Betting on Big Data – Solving the healthcare conundrum 2

Fire Crackers – The Silent Assassin

Photograph courtesy Tapesh Mukherjee

How many people do you think would get sick during Diwali? To some of us, the question may seem either too-large-to-handle or even irrelevant. There is some implicit presumption that doctors and hospitals that provide health care would be adequate to address any such event.  There is also an assumption that it is only older people or people with acute respiratory problems are going to get affected. There is also an assumption that our health care system (should hospitalization be necessary) would be as responsive (or as expected) in the event of an emergency. And then there are of course several who still believe it is alright to burst crackers or light up killer gas laden sparklers in absolute gay abandon or in an uncouth display of wealth to our neighbours. For decades our health care system has been trained to be reactive in a kind of “post-mortem” way instead of being proactive in a “preventive” way. We seem to have thrown healthcare into the proverbial snake pit of expensive medications, consulting fees and an insurance system that are mired in controversies.

Getting to the root of the problem

A very informative article titled “Breathless during Diwali” carried in the Times of India debunked the myth that it is only the loud crackers that are dangerous to our health – in fact as this article revealed it is quite the other way round. The heavy metal content in seemingly harmless sparklers are the ones that do maximum damage.

If healthcare needs to succeed, then it needs to transform itself from being just reactive to events to being more holistic and preventive- It needs to adopt Big Data. Here is my thinking:

1. If the quantum of toxic gases of various types of fireworks are known and we can correlate it with trade volumes of firecrackers during such festivals, then the cumulative effect on air quality in any city can be calculated.
2. When we correlate this data with the population data in a city using “personal location” (Adhaar and other data) and “geo-location” we can begin to identify patterns based on age and other demographic profiles.
3. Care givers can in turn correlate this data to active patient records.  For instance they can help identify patients who are at high risk of developing a specific disease (e.g., obstructive pulmonary disorder) and can also enable the better selection of patients with a preexisting condition (e.g. high blood pressure) and recommend a preventive regime.
4. Once the demand side is known, then the supply side of health care can also be planned through Predictive Modeling. Emergency medical squads, stocking of medicines, manning and/or extension of care-giving facilities can all be planned against a potential demand for their services based on “data-driven” planning as against “hunches or assumptions”.
5. Sharing data with all stakeholders through online communities or mobile messaging could also happen based on available data. Social media enabled by big data can bring together care givers (as in drug companies, practicing doctors), care takers (hospitals, NGOs, nursing homes and volunteers) and care receivers (citizens and ailing patients) for the common good.
6. Activism against use of Fire Crackers can also become stronger through community participation supported by big data

The promise of big data for healthcare

The promise of big data for healthcare requires combining disparate sets of data such as location data, patient records on the one hand and event based data such as the above or even national health statistics. Apart from sharing existing data sets, legal constraints such as privacy laws, a lack of incentives, or incompatible IT systems or formats, it is also the willingness and conscience of all stakeholders that will make it count.

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