide adult primary care. These data are in structured tables within the Precision Medicine Analytics Platform (PMAP). This yields a first-time opportunity to characterize the clinical population of patients who turn to Johns Hopkins for their primary care.

Incorporating AI into Day-to-Day Clinical Workflow: Impact of AI on Healthcare Operations, Physician Behavior, and Quality of Care

PI: Tinglong Dai, PhD, Carey Business School; Haiyang Yang, PhD, Carey Business School; and Risa M. Wolf, MD, School of Medicine

Project Description: Artificial intelligence is rapidly transforming the delivery of health care. This proposal serves better elucidate patient and physician attitudes towards AI using multimodal approaches like focus groups, observation of physician patient interactions to develop stochastic decision and game-theoretic models and observe physician-patient interactions.  The objective of the workgroup is to understand factors involved in the adoption of AI to deploy AI effectively.