Keuzevak - Value based healthcare Flashcards
2 Core characteristics of Porter VBHC
Value chain; all activities of an organization must create a valuable product
- Create value for consumers
central premise: (in any industry) a successful and sustainable enterprise needs to create value for clients (in competition)
Outcomes
effects of care on the health status of patients
Costs
Value comparison and go with the cheaper one with the same value
Patient value;
health outcomes that matter divided by the costs to achieve those outcomes
Value is created
At the level of a medical condition over the full cycle of care
3 concepts in VBHC
- outcomes
- costs
- Value for patients
Medical condition level to create value
a) patients seek HC to address health-related issues
b) issues usually directly related to medical condition
c) ergo: professionals create value by looking at these conditions
Created at specific levels of care; lungcancer, skincancer and not cancer
Value is created over full cycle of care
a) value generated through a set of activities → value chain
b) full care cycle: from diagnosis to rehabilitation → start-to-end care
c) e.g surgery = 1 element of full care cycle
5 goals of VBHC!!
- organize into IPU
- Measure outcomes and costs for every patient
- Move to bundled payments for care cycles
- integrate care delivery across facilities
- Build and enable an IT platform
- expend the excellent services across geography
Measurement & reporting
Providers should measure outcomes and cost of their care cycles per patient
- Publicly reported (competition)
- enabling fair comparison
Systematic outcome measurement
Key Actions to Implement VBHC: 4 keys
- Organizing Around Care Cycles: Health care providers should be organized based on the full care cycle for medical conditions, not just by specialties.
- Integrated Practice Units (IPUs) that consist of multidisciplinary teams focusing on specific medical conditions.
- Measurement & Reporting: Providers should measure and report outcomes and costs for full care cycles, ensuring transparency for comparison.
- Payment Aligned with Value: A shift from fee-for-service to bundled payments that reward outcomes and efficiency, rather than volume of services provided.
Provider competition - value based competition
- Outcome Measurement: Systematic measurement allows for comparisons, and excellent providers are rewarded with more patients.
- Provider Incentives: Providers should be incentivized for achieving good outcomes and efficiency, not for providing more treatments.
Difference between hospital structure and VBHC principle
Hospitals; based on medical specialties
VBHC; per medical condition
IPU
multidisciplinary team of professionals and supporting staff are grouped together to coordinate their independent tasks with the overarching goal to improve value for a particular group of patients
- Hospitals should organize around medical conditions
Payment implication
Bundled payment; reimbursement for the whole care cycle of a medical condition
Rewarding good outcomes and efficiancy
- rewarding more patients
- financial bonusses; p4p
Providers that can not keep up
- Should restructure of go out of business
Confusion around IPUs
concepts refer to organizational units not to care paths
Multidisciplinary collaboration is common, IPU rare
Rare in Netherlands
- No own budget
- No own decision making
units in organization
refer to specific groups, departments, or divisions designed to handle particular functions or tasks.
You can achieve coordination within a unit in two ways-
- Lines of authority; coordination trough supervision decision making power
- Close contact; informal communication
function based grouping
- Vertical lines
- each line represents a group of people with a particular skillset and knowledge
- These groups are based on the means
Market based grouping or condition based grouping
- Each line represents a group (i.e. unit) that serve a particular market (hip replacement)
- serve along the whole cycle of care
- these units are group based on the ends of a production process
long tradition in hospitals and problems
- tradition of medical specialization
- with complex knowledge and skills
- Issues of coordination and dealing with interdependencies
(cross over all different IPU) - Outdaded legacies of medical specialization
Why IPU is solution
- coordination is value based (looked at the patient to provide care)
- to organize around customer needs, not the supply of particular services
VBHC Practical challenges IPU
- History and interest
- Public opinion (concentration of hospitals; specialized)
- radical vs incremental change; lot of time and effort
- informal collaboration vs formal reorganization
- MD teams as liaisons (between coordination mechanisms; work together to improve outcomes) vs. IPU as units
IPU and MD teams
- Relies on communication and cooperation between existing departments or teams to achieve shared goals
- Promotes accountability and alignment with organization by responsibility for patient outcomes
Diabeter as IPU
- Co-located; all care in one facility
- Multidisciplinary Team (endocrinologists, dietitians, nurses, and psychologists))
- Full Care Cycle
- Improving value by routine outcome Measurement and negotiating Bundled Payments
- Diabetes; one medical condition
One team
- One database
- Medical outcome focus
- integrated care delivery
- independent decision making
- Bundled payments and P4P
Bundled payment reward Diabeter
Rewarded with more budget
- More innovation and more patients
Outcomes examples
PREM; Patiënt Reported Experience Measure
(ervaringen)
PROM; patient reported outcome measures
(uitkomsten)
VBHC in EMC
- Function based
- Disease specific (not really medical condition)
- ## outcomes closed monitored
Prom to measure QoL levels
- General measures
- Domain specific
- Disease specific
How to generate prom monitoring? Look at?
- Number of questions
- domain specific and disease questions
- individual / population level
- Suitable for childern
Tracking via E-prom
Challanges to value implementation in EMC
- No answering
- Patient compliance; no burden!
- Rogers innovation curve
- Provider compliance with the PROM measurement (in a already busy schedule)
Change management for innovation such as implementing PROM measurement - determinants
- technical implementation
- Skills
- Knowledge
- Awareness
- Intrinsic motivation (Relatedness, skilss, autonomy)
- extrinsic motivation (punishment or compensation)
Prom measurement in EMC
- Minimal patient burden with minimal loss of information
- Disease overarching approach and visability
Three mechanisms for VB competition via public transperency
- Patient choice; patients choose providers on their outcomes
- Provider comparisons learning; provider learns from systematic comparison of outcome; benchmarking
- Value based competition; providers compete on measurable results with payments tied to the outcomes
Donabedian framework
Quality of care
- Structure
- Process
Results in outcomes; can be predicted by proces and structure indicators + can compare
Ellipse form; structure and process indicators form the quality of care as the provider controls it
structure indicator
What healthcare providers have available to treat patients
- Staff, facilities, systems, equipment
- Certified diabetes team
Process indicator
What providers actually do to treat patients ( how well do they follow the medical guidelines)
- Guidelines; are you doing the right thing
- Diabetes pump
Outcome indicators
Measures of what happens to the patient health
- Manifest outcomes maybe much alter
- QoL, mortality
VBHC and donabedian differences
Freedom of providers
- VBHC; free to experiment
- D; improvement described by indiciators
Level of analysis
- VBHC; medical condition over the full cycle of care, not Individual services
- D: can be individual services to evaluate
Value;
- VBHC; outcomes as proxy for value; costs also into account
- D; Direct focus on outcomes only to patients; structure and process secondary
Freedom for providers in VBHC to experiment with outcomes
Provider encouraged
- design care process
- organize teams
- choose technology
- innovate to delivery methods
ICHOM
Defines indicators that matter the most to patients over the full cycle of care per disease
- Enable worldwide benchmarking
- enables provider comparison
How in NL outcomes public for provider comparisons
- 32 disease specific outcome measures
- using proms
Inform patients, insurers, and providers
Provider comparisons difficulties in interpretation
- Observered differences in outcomes
- Statistical uncertainty
- Case Mix
- Residual confounding
- Registration bias (methodological)
Remains; quality of care or outcomes that have to do with just the
- Observed differences in outcome dimensions
Outcomes matter to patients
- Relevant for patient
- Between provider variation; variance in outcome
Validity
- does the instrument really measure what we know
- systematic biases or error
Reliability
- Repeated measurements give the same result
- random error
Standardization
- each provider collects information the same way?
- error
Reliable between provider differences need to work, because?
- Patient choice for value
- Provider learning
- Value based competition
- statistical uncertainty (within provider differences)
Makes differences harder to detect
- Smaller sample size; large CI
- Extremely rare events
- Extremely common events
Adjust random effects model
random effects model
to tackle statistical uncertainty
- Doesn’t use the distance between intercept and individual centre
- Schrunk factor; put intercept closer to the mean for small sample sizes
- tackle rare events in small hospital sizes
Put it closer to the mean to correct for noise and unreliable data; no overinterpretation of differences
Goal; small hospitals are not punished shift more towards benchmark
Random effects model + formula
method to separate true differences from noise when analyzing data across facilities
between hospital variance (T^2)/
T^2 + (within hospital variance (sigma ^2)/ sample size)
More shrinkage when
Small between-hospital variance - Hospitals appear very similar; small differences are treated as noise.
Large within-hospital variance (
High variability within hospitals increases uncertainty in estimates.
Small sample size: Limited data per hospital amplifies uncertainty and shrinkage.
- Casemix and residual confounding
Casemix; characteristics or co variables influence the outcomes (sex, age, ses, severity); patient population
Residual confounding; confouding that you can not measure (because of no data on variables) and is left
registration bias
differences in outcomes due to how the data for outcomes are collected by the providers
- association with quality of care
Once controlled fro
- Case mix
-statistical uncertainty
- registration bias
Left is quality of care that correlated with the outcomes
Outcomes are the outcomes of provider work and resources and not characteristics of context
VBHC why correcting for these influences
Incorporating Structure and process outcomes
- Otherwise no fair comperison on what the provider does and the resources
Verify that the outcomes are the result of what the provider does and what he has available
Rankability
proportion of variation that is not due to chance?
Between provider variance/ median within provider precision
= T2 / (T2 + σ2)
Needs to be high as possible
- <50% LOW
-50-75% moderate
- >75% high
AUC
Area under curve
- how well the predictors in a model predict outcome
- 0,5 –> do not predict, due to chance
- 1; fully predict outcomes
O/E ratio
The OE ratio compares observed outcomes (e.g., mortality rates) to expected outcomes (adjusted for case mix).
- Ratio above 1; more than average
- Ratio under 1; less than average
Perfect in line; no casemix
Reliability function of
Magnitude between hospital variation
- significant variation are easier to detect, making the measurement more reliable (similar hard to detect)
Certainty of estimation
- Sample size
- statistical noise lower
How to improve rankability
Aggregation
- more information; combining information for more robust model
- Regional indiciators; improves entire health system value, not one hospital - precise estimates large N
Reporting data over 5 years (more robust data)
- conflicting goal to steer by when needed (feedback loop)
-reducing short term fluctuations
Composite indicators; one overarching indicator
- overall quality indicator;
- reduces noise and balances out variability in individual measures
Prioritize impactfull indicators; indicators with low validity and public relevance low priority
Discrimination
the ability to distinguish high and low performing hospitals
all or nothing approach
textbook outcome; if they included all quality indicators are met
- Stricter standard of measuring
Why HSMR not good
- Refferal bias
- Inadequate case mix adjustment; not adjusting for severity
- End of life care inflated mortality rates
- differences in capturing data; comorbidity and mortality
Not reflecting avoidable deaths
HSMR recommendations
- Enhanced adjustments; disease severity, end of life care and regional factors
- Regional perspective; regional level to look at referrals and specialized centres
- Alternative metrics; condition spesific mortality or proces indicators; adherence to guidelines
- Standardized coding; uniform data capturing
low rankability
Quality indicator is unreliable for benchmarking
- case mix
- random variation
Why is little casemix favourable
- Easier comparisons: Differences in outcomes are less influenced by patient characteristics.
- Less adjustment needed: Results are more reliable without complex corrections.
- Understand residuals: Small differences are easier to attribute to other factors!!!
Little casemix makes comparisons easier
Rankability - Between hospital variation
Shows how much hospitals differ in performance.
If high: Hospitals differ significantly, making rankings meaningful.
If low: Hospitals are similar, making it harder to rank them reliably.
Rankability - within hospital variation
Refers to variation within a single hospital, due to randomness or small sample sizes.
If high: Outcomes within a hospital are unstable or noisy, making true differences harder to see.
If low: Results within a hospital are consistent, making hospital ranking easier.
Rankability - N
Small N: Increases uncertainty (n) and lowers rankability.
Large N; Reduces noise σ ^2 and improves rankability by making estimates more precise.
relative risk
worst outcome percentage/ best outcome percentage
random effects model CI
CI becomes smaller
- if the CI is in the mean not significant
Correct noise of low sample sizes
Important with beta in casemix
- Look at the mean, is left or right worse
excersise case mix
Define the case mix variable which is worst condition in the population
- compare patient population and not the mean
Describe what is the worst outcome in case mix example
Right side is more than one, more worse outcomes. Beta is above average
- Confidence interval overlapping with mean
aftercase mix; were has the dot switched to
Vos et al breastcancer aim
Quantifies how much observed variation in these indicators was true to quality, taking into account
- Casemix
- Random variation
Argue for which of these quality indicators case-mix correction is most needed when comparing hospitals and argue why.
Look at O/E ratio, which are of line are due to casemix
- or look at rankability
AUC based on casemix predictors ; low AUC
A low AUC means the predictors (case-mix variables) cannot predict the outcome well.
- The predictors in the model are not relevant or insufficient to explain the outcome.
- The outcome is not strongly determined by patient characteristics, which can be a good sign for process indicators.!!!
AUC based on casemix predictors ; High AUC
A high AUC means the predictors (e.g., age, comorbidities) can reliably predict the outcome (e.g., mortality or receiving treatment).
- This indicates that patient characteristics play a significant role in outcome differences.
Low AUC based on casemix and implications for validity
Low AUC means, based on the casemix no strong predictor of outcomes;
- Casemix less influence on this outcome
Making it more valid, indicator strongly determines outcomes, variation less explained by fluctions in patient population
Rankability def
“The proportion of variance in a quality indicator between hospitals which is not due to chance.”
Rankability increases
When N becomes larger –> less random variation
disadvantage using more data for rankability
- Delay in feedback, want to steer timely
advantage using more years data for rankability
It can lower the prevalence of random variation due to higher N. making comparisons more reliable
- Smaller hospitals benefits the moest, as it smooths out the fluctuations
Purposes of quality indicators
- Hospital Feedback: Provide fairer, more reliable performance insights.
- Internal Monitoring: Track quality trends over time.
- Benchmarking: Enable fair comparisons across hospitals by reducing the impact of chance
costs
Represent value of resources to deliver healthcare services
- Direct costs; directly tied to patient care
- Indirect costs; related to general operation of healthcare facilities
- Fixed; remain constant over time regardless patient volume
- Variable; change with patient volume
costs =/= price, tarrif or charges
costs price = direct costs + indirect costs
Price = selling price (costprice + margin)
Calculation of costprice is really outdaded and not reflecting true cost prices
Compas
Al direction to patient volume
- High quality care (north)
health outcomes per dollar spent
- outcome measurement quality of care
- cost measurement; resources efficiently
When something added value
- Outcomes same costs lower
- Outcomes higher costs stable
- costs lower outcomes neutral
methods for measuring costs
- Division calculation
- Percentage calculation
- Equivalence method
- cost alloction method
- Time driven activity based costing
benefits of cost measuremetn
- benchmarking; allows comparisons
- transparency; provides insights in resource use
- Resource allocation; informs strategic investments in services and processes
Division calculation
total costs per department / number of patients
Division calculation pro and cons
pro
- Simple and easy
- Useful for straightforward cost allocation in low-complexity
environment
cons:
- Oversimplification; not taking into account service variation and patient needs
- Minimal insight in the total into the specifics of the care process.
NOT VBHC
Why cost measurement underlight?
- Complexity; takes time and advanced methods
- historical focus on outcomes
- knowledge and training caps; limited expertise in advanced cost methods
- data limitations; fragmented data
- challenges with costs allocation; assigning indirect costs to specific care pathways
Why cost measurement is more challenging than other industries!!!!!!!!!
- Complexity of care pathways; individualized patient care involves multiple departments and professionals unlike standardized industrial processes
- Service variation: variation in medical conditions (cost differs between conditions)
- Patient variation; each patient may require different levels of care
Cost allocation method
Direct and indirect costs put to services or departments based on their consumption of
resources (such as labor or equipment). Allocation keys are variable (e.g., time, volume of services, number of patients).
- Where do resources go to?
o Can deal with service variation
o See good the costs in your care pathways; not dealing with care pathways complexity
Cost allocation method pro and cons
pro
- Suitable for calculating different types of services.
- Accommodates both direct and indirect costs.
- More accurate than simpler methods like division calculation.
cons
- Assumes a certain level of homogeneity among patients (meaning: all patients consume resources roughly the same way), especially when it comes to allocation of indirect costs.
TABC
Allocates costs based on time spent on different activities and the cost of resources used during that time.
- detailed
- dynamic
- suitable patient journeys
- direct costs direct
- Capacity Cost Rates
Takes into account;
- care pathway complexity
- service variation
- patient variation
TABC pro and cons
pro
- Uses only two components: the cost per time unit for used resources ($/min) and the time spent on each activity.
- TDABC is very effective in complex and variable settings with diverse services and diverse patients.
- The cost model is detailed, dynamic and adaptable.
cons:
- Requires accurate tracking of time spent on activities, which can be challenging.
- May be difficult to implement without sufficient data infrastructure and data availability.
Capacity Cost Rates
- Direct fixed costs and indirect costs are allocated through Capacity Cost Rates (CCR)
- (total cost/total practical capacity)($/min)
Only two components: annual organizational cost data and activity time durations
TABC steps
1) Identify the study’s aim and the medical condition to be costed
2) Map the process of the care pathway at the activity level
3) Identify the direct and indirect resources supplied in the care pathway
4) Estimate the total cost of each resource
5) Estimate the practical capacity of each resource and calculate the CCR
6) Obtain and analyze time estimates for each activity in the care pathway
7) Calculate the cost of the care pathway by multiplying the capacity cost rates by the corresponding time estimates
example of direct costs
- Vaccination supplies
- Nursing time
examples of indirect costs
- Administrative staff
- Facility overhead
- IT support
CCR (capacity cost rate);
total cost resource x / practical capacity of resource x
- total costs per year
- Practical capacity in min
eg. 120.000 min and 60.000 pj
= 60.000 / 120.000 =0,50
TABC dynamic because
- Can add extra stepp for specific patient or change the costs of an resource
Practical challanges of TABC
- Feasability and details; cannot obtain full cycle of care in most cases, but the change
- Lack of annual acces to indirect cost data (outdated costprices)
- Time observing for CCR time consuming an difficult; day to day task, non clinical tasks, simultaneous tasks
4.
Standard rule Step 6
Standard time use for short, in-expensive activities with minor variations
Strategic agenda VBHC
- organize into integrated practice units
- measure outcomes and costs for every patient
- move towards bundled payments
- integrate care delivery across different facilities
- Expand excellent services and reward them with patient volume or innovative budgets
Why do we need payment reform; rationele of payments
- Financial incentive influence behavior
- Providers can influence demand
Paper McGuire
Healthcare professionals can influence demand and sometimes do for there own purposes PID
Gruber & Owens
Subsitution between normal birth and c-section births, SID
Chalkley and Listl
Fixed salary towards F4S, more Xrays provided
Why do we need reform of mayment method, as it now is F4S
- Link with volume in stead of value
- Discourage of prevention (more sick better)
- No link with quality
- Conflicts with intrinsic motivation (Also a business and revenue to make, even when people get better)
- Maintains fragmentation; doesn’t stimulate cooperation
Bundled payments
A single prospective payment per period for accepting accountability for the provision of a bundle of care services related to a condition
- Prospective
- Cylce of care
- One amoun
Aggregation two dimensions bundled paymetns
across time; prospective management
Across providers; need to work togehter
Benefits Bundled payments
- Prevents overprovision; as soar part of bundle
- Improves care coordination and collaboration; reimbursed as a (Need to work together to reach the outcome otherwise no payment)
- Cost controlling; eliminate waste within the bundle
- Improve quality; eg by reducing harmful overtreatment
Pitfalls bundled payments
- Unwarranted increase; in the number of bundles
(surgeries instead of medication, because of a bundled payment that is more profitable) - Underutilization of necessary services
- Risk selection of profitable patients
- Compartmentalizing patients into separate conditions
- Complexity of design and implementation (steenhuis et al)
compartmentalizing
Putting patients into seperate conditions in a bundled payment
- Comorbidity; implement of diabetes, risk of separting care for other diseases (less value)
Paradox; prevents fragmentation but also introduces fragmentation for comorbidity (putting them into different boxes)
P4P
Explicit financial incentives for performing well on a set of predefined performance indicators
- Quality of care; structure or process
- Process indicator; extend to which provides follow clinical guideliness
- Added on pre existing payments, because otherwise deviate from incentives (maximize the matrix otherwise)
Outcomes difficult as indicator for P4P
Has to do with;
- Bad clinical practices
- Coincedence
- Patient circumstances case mix
advantages of P4P
- Quite intuitively appealing; prioritize thing we value as important
- May contribute to better can if done well
- May enable providers to invest in prevention; This because of additional add ons; they can use in prevention
cons of P4P
- Flawed incentives in underlying payment system are left in tact (F4S leaved in tact; only 3 % extra when outcomes)
- design and implementation very complex
Practical evidence P4P or Bundled payments
- More evidence for BP; positive impacts with wide ranging effects
- P4P; inconsistent evidence of improved health outcomes
Complexity with evaluating Bundled payments or p4p
Short term effects; only focus on two years
- change in provider behavior takes more time
More correlation than causal effects
- DIF in DIf
KPI
Metrics used to evaluate succes in achieving healthcare objectives
- Tool to monitor and incentivize
- 20% revieve telemonitoring (proces), at least % should include SDM, or training
- Used in P4P
- Mostly 4%
Designing P4P
- What to incentivize
- conditions and which indicators
- Who collects; PROMS
- Reliable - Who to incentivize
- Individual groups or organizations - How to incentivize
- rewards and penalties
- how much
- how often (mostly half year or 2 years)
- Translate scores to payments
Practical issues with bundled payments
Extramural care in care path; thousands of providers to incentivize as insurer
- Focus only on intramural care
Scope very limited; much are fixed costs such as surgery and spending adjustment is very difficult
Bundled payment three segment model GP
Gatekeeper and substitute to second line
- Prevention as gatekeeper
- Billable codes
- Fee for service outside core activities
- Small bonusses (p4p)
- Bundled payment for chronic diseases
ZK value based formula
- Appropiateness x outcomes/ costs
Steps of zk to bundled payment
- F4S
- value based contracts; bonuses or penalties
- Bundled payments; shared savings taken into account
- Population based; shared savings also
Monitoring differences between F4S and bundled payments
F4S
- Volume per provider
- Costs per provider
Value based
- Costs per condition/ population
- quality per condition/ population
Challanges of insures VBHC
- No monitoring system available for quality and costs per patient or per provider
- cannot disclose inappropriate care due to limited provided information (what happens on site)?
- Costs full cycle of care hardly available
- Regulation complex
when VBHC needed as insurer and cons
- Hinders improvement in quality by providers
- If care is being fragmented -> cooperation
- ## Can conflict with freedom of choice due to chain selection
VBHC contracting insurer
- Fixed budget
- Process KPI
- Variable budget for value based projects
- Bonus for outcome KPIs
- Contract evaluation
KPI for accesability
- Amount of weeks waiting list
- Amount waiting patients on operation
- Capacity for monitoring at home
- % conversation about SDM
KPI for approproate care
- % of vulnerable elderly seen by a geriatrician
- % of patients in hospital ready for dismissal
- Decrease of patients with repeat consultations
- % of patients treated compliant to ZEGG-list (= Dutch appropriate care program)
Care excluded from bundle payment
What is inside
- standard care for patient journey
Influenced by the bundle
- avoidable complications; shared savings
Outside bundle
- Prevent cherry picking, organ donation
Future challanges for insurers VBHC
- Affordability; rising spending and thus rising premiums
- Price pressures; rising wages and prices, negociations difficult
- Scares availability of healthpersonell, increase waiting times
- Transformation of healthcare; more regional
Teaching to the test
providers start focusing on the outcomes in stead of providing better care of the full cycle –> P4P
Gaming
Manipulate system to get better outcomes on the monitor
- Cherry picking; low risks with better profitable
- Upcoding; more severe
Which levels to incentivize
Look at two levels
- individual; track outcomes
- group level; be aware of free riding also track individual outcomes
- organizational level
Individual level outcomes pro and cons
Directly rewards personal performance and effort.
Encourages GPs to take responsibility for their own patient outcomes.
Supports individual motivation
Challenges
Too much pressure on the GP
Discourage teamwork
group level outcomes pro and cons
collaboration and knowledge sharing among GPs in a practice.
Encourages resource pooling for patient care improvements (hiring diabetes educators
Reduces the risk of competition within practices, fostering a team-based approach.
Challenges:
Performance disparities between partners may cause conflicts.
Rewards may not reflect individual contributions adequately.
organizational level outcomes pro and cons
Facilitates systemic improvements
Encourages standardization of care processes and protocols.
Suitable for larger-scale outcomes
Challenges:
Individual GPs may feel disconnected from incentives.
Risk of misalignment between organizational priorities and individual GP practices.
Payouts
BE; Loss aversion also penalties
- depends on yearly or multipele year contract
- 3 months or per year
be aware how tangible it becomes
Link VBHC ans SDM
What is important to an patient can add extra value for the patient
- from population towards individual;
SDM def
approach were clinicans and patient share there best evidence when faced with decisions, where the patients are supported to consider options and achieve informed preferences
- Informed preferences
- Supported to consider options
Steps of SDM (Ellwyn)
- Introducing choice; make clear they have choice
- Describing all options; based on information
- Helping to explore preferences and decisions; help with decision in line with preferences
Decision aids of SDM
Tools with the aim to support the decision making process more evidence based
evidence based on?
- EBM guidelniess
- PROMS
Decision aids of SDM are sketchy? Evidence on impact
- Increases knowledge also about harms
- Effects on clinical outcomes/adherence and services are not consistent
Complexities of SDM
What matters to paitens
- Creating value, not always clear what
- What matters most, not always known
- Becomes clear when doctors become patients
How disease impacts life
- talk about how ideally implement it into life
- Adjustments to what fits best
Performativity of numbers (essen en oborn 2017)
Numbers represent objective reality and therefore have authority
- Performative
- Frame action
- construct boundaries between lives and disease
- Performative numbers
They construct reality, determining what is important
Boundries between disease and lives
- Mystery disease; cant describe when doing better or worse, numbers cant fix that
- PROMS do not capture lived experience
!!!!
- Outcomes are sometimes registration by doctors; dont know how they are doing
Framing action 2 questions
Numbers construct illness active and the consequences
- Who gets what?
- How inclined to become own self manager (if everything is prescibed?)
What role should numbers play in managing?
- Numbers vs patient stories
- Using numbers to make a story
- Use own scale, context specific
- what is objective pain
Critique on SDM with a focus on choice and numbers
SDM focus on numbers and choice in standardized trajectory neglects:
* Uncertainties in care trajectories
* Searching character of much (chronic) care
* Lack of identifiable moments of ‘choice’
Tinkering
Accent not on only clinical guideliness and number but also taking into account the holistic patient stories and the context of their lives
SDM in some cases is
Allowing to make bad choices
How to deal with complexities
- Creating flexibility in registration; stories or scale?
- Using patient input to create SDM
- Using numbers as a start, but not end
Every number is important but needs a story
Using information in the process of decision making
- Track outcomes through patient journey
- Doctor is also better informed on what is there and what are the preferences
- Shorter consultation time and better prepared
- Fill in before visit
Golden circle health monitor
- Strengthen patient empowerment
- ## Provide outcomes on the whole patient journey
Dashboard objectives
- Monitoring QOL
- Detecting; abnormalities or complaints
- Discussing; results with patietn
- Empowering; insight in how they are doing
Prognoastic dashboard
Make more predictive
- How they are doing in the future
- Trendline how they are doing
-
Care level of dashboard
Micro level; QoL, prognostic, quantity of life
Meso; patient groups using medical data
- Optimize outcomes to specific target outcomes
Benefits of dashboard outcome monitoring
- Timing
- Number of consulations
- Better informed
- E-consult enabling; no burden of travelling
Learnings dashboard
- develop with end users
- Ongoing proces
- Medical record intregation
- Fast overview
- ## Integration other outcome measures
Decision aid tool consequences for doctor patient interaction cons and pros
Pro
- Better informed as patient
- Assignment at home, more time questions
cons
- Interaction dependent on that information less for stories
- No viewing of better options outside the decision aid tool
To what extent does the decision aid help in a process of SDM? In your answer go into how the decision aid influences the doctor-patient interaction; does it strengthen the role of patients and how, and how does it affect the role of the professional?
Role of patient
- Better informed
- Knows what to ask in terms of the consequences
- Less time for stories
Role for doctor
- More time efficient; because of patients know that to ask, reduces consultation time
- Better knows preferences of patients
What issues left out of decision aid?
- Pshycological factors; only evidence based
- Equity; low SES not understandable
- Own stories and viewing, maybe having noting to do with a scale
Numerical commensuration
The process of translating complex, qualitative experiences into measurable metrics, enabling comparisons and standardization.
Numbers draw boundries for patients and physicians
For patients:
- Numbers separate disease-related symptoms from broader life experiences, making the illness more manageable but potentially sidelining non-measured aspects.
For physicians: Numbers define the scope of their responsibilities, focusing on measurable parameters aligned with clinical guidelines.
Framing actions with numbers
For physicians: Numbers guide medical decisions, such as adjusting medications or determining treatment plans.
For patients: Scores influence self-management decisions and behavioral changes.
Alignment of numbers in SDM
Uniform language for effective patient and physician
Can predict, stories only from the present and past
Tension of numbers SDM
when numbers dictate actions that conflict with patient expectations or priorities.
What is numerical commensuration, and what are its implications for decision aids?
the transformation of diverse qualities into a common numerical metric, enabling comparison
- Implication; more efficient to use but may neglect complext and qualitive aspects of patient measurements
How do numbers ‘discipline,’ and what does this mean for decision aids?
Numbers discipline by creating standards and benchmarks that guide or constrain behaviors (p. 135-136).
implication; could enforce to stick to adherence of medical guidelines, limiting flexibility in holistic patient approach
- How do numbers create boundaries between illness and broader life, and what are the potential consequences?
Concept: Numbers isolate disease-specific symptoms from broader life experiences (p. 136, 138).
Consequences: While this separation helps patients manage their illness, it can marginalize non-measured aspects like emotional well-being or lifestyle factors, which may be equally important (p. 142).
- How do numbers create boundaries for physicians, and how does this apply to decision aids?
Concept: Numbers focus physicians’ attention on measurable disease parameters and standard treatments, aligning their work with clinical guidelines (p. 138).
o Implications: Decision aids may reinforce this focus, potentially overlooking psychosocial aspects or patient-specific needs.
- How can numbers or decision aids ‘frame action’ for patients and physicians?
Numbers frame action by presenting measurable goals and categories that legitimize specific actions (p. 139-142).
Examples for physicians: Adjusting medication dosages to meet specific score thresholds (e.g., reducing “red” DAS28 scores), while they have another scale in mind
Examples for patients: Modifying lifestyle choices (e.g., diet or exercise) in response to high scores. Decision aids might similarly shape behavior but risk promoting mechanistic responses over comprehensive care.
PRO
Patient reported outcomes
- Refer to outcomes directly form patietns about how they feel and function in relation to an health condition, without the interpretation of a professional
Prems adress perceptions of their experience
PRoms and prems
Proms; are more objective they measure more patient outcomes
PRoms valuable for physician because
- because physicians underreported symptoms
-Improves patient-provider communication and also improve problem detection, management and outcomes
PFS
Progression free survival; assumption that shrinkage or delayed growth of cancer correlates with better overall survival or quality of life
- Hard to asses depends on timing of the point
- Often mis understood by patients
Generic PROMS pros and cons
Pro
- Normative values of groups
- Can compare across population or patient groups
Cons
- Ceiling and floor effects; some score high and some very low
Disease PROMS pros and cons
- Providers insight to relationships among structure limitations within the disease
- Does not allow comparisons across different groups
HRQOL
The impact of disease and treatment on domains of physical, psychological, and social functioning
HRQOL measures
- Standard gamble: determines the risk of a bad outcome, such as death, that a patient would be willing to take to avoid the outcome for which the utility is being assessed (e.g., stroke with severe long-term neurological sequelae)
- Time trade-off: reflects the length of remaining life expectancy that a person may be prepared to trade-off in order to avoid remaining in a sub-perfect health state
- Willingness to pay: reflects the willingness to pay for an improvement in health state
Characteristics of HRQOL
Quantifiable: Scores must be amenable for statistical analysis
Validity: Should be a true measure of HRQoL; should measure what is supposed to measure
Reproducible: Should produce similar results in comparable patients
Responsiveness: Should detect clinically important changes
Simplicity: Should be as short as possible
Phases HRQOL
- Phase 1; HRQoL issues based on three sources: literature, patients and health care professionals
- Phase 2; Construction of the item list
- Phase 3; Pre testing
- Phase 4; Field testing
Now data bank with constructs easy to tailor made an disease specific PROM
AYA as example
- Adolescents and Young Adults (AYA) with cancer
Look at what they find important and made an disease specific PROM to know where to add value for patients
