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Not Getting What You Want From Current Data Aggregation Methods?


Sayings fall in and out of favor, but “Big Data” has sustained its buzzword status since the 1990s. As the healthcare industry—which admittedly arrived late to the Big Data party—strives to realize the advantages of patient data, a number of barriers impede progress. Technical roadblocks. Inconsistent terminology. Budgetary consideration.

"Healthcare must be personalized, preventive, and most importantly continuous."

Yet data aggregation in healthcare may hold the keys to success in today’s healthcare landscape. With data insights, healthcare providers will be better positioned to transition to new payment models, to improve patient outcomes and to address population health. What should you do if your current data aggregation methods fail to deliver results?


Don’t Let Big Data Intimidate You

Little wonder that the sheer volume of data, combined with aggregation challenges, leaves healthcare providers feeling uncertain. According to Managed Healthcare Executive’s 2015 State of the Industry Survey, only 10 percent of those surveyed believe they are making good use of health data to improve care while reducing costs. Experts suggest several practices to overcome the problem:


1. Start with small projects.

Greg Scott at Deloitte Consulting recommends narrowing your focus. “Sometimes focusing on specific clinical questions, on specific subpopulations, can lead to the early wins that build trusted relationships, and pave the way for incremental expansion of those sort of analytic and intervention collaborations.” Deloitte helped a customer do just that: The Medicaid health plan provider decided to address potential prescription abuse. Looking at aggregated data with a more limited scope enabled the organization to see patterns that helped them reduce fraud, waste and abuse. 

One example of starting small and gaining a foothold (and proof of concept) before expansion is PatientBond. PatientBond automates patient engagement (emails, texts, phone calls, apps), tailoring messages to patients’ personalities and communications preferences. Its capabilities apply to a significant number of use cases in a hospital, health system, insurance company or any other healthcare organization, so the decision to institute PatientBond can be overwhelming. However, the company suggests starting with only one application — surgical discharge follow-up, for example — and gaining experience and data before expanding to other applications and departments. This approach has been very effective for both the healthcare organization and PatientBond.


2. Set strategic KPIs.
Like suggestion 1, this is about focusing on the right data. Determine Key Performance Indicators tied to organizational goals and then identify specific data sets needed. A Managed Healthcare Executive article on turning data into action notes, “If you think about it [data] in specific chunks of information that is really related to the business problems you are trying to solve and the piece of the healthcare ecosystem you are trying to deal with, it makes it less overwhelming.”


3. Move data to the cloud.

Right now, hospitals and other healthcare providers often maintain their data in separate silos: electronic health records (EHRs), clinical and financial systems. In addition, healthcare consumers generate their own data—wearables that track activity, health and wellness apps and a growing number of smart health technologies like insulin pumps. HIT Consultant likens clinical data to a “hub-and-spoke structure, with the patient as the center of the hub and each institution or doctor as a spoke around that hub.” As a result, the article contends, the data needs to live outside any single institution. A secure cloud environment allows all providers to see a more comprehensive view of individual patients. Many healthcare organizations are hesitant about moving their data to the cloud due to data security reasons; however, once they become more familiar and practiced in leveraging it, the cloud will facilitate many data efficiencies.


Supplement Your Patient Data with Segmentation

In a Healthcare IT News article, Samir Damani, MD, cardiologist and CEO of MD Revolution wrote that: “Technology will be an important component of both increasing awareness and accountability. If leveraged properly, apps, wearable technologies, genomic testing, wireless medical devices and other emerging technologies can help millions improve their health.”

The problem—then and now—lies in an infrastructure that isn’t aligned effectively with this vision of leveraging big data. And it’s not just the technical infrastructure that has shortcomings—the current healthcare system lacks the human infrastructure needed to support healthcare consumers beyond the four walls of hospitals and medical practices. As Dr. Damani noted, “We need a system of physician extenders: nutritionists, fitness professionals, health coaches and nurses to interact frequently with patients.”

But even more, healthcare providers need insights into the individuals because even the most hands-on approach drive positive results with every healthcare consumer. “Healthcare must be personalized, preventive, and most importantly continuous,” Dr. Damani said. “We need to use what we know about sustaining behavior change and build that into new healthcare delivery models.”


Psychographic segmentation can help by classifying healthcare consumers into distinct segments based on:

• How they feel about health and wellness

 Which communication channels they prefer 

 Where they seek out medical advice

 Motivational triggers for behavior change


Armed with this knowledge, healthcare providers can use aggregated health data more effectively. Rather than communicating with patients based on a shared diagnosis alone, they can customize their patient engagement plans to suit their motivations and preferences. Not only does this increase the likelihood of patient activation, but when used strategically, it also helps you identify which patients will respond to greater levels of personal interaction so that you can make the most efficient use of your human resources.


Effective data aggregation in healthcare isn’t about compiling ALL of the data available. It’s about bringing together the right data, the right tools and the right questions to ensure that big data yields the answers needed to positively impact patient outcomes and healthcare costs.

Psychographic Segmentation and its Practical Application in Patient Engagement and Behavior Change


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