When Future Change Matters: Modeling Future Price and Diffusion in Health Technology Assessments of Medical Devices
Open accessAbstract
Background Health technology assessments (HTAs) that take account of future price changes have been examined in the literature, but the important issue of price reductions that are generated by the reimbursement decision has been ignored.Objectives To explore the impact of future price reductions caused by increasing uptake on HTAs and decision making for medical devices.Methods We demonstrate the use of a two-stage modeling approach to derive estimates of technology price as a consequence of changes in technology uptake over future periods on the basis of existing theory and supported by empirical studies. We explore the impact on cost-effectiveness and expected value of information analysis in an illustrative example on the basis of a technology in development for preterm birth screening.Results The application of our approach to the case study technology generates smaller incremental cost-effectiveness ratios compared with the commonly used single cohort approach. The extent of this reduction in the incremental cost-effectiveness ratio depends on the magnitude of the modeled price reduction, the speed of diffusion, and the length of the assumed technology life horizon. Results of value of information analysis are affected through changes in the expected net benefit calculation, the addition of uncertain parameters, and the diffusion-adjusted estimate of the affected patient population.Conclusions Because modeling future changes in price and uptake has the potential to affect HTA outcomes, modeling techniques that can address such changes should be considered for medical devices that may otherwise be rejected.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most technicians or users of machines do not have enough knowledge on using the technologies or machines.
“Most of the technicians or users of the machines do not have enough knowledge on using the technologies/machines [73].”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [73]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,557 · see citing works
Valente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 20,555 · see citing works
A new product growth for model consumer durables
Bass, F. M. (1969) · Management Science · doi:10.1287/mnsc.15.5.215 · Cited by 6,008 · see citing works
A New Product Growth Model for Consumer Durables
Bass, F. M. (1976) · Lecture notes in economics and mathematical systems · doi:10.1007/978-3-642-51565-1_107 · Cited by 3,971 · see citing works
Caro, J., Briggs, A., Siebert, U., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.012 · Cited by 1,006 · see citing works
Modelling and forecasting the diffusion of innovation – A 25-year review
Meade, N., & Islam, T. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.01.005 · Cited by 821 · see citing works
A Meta-Analysis of Applications of Diffusion Models
Sultan, F., Farley, J. U., & Lehmann, D. R. (1990) · Springer eBooks · doi:10.1007/978-3-031-88060-5_16 · Cited by 576 · see citing works
A Meta-Analysis of Applications of Diffusion Models
Sultan, F., Farley, J. U., & Lehmann, D. R. (1990) · Journal of Marketing Research · doi:10.2307/3172552 · Cited by 325 · see citing works
Strong, M., Oakley, J. E., & Brennan, A. (2013) · Medical Decision Making · doi:10.1177/0272989x13505910 · Cited by 248 · see citing works
Honest, H., Ca, F., Durée, K., Norman, G., Duffy, S., Tsourapas, A., Roberts, T. E., Barton, P., Jowett, S. M., Hyde, C., et al. (2009) · Health Technology Assessment · doi:10.3310/hta13430 · Cited by 204 · see citing works
Universal cervical‐length screening to prevent preterm birth: a cost‐effectiveness analysis
Werner, E. F., Han, C. S., Pettker, C. M., Buhimschi, C. S., Copel, J. A., Funai, E. F., & Thung, S. (2010) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.8911 · Cited by 174 · see citing works
The Value of Implementation and the Value of Information: Combined and Uneven Development
Fenwick, E. A. L., Claxton, K., & Sculpher, M. J. (2007) · Medical Decision Making · doi:10.1177/0272989x07308751 · Cited by 172 · see citing works
Optimal clinical trial design using value of information methods with imperfect implementation
Willan, A. R., & Eckermann, S. D. (2009) · Health Economics · doi:10.1002/hec.1493 · Cited by 97 · see citing works
Hoomans, T., Fenwick, E. A. L., Palmer, S., & Claxton, K. (2008) · Value in Health · doi:10.1111/j.1524-4733.2008.00431.x · Cited by 82 · see citing works
The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions?
Philips, Z., Claxton, K., & Palmer, S. (2008) · Medical Decision Making · doi:10.1177/0272989x07312724 · Cited by 79 · see citing works
IMPLEMENTING THE FINDINGS OF HEALTH TECHNOLOGY ASSESSMENTS
Drummond, M., & Weatherly, H. L. A. (2000) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462300016111 · Cited by 71 · see citing works
Minimal Modeling Approaches to Value of Information Analysis for Health Research
Meltzer, D., Hoomans, T., Chung, J. W., & Basu, A. (2011) · Medical Decision Making · doi:10.1177/0272989x11412975 · Cited by 62 · see citing works
Future Drug Prices and Cost-Effectiveness Analyses
Hoyle, M. (2008) · PharmacoEconomics · doi:10.2165/00019053-200826070-00006 · Cited by 57 · see citing works
Accounting for the Drug Life Cycle and Future Drug Prices in Cost-Effectiveness Analysis
Hoyle, M. (2010) · PharmacoEconomics · doi:10.2165/11584230-000000000-00000 · Cited by 53 · see citing works
Hoyle, M., & Anderson, R. (2010) · Medical Decision Making · doi:10.1177/0272989x09353946 · Cited by 30 · see citing works
Forecasting the adoption of new medical technology using the Bass model.
Sillup, G. P. (1992) · PubMed · Cited by 25 · see citing works
Marketing InnovationMedical device prices follow the experience curve
Brown, A., Meenan, B. J., & Young, T. P. (2007) · Journal of Medical Marketing Device Diagnostic and Pharmaceutical Marketing · doi:10.1057/palgrave.jmm.5050086 · Cited by 22 · see citing works
Philips, Z., Claxton, K., Palmer, S., Bojke, L., & Sculpher, M. J. (2006) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462306051282 · Cited by 21 · see citing works
A dynamic perspective on pharmaceutical competition, drug development and cost effectiveness
Camejo, R. R., McGrath, C., & Herings, R. M. C. (2010) · Health Policy · doi:10.1016/j.healthpol.2010.08.021 · Cited by 20 · see citing works
Chapman, A. M., Taylor, C. A., & Girling, A. J. (2014) · Applied Health Economics and Health Policy · doi:10.1007/s40258-014-0104-y · Cited by 19 · see citing works
Hoyle, M. (2010) · Value in Health · doi:10.1111/j.1524-4733.2010.00778.x · Cited by 12 · see citing works
Systematizing the Use of Value of Information Analysis in Prioritizing Systematic Reviews
Hoomans, T., Seidenfeld, J., Basu, A., et al. (2012) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 11 · see citing works
Brown, A., Meenan, B. J., Dixon, D., et al. (2008) · Journal of Medical Marketing Device Diagnostic and Pharmaceutical Marketing · doi:10.1057/jmm.2008.7 · Cited by 10 · see citing works
Grimm, S. E., Dixon, S., & Stevens, J. (2013) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Influential Usage of Big Data and Artificial Intelligence in Healthcare
Yang, Y. C., Islam, S. U., Noor, A., Khan, S., Afsar, W., & Nazir, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5812499 · Cited by 68 · see citing works
“Most of the technicians or users of the machines do not have enough knowledge on using the technologies/machines [73].”
BackgroundRetracted
- Authors
- Grimm, S. E., Dixon, S., & Stevens, J. W.
- Affiliations
- University of Sheffield (GB)
- Corresponding author
- Grimm, Sabine E.
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 19
- Issue
- 6
- Pages
- 720-726
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W2487141499
- Cited by
- 15
- References
- 29
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2021: 1 · 2020: 5 · 2019: 2 · 2018: 4
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Technology Assessment and Management
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Keywords
- health technology assessment, medical devices, technology diffusion, reimbursement decisions, discounts, cost-effectiveness, value of information analysis, preterm birth screening, two-stage model, technology adoption
- MeSH terms
- Commerce, Cost-Benefit Analysis, Diffusion of Innovation, Equipment and Supplies, Technology Assessment, Biomedical, Drug Costs, Economics, Pharmaceutical, Models, Economic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
