Health Technology Assessment and Pharmaceutical Economics

Introduction and Context

Health systems must make decisions under conditions of scarcity. New medicines, devices, diagnostics, digital tools and procedures may improve health, but they also consume budgets, professional time, infrastructure and patient resources. Health Technology Assessment (HTA) provides a systematic framework for evaluating whether a technology is clinically effective, safe, economically valuable, ethically acceptable and feasible to implement.

Pharmaceutical economics, often called pharmacoeconomics when focused on medicines, applies economic reasoning to pharmaceutical development, pricing, reimbursement and use. Its central question is not simply whether a product works, but whether the additional health gained is worth the additional resources required compared with relevant alternatives.

HTA is therefore both an analytical and a governance activity. It connects clinical evidence, economics, patient preferences, public policy and organisational decision-making. Its outputs may influence regulatory approval, formulary inclusion, procurement, coverage restrictions, price negotiation, clinical guidelines and disinvestment.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Explain the purpose, scope and stages of health technology assessment.
2. Distinguish efficacy, effectiveness, comparative effectiveness, safety and value.
3. Apply major economic evaluation methods to medicines and other technologies.
4. Calculate and interpret incremental cost-effectiveness ratios and related measures.
5. Analyse pricing, reimbursement, patent policy and market-access strategies.
6. Evaluate uncertainty, equity, ethics and implementation constraints.
7. Design a lifecycle value-management approach for a health technology.

1. The Scope and Architecture of HTA

A health technology includes any intervention used to promote, prevent, diagnose, monitor or treat disease. Examples include pharmaceuticals, vaccines, surgical procedures, medical devices, diagnostic tests, clinical pathways, telehealth platforms, artificial-intelligence systems and public-health programmes.

HTA normally considers several domains:

Regulatory approval and HTA are related but distinct. A regulator generally determines whether a product satisfies standards of quality, safety and efficacy for a specified indication. An HTA body or payer may ask a different question: whether the product offers sufficient value relative to its opportunity cost within a particular health system. Approval does not guarantee reimbursement, and reimbursement decisions may differ across jurisdictions.

A typical HTA pathway is:

1. Define the decision problem and target population.
2. Identify the current standard of care and all relevant comparators.
3. Systematically review clinical evidence.
4. Estimate costs, outcomes and resource consequences.
5. Analyse uncertainty and heterogeneity.
6. Consider ethics, equity, feasibility and patient preferences.
7. Deliberate and make a recommendation.
8. Monitor real-world performance and revise the decision when evidence changes.

The comparator is crucial. Comparing a new medicine with placebo may establish efficacy, but a reimbursement decision usually requires comparison with the best available or most commonly used alternative. An inappropriate comparator can make a technology appear more or less valuable than it really is.

2. Evidence: From Efficacy to Real-World Value

Efficacy is the effect of an intervention under controlled research conditions. Randomised controlled trials are often the strongest design for estimating relative treatment effects because randomisation reduces confounding. However, trial participants, adherence, follow-up and clinical settings may differ from routine practice.

Effectiveness is the effect achieved in ordinary clinical practice. It reflects adherence, access, comorbidities, provider behaviour and variations in implementation. Comparative effectiveness research evaluates the relative benefits and harms of alternative interventions, often using pragmatic trials, registries, electronic health records, claims data and systematic reviews.

Evidence synthesis may require indirect comparisons. If treatment A has been compared with B, and B with C, analysts may estimate A versus C through network meta-analysis. Such analyses depend on the transitivity assumption: the studies must be sufficiently comparable in effect modifiers, populations, outcomes and treatment context.

Important outcome categories include:

• mortality and survival;
• disease events, complications and hospitalisations;
• symptom relief and functional status;
• health-related quality of life;
• treatment burden and adverse effects;
• caregiver time and productivity;
• adherence and persistence.

A clinical endpoint, such as blood pressure reduction, may be a surrogate rather than a patient-important outcome. Surrogates are useful only when changes in the surrogate reliably predict changes in outcomes that matter to patients. Poorly validated surrogates can lead to overestimation of value.

Quality-adjusted life-years

The quality-adjusted life-year (QALY) combines survival and health-related quality of life. If a patient spends time t in a health state with utility u, the QALYs accumulated during that period are:

Here, t_i is time in health state i, measured in years, and u_i is the utility weight, usually ranging from 0 for death to 1 for full health, although values below zero may be used for health states considered worse than death. The formula assumes that utility weights are sufficiently stable over each interval and that time periods can be appropriately aggregated.

For example, a patient receiving treatment lives 4 years with utility 0.75 and then 2 years with utility 0.50. The total is:

QALY = (4 × 0.75) + (2 × 0.50) = 4.0 QALYs.

QALYs permit comparison across diseases, but they are not ethically neutral. They may undervalue benefits for people with disabilities, fail to capture distributional concerns and represent preferences imperfectly. Analysts should therefore report QALYs alongside clinical, patient-reported and equity outcomes.

Other measures include life-years gained, disability-adjusted life-years (DALYs), symptom-free days, event-free survival and monetary benefit. DALYs generally combine years of life lost and years lived with disability; they are frequently used in population and global-health analysis but should not be treated as interchangeable with QALYs.

3. Economic Evaluation Methods

Economic evaluation compares the costs and consequences of alternative courses of action. The basic requirement is comparison: a cost or outcome has meaning only relative to a specified alternative.

Cost-minimisation analysis

Cost-minimisation analysis is appropriate only when competing interventions have equivalent outcomes and safety, an assumption that must be demonstrated rather than presumed. It identifies the least costly option. If therapeutic equivalence is uncertain, this method can be misleading.

Cost-effectiveness analysis

Cost-effectiveness analysis expresses outcomes in natural units, such as strokes avoided, cases detected or life-years gained. It is useful when the decision concerns a common clinical outcome and stakeholders do not require a single cross-programme measure.

Cost-utility analysis

Cost-utility analysis commonly uses QALYs or DALYs. It permits comparison between programmes affecting different diseases, but results depend on utility measurement, perspective and assumptions about equity.

Cost-benefit analysis

Cost-benefit analysis expresses both costs and benefits in monetary terms. Benefits may be valued using willingness-to-pay, stated-preference methods, revealed preferences or productivity approaches. It can calculate net benefit but raises difficult questions about whose willingness to pay counts and how income differences influence valuation.

Budget impact analysis

Budget impact analysis estimates the financial consequences of adopting a technology for a particular payer over a defined time horizon. It complements, rather than replaces, cost-effectiveness analysis. A technology may be cost-effective but unaffordable in the short term if the eligible population is large.

4. Incremental Analysis and Decision Rules

The central economic comparison is between an intervention and its comparator. Let C_1 and C_0 denote costs, and E_1 and E_0 denote effects. The incremental cost-effectiveness ratio (ICER) is:

The numerator must be expressed in currency units and the denominator in outcome units, such as QALYs. The ICER is meaningful only when the intervention and comparator are compared on the same time horizon, using consistent discounting, perspective and outcome definitions.

An intervention is dominant when it costs less and produces better outcomes. It is dominated when it costs more and produces worse outcomes. Extended dominance occurs when a combination of other options provides more effect per unit of additional cost, making the dominated option inefficient in the incremental frontier.

Worked example

Suppose a new medicine costs 30,000 monetary units per patient and produces 3.2 QALYs. Standard care costs 18,000 and produces 2.8 QALYs.

Incremental cost = 30,000 − 18,000 = 12,000.

Incremental effect = 3.2 − 2.8 = 0.4 QALYs.

If the decision-maker’s threshold is 40,000 monetary units per QALY, the result may support adoption, subject to uncertainty, affordability and ethical considerations. A threshold is not a universal law. It may be explicit or implicit, and the opportunity cost of displacing other services may be lower than the threshold suggested by historical decisions.

Net monetary benefit

Net monetary benefit (NMB) transforms health effects and costs into a common monetary scale:

λ is the decision-maker’s willingness-to-pay threshold per unit of health, E is health outcome and C is cost. The incremental form is:

A positive incremental NMB indicates that the intervention is preferred at the selected threshold, assuming the analysis captures relevant costs and effects. NMB is especially useful in probabilistic analysis because expected values can be compared directly.

5. Perspectives, Time Horizons and Discounting

The perspective determines which costs and consequences are included.

• Payer perspective: includes expenditures borne by an insurer or public purchaser.
• Health-system perspective: includes healthcare resources regardless of payer.
• Societal perspective: may include patient costs, informal care, transportation and productivity effects.
• Patient perspective: focuses on out-of-pocket payments, time, convenience and outcomes valued by patients.

No perspective is automatically correct. The analyst must follow the decision context and state the perspective transparently. Double counting must be avoided; for example, wage losses should not be counted both as a patient cost and as a societal productivity loss.

The time horizon should be long enough to capture all important differences in costs and outcomes. A one-year horizon may be inadequate for a treatment that changes long-term survival or disease progression.

Future costs and outcomes are often discounted because societies may value present consequences differently from future consequences. If a constant annual discount rate r is used, a future value X_t occurring after t years has present value:

X_t may represent a cost or outcome, provided the same discounting convention is applied according to the applicable methodological guidance. Rates, timing conventions and discounting of health benefits vary across jurisdictions; analysts must follow the relevant national reference case.

6. Modelling Disease and Economic Consequences

Decision models are required when trial follow-up is shorter than the decision horizon, when evidence comes from multiple sources or when long-term outcomes cannot be observed directly. Common models include decision trees, cohort Markov models, microsimulation and discrete-event simulation.

A decision tree represents a finite sequence of events. A Markov model represents transitions among health states over repeated cycles. If p_ij is the probability of moving from state i to state j during one cycle, transition probabilities form a matrix P, where each row sums to 1:

If s_t is a row vector of state occupancy probabilities at cycle t, then:

The Markov approach assumes that future transitions depend on the current state and model inputs, not on the complete prior history, unless tunnel states or additional memory structure are introduced. This “memoryless” assumption may be inappropriate when duration since an event affects prognosis.

Models should be structurally transparent. Analysts should justify health states, transition probabilities, treatment waning, adverse events, treatment switching, mortality, costs, utilities and validation procedures. Calibration against external epidemiological or clinical data is important when direct evidence is incomplete.

7. Uncertainty and Value of Information

Uncertainty is unavoidable. It may arise from:

• Parameter uncertainty: imprecise estimates of costs, probabilities or utilities.
• Structural uncertainty: alternative model structures or assumptions.
• Heterogeneity: genuine differences among patients, providers or settings.
• Methodological uncertainty: disagreement about perspective, discounting or valuation.

Deterministic sensitivity analysis varies one or more inputs over specified ranges. Scenario analysis examines alternative structural assumptions. Probabilistic sensitivity analysis assigns probability distributions to uncertain parameters and repeatedly samples from them. Results can be displayed using a cost-effectiveness plane and a cost-effectiveness acceptability curve.

The probability that an intervention is cost-effective at threshold λ is:

This is not the same as certainty that the intervention is good value. It describes decision uncertainty conditional on the model and threshold.

Value-of-information analysis estimates the value of collecting additional evidence. Expected value of perfect information represents the maximum a decision-maker should theoretically pay to eliminate all uncertainty, while expected value of sample information estimates the value of a particular future study. These methods can guide research prioritisation, adaptive coverage and evidence-development agreements.

8. Pricing, Reimbursement and Market Access

Pharmaceutical pricing reflects research and development expenditure, manufacturing costs, expected demand, competition, intellectual-property protection, bargaining power, therapeutic alternatives and the value attributed to health gains. Price is not equivalent to value: a high-priced product may be cost-effective in a narrowly defined population, while a low-priced product may still be poor value if it offers little benefit.

Common pricing approaches include:

• cost-plus pricing;
• reference pricing based on prices in other countries or therapeutic classes;
• value-based pricing linked to outcomes or cost-effectiveness;
• tendering and competitive procurement;
• risk-sharing and performance-based agreements;
• indication-based pricing;
• differential pricing across markets.

Reimbursement may be unconditional, restricted to subgroups, conditional on further evidence, or refused. Managed-entry agreements can link payment to utilisation controls, financial caps, outcomes or evidence generation. They may be useful where early evidence is promising but immature. However, complex agreements impose administrative costs, require reliable data and may reduce transparency.

Market access is the process through which a manufacturer secures regulatory approval, reimbursement, procurement access and appropriate clinical adoption. It requires alignment among clinical evidence, health-economic evidence, budget impact, supply planning, stakeholder engagement and implementation capability.

9. Patents, Generics, Biosimilars and Competition

Patents provide time-limited exclusivity in exchange for disclosure of an invention. Their policy rationale is to encourage innovation by allowing innovators to recover investment, while eventual competition is expected to improve affordability. Patent systems must balance incentives with access to essential medicines.

After exclusivity expires, generic competition can reduce prices substantially when products are therapeutically equivalent and substitution is trusted. Biosimilars are highly similar biological products with no clinically meaningful differences in safety, purity and potency from the reference product, subject to regulatory requirements. Their development and interchangeability rules may differ from those for conventional generics.

Patent evergreening, secondary patents, regulatory exclusivities, compulsory licensing and voluntary licensing are contested policy areas. Strong intellectual-property protection may stimulate research but can delay competition; weak protection may reduce incentives for high-risk innovation. Policy should also consider neglected diseases, antimicrobial resistance and public funding contributions to research.

10. Lifecycle Value Management

Value is not fixed at launch. It changes as evidence, prices, competitors, indications and treatment patterns evolve. Lifecycle management includes:

1. Pre-launch: define unmet need, comparator, target population, trial strategy and evidence gaps.
2. Launch: establish price, reimbursement position, access criteria, supply and education.
3. Early post-launch: monitor utilisation, safety, adherence, outcomes and budget impact.
4. Maturity: respond to competitors, generic or biosimilar entry and new indications.
5. Reassessment: update cost-effectiveness and revise restrictions when evidence changes.
6. De-adoption: withdraw or reduce use when benefits are inferior, harms emerge or better alternatives become available.

Real-world evidence can identify treatment-effect heterogeneity and implementation problems. It should not automatically replace randomised evidence; rather, each design should be matched to the causal question. Artificial intelligence may support evidence extraction, patient stratification, outcome prediction and pharmacovigilance, but algorithmic bias, data quality, explainability, privacy and automation bias require active governance.

11. Ethics, Equity and Governance

Efficiency is only one criterion. A technology may be cost-effective but raise concerns about fairness, severity of illness, rare disease, age, disability or access for disadvantaged groups. Conversely, an intervention with a high ICER may be prioritised because it addresses catastrophic illness or a historically underserved population.

Key governance principles include transparency, consistency, independence, stakeholder participation, appeal mechanisms and management of conflicts of interest. Patient and public involvement can improve outcome selection, acceptability and understanding of treatment burden, but participation must be representative rather than symbolic.

Distributional cost-effectiveness analysis examines how costs and health benefits are distributed across population groups. It can reveal trade-offs hidden by a single aggregate ICER. Equity weights may be applied, but they require explicit social and ethical justification.

Confidential discounts create tension between commercial confidentiality and public accountability. Decision-makers should disclose methods, evidence sources, assumptions and reasons for recommendations even when net prices cannot be published.

12. Applied Case: Conditional Reimbursement of a Digital Diagnostic

This is an illustrative case.

Background

A digital diagnostic system uses machine learning to identify possible diabetic retinopathy from retinal photographs. The manufacturer claims that it can improve detection in primary care and reduce specialist workload. The system has received regulatory clearance, but evidence comes mainly from a controlled validation study.

Problem

The health ministry must decide whether to reimburse the system nationally. Uncertainty concerns accuracy in rural clinics, effects on referral rates, patient outcomes, implementation costs, cybersecurity and whether specialist capacity will actually be released.

Analysis

The relevant comparator is usual screening with existing referral pathways, not no screening. The HTA team defines the population as adults with diabetes eligible for screening and uses a health-system perspective. Outcomes include cases detected, vision loss prevented, false-positive referrals, patient time and costs of equipment, training, maintenance and specialist assessment.

A decision model links diagnostic accuracy to referral, confirmatory assessment, treatment and long-term visual outcomes. The model uses sensitivity and specificity estimates from validation studies, but varies them in sensitivity analyses because performance may differ by image quality and population.

The budget impact model estimates the number of eligible patients, adoption rates, replacement cycles and staff training. It also models a capacity constraint: if referrals rise sharply, waiting times may increase and reduce the expected benefit.

Approach and solution

The ministry provides conditional reimbursement for two years. Requirements include:

• prospective data collection in urban and rural settings;
• monitoring of sensitivity, specificity and subgroup performance;
• cybersecurity and data-governance audits;
• reporting of referral rates, waiting times and vision outcomes;
• a predefined reassessment trigger;
• a price ceiling linked to demonstrated workload reduction and outcomes.

The programme begins in sites with sufficient imaging quality and referral capacity. Clinicians retain responsibility for exceptions and cases that the algorithm cannot classify.

Outcome

After two years, detection improves in participating clinics, but the benefit is smaller where image quality is poor. Referral volumes initially rise, exposing a shortage of ophthalmology appointments. The health ministry expands technician training, improves referral coordination and renegotiates the payment arrangement. The technology remains reimbursed for sites meeting quality and capacity standards, while lower-performing sites receive technical support before expansion.

Learning points

The case demonstrates that diagnostic accuracy is not the same as health benefit. Value depends on the entire care pathway, including confirmatory testing, treatment capacity and patient follow-up. Conditional coverage can manage uncertainty, but only when evidence requirements, monitoring responsibilities and reassessment criteria are specified in advance.

Challenges and Best Practices

Major challenges include immature clinical evidence, indirect comparisons, rapid technological change, weak local cost data, inconsistent utility measurement, confidential pricing, model opacity and implementation gaps. HTA may also be pressured by political urgency, lobbying or public expectations.

Best practice includes:

• defining the decision problem before analysing results;
• selecting clinically relevant comparators;
• using systematic and reproducible evidence reviews;
• separating observed evidence from assumptions;
• reporting perspective, time horizon and discount rates;
• testing structural as well as parameter uncertainty;
• presenting cost-effectiveness and budget impact separately;
• incorporating patient and equity perspectives;
• validating models and documenting code where possible;
• planning post-launch evidence and reassessment;
• protecting independence and managing conflicts of interest.

Contemporary Developments

HTA is increasingly concerned with precision medicine, companion diagnostics, cell and gene therapies, digital therapeutics, real-world evidence and antimicrobial stewardship. High upfront costs and potentially durable benefits challenge conventional annual-budget systems. Novel payment models include instalment payments, annuity arrangements and outcomes-based contracts, although their administrative and legal complexity remains substantial.

International collaboration can reduce duplication in evidence assessment, but transferability is not automatic. Relative treatment effects may travel more readily than costs, epidemiology, utilities, clinical pathways and affordability. Local deliberation remains necessary.

The expanding use of AI creates both opportunities and risks. HTA should assess not only algorithm performance but also dataset representativeness, calibration, clinical workflow, human oversight, model drift and consequences of errors. A technically accurate model may still have negative value if it worsens inequity or creates unsustainable demand.

Chapter Summary

Health Technology Assessment evaluates medicines, devices, diagnostics and other interventions through clinical, economic, patient, ethical, organisational and legal lenses. Regulatory approval establishes acceptable quality, safety and efficacy, whereas HTA addresses comparative value and affordability within a decision context.

Economic evaluation includes cost-minimisation, cost-effectiveness, cost-utility, cost-benefit and budget impact analysis. Incremental analysis compares additional costs with additional outcomes. The ICER, QALY and NMB are useful tools, but their interpretation depends on perspective, time horizon, discounting, comparator selection and opportunity cost.

Decision models extend evidence beyond trial follow-up but introduce structural and parameter uncertainty. Sensitivity analysis, probabilistic analysis and value-of-information methods clarify whether additional evidence is needed. Pricing and reimbursement are shaped by value, competition, patents, bargaining power and budget constraints. Lifecycle management recognises that value changes after launch as evidence, prices and alternatives evolve.

Effective HTA is transparent, evidence-informed, participatory and attentive to equity. Conditional reimbursement and real-world evidence can support decisions under uncertainty, while AI and digital technologies require assessment of both technical performance and system-level consequences.

Key Terms

Health Technology Assessment: A systematic evaluation of the clinical, economic, ethical, organisational and social consequences of a health technology.

Pharmacoeconomics: Economic analysis of pharmaceutical products and medication-related decisions.

Efficacy: Effect of an intervention under controlled research conditions.

Effectiveness: Effect achieved in routine clinical practice.

Comparative Effectiveness: Relative benefits and harms of alternative interventions.

QALY: A measure combining length of life and health-related quality of life.

DALY: A measure combining years of life lost and years lived with disability.

ICER: Additional cost divided by additional health outcome compared with an alternative.

Net Monetary Benefit: Monetary value of health gains minus intervention cost.

Dominance: A situation in which an option is cheaper and more effective, or more costly and less effective, than its comparator.

Budget Impact Analysis: Estimate of the financial consequences of adopting a technology for a specified payer and time period.

Discounting: Adjustment of future costs or outcomes to present-value terms.

Decision Model: A formal representation of pathways, events, costs and outcomes used to analyse alternative strategies.

Parameter Uncertainty: Uncertainty about the numerical values of model inputs.

Structural Uncertainty: Uncertainty about model design or assumptions.

Value of Information: The potential benefit of obtaining additional evidence to reduce decision uncertainty.

Market Access: The process of achieving regulatory, reimbursement, procurement and clinical adoption.

Biosimilar: A biological product highly similar to a reference product without clinically meaningful differences.

Lifecycle Value Management: Ongoing reassessment and optimisation of a technology from development through de-adoption.

Conditional Reimbursement: Coverage granted subject to evidence generation, restrictions or later reassessment.

Review Questions

5 Short Answer Questions

1. Distinguish regulatory approval from health technology assessment.
2. Define efficacy and effectiveness.
3. What does an incremental cost-effectiveness ratio measure?
4. Why is budget impact analysis not a substitute for cost-effectiveness analysis?
5. Identify two types of uncertainty in economic evaluation.

5 Long Answer Questions

1. Explain the domains of HTA and how they contribute to a reimbursement decision.
2. Compare cost-effectiveness, cost-utility and cost-benefit analysis.
3. Discuss the importance of perspective, time horizon and discounting in HTA.
4. Analyse how patent policy affects pharmaceutical innovation and access.
5. Explain how lifecycle value management can improve decisions after product launch.

3 Analytical / Application Questions

1. A medicine costs 25,000 monetary units and produces 2.5 QALYs, while standard care costs 15,000 and produces 2.2 QALYs. Calculate the ICER and discuss what additional information is required before reimbursement.
2. Design a conditional-reimbursement arrangement for a digital diagnostic whose accuracy is established but whose effect on patient outcomes is uncertain.
3. A technology is cost-effective but would increase annual expenditure beyond the payer’s available budget. Analyse the policy options and the ethical trade-offs involved.

Multiple Choice Questions with Answers

Question 1

Which question is most characteristic of health technology assessment rather than regulatory approval?

A. Does the product meet manufacturing quality standards?

B. Does the product offer sufficient value compared with relevant alternatives?

C. Is the product chemically stable during storage?

D. Has the product been manufactured under controlled conditions?

Correct Answer: B. Does the product offer sufficient value compared with relevant alternatives?

Question 2

A new treatment costs 30,000 monetary units and produces 3.2 QALYs. Standard care costs 18,000 and produces 2.8 QALYs. What is the ICER?

A. 3,750 monetary units per QALY

B. 12,000 monetary units per QALY

C. 30,000 monetary units per QALY

D. 48,000 monetary units per QALY

Correct Answer: C. 30,000 monetary units per QALY

Question 3

Which method expresses outcomes in a natural clinical unit such as strokes avoided?

A. Cost-effectiveness analysis

B. Cost-benefit analysis

C. Cost-minimisation analysis

D. Budget impact analysis

Correct Answer: A. Cost-effectiveness analysis

Question 4

In the formula NMB = λ × E − C, what does λ represent?

A. The discount factor

B. The willingness-to-pay threshold per unit of health

C. The probability of an adverse event

D. The total treatment cost

Correct Answer: B. The willingness-to-pay threshold per unit of health

Question 5

Which situation describes dominance?

A. An intervention costs more and produces more health

B. An intervention costs less and produces less health

C. An intervention costs less and produces more health

D. Two interventions have identical costs and outcomes

Correct Answer: C. An intervention costs less and produces more health

Question 6

Why is the choice of perspective important in economic evaluation?

A. It determines which costs and consequences are included

B. It eliminates all uncertainty from the analysis

C. It guarantees that the intervention is cost-effective

D. It replaces the need for a comparator

Correct Answer: A. It determines which costs and consequences are included

Question 7

Which type of uncertainty concerns alternative model structures or assumptions?

A. Parameter uncertainty

B. Structural uncertainty

C. Sampling precision

D. Treatment adherence

Correct Answer: B. Structural uncertainty

Question 8

What is the principal purpose of budget impact analysis?

A. To estimate affordability for a specified payer and time period

B. To prove that a technology is clinically efficacious

C. To determine whether a patent is legally valid

D. To measure only patient utility values

Correct Answer: A. To estimate affordability for a specified payer and time period

Question 9

Why may conditional reimbursement be used for a promising technology?

A. It removes the need for evidence collection

B. It permits access while requiring additional evidence and reassessment

C. It guarantees permanent reimbursement

D. It applies only to generic medicines

Correct Answer: B. It permits access while requiring additional evidence and reassessment

Question 10

Which statement best describes lifecycle value management?

A. Setting a product price once and never revisiting it

B. Managing value from development through post-launch reassessment and possible de-adoption

C. Replacing clinical trials with marketing research

D. Evaluating only manufacturing costs

Correct Answer: B. Managing value from development through post-launch reassessment and possible de-adoption