Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure

Comprehensive Depression CenterComprehensive Depression CenterUniversity of Michigan Medical SchoolUniversity of Michigan Medical School

Ann Arbor, January 3, 2002Ann Arbor, January 3, 2002

Population ManagementPopulation Managementof Chronic Illness:of Chronic Illness:Towards a Scalable Towards a Scalable

Healthcare InfrastructureHealthcare Infrastructure

Bruce R. SchatzCANIS Laboratory

School of Library & Information ScienceSchool of Biomedical & Health Information Sciences

University of Illinois at Urbana-Champaignschatz@uiuc.edu , www.canis.uiuc.edu

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 12-19 Full-Spectrum Quality-of-Life Indicators

The Plan (Here to There) slides 20-27 Pilot Projects for Population Management

The PromiseThe Promise

Population Monitoringof

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 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

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 TechnologyThe Technology

Full-SpectrumQuality-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) 2nd 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 2010 467 objectives in 28 focus areas *www.health.gov/healthypeople

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 PlanThe Plan

Pilot Projectsfor

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 SETI@Home Public Health applications already 1M users!

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 Individualsat 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

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

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).