The Role of Informatics in CIN’s, ACO’s and the Triple Aim

1. The Role of Informatics in CIN’s, ACO’s and the Triple Aim October 13, 2016 Dave Ramsey Director of Informatics CCI Labs

2. A bit about CCI CCI is an LLC created to consult and support organizations seeking to form CIN’s and ACO’s It has capability to deliver: • Data aggregation • Data analytics • Organizational consulting • Care management tools CCI Labs is a not for profit 501(c)3 aligned with CCI and the University of South Carolina School of Medicine CCI Labs conducts applied research in population health The two companies have approximately 60 staff members in aggregate

3. What’s a CIN? A Clinically Integrated Network • A collection of practices and their associated physicians who agree to share patient data for the purposes of quality improvement on behalf of the patients served • Employed and affiliated physicians may negotiate collectively with payers regarding reimbursement arrangements • They are not just a loose collaboration to “gain leverage” and are regulated by the FTC

4. What’s an ACO? An Accountable Care Organization • A collection of practices, hospitals and their associated physicians who agree to share coordinated care for a specific population • Typically there is some reward (upside potential) and maybe some risk (downside potential) for managing these patients against a defined set of criteria which include • Medical quality metrics • A threshold of expense • Patient satisfaction The most widely known ACO is the MSSP (Medicare Shared Savings Program) of CMS

5. What’s a Population A “population” is a group of patients who share some common persistent characteristics • Geography • Ethnicity • Chronic disease • Economic status • Employees • Age brackets The health of these populations leads to commonality of medical treatment or medical policy So for the MSSP, the population is those served by Medicare and hence generally of age 65+

6. What Role Does Informatics Play Data Collection • Acquisition • Normalization • Quality assurance Metric Reporting • Periodic calculation of standardized metrics for all participating physicians, practices and hospitals • Reporting of these same metrics to governmental agencies Support for Quality Improvement Initiatives • Huddle reports • Gaps in care • Custom metrics (ex. for a PCMH quality initiative) Support for Care Coordination • Care transitions • Case management Price of care assessment (and how it relates to relevant payment models) • Comparison to regional norms • Understanding underlying costs

7. Data that is systematically used Patient data: • Demographic • Vitals • Labs • Meds • Visit and scheduling • Diagnosis • Insurance • Billing Provider data: • Identification (NPI, address …) • Association with a practice • Title/Role (MD, DO, PA, NP, practice manager …) • Membership (ACO, CIN, research projects …) Practice or Hospital: • Addresses • Sites and identifiers • EMR’s used • IT point of contact • Billing data processors

8. Reporting Reports are program or institution specific • MSSP for CMS • DRP/HSRP for NCQA • PCMH for NCQA • MACRA for CMS • Homegrown reports for employers Typically CCI is asked to keep statistics on providers accessing reports as a measure of provider engagement Reports are, at the highest level, just statistical but patient data relative to each metric is available from the reporting system with additional authentication and transmission techniques

9. Physician Report Cards Your results Comparator results Choose report Choose who the report is about Above metric of success Below metric of success Choose who to compare results against Your missing data Metric name (click for definition) Comparator missing data Email’s you patient data used in this metric

10. Huddle Reports • Program Specific or general purpose • Who a provider is seeing today • Red shows area needing attention • Right column are recommendations for action at this visit

11. Other data and metrics Some institutions have local quality measures and these may require additional data • Time to administer antibiotic from time of diagnosis of sepsis • Percent of new mothers given breast feeding education • Percent of generic drugs relative to brand drugs CCI does calculate these institution specific metrics • They may require chart reviews if the data isn’t discrete or well recorded • When chart reviews are conducted, they are often sampled to contain the cost of collecting the data and therefore are estimates which are institutionally interesting but not valid as measures of individual provider quality

12. Quality Improvement Coding and data collection for improved analytics and metric achievement Addressing gaps in care Evaluating patient health outcomes over time Evaluating population health outcomes over time

13. How does Informatics support Payment Models Fee for service • Cost/Price • Measures to determine if the service lead to a positive outcome or to a readmission • Patient Satisfaction Bundled payments • Annotating the team members • Evaluating the members contribution to cost • Measures to determine if the service lead to a positive outcome or to a readmission MACRA MIPS AAPM • Quality measures • EMR utilization measures • Quality Improvement activities • Prediction of costs (run rate) • Risk assessment

14. What CCI Labs (research) is studying Advanced protocols for the treatment of chronic diseases or conditions • Hypertension (AMA MAP) • Heart attack and stroke reduction (CMS Million Hearts study) • Smoking cessation (Tip Top protocol) Rapid learning systems for understanding populations • New data sources • Psychosocial data • Environmental data • Geospatial data • Community data • Public policy data • Non-hypothesis driven analytics (e.g. machine learning) • Continuous mining of data and evaluation of impact of changes Primary Care Provider guidance for treatment of clusters of chronic diseases

15. A deeper look at data analysis behind our cluster work Characterized just data from patients in our MSSP ACO Agglomerative Approach (bottom-up) Start with each patient in a separate group Merges patients that are ‘close’ Keeps merging groups that are ‘close’ Continues until all groups are merged into one Creates a tree or dendogram

16. 11 most frequent conditions > 50,000 patients • 72% Hypertension • 69% Lipid Metabolism Disorders • 39% Vascular Complications • 38% Obesity • 35% Osteoarthritis • 29% Mental Health (Depression, Abuse) • 28% COPD • 28% Diabetes • 15% Cancer • 14% Renal Failure • 12% Congestive Heart Failure Easy to calculate – just count

17. If we look at clusters we find … Computationally difficult to define clusters but important results

18. Current work – guidance for primary care Looking at all treatment regimes for each disease in each cluster Evaluating medications that are in conflict among diseases in a cluster Evaluating hierarchy of treatment urgency Reducing the Cluster to a single protocol with just a few pages of recommendation rather than hundreds of pages Giving guidance to physicians via a portable device • Must do • Should do • Should not do • Must not do • Threshold for referral

19. Questions