Data-driven Healthcare Analytics: From data to insight for individualized care

Editor’s note: This article is by Dr. Shahram Ebadollahi, senior manager of Healthcare Systems and Analytics Research at IBM Research.

The more longitudinal medical data records that are becoming available should mean that healthcare providers – from nurses and public health officials, to specialists – have more insight into helping solve their patients’ problems in the here and now. But now, the challenge is how to elegantly analyze all that data and derive insights from it to help those providers deliver better care to their patients.

My team at IBM Research developed the foundational analytics for a healthcare solution, now called IBM Patient Care Insights. These analytics can take into account all patient characteristics, such as treatments, procedures, outcomes, costs, etc. – basically everything about a set of patients that could be observed and captured over time (even the unstructured information, such as a doctor’s notes on a chart).

The data, in a sense, captures the collective memory of the care delivery system and embedded in it are insights about all the procedures and outcomes for all the patients. The analytics that can help us extract that insight promises to lead to better, more-efficient, and lower cost patient care. 

How IBM Care Insights derives insight from population data to better personalize decision making.

Medical data: analyzing, visualizing, predicting

So, how does Care Insights make sense of years of data, from multiple sources, about thousands of people? All to give healthcare providers a way to identify treatment and early intervention options.

Care Insights’ suite of tools use innovative algorithms rooted in machine learning, data mining and information retrieval techniques to look for patient similarity to derive tailored insights regarding a custom course of actions that are delivered through easy-to-understand visuals.

The Patient Similarity analytics tool finds all patients who display similar clinical characteristic to the patient of interest. The resulting individualized insight includes suggestions on how to manage care delivery to the patient, but perhaps more-importantly predicts health issues that could arise in the future (because patients with similar characteristics had experienced such health issues).

It can then match patients to specific physicians or specialists who can potentially provide a better outcome; understand and analyze utilization patterns (utilization of resources in the care delivery network) of patients, and identify abnormal utilizations (over utilization or under utilization), which could be an indicator of a potentially poor outcome or unnecessarily high cost.

The similarity analytics suite of tools can also predict the patient’s potential future adverse outcomes and conditions. Therefore, it can identify opportunities for early intervention. 

Visualizing the evolution patterns of patients with similar attributes to the patient of interest.

What about Watson?

Watson can provide tailored and to-the-point answers with supporting evidence to questions based on the corpus of knowledge it is connected to. IBM Care Insights complements this knowledge-driven evidence, obtained from medical knowledge sources, with data-driven insights derived from the large patient population medical records discussed here.

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