The news is full with stories about predictive analytic and precision medicine programs being unveiled at prestigious institutions such as the Mayo Clinic or Duke University. But what about analytics at your local clinic, family doctor, or specialist? Aren’t they health analytic pioneers, too?

While large hospitals have been implementing their Electronic Health Records (EHR) systems during the past decade, it wasn’t until the past few years that smaller medical groups started transitioning from paper to digital records. Even as of January 2015, hospital or health-system owned physician sites were about 10% more likely to be using EHR systems compared to private groups. Without digital records, there can’t be data analytics.

Internal dynamics and resources shape the extent to which healthcare providers adopt analytics to manage revenue cycle and population health. As compared to hospital settings, in physician groups, resources (staff and their time) are often limited. For example, a large hospital might employ 20 full-time analysts or dedicated IT staff to address health analytic needs, but smaller physician groups can’t afford that luxury. For these groups, it is often an executive, or even clinicians themselves, spending hours each week manually compiling Excel reports to measure efficiency, with dozens of versions and formats flying around the office’s email system.

Fixing our healthcare system depends on an effective continuum of care–tracking and treating patients throughout life, and at all levels–from the family doctor’s office to the large hospitals they visit. Yet how can we expect analytic maturity along this continuum when there is such imbalance between various organizations’ analytic capabilities?

One might assume doctors are all involved, to some degree, in data-derived population health monitoring, but that isn’t the case. In a 2013 McKesson study of healthcare providers , 90% were using “analytics” to track some sort of revenue cycle management (think insurance and billing), while 23% were not doing any sort of population health analytics (identifying high-risk patients, for example).

Our current health system rewards these analytic priorities, but that is rapidly changing. Earlier this yearthe government set a goal of tying 90% of all traditional Medicare payments to quality or value by 2018 through various programs. This is the first time in the 50-year history of the Medicare program that HHS set explicit goals for alternative payment models and value-based payments. In the next five years, revenue cycle and population health analytics will be intertwined.

At Mimir Health, we are focused on working with the little guys, the physician groups and community clinics that are just starting to dedicate resources to health information technology in order to survive and thrive in a changing healthcare landscape. In our experience, these groups’ analytic priorities often center around revenue cycle, patient referral networks, and government-defined quality of care measures.

Our analytic solutions mirror these core analytic requirements, and that is a necessity if we want to level the analytic playing field between hospital systems and physician groups to create an effective continuum of care.

Still, we need to start with the basics, tracking the dollars and cents coming in and out of these practices, and help healthcare providers transition with the changing health industry. The next step is working with them to prove the quality of the care they provide by helping them satisfy quality reporting requirements. Once this analytic foundation is built we can then work towards advanced population health and precision medicine analytics.