Kevin Sha Li
Graduated: June 10, 2018
Understanding the practical utility of using the analytic potential of patient data in Identifying the High Cost patients
It is widely known that the minority of patients make up the majority of healthcare costs. Research being done aims at predicting/identifying these patients through predictive modeling. All in the hopes that an increase of targeted resources can prevent the inurnment of the high cost, which can help the patient and hospital. Yet what is the actual utility in these models? Most models only apply to the particular institution the model was conceived in or are hindered by limitations of size. In this study, I went through patient’s clinical notes to better understand how practical such predictive models are. First, I sought after literature to better understand what variables most predictive models use as their base. From there, I compare it to what was available in the patient’s profile. I revised what was necessary to predict high cost given what was accessible in the database. With access to UWMC/Harborview database, I went through clinical notes to evaluate each patient’s possible predictability. These determinations were later verified by a physician for accuracy. This was further reflected at Northwest (NW) Hospital, which is a relatively smaller hospital with a focus on inpatient/outpatient. NW Hospital provides a contrasting site for comparison. Afterward, each patient was categorized in what was the nature of their high cost. This work's importance is in how to consider predictive models moving forward. Assuming modeling will always have the solution to predict high-cost patients is misguided. Instead, understanding the underlying dynamic of the patient's cause is a better target. The conclusions made in this study can help better structure models to be more effective in how they predict patients.
Last Known Position:
Data Research Scientist, UWMC
Drs. Adam Wilcox (Chair), Thomas Payne