Knowledge and Data-Driven Framework for Designing a Computerized Physician Order Entry System

Abstract

A major concern related to the healthcare industry is uniformity in healthcare delivery. There is considerable variation in the diagnosis and treatment of patients depending on the experience and expertise of the doctors. Information technology can play a major role in addressing this issue. Research investigating the use of data-driven approaches and knowledge-driven clinical pathways to achieve uniformity in the delivery of healthcare is ongoing. Specifically, the integration of data and knowledge-driven approaches can be used to ensure uniformity in the delivery of patient care, thus avoiding inappropriate variance. The data-driven approach can utilize the bulk of medical data being generated. The knowledge-driven approach in the form of clinical pathways incorporates evidence-based care. In this context, knowledge and data-driven computerized physician order entry (CPOE) systems are gaining importance in healthcare delivery systems. This paper proposes a knowledge and data-driven framework for a CPOE system. This work is based on a knowledge base populated with disease quadruples, each of which comprises a list of symptoms, tests, results, and medications for a particular disease. The data used in the proposed system are obtained from two datasets, namely, the MIMIC (Medical Information Mart for Intensive Care) and the Disease-Symptom Knowledge Database, which is based on the operational data of New York-Presbyterian Hospital (NYPH). We combined both datasets using the common attribute of disease to generate data with more attributes to aid decision making. This was performed with the help of specialists and clinical knowledge. The resulting patient data are further integrated with clinical pathways before the extraction of disease quadruples. The novelty of this work lies in the extraction of disease quadruples from the integration of patient data with clinical pathways. The list dynamically ranks each element of the quadruple based on its association score to facilitate the generation of prescription order sets for the CPOE. The effectiveness of the proposed system in providing uniform patient care delivery has been validated by experts. The proposed system can significantly improve patient safety and the quality of healthcare delivery due to the integration of data-driven capability with clinical pathways.

Keywords

• CPOE
• disease-symptom knowledge database
• New York-Presbyterian Hospital
• complaints
• knowledge and data-driven system
• framework