Data-driven decision making in patient management: a systematic review

Abstract

INTRODUCTION: Data-Driven Decision Making (DDDM) plays a pivotal role in healthcare, specifically patient management. This review aims to provide a comprehensive understanding of the technologies used in DDDM and provide a framework of how DDDM is involved in patient management. METHODOLOGY: This study follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) framework, studies from Web of Science, Pubmed, and Embase are screened for consideration. The inclusion criteria are outlined to identify studies on patient management utilizing DDDM. RESULT: The studies included in the review explore DDDM in patient management from data-driven approaches to decision making methods. In the former, artificial intelligence, together with other methods, is the dominant method utilized. As a comparison, the decision support system, Markov decision process, and shared decision making are exploited in the latter. Disease diagnosis and treatment was the most common area of patient management application along with precision medicine, patient care, nursing, and other related fields of patient management. A framework of how DDDM is involved in patient management was identified. CONCLUSION: While challenges such as data quality and interpretability exist, advantages of DDDM lie in unprecedented personalization, streamlined decision-making, and the potential for a future where technology complements healthcare expertise for more effective and patient-centered care. DDDM is not only a useful option for patient management but also to many other aspects of healthcare and the systems around healthcare.

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