Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure

1. Population Managementof Chronic Illness:Towards a Scalable Healthcare Infrastructure Bruce R. Schatz CANIS LaboratorySchool of Library & Information ScienceSchool of Biomedical & Health Information Sciences University of Illinois at Urbana-Champaign schatz@uiuc.edu , www.canis.uiuc.edu Comprehensive Depression Center University of Michigan Medical School Ann Arbor, January 3, 2002

2. Severe versus Average Health • Depression Center for 35K visits per year • At this Scale: • Multidisciplinary teams can treat patients • Telephone questionnaires can follow-up • State of Michigan has 1.5M at-risk persons • At this Scale: • Need Healthcare Infrastructure for Population Monitoring

3. Outline of Talk • The Promise (What) slides 4-11 • Population Monitoring of Average Health • The Technology (How) slides 12-19 • Full-Spectrum Quality-of-Life Indicators • The Plan (Here to There) slides 20-27 • Pilot Projects for Population Management

4. The Promise Population Monitoring of Average Health

5. The Problem of Chronic Illness • Chronic Illness is the Economy! • Acute – can cure immediate symptom • Chronic – must manage over long time • No Infrastructure for Chronic Healthcare • twice a year community clinic • twice a month alternative medicine • twice a day self-care home monitors • Most of Population has Chronic Illness • Heart Diseases – physical cause of death • Affective Disorders – mental burden of life • Cancer, Arthritis, Asthma, Diabetes

6. What Works • Multidisciplinary Teams treating Lifestyle • Medicine: physicians and nurses • Health: psychologists and social workers • Decreases Readmissions for Heart Disease • Why are these Teams effective? • Treat all lifestyle factors (full-spectrum) • Treat actual disease stage (dynamic) • Treat actual patient status (adaptive) • No Infrastructure for Chronic Healthcare • Expert teams need expert training • Doesn’t scale to whole populations • Can’t reach underserved populations

7. Solution of Healthcare Infrastructure • Specialty Center (100 at a time) • Like Depression Center, use a team • Treat each patient as an individual • QoL Questionnaire (10K longitudinally) • Assess Quality of Life with questions (SF-36) • Patients administer, Physicians analyze • Gross screening for immediate treatments • At-Risk Population (1M continuously) • Full range of stage and status • Prevention requires early detection

8. What Scales • Provider Pyramid • Range of providers for range of needs • More expert is more expensive • Level of Service for Volumes of Persons • Top (few severe): professionals (physicians) • Middle: screening and follow-ups • Bottom (many average): amateurs (patients) • Analogues from other Infrastructures • Evolution of the Telephone (logical/physical) • Medicine versus Health • Railroads (physical) versus Banking (logical)

9. Population Management • Strategy of Preventive Medicine (G. Rose) • All Chronic Illness is Continuous • To change Extreme, must change Average • Infrastructure for Chronic Healthcare • Must manage the Average (healthy) • Now treat the Extreme (sick, severe) • Decrease Average will Decrease Extreme • Population versus Individual Management • Population Management by Health Monitors • Screen All the People All the Time • Locate at-risk cohorts across population

10. Managed Expectations • Quality of Life is the Goal • Improve overall quality across spectrum • Beyond simply damping down symptoms • Many Features for Health Status • in Canada: R. Evans economic model • in America: Healthy People 2010 • Beyond Managed Care to Expectations • Understand spectrum and make choices • 80-year-olds are not 20-year-olds • Empowering individuals at base of pyramid

11. Population Monitoring • Possible to Monitor Whole Populations • Daily Monitors, Full Spectrum of Features • Relies on Internet to handle Questionnaires • Cohort Clusters supplement Diagnoses • Daily Feature Record for each Individual • Detailed Records for whole Population • Group Clusters of Similar Patients • Cohort Clusters drive Treatments • Treat by comparing Similar Cases • Manage Expectations with Actual Cases • Identify Risk based on Cohort Clusters

12. The Technology Full-Spectrum Quality-of-Life Indicators

13. Quality of Life Indicators • General Purpose Instruments • Paper-Based Assessment – 30 questions • Answerable by Patients across Populations • Medical Outcomes Study (A. Tarlov) • MOS produced general-purpose SF-36 • Specialty Practices in Big Cities • Cure status for Acute condition • Utility of QoL questionnaires • Effective at gross screening • VA study (3K) – survival of heart surgery

14. Disease-Specific Questionnaires • Specific Questions for Specific Disease • 1000 QoL questionnaire instruments • Paper-based, clinical trial screening • Causal Model drives Questions • KCCQ for Cardiomyopathy (CHF) • Model based on fluid retention overload • Majority of seniors with CHF don’t have! • Caring for Depression (K. Wells) • MOS specific for Depression • CES-D, Center Epidemiological Studies • DIS, NIMH Diagnostic Interview Schedule

15. Health Status Indicators • General-Purpose for Social Correlations • Whitehall study (M. Marmot) • 12K civil servants in England • SF-36 longitudinal screening (8K) • Health status inverse of Socioeconomic • Special-Purpose for Treatment Outcomes • Depression Center Outreach (M-DOCC) • IVR (Interactive Voice Response) • Brief CDS (21 questions) plus SF-12 • Treatment Outcomes and Screening

16. Depression Screening • MOS Depression Study (Rand/UCLA) • 2K patients out of 22K in MOS • In specialty practices Boston, Chicago, LA • 5 longitudinal assessments over 4 years • Every 6 months for 2 years then at 4 years • Details of the Screening • 2 stages of screening with CES-D and DIS • Screen for MDD (major depressive disorder) • 2nd for chronic dp (dysthymic disorder) • Telephone follow-up for COD interview

17. Beyond Screening • Why are Some People Healthy? (R. Evans) • Major categories are: disease, health care, health function, genetic endowment, physical environment, social environment, individual response, behavior, well-being, prosperity. • Healthy People 2010 • 467 objectives in 28 focus areas • *www.health.gov/healthypeople • Measure Full-Spectrum Health Status • Detailed QoL in each detailed category

18. Full-spectrum Dry-runs • Our first dry-run • 500 questions from 20 QoL questionnaires • Use Evans categories with 2 more levels • Needed more Breadth & especially Depth • Collection & Software by Medical Scholars • Plans for next dry-run • Multiple categorization for different views • Encode nurses at Carle and at Barnes (Rich) • For Depression, Encode the Center!

19. Computer-based Questionnaires • Treat actual disease stage (dynamic) • Computer assessment handles full-spectrum • Database of all questions (500K) • Individual session asks only 30 questions • Tree-walking Categories by Breadth-First • Treat actual patient status (adaptive) • MOS knows this *the* problem (McHorney) • GRE as the paradigm • Session answers determine questions • Historical answers determine questions

20. The Plan Pilot Projects for Population Monitoring

21. Population Management • Possible to Monitor Whole Populations • Daily Monitors, Full Spectrum of Features • Internet Software handles Questionnaires • Cohort Clusters supplement Diagnoses • Daily Feature Record for each Individual • Detailed Databases for whole Population • Analyze Clusters of Similar Patients • Cohort Switching drive Treatments • Manage Expectations with Actual Cases • Improve Health by Switching Cohorts

22. Peer-Peer Computations • Local Interaction • Your PC does small computations • e.g. screensaver for SETI • Global Merging • Partition computation into small parts • Each local forms part of global whole • Large-Scale Distribution • 3M users of SETI@Home • Public Health applications already 1M users!

23. Peer-Peer for Medicine • Intel Philanthropic P2P Program • *www.intel.com/cure • Evolved engine from SETI • United Devices commercial software • 1M volunteers for Cancer computation • Cancer Research Project (Oxford University) • Partitioned Screening of Molecules • Data-centered driven by Indexing needs • Health monitors feasible for Individuals at Scale of whole Populations!

24. Getting from Here to There • Develop Full-spectrum Questionnaire • Merge existing Quality of Life instruments • Encode knowledge from Medical Professionals • Develop Dynamic Adaptive Administration • Software to handle Interactive Sessions • Software to build Individual History • Software to build Population Database • Deploy to test Population (30-50 persons) • Develop Cohort Similarity Clustering • Algorithms for Statistical Feature Matching • Lifestyle Coaching via Cohort Switching

25. Healthcare Infrastructure • Scalable Pilot Project • 3000-5000 patients across ranges for 3-5 years • Full-spectrum depth-first for Depression • Provider Pyramid across County from Center • Towards Ordinary Medicine • Handle 1M persons for clinical trial • Push out from M-CARE, Ford/GM • All of Michigan, clusters not categories • Automated questionnaires and data analysis • Affective computing for Affective disorder

26. Ordinary Medicine • Centralized Medicine does not Scale • Distributed Healthcare does Scale • Pilot is thousands of persons (1K) • Customary to push down to Individual • MOS to screen single person (1) • Revolutionary to push up to Population • IHM to screen millions of persons (1M)

27. Further Reading • Richard Berlin and Bruce Schatz Population Monitoring of Quality of Life for Congestive Heart Failure, Congestive Heart Failure, 7(1):13-21 (Jan/Feb 2001). • G. Rose, The Strategy of Preventive Medicine (Oxford University Press, 1992). • K. Wells, R. Strum, C. Sherbourne, L. Meredith, Caring for Depression (Harvard University Press, 1996). • R. Evans, M. Barer, T. Marmor (eds), Why are some People Healthy and Others Not? The Determinants of Health of Populations (New York: Aldine de Gruyter, 1990).