Business Process Modeling and Prescriptive Data Analytics in Healthcare Informatics

Abstract

Business process modelling proved to be an effective way in creating quantifiable and qualitative business impact with efficient and effective deployment of new age technologies like Machine learning, Artificial intelligence, Natural language processing etc. for enhancing business growth newlinein the fiercely competitive and commoditized market. Combination of the business process newlinemodels, and new age technology approaches will result in increasing the efficiency and newlineproductivity of key business process metrics associated with business process agility and efficacy. newlineBusiness process modeling principles are getting used in the literature review resulted in identifying the gaps in the front and back-end processes of the hospitals and other healthcare facilities. The identified research gaps were: 1.Usage of complex and heterogenous systems creating nteroperability issues. newline2.Traditional process being followed resulting in lack of efficacy due to extensive involvement of newlinehuman intervention, intensive training for relating the complex medical terminology with the newlinemedical procedures that were conducted along with the international classification of diseases. newlineThis research focused on bridging the gaps and proposed a simulated model and method for newlineanalyzing healthcare patient-related data and present useful process information for healthcare organizations. The focus was on the application of conjunctive artificial intelligence technology newlineapproaches to the traditional business process that is followed in the Medical coding of the newlinehospital processes. Business process modeling will specialize in identifying hidden patterns more newlineeffortlessly, as traditional practices are time-consuming, challenging, inefficient, and costly. newlineThe Autonomous coding Engine model developed during this research literature review is a newlineconjunctive application of three different technology approaches like text synthesis, machine learning and rule-based NLP to improve on the cost of processing the medical codes, improve the newlineaccuracy and eliminate the