Using Big Data Analytics in Healthcare

The healthcare industry has been a late adopter of technology when compared to other industries such as banking, retail and insurance. As per the McKinsey report on big data from June 2011, “…if US health care could use big data creatively and effectively to drive efficiency and quality, we estimate that the potential value from data in the sector could be more than $300 billion in value every year, two-thirds of which would be in the form of reducing national health care expenditures by about 8 percent…”

To realize the true potential value of big data in healthcare, the industry will need major structural changes, including legislative reforms, incentives, privacy laws, reimbursement and incentive schemes. With recent health reform paving the way for health information exchange (HIE) across the nation, data is going to flow in at a much faster rate and with more variety with real-time information about members and patients. The HIPAA 4010 to 5010 move has already paved the way to capture more granular and specific information with procedure codes, revenue codes, drug codes and diagnosis codes.

Now, without getting too distracted with macro indicators, I would like to delve into industry domain-specific example use cases for improving health outcomes:

Reduce hospital re-admissions

One major cost in healthcare and one that is severely impacting Medicaid is hospital readmission costs due to lack of sufficient follow-up and proactive engagement with patients. These follow-up appointments and tests are often only documented as free-text in patients’ hospital discharge summaries and notes. This unstructured data can be mined using text analytics. If timely alerts were to be sent, appointments scheduled and education materials dispatched, proactive engagement could potentially reduce readmission rates by over 30 percent.

Patient Monitoring: Inpatient, out-patient, emergency visits and ICU

Everything is becoming digitized. With rapid progress in technology, sensors are embedded in weighing scales, glucose devices, wheel chairs, patient beds, X-Ray machines, etc. Digitized devices generate large streams of data in real-time that can provide insights into patient health and behavior. If this data is captured, it can be put to use to improve the accuracy of information and enable practitioners to better utilize limited provider resources. It will also significantly enhance patient experience at healthcare facility by providing proactive risk monitoring, improved quality of care and personalized attention. Big data can enable complex event processing (CEP) by providing real-time insights to doctors and nurses in the control room.

Preventive care for ACO

One of the key accountable care (ACO) goals is to provide preventive care. Disease identification and risk stratification will be very crucial to business function. Managing real-time feeds coming in from HIE, pharmacists, providers and payers will deliver key information to apply risk stratification and predictive modeling techniques. In the past, companies were limited to historical claims and HRA/survey data but with HIE, the whole dynamic to data availability for health analytics has changed. Big data tools can significantly enhance the speed of processing and data mining.


Through HIE, most of the providers, payers and pharmacists will be connected through networks in the near future. These networks will facilitate the sharing of data to better enable hospitals and health agencies to track disease outbreaks, patterns and trends in health issues across a geographic region or across the world allowing determination of source and containment plans.

Patient care quality and program analysis

With exponential growth of data and the need to gain insight from information comes the challenge to process the voluminous variety of information to produce metrics and key performance indicators (KPIs) that can improve patient care quality and Medicaid programs. Big data provides the architecture, tools and techniques that will allow processing terabytes and petabytes of data to provide deep analytic capabilities to its stakeholders.

While this is the tip of the iceberg for the healthcare industry use cases for big data, there are lots of unique opportunities specific to your line of business, organization and department. Most healthcare organizations have begun their journey along a data analysis continuum. Assessing and prioritizing the initiatives for big data based on value is key to success and will have significant impact on your organization in years to come.

IBM has a team of experienced consultants and leading products and solutions that can help cross-organization teams assess data sources, develop a roadmap and strategy, and implement a flexible and scalable big data platform with clinical and advanced analytics capabilities.

You can start small, but remember to Think Big.


2 thoughts on “Using Big Data Analytics in Healthcare

  1. I like most of the use cases, however, outside of patient specific uses lies internal uses that also can drive patient benefits. It’s true that there are many variables at play, though one aspect we’re tackling is the physician economics. With, we aggregate billing data make it instantly useful for practice managers and others within the health system.

  2. Pingback: Top Big Data Posts for Year 2013 !!! | The Big Data Institute

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