Comprehensive Depression Center University of Michigan Medical School Ann Arbor, January 3, 2002 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

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

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

The Promise Population Monitoring of Average Health

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 yearcommunity clinic twice a month alternative medicine twice a dayself-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

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

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

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)

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

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

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

The Technology Full-Spectrum Quality-of-Life Indicators

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

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

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

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) 2 nd for chronic dp (dysthymic disorder) Telephone follow-up for COD interview

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 objectives in 28 focus areas * Measure Full-Spectrum Health Status Detailed QoL in each detailed category

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!

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

The Plan Pilot Projects for Population Monitoring

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

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 Public Health applications already 1M users!

Peer-Peer for Medicine Intel Philanthropic P2P Program * 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!

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

Healthcare Infrastructure Scalable Pilot Project 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

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)

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).