Measuring the impact of predictive analytics on patient satisfaction

Abstract

A critical factor in determining the quality of healthcare is patient satisfaction , it is the patients’ compliance to their treatment plans and overall well-being and success of the health care organizations. It has emerged as a strong weapon in the recent years to overall enhance the patient centeredness with patient-specific care needs. This may lead to improvement of the satisfaction level of the patients. This chapter is devoted to the analysis of the theoretical framework that links patient satisfaction and predictive analytics , which shows the possibilities of the data-driven methods in the healthcare domain. Big data is used to proactively identify patients’ needs and tailor patient care and support by analyzing, among others, demographic data, treatment records, patient’s electronic health records , and even social factors that may influence health status . Risk analysis and possible problems can be identified with more precision by health care services through the use of predictive analytics; thus, the business can allocate resources in the most effective manner, while service to the clients becomes much more proactive and client oriented. This chapter focuses on patient satisfaction of the applied predictive analytics in the field of healthcare and discusses it in detail. Some specific examples of specific cases as well as factual information describing the degree of the changes that affect the patient satisfaction index and caused by the application of predictive analytics are described in the chapter. Some examples of benefits include, patient’s wait time, coordinated care, and better management of chronic diseases . Ethical issues of data patient use including informed consent and privacy in the usage of the patient data are also asserted. This, in turn, strengthens the role that was taken by strong data management frameworks and open data sharing procedures for developing and maintaining patients’ trust. These technologies can revolutionize patient experience from care providers by providing the interventions and treatment regimens that have the likelihood of yielding the best results as well as identifying potential adverse outcomes so that health care providers can react proactively. Analytics is going to make patient satisfaction, superior medical outcomes, and better health care delivery systems predictable.

Ähnliche Arbeiten