A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity. (December 2020)

• Record Type: Journal Article
• Authors: Nour, Majid; Sindi, Hatem; Abozinadah, Ehab; Öztürk, Şaban; Polat, Kemal
• Is Part Of: International journal of medical informatics. Volume 144(2020)
• Publication Date: 2020-12
• Subjects: Healthcare system evaluation -- Hospital indicators -- Random forest regression -- Weighted indicators -- Cross-indicators; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104300
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: This paper includes a framework for healthcare system performance evaluation. The proposed method generates a performance score using hospital performance indicators. Hospital performance analysis can be performed using the proposed framework. Abstract: Objective: Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process. Method: As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured. Results: As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model. Conclusion: Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.