Data-Driven Clinical Decision Support Systems Theory and Research

Introduction

Digitization, which has had a significant influence on the healthcare industry, is motivated by the prospect of optimizing performance, reducing costs, and improving the quality of patient care and healthcare (Lee et al., 2017). This digital process has transformed how patients and physicians engage with medical services. Although healthcare has been slow to accept technology, it is now embracing digital transformation. The use of information technology (IT) in the healthcare industry has transformed interactions between physicians and patients, giving way to the implementation and usage of clinical decision support systems (CDSS).

CDSS are examples of the transformative healthcare evolution because they analyze data and assist physicians in making timely patient-related decisions (Agency for Healthcare Research and Quality, 2013)¹. Healthcare professionals also utilize these systems to enhance treatment by increasing patient safety, eliminating needless testing, and reducing costs. For example, medical errors can decrease through enhanced healthcare, safety, and efficiency. Clinicians use these systems in the preparation of diagnoses and subsequent evaluations and outcomes (Osheroff et al., 2012). Although CDSS have the potential to improve healthcare delivery, it has been a challenge to fully realize their potential (Linder et al., 2006).

Clinical decision making uses descriptive, predictive, and prescriptive analytics. Descriptive analytics analyzes and presents past data regarding patients and treatment (Gensinger, Jr., 2014). This type of analytic utilizes visualization, alerts, and reports to describe patient activities (Gensinger, Jr., 2014). Predictive analytics uses historical data to make predictions by performing an analysis with rule-based or artificial intelligence (AI) techniques like machine learning and deep learning (Bartley, 2017; Gensinger, Jr., 2014). Medical professionals can identify trends and patterns in treatment plans or uncover chronic diseases in patients based on age, location, and ethnicity. Prescriptive analytics recommends actions that can produce the best outcome. This allows healthcare professionals to develop optimal clinical pathways for patient care (Bartley, 2017; Gensinger, Jr., 2014)

A recent Stanford study on the future of AI’s impact on society suggests that AI-enabled systems will change the future by replacing tasks rather than eliminating jobs (Grosz & Stone, 2018). In fact, AI technologies and data analytics are transforming the way organizations operate. Tech giants like Google, Microsoft, and Amazon are investing heavily in data collection (Newman, 2020). Organizations are focusing on feeding large datasets into data analytic models to produce predictive and prescriptive information to obtain meaningful projections. This application in the healthcare field facilitates in the reduction of operating costs, improves treatment outcomes, increases access to patients and clinician resources, and optimizes healthcare provider satisfaction (Bartley, 2017). Using predictive and prescriptive analytics in healthcare can improve forecasting, real-time insights, and automated decision making (Bartley, 2017). In turn, physicians and clinicians will experience enhancements in their daily tasks.