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Economic evaluation identifies, measures, and compares the costs and consequences of different medical treatments and therapeutic interventions, as well as operational systems for efficiency.
Traditionally used in the pharmaceutical industry, today, there are many new models of care that need economic evaluation. While evidence mounts as to the financial benefits of novel approaches—like care management, digital therapeutics, value-based care, behavioral health integration, and others—they don't easily fit the mold of traditional clinical care and can be challenging to assess.
To address this challenge, organizations turn to healthcare economists, who apply various data and mathematical techniques to calculate the health and efficiency benefits associated with a particular medical intervention to inform whether the investment allocation is worth the outcome.
They aim to conduct a robust, data-driven evaluation that helps leaders decide which interventions provide good clinical outcomes at a reasonable price. They seek to answer the question: Which interventions should be piloted, implemented, or scaled to larger populations of patients?
Traditional methods of economic evaluation
Because economic evaluations are based on underlying value judgments on how best to allocate resources toward effective and safe interventions, the mathematical method used is vital to creating useful insights. The design and construction of the economic model will significantly influence its results.
Traditional methods are based on clinical trial data, assessing an intervention's productive efficiency relative to health outcomes realized for a particular cost.
These 3 techniques are most commonly used:
- Cost-benefit analysis (CBA): Compares the costs and outcomes of available interventions in monetary terms.
- Cost-effectiveness analysis (CEA): Compares the costs of available interventions for a chosen outcome, like the cost per surgery avoided.
- Cost-utility analysis (CU): Compares the costs of interventions utilizing quality-adjust life-years (QALY) as the outcome focus.
While the first 2 methods are used for about 90% of economic evaluations—assessing cost-effectiveness and budget impact—these may be inadequate or inappropriate to appraise specific types of medical interventions.
New models of healthcare require novel approaches to economic evaluation
In today's cost-sensitive healthcare environment, where emerging models of care strive for evidence-based and efficient care while meeting the demand for enhancing patient experience, new approaches to economic evaluation are essential to high-stakes decision making.
When evaluating new medical treatments and models of care, it may look like there is no accessible data to use. But that's not the end of the story.
RTI Health Solutions' Associate Director of Health Economics, LaStella Miles, had this to say in a recent interview, “Where there is no path, we make a path." She spoke about how, as a health economist and industrial engineer, she approaches client projects with little to no precedent. The health economics team builds mathematical models and test their feasibility, relevance, and accuracy to make predictions.
She encourages evaluators to ask themselves, “What can we do with the data we have?" Starting with available data offers an opportunity to determine if existing sources can be useful.
Luckily, there are emerging measures, standards, and assessments for new models of care that can be leveraged to create reliable and productive mathematical models. Here are ideas for data sources to consider that may serve as a proxy when applying economic evaluation to novel medical, social, or financial care interventions.
Tapping newer comparative effectiveness models to reduce discrimination
While using quality-adjust life-years (QALY) has been deemed an important outcome measure by cost-effectiveness researchers, there is controversy around its impact as a tool for making comparative effectiveness assessments of interventions for populations.
QALY doesn't account for variation in populations and sub-populations, including demographics, level of need, social needs, and risk. In fact, Congress considered new legislation in early 2023 that would prohibit the use of QALY and similar measures for Medicare and Medicaid coverage and payment determinations because of concerns that the measure "systematically discriminates against elderly, disabled, or terminally ill Americans when used to inform resource allocation decisions or price determinations."
While QALY has been found to provide valid reasons for measuring both quality of life and longevity improvements, a report by the National Academy of Sciences highlighted concerns around accounting for "distributive justice" and whether these measures discriminate.
The National Disability Council published a 2022 report titled “Alternatives to QALY-Based Cost-Effectiveness Analysis for Determining the Value of Prescription Drugs and Other Health Interventions." Their policy brief examined the design and discriminatory potential of QALY-based cost-effectiveness analysis (Standard CEA), providing alternatives to standard CEA, including:
- Augmented or extended traditional CEA
- Multi-criteria decision analysis
- Frameworks for determining value to individual patients
As alternatives to QALY, these new measures maintain a researcher's capacity to calculate wellbeing and quality of life for informing policy and resource distribution decisions without discriminating against any members of society.
Data sources for evaluating new models of healthcare
Data is necessary to not only assess the total cost of these programs accurately but to uncover types of measures that can tie an outcome to the intervention in a meaningful way.
Validated assessments: Using data from standardized, validated assessments is a valid option for creating a rubric for evaluating non-traditional care models. These tools can be fine-grain questionnaires that ask questions about concrete measures.
For example, the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) is a standardized patient social risk assessment protocol. Their validated assessment tool has been translated into 25 languages and is based on national core measures. PRAPARE is evidence-based, as well as paired with an Implementation and Action Toolkit and standardized across ICD-10, LOINC, and SNOMED.
It asks, “How often do you see or talk to people you care about and feel close to? (For example: talking to friends on the phone, visiting friends or family, going to church or club meetings)?"
The answer choices—less than once a week, 1-2 times a week, 3-5 times a week, 6 or more times a week—can be used to correlate social program intervention frequency with other metrics like scores that measure feelings of loneliness.
Because many assessment tools are based on scales with interval properties, these utility measures can support comparing different interventions. If intervention A improved a patient's outlook or health, on average, by 10 points on a utility scale and intervention B by 5 points, then intervention A is twice as effective.
Generic measures: Other tools that use generic measures, like the 36-item short-form survey instrument (SF-36), asks time-bound questions about general health. These can be taken as a baseline before an intervention and after to zero in on changes that may reveal an intervention-driven improvement.
Willingness to pay: The “willingness to pay" measure also assesses the value that a stakeholder would pay for a particular outcome delivered by an intervention. The stakeholder could be a patient, health plan or provider program leader, local or state government agency, etc. This technique not only captures non-health benefits but enables them to be expressed in monetary terms, further allowing cost-benefit analysis.
Social risk scores: Spending targets can be used to establish program costs that account for the equity needs of individuals with higher social risk. Because lower spending could indicate underuse or insufficient access rather than appropriate cost-efficiency efforts, spending targets should include patients' clinical and social needs.
A local area-level risk score like the Local Social Inequity (LSI) score generated by the RTI Rarity™ tool could be used to inform spending targets and act as a proxy for other measures in economic evaluation.
Time-driven activity-based costing (TDABC): Viewed as a best practice for cost measurement, TDABC matches direct and indirect costs to activities based on the effort calculated in time value. Accurate cost information for a care model and its workflow of activities establishes a reliable sum that accounts for all costs relevant to each activity in the care model, getting a more accurate evaluation of the value of a healthcare activity.
Patient-reported outcomes: While patient-reported outcome measures (PROMs) are becoming more prevalent in pharmaceutical, medical device, and digital therapeutic clinical trials, they can also be invaluable in economic evaluation of new models of care. These non-disease-specific measures could come from assessment tools that look at health-related quality of life (QoL), wellbeing, and social care-related QoL.
Economic evaluation is a single tool in the decision making toolbox
Economic evaluation for healthcare assesses the value of a treatment, medication, or program compared to the next best alternative. While this can be challenging for new models of care, there are more expansive data sets that health economists can investigate and determine if they can accurately and reliably support the valuation of novel care interventions.
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