Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology
Abstract Background The potential for artificial intelligence (AI) to transform healthcare cannot be ignored, and the development of AI technologies has increased significantly over the past decade. Furthermore, healthcare systems are under tremendous pressure, and efficient allocation of scarce hea...
| Egile Nagusiak: | , , |
|---|---|
| Formatua: | Artikulua |
| Hizkuntza: | English |
| Argitaratua: |
BMC
2024-07-01
|
| Saila: | BMC Digital Health |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1186/s44247-024-00088-7 |
| _version_ | 1827195774218272768 |
|---|---|
| author | Nanna Kastrup Annette W. Holst-Kristensen Jan B. Valentin |
| author_facet | Nanna Kastrup Annette W. Holst-Kristensen Jan B. Valentin |
| author_sort | Nanna Kastrup |
| collection | DOAJ |
| description | Abstract Background The potential for artificial intelligence (AI) to transform healthcare cannot be ignored, and the development of AI technologies has increased significantly over the past decade. Furthermore, healthcare systems are under tremendous pressure, and efficient allocation of scarce healthcare resources is vital to ensure value for money. Health economic evaluations (HEEs) can be used to obtain information about cost-effectiveness. The literature acknowledges that the conduct of such evaluations differs between medical technologies (MedTechs) and pharmaceuticals, and poor quality evaluations can provide misleading results. This systematic review seeks to map the evidence on the general methodological quality of HEEs for AI technologies to identify potential areas which can be subject to quality improvements. We used the 35-item checklist by Drummond and Jefferson and four additional checklist domains proposed by Terricone et al. to assess the methodological quality of full HEEs of interventions that include AI. Results We identified 29 studies for analysis. The included studies had higher completion scores for items related to study design than for items related to data collection and analysis and interpretation of results. However, none of the studies addressed MedTech-specific items. Conclusions There was a concerningly low number of full HEEs relative to the number of AI publications, however the trend is that the number of studies per year is increasing. Mapping the evidence of the methodological quality of HEEs of AI shows a need to improve the quality in particular the use of proxy measures as outcome, reporting, and interpretation of the ICER. |
| first_indexed | 2025-03-21T09:34:40Z |
| format | Article |
| id | doaj.art-5b51e0018e5c44a0892e29edefbb7fd7 |
| institution | Directory Open Access Journal |
| issn | 2731-684X |
| language | English |
| last_indexed | 2025-03-21T09:34:40Z |
| publishDate | 2024-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Digital Health |
| spelling | doaj.art-5b51e0018e5c44a0892e29edefbb7fd72024-07-07T11:37:12ZengBMCBMC Digital Health2731-684X2024-07-012111210.1186/s44247-024-00088-7Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodologyNanna Kastrup0Annette W. Holst-Kristensen1Jan B. Valentin2Department of Clinical Medicine, Danish Center for Health Services Research, Aalborg UniversityDepartment of Clinical Medicine, Danish Center for Health Services Research, Aalborg UniversityDepartment of Clinical Medicine, Danish Center for Health Services Research, Aalborg UniversityAbstract Background The potential for artificial intelligence (AI) to transform healthcare cannot be ignored, and the development of AI technologies has increased significantly over the past decade. Furthermore, healthcare systems are under tremendous pressure, and efficient allocation of scarce healthcare resources is vital to ensure value for money. Health economic evaluations (HEEs) can be used to obtain information about cost-effectiveness. The literature acknowledges that the conduct of such evaluations differs between medical technologies (MedTechs) and pharmaceuticals, and poor quality evaluations can provide misleading results. This systematic review seeks to map the evidence on the general methodological quality of HEEs for AI technologies to identify potential areas which can be subject to quality improvements. We used the 35-item checklist by Drummond and Jefferson and four additional checklist domains proposed by Terricone et al. to assess the methodological quality of full HEEs of interventions that include AI. Results We identified 29 studies for analysis. The included studies had higher completion scores for items related to study design than for items related to data collection and analysis and interpretation of results. However, none of the studies addressed MedTech-specific items. Conclusions There was a concerningly low number of full HEEs relative to the number of AI publications, however the trend is that the number of studies per year is increasing. Mapping the evidence of the methodological quality of HEEs of AI shows a need to improve the quality in particular the use of proxy measures as outcome, reporting, and interpretation of the ICER.https://doi.org/10.1186/s44247-024-00088-7Artificial intelligenceHealth economic evaluationCost-effectivenessCost-utility analysisCost-effectiveness analysisSsystematic review |
| spellingShingle | Nanna Kastrup Annette W. Holst-Kristensen Jan B. Valentin Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology BMC Digital Health Artificial intelligence Health economic evaluation Cost-effectiveness Cost-utility analysis Cost-effectiveness analysis Ssystematic review |
| title | Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology |
| title_full | Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology |
| title_fullStr | Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology |
| title_full_unstemmed | Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology |
| title_short | Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology |
| title_sort | landscape and challenges in economic evaluations of artificial intelligence in healthcare a systematic review of methodology |
| topic | Artificial intelligence Health economic evaluation Cost-effectiveness Cost-utility analysis Cost-effectiveness analysis Ssystematic review |
| url | https://doi.org/10.1186/s44247-024-00088-7 |
| work_keys_str_mv | AT nannakastrup landscapeandchallengesineconomicevaluationsofartificialintelligenceinhealthcareasystematicreviewofmethodology AT annettewholstkristensen landscapeandchallengesineconomicevaluationsofartificialintelligenceinhealthcareasystematicreviewofmethodology AT janbvalentin landscapeandchallengesineconomicevaluationsofartificialintelligenceinhealthcareasystematicreviewofmethodology |