• 1 Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • 2 The Affiliated Changshu Hospital of Soochow University (Changshu No.1 People's Hospital), Changshu, Jiangsu 215500, China
  • 3 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
  • 4 Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
  • 5 School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, Indonesia
J Healthc Eng, 2021;2021:9208138.
PMID: 34765104 DOI: 10.1155/2021/9208138


Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.