Abstract
Background: Non-communicable diseases are today’s leading killer diseases since they are a common cause of morbidity and mortality as well as contribute to high health costs. These conditions thus have to be continuously managed, and the patient assigned a correct treatment plan to reduce possible complications and enhance the quality of life. Data analysis in the healthcare sector offer novel ways of disease surveillance in real-time and developing unique approaches to health intervention.
Aim: The purpose of the current investigation is to examine the effectiveness of ‘big data’ solutions to the existing healthcare issues of diabetes and hypertension in relation to their monitoring, care plans, and program prognoses.
Methods: The data used in the study include physiological, lifestyle, and behavioural data from wearable devices, EHRs, as well as health applications. Data mining tools also include use of machine learning and predictive analytics used to monitor patient data in real time with a view of customizing treatment plans. Real-time monitoring systems give feedback to both patients and health care providers for necessary modifications in treatment plans in the course of the treatment.
Results: The usage of data analytics enhances the ability of monitoring the essential health standards inclusive of blood glucose levels as well as the blood pressure to detect health risks and disease flare-ups. Individual care maps, which are developed according to patient characters, increase compliance to treatments and self-care, as well as prevent readmissions and long-term complications or comorbidities. Predictive analytics in this context can provide the necessary intelligence for what is to be expected regarding the progression of a disease so that appropriate action can be taken by the health care providers.
Conclusion: It also supports the efficient treatment of such conditions as diabetes and hypertension via timely data tracking, patient-specific solutions, and forecasts. They result in enhanced patient experiences, better functioning of the health systems and more preventive and effective method to managing chronic illnesses, among others. Future technologies of AI and telemedicine are likely to bring more benefits in achieving the above-mentioned objectives.
Keywords: data analytics, diabetes management, hypertension, personalized care, continuous monitoring, predictive analytics, chronic disease management, healthcare