Building a Data Warehouse at Clover (PDF)
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A brief tour of why we focused on building out a data warehouse early on at Clover, and why we think the Data Science function has room to grow in health insurance.
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Building a Data Warehouse at Clover (PDF)
- 1. Clover Footer Reinventing Health Insurance: Using Data to Put Patient Care First Healthcare Analytics Lean in Conference! Oct 23, 2015!
- 2. 2Footer Agenda 1 What is Clover?! 2 Who am I?! 3 Data Science and Clover! 4 What are we doing with all this?! 5 Questions!
- 3. 3 Who am I?
- 4. 4 Data Science at Clover Ian Blumenfeld Platform Health modeling at Archimedes, Lapsed Physics Phd Otis Anderson Product Analytics at Yammer, MS Office, Former affirmative action consultant
- 5. 5 Data Science at Clover Footer!
- 7. 7 • Actuarial Science • Health Economics • Medical Informatics • Finance • Accounting List of Data Science areas of non-expertise
- 8. 8 What is Clover Health?
- 9. 9 A tech company and a health insurance company Healthcare! Technology!
- 10. 10 • Medicare Advantage Part D plan • Why? • More unit cost -> more opportunity • We think chronic disease management represents the biggest opportunity to reduce cost of care by improving outcomes • 7K enrollees in New Jersey (OPEN ENROLLMENT) • Clinical operations and customer service are in-house • More on that later Clover is a health insurance company
- 11. Our goal is to organize and leverage data to fix our healthcare system. Clover is trying to improve health outcomes for our member population. We are using the tools of data science and modern web development to prioritize, assess and iterate upon our interventions. 11
- 12. Why did Clover build the data science function first?
- 14. Clinical Outcomes? So What? There’s measuring clinical outcomes and then there is optimizing them. To see what I mean let us imagine two campaigns around nurse visits.
- 15. Two Campaigns ! !Campaign Discharged Members Clinical Effectiveness Coverage Control Readmission Rate Covered Pop Readmissions Uncovered Pop Readmissions A 100 .15 .4 .4 10 24 B 100 .2 .2 .4 4 32 Total readmissions in campaign A - 34 Total readmissions in B – 36 So A is more effective at preventing readmissions, even though the intervention from B is the more clinically effective campaign
- 17. Two Campaigns ! ! Things that can be affected… sometimes…maybe
- 18. So even when you know the outcomes . . .you still can push to the optimal result by pushing up the processes that lead to the outcomes. If you want to talk about outcomes where the targeting is less obvious than a hospital discharge, then predictive modeling is more important. What do you want to optimize outcomes then? • Flexible clinical operations team • Data warehouse full of joinable outcome and process data • Apps that gather information as they enable operations • Speed – data speed and decision speed
- 19. Footer Which brings us back to this
- 20. Footer Which brings us back to this This part is hard This part is critical
- 21. Difficulties we faced, part I • Assembling catalogue of necessary data • Adding joinable keys into separate data sources • Pinning down when membership starts and stops • Parsing unstructured data • Transforming hard to scrape-data (PDFs, invoices, one actual photograph of a series of data points) • Interpreting claim duplications – different for different files and different use cases • Reconciling multiple sources of truth • Understanding claim semantics • PROVIDER DATA • Interpreting part d accounting rules • Counting hospital visits • Automating all of the above • Making sure that the automation doesn’t break any of the above
- 22. Difficulties we faced, part II A stylized representation of our call logs at one point.
- 23. An example – provider data To someone who has thought about it for a few minutes, provider data seems easy. You want one row per provider with at least address, an identifier, name, specialty, and whether they are in network. What happened? 1. Our provider data migrated from Access 🙀 to a more custom physician data management solution. 2. UI in new solution made it hard to validate data. 3. Most accurate data ended up going onto paper. All sorts of horrible consequences follow when the source of truth is paper 🙀 🙀 🙀
- 24. An example – provider data What did we do? We took control of the data validation process. The object was to get provider data into a good state and be able to maintain and update it. But provider data is bad because it is complex. You need to reconcile multiple sources of truth and update based on occasionally provisional data.
- 25. An example – provider data SIMPLIFIED WORKFLOW!!!
- 26. 26 What have we built out of this?
- 27. 27Footer • We can run all of the things you have to do – star ratings, finance, customer service, claim forensics out of our data warehouse. ! • It can all be joined on a unique of a member, so everything can be related to everything else. ! • We can run lots of things in SQL and Python cutting down on less automatable solutions like Excel and access. ! • Speed and flexibility to answer any ad-hoc question quickly!! A working data warehouse!
- 28. 28 Useful things engineers have built Member profile used by Clover staff surfaces history and captures observations.
- 29. 29 Useful things engineers have built Gaps in care surfaced as clinical reminders
- 30. 30 Questions?