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Southern Institute for Health Informatics 2006 Conference 22nd September 2006 Steffen Bayer. Modelling and planning care services for long-term conditions. Long-term conditions as an increasing concern. Growing long-term care needs aging population - PowerPoint PPT Presentation
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Modelling and planning care services for long-term conditions
Southern Institute for Health Informatics 2006 Conference
22nd September 2006
Steffen Bayer
Long-term conditions as an increasing concern
• Growing long-term care needs– aging population
– improved survival rates for chronic conditions
• Large demand for care services for chronic diseases– 17.5m adults in the UK may be living with a chronic disease
– Around 80% of GP admissions relate to chronic disease
– Patients with a chronic disease or complications use over 60% of hospital beds
– Evidence from the US suggests people with chronic conditions consume 78% of all health spending.
• Pressures on health and social care system– staff shortages
– funding constraints
Planning of care services: the challenge of evidence-based decision-making
• Drive towards evidence-based decision making in medicine, policy and management
• Clinical trials happen in isolation and often under special circumstances
• Randomised control trials for service innovation particularly difficult due to complexity and interconnectedness; often inconsistent findings
• Real-life decision making requires tradeoffs – between different chronic diseases
– between treatment and prevention (and screening)
– between cost (for whom?), quality of life, longevity, etc.
Uncertainty and system behaviour
New technologies
Changing needs
New policiesFuture care services
Whole System Effects
Unintended
consequences
Models can be useful - all models are wrong
• Models simplify: The map is not the territory.
• But sometimes the slightly wrong answer is good enough.
• Models help to think.
Variety of modelling approaches
• Discrete event simulation – operational details
• System dynamics – strategic, aggregate level– interrelationships, feedback– whole systems thinking…
Modelling in action: System Dynamics
Fundamental building blocks of systems: stocks and flows
Stocks and flows are
• as simple as a bath.
• everywhere – from bank accounts to hospitals.
Stock: water in bath tub [litre]
Flow: water flowing in [litre per minute]
Stock and flow comparison
Stock Flow
water in bath tub in and outflow
money in account money paid in and withdrawn
prevalence new incidences, deaths
occupied beds admissions and discharges
Unit: “things”: e.g. £, people, widgets, boxes…
Unit: “things per time unit”: e.g. £/year, people/month, widgets/hour, boxes/day
Bath tube dynamics – simple and fundamental
Stock accumulation is as simple as filling (and emptying) a bath.
The only way to change the stock is via the inflows and outflows.
Care delivery with telecare
healthy HC fL HC fM HC fH
Inst fM
TC fL TC fM TC fH
Inst fH
effect of TC on ftyprogression
share toTC
from healthy toHC fL
from HC fL toHC fM
from HC fM toHC fH
from TC fL toTC fM
from TC fM toTC fH
death rate TCfM
death rate TCfH
aging
effect of TC on fracrate to inst care entry
fH
death rate hdeath rate HC
fL
death rate HCfM
death rate HCfH
death rate Instentry fMwaiting Inst
fM
waitingInst fH
from waiting toInst entry 3
from waiting toInst entry 4
from HC to waitingInst entry 3
TC fH towaiting Inst
to waiting Instfrom TC fM
to waiting Instfrom HC fH
death r w InstfH
HC f2 to fL
effect of TC on fracrate to inst care entry
fM
death rate TCfL
from healthy toTC fL
from HC fL toh
from hc fM tofL
from HC fH tofM
from TC fH tofM
from TC fM tofL
from TC fL toh
death rate Instentry fH
Demand for institutional care
Clients in institutional care
550,000
500,000
450,000
400,000
350,000
55
5
5
5
5
5
5
5
55
55 5 5 5
4 4
4
4
4
4
4
4
4
44
44 4 4 4
3 33
3
3
3
3
3
3
33
33
3 3 3
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 204 216 228 240Time (Month)
run 1 person1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
run 2: 50% share to telecare but no effect of telecare person2 2 2 2 2 2 2 2 2 2
run 3: run 2 plus best guess Effect of telecare on frac rate to institutional care medium frailty entry =0.2 person3 3 3 3 3
run 4: run 3 plus best guess Effect of telecare on frac rate to institutional care high frailty entry =0.8 person4 4 4 4 4 4
run 5: run 4 plus best guess Effect of telecare on frailty progression = 0.8 person5 5 5 5 5 5 5 5
Simulation modelling to investigate treatment and prevention options for chronic illness (heart failure)
high risk asymptomaticunknown
symptomaticusual care
symptomaticTC
frac r dev HF
developing HFdevelopingsymptoms
frac r devsymptoms
frac death r at risk frac death rasympt
frac death r sympt
dying at riskdying
asymptomatic
dyingsymptomatic
frac death rsympt TC
dying sympt TC
transfer to TC
TC effect on fracdeath r sympt
<frac death rsympt>
becoming highrisk
time constanttransfer to TC
asymptomaticand known
detection ofpresymptomatic
HF
developing symptknown disease
dying knownunsymptomatic
cost of riskreduction per
person
investment inprevention
screening costper person
investment inscreening
frac r dev symptknown
effectiveness ofmanaging
unsymptomatic
<frac death rasympt>
TC places
detection fractionwithout screening
leaving high risk
Hospital demand: hospital bed days
total hospital days
100,000
90,000
80,000
70,000
60,000
5 5 5 5 5 5 5 544 4 4 4 4 4 43 3 3 3 3 3 3 3 3
22
22 2
2 2 2 21 1 1 1 1 1 1 1 1
0 25 50 75 100Time (Month)
total hospital days : base hospital days/Month1 1 1 1 1total hospital days : TC 3M hospital days/Month2 2 2 2 2total hospital days : Prevention 3M hospital days/Month3 3 3 3total hospital days : Screening 3M hospital days/Month4 4 4 4total hospital days : TSP 1M hospital days/Month5 5 5 5 5
Number of symptomatic patients
number of symptomatic
400,000
350,000
300,000
250,000
200,000
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
44
44 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 32
22
22
22
22 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 25 50 75 100Time (Month)
number of symptomatic : base person1 1 1 1 1 1 1 1 1 1 1 1 1
number of symptomatic : TC 3M person2 2 2 2 2 2 2 2 2 2 2 2
number of symptomatic : Prevention 3M person3 3 3 3 3 3 3 3 3 3 3 3
number of symptomatic : Screening 3M person4 4 4 4 4 4 4 4 4 4 4 4
number of symptomatic : TSP 1M person5 5 5 5 5 5 5 5 5 5 5 5
Modelling process
• Modelling invites us to question assumptions: – What are the boundaries of our system?
– What do we really need to know to make decisions?
• Modelling can help to uncover information requirements
• Modelling can facilitate a dialogue between stakeholders
• Modelling allows cheap and simple experimentation with different choices
Conclusions
• Trials alone provide only a limited basis for decision-making
• Modelling can highlight– Trade-offs
– Investment and implementation processes
– Time scales of effects to materialise
– Importance of context
– Existence of alternative interventions and of prevention and screening
• Modelling might be valuable – even if it can’t necessarily provide all the answers
