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May 15, 2015 News No Comments

Connecting Smart Data Analytics and Population Health Management
By Shane Pilcher


Healthcare organizations across the country are accumulating copious amounts of clinical and claims-related data. The amount of data, partially driven by Meaningful Use initiatives, is expected to increase exponentially over the foreseeable future. According to studies from McKinsey, Gartner, and others, data are being generated at staggering rates, with volume reaching exabytes (a million terabytes) and even zettabytes (a billion terabytes) each year. The Kaiser Family Foundation forecasts that the volume of healthcare data gathered will grow 50-fold between 2013 and 2020.

Faced with potentially zettabytes of data, it is no wonder that many healthcare organizations around the country are approaching data analytics with some trepidation. According to the third annual Health IT Industry Outlook Survey, conducted by our firm, more than 84 percent of CIOs, CMIOs, IT project managers, IT directors, and consultants had questions around type, quantity, and ways in which to use their healthcare data. In addition, 62 percent stated the biggest barrier to IT initiatives around MU and data analytics was a lack of organizational buy-in or financial resources. As a first step to overcoming these issues, it is important to transition from just a data analytics focus to a smart healthcare data analytics approach.

The transition to a smart healthcare data strategy requires ensuring the organization is collecting the right type of data and in the right quantities. This requires a careful evaluation of the organization’s current HIT tools and an assessment of how and where the data are captured, and how the information can be accessed. Once an organization has the tools and workflow to transform data into smart healthcare data, effective and reliable analytics can begin.

Population Health Management Essential

One of the strongest and growing trends around smart healthcare data and analysis is population health management. With 40 percent of survey participants indicating improved patient care across the healthcare continuum as their key business objective in 2015, the trend toward PHM is no surprise.

In analyzing claims, clinical, lab, imaging, and medication data, profiles begin to emerge to help identify at-risk patients within an organization’s patient population. These at-risk profiles help providers develop customized treatment plans and follow-up methods, which can reduce that risk and improve their outcomes. At-risk profiles can isolate a particular diagnosis population, focusing on conditions affecting the greatest percentage of the healthcare organization’s patients, or targeting a specific diagnosis or demographic subset for strategic results.

The use of smart healthcare data analytics does not stop there. Karen DeSalvo, National Coordinator for Health IT, recently stated that data can provide a broader view of population health that can help communities prioritize which health issues are the most pressing: “There’s an opportunity to be more inclusive of data sources, not only from public health, but social and human services agencies and, of course, patient-generated health data and patient-reported outcomes.”

Patient-Generated Data Fill Gaps

By including patient-generated data, such as blood-glucose readings, blood pressure, daily diet, and exercise routines, providers can take PHM to a new level of provider and patient engagement. This leads to improved monitoring and tracking of treatment regimen adherence and individual patient responses, which can alert providers when patients need treatment changes or increased support between patient visits. The agility this engagement brings to care delivery yields improved patient outcomes with fewer unplanned visits and hospitalizations, ultimately reducing care-delivery costs.

Once we break the surface of smart healthcare data analytics and transition to PHM, imagination is really the only limitation. If an organization can imagine the questions to ask of its smart healthcare data, the right analysis can guide providers to the desired patient outcomes while decreasing the cost of care.

Shane Pilcher is vice president of Stoltenberg Consulting, Inc.


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