“People who run ball clubs, they think in terms of buying players. Your goal shouldn’t be to buy players, your goal should be to buy wins. And in order to buy wins, you need to buy runs. You’re trying to replace Johnny Damon. The Boston Red Sox see Johnny Damon and they see a star who’s worth seven and half million dollars a year. When I see Johnny Damon, what I see is… is… an imperfect understanding of where runs come from. The guy’s got a great glove. He’s a decent leadoff hitter. He can steal bases. But is he worth the seven and half million dollars a year that the Boston Red Sox are paying him? No. No. Baseball thinking is medieval. They are asking all the wrong questions. … and if you want full disclosure, I think it’s a good thing that you got Damon off your payroll. I think it opens up all kinds of interesting possibilities.”

– Peter Brand to Billy Beane in the movie “Moneyball”

Introduction: The Data Management Yeast in the ACO Dough

Data management and analytics are essential pillars of value-based care. Data undergirds many functions of MSSP Accountable Care Organizations (ACOs) which include, but are not limited to:

• Enrollment: Knowing what members are in the ACO
• Provider roster: Identifying the health care providers contracted to serve those members
• Attribution: Associating a member with a provider that is accountable for the patient’s cost and utilization
• Claims analysis: Recording the services provided to the members including diagnoses and costs
• Encounters: Capturing additional information from the patient’s Electronic Health Record (EHR)
• Risk stratification: Analyzing patient demographics and recent utilization to assess a patient’s level of health compared to the rest of the enrolled population
• Care coordination: Connecting patients in need of follow-up care plans to care managers responsible for defining and executing those plans
• Reporting: Analyzing cost, utilization and quality metrics to identify potential improvement areas and help plan improvement initiatives
• Shared savings calculations: Aggregating enrollment, claims, attribution, quality scores, benchmarks and other data elements to identify the spending under the benchmark that the ACO can retain.

Properly managing the data-heavy processes above opens up all kinds of interesting possibilities for improvements in MSSP patient quality, cost and utilization.

“What’s this ‘Data Management’ Thing You Keep Talking About?”

Marshalling the diverse array of data to support the aforementioned activities challenges MSSP ACOs to master the distinctly non-clinical disciplines of

• Data Engineering: Acquiring source data (typically from payors, hospitals or clinics) and transforming disparate data sets into a common, reusable format
• Data Analysis: Using transformed data to answer questions about clinical and financial ACO performance and presenting those insights in an accessible way.
• Data Management: Applying industry best practice data governance concepts and frameworks to maximize the business value of the data, including security, storage, quality, observability, modeling, warehousing and business intelligence.

MSSP ACOs that consistently invest in, and practice, these capabilities will be in position to take action that improves patient health, provider quality and financial performance. As the (somewhat) hypothetical example below illustrates, such investments can offer more than one type of compelling ROI.

Giving Data a Job and Measuring Its Value

Let’s say you are the Chief Medical Quality Officer for a large MSSP ACO. You have made big investments in care management, physician engagement and data analytics. As a result of your quarterly clinical insights review, you notice that your MSSP population has very a large A1C screening care gap for patients with a pre-diabetic diagnosis relative to other MSSP ACOs of similar size and risk profile as yours. Now what do you do?

Similar to a baseball team using analytics to find compelling but previously unknown players, you put your data to work finding compelling but previously unknown clinical and financial opportunities.

1. Find the providers: Using your population health analytics tool (or its underlying data warehouse) to identify all the doctors on your MSSP provider roster with A1C screening care gaps, sorted by the largest number of unscreened, attributed, pre-diabetic patients.
2. Find their patients: For the providers identified in the previous step, pull the list of unscreened, pre-diabetic patients ordered in descending order by risk score.
3. Initiate outreach: Give the list of providers with their list of overdue, pre-diabetic patients to your Provider Engagement team for a 12 month long campaign of “non-embarrassing intervention”.
4. Monitor: Each month, watch the A1C test rate each month for the target providers and patients and adjust the outreach efforts based on the response, as needed.
5. Measure: At the end of the campaign, identify how many patients have completed their annual screenings and estimate the financial impact.

In this example, let’s assume:

• A modestly sized MSSP ACO of 10,000 members (the minimum allowable is 5,000 members)
• 38.1% prediabetic prevalence for people ages 65 and over (See the National Diabetes Statistics Report, Appendix A: Detailed Tables: Table 5 for ages 65+, and Definition 2: “Any of: A1C 5.7–6.4% or fasting plasma glucose 110–125 mg/dL”)
• A 46% average success rate of A1C screening campaigns for pre-diabetic patients
• Upto a 10%/year pre-diabetic to diabetic conversion rate
• $500/year to treat pre-diabetics
• $12,022/year spend on diabetics that is attributable to diabetes
  With these assumptions, the 3810 MSSP pre-diabetic patient clinical initiative would yield
• 1753 additional patients screened
• As many as 176 pre-diabetic patients prevented from converting to full diabetes in a year
• Avoiding up to $2,027,872/year in medical spending (176 patients times the difference between pre-diabetic and diabetic spending per year, $11,522)

The impact from this clinical initiative could be fed into financial KPIs that illustrate the MSSP ACO’s financial impact as well as used in financial forecasting models for future clinical initiatives.

In addition to these impressive financial results, the intangible, patient quality-of-life impacts may be even more rewarding. For those patients screened and diverted from full diabetes, they are spared troublesome impacts like regular dialysis visits, vision loss, kidney damage and risk of premature mortality. Due to the proactive interventions enabled by data analytics, patients in this cohort have a puncher’s chance at a normal, family life well into their autumn years.

Conclusion

Compelling financial and patient outcomes like those above do not come about by accident. The prerequisites require several years’ investment of time and money into a functional data platform and the data management disciplines required to align data development towards clinical insight and impact. But this investment enables you to ask the right questions about your MSSP population and develop a more perfect understanding of where clinical and financial improvements can come from.

As MSSP continues to grow and evolve, organizations need experienced partners who can provide the data strategy, platform, tools and best practices needed to jump start ACO performance. COPE Health Solution’s Analytics for Risk Contracting (ARC) platform and associated consulting services provide proven, ready to use analytics capabilities that identify patient and provider opportunities to improve value-based care performance as well as connect these opportunities to compelling, financial impacts.

Contact us at info@copehealthsolutions.com and explore our analytics tools and case studies that can help accelerate MSSP ACOs’ journey to value.