Exploring Discrepancies: Analyzing Electronic Medical Records Data Against Direct Observations; American Healthcare: The Intersection of Hospital Privatization and Insurance Types

My technical project and sociotechnical research paper deal with current factors in the healthcare industry that affect patient experience and outcomes. My technical project focused on Electronic Medical Record (EMR) system data in a primary care setting. My capstone team analyzed how EMR and observational data can be reconciled to improve both patient and provider experience. My sociotechnical research paper explores this kind of interaction on a larger scale. In my paper, I focus on the interaction between hospital privatization and insurance types, and what factors specific to this interaction lead to different health outcomes by insurance type.

EMR systems are widely used by healthcare providers to track patient and appointment-level data. EMR systems are often expected to result in improvements in efficiency and decrease stress upon their implementation, and their data can even be used to make managerial decisions that affect the operations of healthcare providing institutions. However, factors like institution-specific software configurations and human data entry error can impact the accuracy of the data provided by EMR systems. This capstone project aimed to improve patient flow and healthcare provider experience by exploring the influence of the EMR system at the University of Virginia University Physicians Primary Care Clinic (UPC). My capstone team visited the UPC over the course of months, collecting observational timestamp data on the same appointments that were also being tracked by UPC’s EMR system. We compared and analyzed the different datasets based on standardized metrics and noted discrepancies between the EMR data and observational data. We used further statistical tests to confirm differences between the two datasets and analyze these differences in terms of key time segments for categories of appointments. The paper underscored the importance of using reliable data-collection and interpretation systems and emphasizes that this can improve experience on both the patient and provider side.

My sociotechnical research paper focuses on the intersection of hospital privatization and insurance types and explores what factors contribute to treatment discrepancies between differently insured patients. Following the recent trend of hospital privatization, publicly insured patients have experienced decreasing access to care and quality of care compared to privately insured patients. To understand this problem, I used Leigh Star’s infrastructure framework with a specific focus on the pillar of standards. Healthcare is extremely reliant upon standards due to its size and variability, and changes in standards over time can have ripple effects that affect patient outcomes. I conducted my analysis using primary sources from insurance companies and secondary sources consisting of healthcare-specific studies and statistics-focused policy reviews. I used the primary sources and historical secondary sources to analyze the evolution of public and private insurance standards since their respective inceptions. Next, I used the studies and policy reviews to analyze the standards of hospital privatization and how those interacted with the different insurance types. In my analysis, I found that both public and private insurance standards had become less inclusive over their lifetimes. I also found that private hospital standards cater to the more adjustable framework of private insurance, allowing privately insured patients to better adapt to waves of hospital privatization and in turn receive better coverage. Although discrepancies in coverage do exist, it is important to remember that hospitals and insurance are relatively new entities. I conclude that these current problems also represent significant opportunities for future advancement in the healthcare industry.

While working on my technical project, my first learning experience took place during the period in which we collected observational data. I found the primary care appointment process to be quite structured but still very personal, as each appointment was different based on the patient. When our group transitioned to working with the datasets, I realized that we were beginning to lose touch with this personal side of primary care. Although we were coming to conclusions to help both patients and providers, I realized it was important to keep this personal side in mind and remember the doctors, nurses, and patients that our recommendations would affect. I had a similar realization during my sociotechnical project. After spending so long looking at high-level studies and other secondary sources consisting of heavy data analysis, I realized that data also took the personal aspect out of the project and effectively reduced patient experiences to numbers. My understanding from my sociotechnical research paper grew from my learning from my technical project. I began to see that healthcare is an industry where people can easily be represented by numbers and the human aspect can be lost. I think it is vital to keep this in mind when dealing with data-driven decisions, not just in healthcare, but in any other field where this may apply.

Bachelor of Science in Systems Engineering
Technical Advisor: Robert Riggs
STS Advisor: MC Forelle
Technical Team Members: Avery Schebell, Grace Fitzgerald, Catherine Irons, Sammy Park