Author
Listed:
- Melissa J. Basile
(Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA)
- I. R. A. Retel Helmrich
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- Jinny G. Park
(Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA)
- Jennifer Polo
- Judith A.C. Rietjens
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- David van Klaveren
(Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA)
- Theodoros P. Zanos
(Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA)
- Jason Nelson
(Predictive Analytics and Comparative Effectiveness (PACE) Center at the Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA)
- Hester F. Lingsma
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- David M. Kent
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- Jelmer Alsma
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- R. J. C. G. Verdonschot
(Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands)
- Negin Hajizadeh
(Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA)
Abstract
Introduction Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown. Methods Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs. Results In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment. Conclusions Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs. Highlights While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use. Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19. Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.
Suggested Citation
Melissa J. Basile & I. R. A. Retel Helmrich & Jinny G. Park & Jennifer Polo & Judith A.C. Rietjens & David van Klaveren & Theodoros P. Zanos & Jason Nelson & Hester F. Lingsma & David M. Kent & Jelmer, 2023.
"US and Dutch Perspectives on the Use of COVID-19 Clinical Prediction Models: Findings from a Qualitative Analysis,"
Medical Decision Making, , vol. 43(4), pages 445-460, May.
Handle:
RePEc:sae:medema:v:43:y:2023:i:4:p:445-460
DOI: 10.1177/0272989X231152852
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