Jeanne S. Mandelblatt, Scott D. Ramsey, Tracy Lieu, Charles E. Phelps
Value in Health. 2017;20(2):185-192.
The recent acceleration of scientific discovery has led to greater choices in health care. New technologies, diagnostic tests, and pharmaceuticals have widely varying impact on patients and populations in terms of benefits, toxicities, and costs, stimulating a resurgence of interest in the creation of frameworks intended to measure value in health. Many of these are offered by providers and/or advocacy organizations with expertise and interest in specific diseases (e.g., cancer and heart disease). To help assess the utility of and the potential biases embedded in these frameworks, we created an evaluation taxonomy with 7 basic components. We apply the taxonomy to four representative value frameworks recently published by professional organizations focused on treatment of cancer and heart disease and on vaccine use. We conclude that each of these efforts has strengths and weaknesses when evaluated using our taxonomy, and suggest pathways to enhance the utility of value-assessing frameworks for policy and clinical decision making.
Marc B. Rosenman, Brian Decker, Kenneth D. Levy, Ann M. Holmes, Victoria M. Pratt, Michael T. Eadon
Value in Health. 2017;20(1):54-59.
Implementing new programs to support precision medicine in clinical settings is a complex endeavor. We describe challenges and potential solutions based on the Indiana GENomics Implementation: an Opportunity for the Underserved (INGenious) program at Eskenazi Health—one of six sites supported by the Implementing GeNomics In pracTicE network grant of the National Institutes of Health/National Human Genome Research Institute. INGenious is an implementation of a panel of genomic tests.
We conducted a descriptive case study of the implementation of this pharmacogenomics program, which has a wide scope (14 genes, 27 medications) and a diverse population (patients who often have multiple chronic illnesses, in a large urban safety-net hospital and its outpatient clinics).
We placed the clinical pharmacogenomics implementation challenges into six categories: patient education and engagement in care decision making; clinician education and changes in standards of care; integration of technology into electronic health record systems; translational and implementation sciences in real-world clinical environments; regulatory and reimbursement considerations, and challenges in measuring outcomes. A cross-cutting theme was the need for careful attention to workflow. Our clinical setting, a safety-net health care system, presented some distinctive challenges. Patients often had multiple chronic illnesses and sometimes were taking more than one pharmacogenomics-relevant medication. Reaching patients for recruitment or follow-up was another challenge.
New, large-scale endeavors in health care are challenging. A description of the challenges that we encountered and the approaches that we adopted to address them may provide insights for those who implement and study innovations in other health care systems.
Kathryn A. Phillips, Michael P. Douglas, Julia R. Trosman, Deborah A. Marshall
Value in Health. 2017;20(1)47-53.
The growth of “big data” and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.
To examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.
We focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.
Using a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.
We conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.
Martine Hoogendoorn, Talitha L. Feenstra, Yumi Asukai, Andrew H. Briggs, Sixten Borg, Roberto W. Dal Negro, Ryan N. Hansen, Sven-Arne Jansson, Reiner Leidl, Nancy Risebrough, Yevgeniy Samyshkin, Margarethe E. Wacker, Maureen P. Rutten-van Mölken
Value in Health. 2016;19(6):800-810.
To assess how suitable current chronic obstructive pulmonary disease (COPD) cost-effectiveness models are to evaluate personalized treatment options for COPD by exploring the type of heterogeneity included in current models and by validating outcomes for subgroups of patients.
A consortium of COPD modeling groups completed three tasks. First, they reported all patient characteristics included in the model and provided the level of detail in which the input parameters were specified. Second, groups simulated disease progression, mortality, quality-adjusted life-years (QALYs), and costs for hypothetical subgroups of patients that differed in terms of sex, age, smoking status, and lung function (forced expiratory volume in 1 second [FEV1] % predicted). Finally, model outcomes for exacerbations and mortality for subgroups of patients were validated against published subgroup results of two large COPD trials.
Nine COPD modeling groups participated. Most models included sex (seven), age (nine), smoking status (six), and FEV1% predicted (nine), mainly to specify disease progression and mortality. Trial results showed higher exacerbation rates for women (found in one model), higher mortality rates for men (two models), lower mortality for younger patients (four models), and higher exacerbation and mortality rates in patients with severe COPD (four models).
Most currently available COPD cost-effectiveness models are able to evaluate the cost-effectiveness of personalized treatment on the basis of sex, age, smoking, and FEV1% predicted. Treatment in COPD is, however, more likely to be personalized on the basis of clinical parameters. Two models include several clinical patient characteristics and are therefore most suitable to evaluate personalized treatment, although some important clinical parameters are still missing.
Julia R. Trosman, Christine B. Weldon, Michael P. Douglas, Patricia A. Deverka, John B. Watkins, Kathryn A. Phillips
Value in Health. 2017;20(1):40-46.
New payment and care organization approaches, such as those of accountable care organizations (ACOs), are reshaping accountability and shifting risk, as well as decision making, from payers to providers, within the Triple Aim context of health reform. The Triple Aim calls for improving experience of care, improving health of populations, and reducing health care costs.
To understand how the transition to the ACO model impacts decision making on adoption and use of innovative technologies in the era of accelerating scientific advancement of personalized medicine and other innovations.
We interviewed representatives from 10 private payers and 6 provider institutions involved in implementing the ACO model (i.e., ACOs) to understand changes, challenges, and facilitators of decision making on medical innovations, including personalized medicine. We used the framework approach of qualitative research for study design and thematic analysis.
We found that representatives from the participating payer companies and ACOs perceive similar challenges to ACOs’ decision making in terms of achieving a balance between the components of the Triple Aim—improving care experience, improving population health, and reducing costs. The challenges include the prevalence of cost over care quality considerations in ACOs’ decisions and ACOs’ insufficient analytical and technology assessment capacity to evaluate complex innovations such as personalized medicine. Decision-making facilitators included increased competition across ACOs and patients’ interest in personalized medicine.
As new payment models evolve, payers, ACOs, and other stakeholders should address challenges and leverage opportunities to arm ACOs with robust, consistent, rigorous, and transparent approaches to decision making on medical innovations.
Deborah A. Marshall, Juan Marcos Gonzalez, Karen V. MacDonald, F. Reed Johnson
Value in Health. 2017;20(1):32-39.
We examine key study design challenges of using stated-preference methods to estimate the value of whole-genome sequencing (WGS) as a specific example of genomic testing. Assessing the value of WGS is complex because WGS provides multiple findings, some of which can be incidental in nature and unrelated to the specific health concerns that motivated the test. In addition, WGS results can include actionable findings (variants considered to be clinically useful and can be acted on), findings for which evidence for best clinical action is not available (variants considered clinically valid but do not meet as high of a standard for clinical usefulness), and findings of unknown significance. We consider three key challenges encountered in designing our national study on the value of WGS—layers of uncertainty, potential downstream consequences with endogenous aspects, and both positive and negative utility associated with testing information—and potential solutions as strategies to address these challenges. We conceptualized the decision to acquire WGS information as a series of sequential choices that are resolved separately. To determine the value of WGS information at the initial decision to undergo WGS, we used contingent valuation questions, and to elicit respondent preferences for reducing risks of health problems and the consequences of taking the steps to reduce these risks, we used a discrete-choice experiment. We conclude by considering the implications for evaluating the value of other complex health technologies that involve multiple forms of uncertainty.