“Big Data”​ Can Mean Big Wins for Healthcare Systems and Providers!


Population Health Management and Data Analytics

“Big Data”​ Can Mean Big Wins for Healthcare Systems and Providers! by Dave Amin – As a consultant at a health system provider network, I was invited to a commercial payer’s office for a data analytics demo. I spent half a day with the Director of Payment Innovation. He profiled the payer’s data analytics software system for me. We discussed the future of payment innovation and the ease of data access and delivery.  A detailed demo revealed the wealth of information currently accessible to contracted health systems.  Finally, he shared the risk profile of the population served by the health system where I was consulting.

I was stunned. I had been trying to convince providers that the starting point for any risk-based population health model was data analytics around their current patient population and the demographic they serve.  For an organization trying to determine where their efforts and financial resources should be focused, this was invaluable information.  “Why haven’t you shared this with the health system and the providers?” I asked.

Surprised and smiling wryly, he looked at me, “I have been beating on doors all over this state for years. I feel like a broken record. The providers and health systems just aren’t paying any attention.”

Payers Have Analytics – Why? 

Commercial payers are actuaries first and foremost, plain and simple. Currently every health insurance company across the United States knows exactly what level of risk is associated with each member.  And many of them will willingly share it with providers.  Yet obtaining data from specific payers only covers a percentage of a health system’s patients. To be truly effective, data would have to be shared universally from all payers (including CMS) and merged within a single system easily accessible by the health system and providers.

In researching the increasing availability of payer data analytics and the relative disinterest of providers, it quickly became apparent, with very few exceptions, that not even large health systems maintain their own data analytics or curate the information available to them through payers.  And why should they?  Overall, the system is still operating on a 100-year old fee for service model. Although the times and expectations are changing, the business model is still lagging.

Big Data“​ Can Mean Big Wins for Healthcare Systems and Providers!

In business parlance, there is little emphasis on utilization and value in a fee for service model, just an emphasis on volume.  Patients are sadly just “widgets” to be processed as efficiently as possible. The faster patients get sick and need care, the faster health systems and providers make money – a perverse incentive. Value based medicine is changing that paradigm. Yet, most health systems are operating completely in the blind with respect to the medical risk of their patient populations.  This is the first reason why those health systems are reluctant to enter into risk-based contracts with commercial payers – they lack risk quantification for their patient populations.  It is impossible to successfully negotiate with payers from a position of ignorance.

The second reason why health systems are reluctant to enter into risk-based contracts is much simpler.  Currently, even those with access to state-of-the-art analytics, struggle with how to use the information. What is this data telling us? How do we make it actionable?  How does the data return value?  Where is the ROI in the short run and the long run?

Serving the New Risk-based Population Health Model: Data Analytics

To meet the needs of a risk-based population health model, health systems need a data analytics platform that allows them to think, behave, and act as actuaries – exactly like payers.  This means understanding the risk of the patient population they are serving.  Access to the health system’s own claims data, integration of claims and clinical data, and risk stratifying patient populations is essential.

The potential is staggering.  With this kind of detailed information, health systems could access any one patient record and query: What is this patient’s risk adjustment factor and what is driving it? What care gaps require closure? What are the patient’s social determinants of health and is it cost effective to address them? How is this patient’s annual medical spend trending and where can we intervene? Given existing preventive care reimbursements, is it cost effective to aggressively identify and manage the rising risk population? The list of possible questions and data driven answers goes on and on – I think you get the idea – the utility of data analytics lies in asking the right questions and using highly curated data.

In addition to generating strong clinical outcomes and generating a cost/benefit analysis for social interventions and preventive care, internally generated numbers will allow health systems to negotiate intelligently with commercial payers.  A model of cost-effective care can now be generated which allows realistic benchmarks or targets of per member per year spend to be calculated and continuously monitored. Similar conversations can take place directly with employers with the goal of increasing market share by selling products and services to lower employers’ health care costs. 

“Big Data”​ Can Mean Big Wins for Healthcare Systems and Providers! by Dave Amin

But Data Analytics Can Be Expensive – Is It Worth the Investment?

Again, for emphasis, it is imperative for health systems to become experts at understanding the actuarial risk of their patient population. They need to understand the financial risk or exposure associated with the annual care of any one patient.  Would you buy a house without looking at the basement?  Would you buy a used car without looking under the hood?  Of course not.  Why would a provider commit to care for a patient without understanding that patient’s risk profile and without having actionable data to mitigate that risk?

Data analytics platforms and data analysts are not necessarily short run profit centers – they require some investment. How does a system get started without undertaking a large capital expense? Start by partnering with payers to leverage their existing data. Many payers are anxious to partner with their provider systems, control costs and move to risk-based contracts. Partnerships are possible today that would not have been possible five or ten years ago. There is an increased understanding that we are more effective as partners than adversaries. 

“Big Data”​ Can Mean Big Wins for Healthcare Systems and Providers!

Next, begin exploring options for building out your own infrastructure. There is an explosion of population health data analytic solutions on the market. Organizations such as “KLAS” provide annual reviews of many market solutions and can be a wealth of consolidated information and product comparisons. 

Lastly, pull together your team and think critically about the future of healthcare and how you want to be positioned as value-based medicine and risk-sharing contracts become more the norm. Become students of successful organizations and ask them for advice and learn from their mistakes. Model a relatively small capital investment of just $3.00 per member per month for a data analytics platform and required support staff. Run a quick ROI using some basic assumptions and then refine this model until it captures reality and starts to make sense. 

You may well find, as have many successful provider systems, that the question is not “is it worth the investment” but rather “can we afford NOT to make the investment.”  

“Big Data”​ Can Mean Big Wins for Healthcare Systems and Providers! by Dave Amin