Mapping data-driven strategies in improving health care and patient satisfaction

Health services involve data analytics, central to healthcare service delivery, research, and planning. Based on the literature, this specific article seeks to enlighten the reader about data analytics in health services by discussing its advantages, shortcomings, and issues that come with it. Section one mapping outlines how data analytics optimizes the results of health service delivery through risk factor definition, disease detection, and case-by-case management. It helps inform disease trajectory, determine drug interaction, monitor disease, support decision-making, and enhance service delivery quality. The research also examines data analysis in health services, medicine, and pharmaceuticals. Several big data analytics applications have been identified as relevant to drug development, personalized medication, and specialized trials. Evaluations that rely on biomarkers differentiate between patients who are convenient for specific therapies, improving the patient's status and developing health care services. The third section also stresses the role of data analytics in enhancing efficiency and profitability. They involve detecting fraud, abuse, and unnecessary medical activities, creating savings and better financial returns. Data analytics also leads to identifying high-cost patients and ways of managing healthcare costs, increasing revenues and profitability. The fourth section discusses how big data complements traditional public health surveillance and outbreak platforms. It facilitates collecting data, analyzing different sources, and identifying special patterns or outliers to detect the outbreak early enough, contain it, or allocate resources to control it. It anticipates and addresses the dangers to the public's well-being and effectively controls episodes of infectious diseases. However, some key issues are associated with data analytics implementation, such as data quality, governance, privacy, bias, integration, expertise, and ethics, among others. Therefore, solving these challenges to achieve the optimum shifting of health service paradigms through data analytics in inpatient treatment, organizational management, and affordability is imperative.