Population Management of Chronic Illness: Towards a Scalable Healthcare Infrastructure
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Transcript of 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 [email protected] , 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).