Tuesday, September 8, 2015

mobile tool helps non-physician health workers, such as Accredited Social Healthcare Activists (ASHA), and doctors make evidence-based management decisions to lower their patients’ CVD risk

George Clinical, a research organization, Posted to federaltelemedicine.com | Cardiovascular Disease (CVD) risk
SMARTHealth – Provides Rural Areas with Better Access to Healthcare through a Mobile-Based Tool
Gender, age, systolic BP, cholesterol level, and behavioral risks are entered into the mobile system to estimate the patient’s risk of developing CVD. Knowing this information can encourage patients to adhere to their medication and change behavioral risk factors that may result in CVD, such as drinking or smoking.
Seventy percent of India’s population live in rural areas, where the doctor to patient ratio stands at 1 doctor to 1700 patients. A mobile-based system called SMARTHealth, was developed as a point-of-care, Clinical Decision Support (CDS) tool. The mobile system assists local healthcare workers assess and manage Cardiovascular Disease (CVD) risk, and helps to expand the capabilities of current healthcare workers by providing them with proper equipment to monitor and measure CVD risk within these rural communities.
The CDS tool operating with a mobile health system is able to generate a risk-based prediction and management system along with a server side electronic medical record system.
This mobile tool helps non-physician health workers, such as Accredited Social Healthcare Activists (ASHA), and doctors make evidence-based management decisions to lower their patients’ CVD risk. This helps to prevent severe diseases that may occur later.
The mobile system is able to provide information on the risk of CVD to the patient through a visual projection meter built within the app. Through this projection meter, the cause and effect risk factor for each patient can be visually expressed in an easy to understand format. Gender, age, systolic BP, cholesterol level, and behavioral risks are entered into the mobile system to estimate the patient’s risk of developing CVD. Knowing this information can encourage patients to adhere to their medication and change behavioral risk factors that may result in CVD, such as drinking or smoking.