Home Hypertension M-Health Modeling Implementation and Operation at a Diabetic and…

M-Health Modeling Implementation and Operation at a Diabetic and…

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By Dr. R.A. Ramanujan, MD, Diabetic Care Associates And Dr. Anu Banerjee, System Chief Quality and Innovation Officer, Arnot Health

Dr. R.A. Ramanujan, MD, Diabetic Care Associates

Patients with chronic diseases such as coronary heart disease, stroke, diabetes, chronic kidney diseases, obesity, and many other metabolic disorders require more frequent information exchange and supervision. Chronic Disease Care (CDC) is a complex and challenging issue for patients for many reasons; comorbidity, socioeconomic and demographic qualities, complexity in care, inequality in service through advanced technology-based operation, etc. There is a compelling need for renewed alliance and realignment in service delivery. We evaluated the method of submitting measurements in real-time from personal devices, like smartphones, directly through a secure portal into the patients’ chart. The purpose is to give chronic disease patients self-efficacy, integrate family, ally the patient’s health team in the community, and permit natural transition in care supportive of a multi-sector partnership.

M-health is a platform for the rapid exchange of abundant information. Accomplishing desirable health-related service should guarantee technological support for the robust bidirectional transaction. This is often, distinctive to illness to espouse acceptance with perceptible value to stakeholders with information that is reliable to engage with commitment and confidence. Germane medical conditions where such streaming data conceptually, is attractive are those where numerical data extraction is critical to the outcome; such as hypertension, diabetes, and many of the cardio-metabolic disorders. Affordable technologically simple devices can be graduated to access numerical data such as blood pressure, pulse, weight and blood sugar, and much more securely. Data generation directly from customers or by outsourcing through allied health caregivers allows a better understanding of information generated at unfamiliar sites. It is possible to access and fuse this raw data from diverse locations at secure sites for validation, integration, and dissemination. Data modeling analytical through system behavior permits improvement in hypertension detection and management. The process of implementation and execution of operation will be a creative revolt that extant clinical care beholds. Reframing data acquisition sites from clinics to unfamiliar resource access instruments will be a cultural change. Accommodation of allied health forces and reforming external service integration will be a challenge. Deficiency in data integration between health care vendors is common. Blood pressure, pulse, and blood sugar values are not steady physiological values yet, and decisions are based on observations made in confined clinical location. This operation is incapable of reconciling ecological and contextual perturbations. Implementation of m-health-based operation has become a reality in practice, and wider acceptance holds promise to the economic model of collaborative service with public health and clinic-based service.

The potential for the collaborative endeavor in the health care field is ever expansive. These kinds of mobile applications should have the ability to integrate from the public sector by involving school systems, businesses, social and religious congregations, in the care and treatment of today’s health culture. It can also set a precedent for tracking and trending air pollution and the impact of noise on health, monitoring maternal and child health indices, and impacting disease transition in cardio-metabolic disorders. In any way these applications are being used, we believe that mobile health utilized in a structured way and well-integrated with electronic medical record systems will pave the way for successfully supporting patient-centered clinical care, beginning with Chronic Disease Management.

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