Health technology assessments in personalized medicine: illustrations for cost-effectiveness analysis

Implementation of personalized medicine in a context of moral hazard and uncertainty about treatment efficacy

This paper analyzes the decision of a health authority to implement personalized medicine. We consider a model in which the health authority has three possibilities. It can apply either the same treatment (a standard or a new treatment) to the whole population or implement personalized medicine, i.e., use genetic information to offer the most suitable treatment to each patient. We first characterize the drug reimbursement contract of a firm producing a new treatment with a companion genetic test when the firm can undertake an effort to improve drug quality. Then, we determine the conditions under which personalized medicine should be implemented when this effort is observable and when it is not. Finally, we show how the unobservability of effort affects the conditions under which the health authority implements personalized medicine.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration

OBJECTIVES

We sought to explore to what extent the use of Subpopulation Treatment Effect Pattern Plot (STEPP) may help to identify efficient treatment allocation strategy.

METHODS

The analysis was based on data from the COACH study, in which 1023 patients with heart failure were randomly assigned to three treatments: care-as-usual, basic support, and intensive support. First, using predicted 18-month mortality risk as the stratification basis, a suitable strategy for assigning different treatments to different risk groups of patients was developed. To that end, a graphical exploration of the difference in net monetary benefit (NMB) across treatment regimens and baseline risk was used. Next, the efficiency gains resulting from this proposed subgroup strategy were quantified by computing the difference in NMB between our stratified approach and the best performing population-wide strategy.

RESULTS

The analysis using STEPPs suggested that a differentiated approach, based on offering intensive support to low-risk patients (18-month mortality risk ≤ 0.16) and basic support to intermediate- to high-risk patients (18-month mortality risk > 0.16) would be an economically efficient treatment allocation strategy. This was confirmed in the subsequent cost-effectiveness analysis, where the average gain in NMB resulting from the proposed stratified approach compared to basic support for all was found to be €1312 (95% CI €390-€2346) per patient.

CONCLUSIONS

STEPP provides a systematic approach to assess the interaction between baseline risk and the difference in NMB between competing interventions and to identify cutoffs to stratify patients in a health economically optimal manner.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

INTRODUCTION

The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature. Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies. Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.

Implementing personalized medicine with asymmetric information on prevalence rates

Although personalized medicine is becoming the new paradigm to manage some diseases, the economics of personalized medicine have only focused on assessing the efficiency of specific treatments, lacking a theoretical framework analyzing the interactions between pharmaceutical firms and healthcare systems leading to the implementation of personalized treatments. We model the interaction between the hospitals and the manufacturer of a new treatment as an adverse selection problem where the firm does not have perfect information on the prevalence across hospitals of the genetic characteristics of the patients making them eligible to receive a new treatment. As a result of the model, hospitals with high prevalence rates benefit from the information asymmetry only when the standard treatment is inefficient when applied to the patients eligible to receive the new treatment. Otherwise, information asymmetry has no value. Personalized medicine may be fully or partially implemented depending on the proportion of high prevalence hospitals.

Systematic overview of cost-effectiveness thresholds in ten countries across four continents

AIM

To provide an overview of thresholds for incremental cost-effectiveness ratios (ICERs) representing willingness-to-pay (WTP) across multiple countries and insights into exemptions pertaining to the ICER (e.g., cancer). To compare ICER thresholds to individual country's estimated ability-to-pay.

MATERIALS & METHODS

We included AHRQ/USA, BIQG-GOEG/Austria, CADTH/Canada, DAHTA@DIMDI/Germany, DECIT-CGATS/Brazil, HAS/France, HITAP/Thailand, IQWiG/Germany, LBI-HTA/Austria, MSAC/Australia, NICE/England/Wales and SBU/Sweden. ICER thresholds were derived from systematic literature/website search/expert surveys. WTP was compared with ATP using Spearman's rank correlation.

RESULTS

Two general and explicitly acknowledged thresholds (England/Wales, Thailand), implicit thresholds in six countries and different ICER thresholds/decision-making rules in oncology were identified. Correlation between WTP and ability-to-pay was moderate.

DISCUSSION

Our overview supports country-specific discussions on WTP and on how to define value(s) within societies.

Some economics on personalized and predictive medicine

OBJECTIVE

To contribute to the theoretical literature on personalized medicine, analyzing and integrating in an economic model, the decision a health authority faces when it must decide on the implementation of personalized medicine in a context of uncertainty.

METHODS

We carry out a stylized model to analyze the decision health authorities face when they do not have perfect information about the best treatment for a population of patients with a given disease. The health authorities decide whether to use a test to match patients with treatments (personalized medicine) to maximize health outcomes. Our model characterizes the situations under which personalized medicine dominates the alternative option of business-as-usual (treatment without previous test). We apply the model to the KRAS test for colorectal cancer, the PCA3 test for prostate cancer and the PCR test for the X-fragile syndrome, to illustrate how the parameters and variables of the model interact.

RESULTS

Implementation of personalized medicine requires, as a necessary condition, having some tests with high discriminatory power. This is not a sufficient condition and expected health outcomes must be taken into account to make a decision. When the specificity and the sensitivity of the test are low, the health authority prefers to apply a treatment to all patients without using the test. When both characteristic of the test are high, the health authorities prefer to personalize the treatments when expected health outcomes are better than those under the standard treatment. When we applied the model to the three aforementioned tests, the results illustrate how decisions are adopted in real world.

CONCLUSIONS

Although promising, the use of personalized medicine is still under scrutiny as there are important issues demanding a response. Personalized medicine may have an impact in the drug development processes, and contribute to the efficiency and effectiveness of health care delivery. Nevertheless, more accurate statistical and economic information related to tests results and treatment costs as well as additional medical information on the efficacy of the treatments are needed to adopt decisions that incorporate economic rationality.

[Evaluation and incorporation of health technologies: process and methodology adopted by a high-complexity care university]

The demographic and epidemiological transitions tend to increase the role of hospitals in medical care. Within such organizations, effective, safe, and cost-effective health technologies ensure better quality of care and increase users' survival, thus emphasizing the importance of evaluation of such hospital-based technologies. This article aims to present a model for the evaluation and incorporation of technology in a teaching hospital that provides high-complexity care. The article describes an approach to methods/processes assessment that can be used easily by any hospital. The model allowed proper health technology assessment (HTA), thereby legitimizing decisions on technology incorporation by the hospital administration with high levels of acceptance and adoption by the clinical staff, suggesting that hospital-based HTA (provided that it is well-structured, with the support of institutional administration) can be a powerful tool for dissemination and valorization of HTA culture in an environment with the widest use and the greatest impact on the health system's budget.

Health technology assessment and personalized medicine: are economic evaluation guidelines sufficient to support decision making?

BACKGROUND

Many jurisdictions delivering health care, including Canada, have developed guidance for conducting economic evaluation, often in the service of larger health technology assessment (HTA) and reimbursement processes. Like any health intervention, personalized medical (PM) interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. However, current approaches to economic evaluation to support decision making have been largely developed from population-based approaches to therapy-that is, evaluating the costs and consequences of single interventions across single populations. This raises the issue as to whether these methods, as they are or more refined, are adequate to address more targeted approaches to therapy, or whether a new paradigm for assessing value in PM is required.

OBJECTIVES

We describe specific issues relevant to the economic evaluation of diagnostics-based PM and assess whether current guidance for economic evaluation is sufficient to support decision making for PM interventions.

METHODS

Issues were identified through literature review and informal interviews with national and international experts (n = 10) in these analyses. This article elaborates on findings and discussion at a workshop held in Ottawa, Canada, in January 2012.

RESULTS

Specific issues related to better guiding economic evaluation of personalized medicine interventions include: how study questions are developed, populations are characterized, comparators are defined, effectiveness is evaluated, outcomes are valued and how resources are measured. Diagnostics-based PM also highlights the need for analyses outside of economic evaluation to support decision making.

CONCLUSIONS

The consensus of this group of experts is that the economic evaluation of diagnostics-based PM may not require a new paradigm. However, greater complexity means that existing approaches and tools may require improvement to undertake these more analyses.

The application of economics concepts to stratified medicine--use of health economics data to support market access for stratified medicine interventions

Stratified medicine (SM), as opposed to empirical medicine, is the practice of using biomarkers or diagnostic tests to guide the choice of therapeutic treatments. The link between the diagnostic test and the therapy provides new opportunities for value creation and may strengthen the value proposition to pricing and reimbursement authorities. However, SM provides new challenges for the value assessment process, in particular health technology assessment (HTA) and pricing and reimbursement (P&R) decisions. Although health economics (HE) should be relevant for all stakeholders, not all stakeholders are comfortable with analysis/interpretation of economic data relevant to SM interventions as this approach is still in an early/emergent stage in most markets. This article addresses how different stakeholders are using health economic data in the overall value of information analysis to inform prioritization and reimbursement of SM interventions. Findings of an expert discussion outlines key challenges affecting various stakeholders when applying health economic data in the healthcare decision-making process for SM interventions.

Stratified medicine and reimbursement issues

Stratified Medicine (SM) has the potential to target patient populations who will most benefit from a therapy while reducing unnecessary health interventions associated with side effects. The link between clinical biomarkers/diagnostics and therapies provides new opportunities for value creation to strengthen the value proposition to pricing and reimbursement (P&R) authorities. However, the introduction of SM challenges current reimbursement schemes in many EU countries and the US as different P&R policies have been adopted for drugs and diagnostics. Also, there is a lack of a consistent process for value assessment of more complex diagnostics in these markets. New, innovative approaches and more flexible P&R systems are needed to reflect the added value of diagnostic tests and to stimulate investments in new technologies. Yet, the framework for access of diagnostic-based therapies still requires further development while setting the right incentives and appropriate align stakeholders interests when realizing long-term patient benefits. This article addresses the reimbursement challenges of SM approaches in several EU countries and the US outlining some options to overcome existing reimbursement barriers for stratified medicine.

A Dutch and American commentary on IT in health care: roundtable discussions on IT and innovations in health care

PURPOSE

This chapter reports on experts' perspectives on health information technology (HIT) and how it may be used to improve health care quality and to lower health care costs.

DESIGN/METHODOLOGY/APPROACH

Two roundtables were convened that focused on how to best use HIT to improve the quality of health care while ensuring it is accessible and affordable. Participants drew upon lessons learned in the Netherlands, the United States, and other countries.

FINDINGS

The first roundtable focused on the use of (1) electronic health records (EHRs) by health care providers, (2) cloud computing for EHRs and health portals for consumers, and (3) data registries and networks for public health surveillance. The second roundtable highlighted (1) the rapid growth of personalized medicine, (2) the corresponding growth and sophistication of bioinformatics and analytics, (3) the increasing presence of mobile HIT, and (4) the disruptive changes in the institutional structures of biomedical research and development.

PRACTICAL IMPLICATIONS

Governmental sponsorship of small pilot projects to solve practicable health system problems would encourage HIT innovation among key stakeholders. However, large-scale HIT solutions developed through small pilot projects--should be pursued through public-private partnerships. At the same time, governments should speed up legislative and regulatory procedures to encourage adoption of cost-effective HIT innovations.

SOCIAL IMPLICATIONS

Mobile HIT and social media are capable of fostering disease prevention and encouraging personal responsibility for improving or stabilizing chronic diseases.

ORIGINALITY/VALUE

Both health services researchers and policy makers should find this chapter of value since it highlights trends in HIT and addresses how health care quality may be improved while costs are contained.