September 21 2016 | 0 Comments | 193 reads Average Rating: 4.5

New Data, New Insights: Improved Care

by David Hom in Health Analytics

Healthcare organizations have traditionally used claims and clinical data to guide their performance improvement efforts. But they are now realizing that there’s value in stretching beyond their comfort zone.

Indeed, they are seeing the good in non-traditional data such as the Zip+4 and Metropolitan Statistical Area (MSA) data commonly used by retail companies. An informal survey conducted during a webinar entitled Predictive Analytics: A Catalyst to Drive Behavior and Engagement in a Value-based World revealed that more than half of the healthcare professionals responding were already using non-traditional data, with 47 percent implementing a variety of multi-source/non-traditional data and 7 percent already leveraging such data to realize positive outcomes.

Not only are healthcare organizations using a wider array of data, they are applying more sophisticated tools to analyze it as well. Natural language processing, text mining and machine learning are enabling organizations to get the most out of all those bit and bytes of information.

The upshot? Organizations are sitting on the precipice of something truly powerful. They no longer just have an understanding of what could work with specific patient populations but what will work. Indeed, by analyzing this wide array of data, healthcare organizations can develop more effective care improvement programs by gaining insight into factors such as:

Impactability or the likelihood that particular interventions will have an impact on patient or population outcomes. In essence, when assessing impactability, healthcare providers gain a better understanding of whether or not interventions will result in real improvements. In other words, the analysis could provide insight into where healthcare providers will get the most “bang for their buck” in terms of which diseases to target and what approaches to take. For example, such analysis could reveal that diabetes is a condition well worth targeting. In addition, when assessing impactability, providers could determine if a patient education program would actually make a difference in the utilization of the emergency department or the number of inpatient admissions – or if it would merely be a “nice to have.”

Intervenability or the likelihood that the patient or population will become actively engaged in their own care. Taking a page out of the retail industry’ s handbook, healthcare organizations could segment consumers by type to realize if they are actually going to take action. With insight into a person’s or a population’s expected behavior, organizations will have a better idea if there is any likelihood for change. As such, an organization would be able to determine if an education program would be likely to result in actual behavior change – or if it would turn into an in-one-ear, out-the-other exercise. Indeed, if a healthcare organizations has some insight into how a person or group will or won’t engage in their own care, then it can become more strategic in the development of healthcare programs.

What others types of non-traditional data could help your organization better assess impactability and intervenability? And how?

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Author
David Hom
Chief Evangelist

Dave is an internationally-recognized expert in the field of consumer engagement through programs such as Value Based Benefits and Employee Wellness.

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