Carnegie Mellon's Initiative for Patient Safety Research (IPSR) is a partnership and grant between CMU and the Jewish Healthcare Foundation (JHF) to build and engage a multidisciplinary community of researchers to analyze data with the goal of:

• Detecting medication errors and developing proof-of-concept innovations to reduce them.
• Developing new computational and analytical methods to identify and define medication errors within electronic medical record data.
• Identifying trends associated with the errors to provide an understanding of the precursors to, and potential causes of, medication errors.

The IPSR will foster and support research across the patient safety landscape, with a first phase of research that will collect and analyze data on medication errors using foundational data-driven concepts focused on data availability, pattern recognition and assessment. The ultimate goal of IPSR is to create a better health system and a healthcare environment dedicated to safety — addressing medication errors is just the first step in using data-driven, systems-based solutions to anticipate medical errors and prevent them before they occur.

To date, the IPSR grant from the JHF has supported Ph.D. students and faculty with two projects beginning in 2023:

Capstone Project: Four students from the Heinz College of Information Systems and Public Policy worked on a CMU capstone project led by faculty advisors Rema Padman, Ari Lightman and Alan Scheller-Wolf.  Assisting the CMU team was the Department of Biomedical Informatics (DBMI) team at the University of Pittsburgh, led by Professor Richard Boyce. The Pitt team provided access to the Medication Error at Regional Scale (MEARs) database, guidance in understanding the observational health data sciences and informatics (OHDSI) framework, and assistance in defining a representative patient cohort for the analysis. 

The capstone project objective was to explore and validate signals of adverse drug events (ADE), adverse drug reactions (ADR) or medication errors with electronic health record (EHR) data. Doing so required the students to build an understanding of the patient journey in predefined cohorts through visualization and analysis, as well as for novel cohorts that they defined. The students used two data sources: EHR data (MEARS) from the University of Pittsburgh and adverse event reporting data (FAERS/AEOLUS). The CMU team, along with Padman and Boyce, identified a representative cohort (patients taking colchicine) and further narrowed down that cohort to patients taking colchicine and clarithromycin (an antibiotic). This was done to focus on a known drug-drug interaction and provide a smaller dataset suitable for mining and analysis. The student team used several algorithmic methods for analysis but settled on association rules to uncover patterns in prescribing practices within the EHR data. They also worked with subject matter experts to identify rationale and patterns within the EHR data.  

The capstone project was successful in identifying high-risk patient groups based on a high degree of confidence within the association rules identified, including patients taking colchicine and metaprolol (a well-known beta blocker). Other important achievements of the project were: introducing students to the OHDSI framework; strengthening our partnership with the University of Pittsburgh; developing techniques for extracting and processing data for analysis; identifying algorithmic methods to assess EHR data for indicators for ADR/ADE; and defining rules to determine levels of confidence in signal detection. We believe the methods and procedures developed by the team can be used, modified and expanded to undertake several student- and faculty-run projects on detecting ADR/ADE through EMR data to develop risk indicators, and eventually in applications reducing the incidence of ADEs and severity of ADRs.

New Project in 2024:  A Ph.D. student in the Heinz College's Information Systems & Management program is conducting research with Padman on “Medication Reconciliation To Improve Patient Safety With Artificial Intelligence and Operations Research (OR) Methods." A second researcher with a background in quantitative biology and biomedical informatics will also be working on the project. Funding and work on this research began in February 2024.