THE ROLE OF HEALTHCARE ANALYTICS IN ENHANCING DECISION-MAKING: A STUDY ON ITS IMPACT AND APPLICATIONS

This research examines the effect of healthcare analytics on clinical decision making in five important variables: Real-time Data, Data Quality, Patient Outcome, Data Interoperability, and Health Disparities. Data were examined through multiple linear regression to investigate the connection between the independent variables and clinical decision making. The regression model was statistically significant and accounted for 55.1% of the variance in clinical decision making (R² = 0.551). Real-time Data, Data Quality, Patient Outcome, and Health Disparities were revealed to have substantial impacts on clinical decision making. Interestingly, Real-time Data contributed most strongly positively, while Patient Outcome contributed significantly but negatively. Data Interoperability did not reveal a statistically significant effect. Key outcomes of the work highlight the vital importance of using real-time, high-quality data to inform clinical decisions and incorporating equity considerations within healthcare analytics. The negative direction of the observed relationship with patient outcomes indicates an imperative to drill further into whether and how the data on patient outcomes are currently being used within practice. With a small sample size, a convenience sampling process, and utilizing self-reported information, the current study is bounded by limitations around generalizability. The study adds to what is known regarding the impact that certain elements of healthcare analytics have on clinical decision making and presents actionable, strategic, and policy-level recommendations for improving data-informed healthcare provision.