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A data‐driven integrated system
approach to tackling chronic diseases
Prof. Ran Balicer, MD, PhD, MPH
Director, Clalit Research Institute
Director, Health Policy Planning, Clalit
‘The New Normal’
• 1 of 2 members >45 have chronic multi‐morbidity
• 3 of 4members aged >65 are multi‐morbid
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‘The New Normal’
• 1 of 2 members >45 have chronic multi‐morbidity
• 3 of 4members aged >65 are multi‐morbid
10.0%
13.0%
16.0%
19.0%
22.0%
2006 2007 2008 2009 2010 2011
20.0%
21.0%
22.0%
23.0%
24.0%
2006 2007 2008 2009 2010 2011
Age 45-65, 2+ NCDs
Age 65+, 5+ NCDs
25% of
60% of $$
20% of
50% of $$
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NCD multi‐morbidity – key challenge
• >50% of 45+ old have NCD multi‐morbidity
• 3 of 4members aged >65 are multi‐morbid
• Rapidly aging population
Do more?
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Paradigm shift is a requisite
• Increasing demand, decreasing resources
• ‘Do more’ option - exhausted
Our system is (also) broken
Work in silos
‘Equal’
Therapeutic
Reactive
Paternalistic
Waste everywhere
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Helpful attributes in Israel
• Universal health insurance
– 4 Sick funds, wide basic ‘basket’ of services for 8M citizens
• Strong emphasis on community primary care
– 90% are happy with their health fund
• Incentives well aligned for value vs. volume
– Life‐long insurer membership (1% ann. attrition)
– Salaried physicians, bundled procedure hospital payments
• Innovative spirit, strong health IT infrastructure
• Covers 50% of Israelis – 4.2 Million members
– Over‐representing minorities, low SES, elderly
– Funding by age‐gender capitation
• 1,500 primary care clinics,
– Multi‐disciplinary teams
– Most physicians ‐ salaried
• 30% of hospital acute beds in Israel
Clalit Health Services:Israel’s integrated healthcare provider
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Policy planning: strategy principlesParadigm shift: the Clalit strategy
= a requisite and driving force
for transforming chronic disease care
Data-driven management of chronic disease
Data in Clalit
• Centralized Data Warehouse – real‐time data:
– Electronic information since late 1980’s
– Single EMR Coverage in all community clinics
– Inpatient and outpatient detailed data
– Smoking, BMI, BP measures…
– Detailed Socio‐demographic data
– Labs, Pharmacies, Imaging
– Full data on Costs
– >100 chronic disease registries
Decades of full life-span, ID-tagged, Geo-coded,
EMR-based data on > 4M people
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• Multi-disciplinary group, established March 2010
• A marked change in the organization mindset
• Mandate: Turn data to insights, insights to policy
Clalit Research Institute
• Real-life Outcome Research
• Data-driven care models design
• New measures for patient-centered care
• Multidisciplinary group:• Physicians
• Epidemiologists
• Biostatisticians
• IT specialists
• Mathematicians / algorithm specialists
• International collaborations
• WHO
• Academic institutes
• Science and development
Clalit Research Institute
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Brief Case studies: Data ‐> Insights ‐> Policy
• Coordinated: Readmission reduction
• Preventive: Predictive proactive prevention
• Equitable: Reducing disparities in quality of care
• Proactive :Care of the multi‐morbid
• Real World Evidence :What really works?
Data driven policy: Implementation
Case study 1:Data‐driven integrated care for
preventing readmissions
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Readmissions: a common challengethe UK experience
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Readmissions are just the symptom
Patient‐centered care coordination is the real target
(%)
Variance after case‐mix adjustment
7‐day readmissions
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Readmissions ‐ Israel
Clalit
rates
Readmission rates
Subgroupcountry
16.7%Internal med wards
2007
Hong Kong(Wong et al, BMC HSR, 2011)
19.6%Medicare (65+)
2003/4
US (Jencks et al, NEJM, 2009)
9.1%16-74 yo
UK(Department of Health, 2008)
Shadmi, Balicer et al, Clalit 2012
Readmissions ‐ Israel
Clalit
rates
Readmission rates
Subgroupcountry
14.3%(age adjusted)
16.7%Internal med wards
2007
Hong Kong(Wong et al, BMC HSR, 2011)
17.5%(Urgent & planned readmission)
19.6%Medicare (65+)
2003/4
US (Jencks et al, NEJM, 2009)
8.9%
9.1%16-74 yo
UK(Department of Health, 2008)
Shadmi, Balicer et al, Clalit 2012
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Hospital
Data may be misleading7 days readmission rate
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Hospital Blue – to same hospitalRed – to any hospital
Data may be misleading7 days readmission rate
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• Across life course, between providers and care settings:
– Continuity of care
– Continuity in communication
– Continuity of data
Aiming for integration
• Discharge planning from day 1
– Direct nurse‐to‐nurse hospital‐community communication
• Post‐discharge outreach to patients by GP clinic
• Home teams for the bed‐ridden patients
Continuity of care
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• Most patients treated by a multidisciplinary team (GP, nurses, allied health professionals)
– Coordinated teamwork at clinic level
• Hospital‐community direct communication
– Discharge planning e‐letters
– Transition care nurses
Continuity of communication
• GPs, specialists share the same EMR system
– Direct delivery of consult e‐summary in GP inbox
• Interoperable online system links hospitals and GPs
– Clalit hospitals but also most key out‐of‐chain facilities
– Soon – all hospitals in Israel are joining
• Single organization‐wide database + BI
Continuity of data
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•Work in the hospital (i.e. discharge planning)
•Work in the community (i.e. home care)
• Bridge the gap:
• Continuity of data
• Transition care
• Coordinated Teamwork
Improving: Where should we aim
CoC nurses: Hospital‐based community nurses, in every hospital
Structured outreach protocols for GP clinics, CTM
Home teams enhanced, activated by GP clinic nurse
Dedicated targets, indicators & IT support systems
Set of care coordination indicators
New work processes in the hospitals and in the clinics
Further care coordination: Action taken 2012
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Risk Stratification
30‐Day readmission rates among 65+ years old in 2012
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6
30‐day Read
mission Rate
Clalit Pre‐admission Risk Score ‐ Stratum
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Implementation% of elderly patients not approached by GP within 7 days of
discharge(lower = better)
Project onset Q3, 2012
20.0%
20.5%
21.0%
21.5%
22.0%
22.5%
23.0%
1 2 3 4Annual Quater
Readmissions among the elderly: 2012 vs 2010 by quarter
2012
2010
2012 vs 2010By quarter
Interven
tion
Readmission reduction30‐Day readmission rates, 65+ years old
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Case study 2:
Personalizing preventive caredriven by integrated data
Preventive Nephrology
11.50%
4.60%
1.80%
0.60%0.20% 0.10%
0%
2%
4%
6%
8%
10%
12%
14%
<96% 92-96% 80-92% 60-80% 30-60% 0-30%
5‐year deterioration rates to RRTamong CKD stage 3 patients, Clalit
Clalit Research Institute Risk Scores
100-foldRRT increased risk !
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Proactive prevention: NephrologyPreventive Nephrology
Case study 3:
Using integrated data for reducing disparities while improving quality
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Despite decades of work…
Clalit, in 2008 made a strategic decision to reduce
inequality driven health gaps
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Strategy assumptions
• Equal treatment will not reduce disparities
• Must use existing successful platforms
• Primary care is most important
• (almost) All solutions are local
• Measure, measure, measure
• EMR-data based quality measures (e-QM)
Utilize existing platforms
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Data-driven policy
EMR-based care indicators used for:
1. Target setting
2. Real-time achievements monitoring
1. Diabetes control
2. Blood pressure control
3. Hyperlipidemia control
4. Influenza immunization
5. Mammography tests
6. Fecal occult blood tests
7. Anemia in infants
Composite Score
Disparity Reduction in chronic disease prevention & management
7 Selected Indicatorslowest
performing clinics(400,000 members)
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Use of Data in ClalitClalit: Disparity Reductionin preventive medicine measures
Intervention plan components: bottom‐up planning
Disparity Reduction
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Low SES High SES
2008 498 350
2011 474 340
% reduction 2008-2011
4.8% 2.8%
• Compared to 2008, a decrease in AMI incidence was observedin all SES groups.
• A relatively higher decrease was observed in the low‐SES groupcompared to high‐SES group.
AMI morbidity – promising trend
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Case study 4:
Care for the most complex multi‐morbid patients
System‐wide change:GP time allocation by morbidity
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Annual visits per patient by comorbidity level
Clalit ACG primary care comorbidity score category
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Proactive care: Risk‐based outreach interventions
Proactive Care for the chronically ill:The Clalit adapted model
X 18
X 7
$$ Impact: Cost per patient
X 8
X 4
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Care for the ‘pyramid tip’
• Key Challenge – patient selection:– Multi‐morbid, High Risk patients
– Balance impactability and predicted risk• Complex integration of multifaceted data
• Selection process chosen: – Available EMR‐data risk‐based prediction system
– Excluding low‐impactability patients using criteria determined through physician input
– 40% positive predictive value for future highest costs• C‐statistic >0.75
Care for the ‘pyramid tip’
• Inspired by Chad Boult’s Guided Care model– Select multi‐morbid High Risk patients (not high cost!)
– Dedicated qualified nurse (1:100)
• Training process
• Proactively assess overall patient needs
• With GP, create care plans for patients
• Provide self management and caregiver support
• Coordinate transitions
• Facilitate access to community resources
– 3 Year controlled clinical trial in process
PI: Dr. Efrat Shadmi
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In Summary
Using data to drive care transformation
Identifying priorities and achieving mgmt support
Risk stratification
Individually targeted intervention programs
Clinical staff decision support
Patient participation
Real‐time continuous feedback
Comparative effectiveness… Signal assessment… Research… Precision medicine… genetics…
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Requisites
Harmonization of EMR systems Central database
ID issues
Coding/software
Data sharing
Analytic capacity
Intervention platform willing to innovate
Aligned incentives
To meet the challenges ahead (plus more)…
• Data driven policy – Maximize data streamlining and use
• Build on infrastructure strengths – Improve in areas of fragmentation
• Develop innovative approaches – Patient‐centered care
– Proactive self‐care support
– Integrated care
– Right incentives
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Thank you!
“It is not enough to do your best;
you must know what to do, and then do your best.
W. Edwards Deming
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Contact
Prof. Ran Balicer
Director, Clalit Research Institute
& Director, Health Policy Planning, Clalit
Tel:+972‐3‐6923104
101 Arlozorov St. Tel‐Aviv 62098