We need to use technology to get smarter about care | Collective Intelligence & Distance Learning | Scoop.it

According to the Institute for Alternative Futures, healthcare accounts for only 10-25% of the variance in health over time. The remaining variance is shaped by genetic factors (up to 30%), health behaviours (30-40%), social and economic factors (15-40%), and physical environmental factors (5-10%).


Too often, every stakeholder in the system views care through their own lens – the data they collect and the interventions they can sponsor. Doctors want to identify symptoms and treat them. Hospitals want to bring patients in for procedures that will cure them. Pharmaceutical companies want to find people who might benefit from their medication. Public health specialists want to cut the number of premature births or the incidence of diabetes. Social workers want to change harmful behaviours.


Unfortunately that information is scattered in various databases and departments, making it hard to achieve a holistic picture of the patient. Healthcare organisations can magnify their impact on individual health by dealing with issues beyond office visits and hospitalisations.


There's an opportunity to dramatically improve the care ecosystem (Smarter Care), making it more efficient, by applying analytics to data generated at every point in the care cycle. This phenomenon, known as big data, would develop a fuller understanding of individuals and the factors affecting their social and physical health.


Smarter Care systems have five common attributes:


• Intervention – Discovering the points in their lives where individuals can be influenced, and the most effective intervention strategy


• Knowledge – Assessing what has worked and applying that information to improving the system going forward


• Collaboration – Leading individuals to work with the right care-givers to make healthy choices or change their social determinants


• Coordination – Sharing care, knowledge and accountability across clinical and social boundaries


• Learning – Using analytics to study communities and understand who is at medical risk and how those risks are created, whether by medical, psychological or social factors