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Under careful construction: combining findings, arguments, and values into robust health care coverage decisions
BACKGROUND: Health care coverage decisions deal with health care technology provision or reimbursement at a national level. The coverage decision report, i.e., the publicly available document giving reasons for the decision, may contain various elements: quantitative calculations like cost and clini...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175321/ https://www.ncbi.nlm.nih.gov/pubmed/35672735 http://dx.doi.org/10.1186/s12913-022-07781-1 |
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author | Kleinhout-Vliek, T.H. De Bont, A.A. Boer, A. |
author_facet | Kleinhout-Vliek, T.H. De Bont, A.A. Boer, A. |
author_sort | Kleinhout-Vliek, T.H. |
collection | PubMed |
description | BACKGROUND: Health care coverage decisions deal with health care technology provision or reimbursement at a national level. The coverage decision report, i.e., the publicly available document giving reasons for the decision, may contain various elements: quantitative calculations like cost and clinical effectiveness analyses and formalised and non-formalised qualitative considerations. We know little about the process of combining these heterogeneous elements into robust decisions. METHODS: This study describes a model for combining different elements in coverage decisions. We build on two qualitative cases of coverage appraisals at the Dutch National Health Care Institute, for which we analysed observations at committee meetings (n = 2, with field notes taken) and the corresponding audio files (n = 3), interviews with appraisal committee members (n = 10 in seven interviews) and with Institute employees (n = 5 in three interviews), and relevant documents (n = 4). RESULTS: We conceptualise decisions as combinations of elements, specifically (quantitative) findings and (qualitative) arguments and values. Our model contains three steps: 1) identifying elements; 2) designing the combinations of elements, which entails articulating links, broadening the scope of designed combinations, and black-boxing links; and 3) testing these combinations and choosing one as the final decision. CONCLUSIONS: Based on the proposed model, we suggest actively identifying a wider variety of elements and stepping up in terms of engaging patients and the public, including facilitating appeals. Future research could explore how different actors perceive the robustness of decisions and how this relates to their perceived legitimacy. |
format | Online Article Text |
id | pubmed-9175321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91753212022-06-09 Under careful construction: combining findings, arguments, and values into robust health care coverage decisions Kleinhout-Vliek, T.H. De Bont, A.A. Boer, A. BMC Health Serv Res Research BACKGROUND: Health care coverage decisions deal with health care technology provision or reimbursement at a national level. The coverage decision report, i.e., the publicly available document giving reasons for the decision, may contain various elements: quantitative calculations like cost and clinical effectiveness analyses and formalised and non-formalised qualitative considerations. We know little about the process of combining these heterogeneous elements into robust decisions. METHODS: This study describes a model for combining different elements in coverage decisions. We build on two qualitative cases of coverage appraisals at the Dutch National Health Care Institute, for which we analysed observations at committee meetings (n = 2, with field notes taken) and the corresponding audio files (n = 3), interviews with appraisal committee members (n = 10 in seven interviews) and with Institute employees (n = 5 in three interviews), and relevant documents (n = 4). RESULTS: We conceptualise decisions as combinations of elements, specifically (quantitative) findings and (qualitative) arguments and values. Our model contains three steps: 1) identifying elements; 2) designing the combinations of elements, which entails articulating links, broadening the scope of designed combinations, and black-boxing links; and 3) testing these combinations and choosing one as the final decision. CONCLUSIONS: Based on the proposed model, we suggest actively identifying a wider variety of elements and stepping up in terms of engaging patients and the public, including facilitating appeals. Future research could explore how different actors perceive the robustness of decisions and how this relates to their perceived legitimacy. BioMed Central 2022-06-07 /pmc/articles/PMC9175321/ /pubmed/35672735 http://dx.doi.org/10.1186/s12913-022-07781-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Kleinhout-Vliek, T.H. De Bont, A.A. Boer, A. Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title | Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title_full | Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title_fullStr | Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title_full_unstemmed | Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title_short | Under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
title_sort | under careful construction: combining findings, arguments, and values into robust health care coverage decisions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175321/ https://www.ncbi.nlm.nih.gov/pubmed/35672735 http://dx.doi.org/10.1186/s12913-022-07781-1 |
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