1 / 27

Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure

Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure. Bruce R. Schatz CANIS Laboratory School of Library & Information Science School of Biomedical & Health Information Sciences University of Illinois at Urbana-Champaign schatz@uiuc.edu , www.canis.uiuc.edu.

naasir
Download Presentation

Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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).

More Related