Syncing personal health data with big data to reduce healthcare costs

Big Data has been a hot topic as of late in healthcare.

Many see big data as a way to reduce healthcare cost by managing patient oriented outcomes through analytics.

Can high risk patients be identified and targeted for earlier interventions?

While this is the norm for most institutions, the ability of integrating specific technology to mine big data and analyze it allows for the scaling up of healthcare to a larger population.

At the mHealth + Telehealth World 2013 Congress in Boston, MA, I had the opportunity to listen to a great discussion regarding the implication of data on healthcare. It was titled, “The Financial Impact of Technology, Electronic Tools and Data Mining.” The speaker was Philip Fasano, Executive Vice President and Chief Information Officer of Kaiser Permanente.

One of the most interesting comments Mr. Fasano made was that Big Data is not necessarily an IT thing, rather it has roots in financial services and banking. These institutions have been using data to analyze problems and create solutions that lead to innovative products for awhile. Rather, big data for the healthcare industry offers the opportunity to perform patient assessments, research, and studies on a larger scale.

While Big Data has been making the headlines, Mr. Fasano calls for a progression of Big Data to Massive Data. The inherent issue is that Big Data is relegated to institutions and groups that collect information from their patient population. This data includes that which is collected in hospitals and at institutional clinics.

However, with the influx of mobile technology, Mr. Fasano sees the opportunity to break the boundaries of data collection from brick and mortar, and reach patients in their own homes and in their daily environment. Massive data’s potential lies in self-monitoring and the use of mobile technology to collect patient specific data.

This is already being accomplished with the rise of mobile apps that serve as patient data trackers and diaries, and also with physical activity trackers (e.g. Fitbit, Nike FuelBand). However, integrating this data into the overall data pool collected to be analyzed is the issue. This issue will need to be addressed in the next few years. This includes the creation of data centers so information can be stored appropriately.

Massive data will play a large role in monitoring and treating chronic diseases. Not only will data from patient records within a hospital be used, but also the data from their homes can be incorporated. For the post-discharge heart failure patients, data can be collected that goes beyond vitals and weight.

For instance, is the patient sitting down all day? What are they eating? Data that can identify their lifestyle and identify areas for intervention can be key in preventing further disease progression. By doing so, it may be possible to reduce overall healthcare cost and improve the patient’s quality of life.

Author:

Timothy Aungst, PharmD Follow Me
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