Clinicians place orders for patients in the electronic health record (EHR). There is currently no internal mechanism to detect medical errors in EHR systems. Identifying and monitoring medical errors has relied on voluntary reporting and chart review, methods that are subject to substantial self-reporting bias. To quantify the magnitude of wrong-patient errors, I developed and validated the Wrong-Patient Retract-and-Reorder (RAR) Measure. The Wrong-Patient RAR measure overcomes limitations of voluntary reporting by using an electronic query to objectively detect wrong-patient orders in EHR data. Whereas previous data indicated an average of 9 wrong-patient medication errors per hospital per year based on voluntary reporting,2 the RAR measure identified 5,246 wrong-patient orders in a large healthcare system in 1 year. The vastly greater volume of errors detected provides insights into the epidemiology of wrong-patient orders, informs targeted intervention strategies, and yields sufficient numbers of events to power health IT safety intervention studies.

Applying the retract-and-reorder (RAR) method, my research group developed and validated new Health IT Safety Measures to capture wrong patient, drug, dose, route, and/or frequency. We currently have a dataset of 5 years of orders from NYP that includes these safety measures. We want to help other hospitals implement these automated measures to improve patient care, potentially as part of existing national safety testing procedures. We envision a process whereby a hospital can deliver us a standardized flat file of patient orders, we process the file with a program, and then we output rates of safety events. This information can then be used to improve patient care. The goal of this project would be to take existing SQL queries for automated measures and convert the queries to python code that can process a standardized input file. There are also numerous research questions to pursue related to this dataset depending on interest of the applicant.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Jason Adelman
  • Center/Lab: Center for Patient Safety Research at Columbia University Irving Medical Center
  • Location: Medical Campus - PH9
  • My research program, within the Center for Patient Safety Research at Columbia University Irving Medical Center (www.columbiapatientsafety.org) which I founded in 2019, focuses on measurement and prevention of medical errors in electronic health record (EHR) systems. As a health services and health information technology (IT) safety researcher, my research aims to 1) develop and validate systematic methods to quantify medical errors that occur frequently in the course of clinical care, 2) elucidate the epidemiology of medical errors across clinical and health system settings, and 3) design and test system-level interventions to prevent errors that can be widely disseminated and implemented in healthcare settings nationally and internationally.

Project Timeline

  • Earliest starting date: 10/15/2022
  • End date: 6/30/2023
  • Number of hours per week of research expected during Fall 2022: ~10

Candidate requirements

  • Skill sets: Python, SQL, relational databases
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes
  • Additional comments: Depending on applicant interest, there will be opportunity to be involved in academic output (publications)