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The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.
Zondag, Anna G M; Rozestraten, Raoul; Grimmelikhuijsen, Stephan G; Jongsma, Karin R; van Solinge, Wouter W; Bots, Michiel L; Vernooij, Robin W M; Haitjema, Saskia.
Afiliação
  • Zondag AGM; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Rozestraten R; Utrecht University School of Governance, Utrecht University, Utrecht, Netherlands.
  • Grimmelikhuijsen SG; Utrecht University School of Governance, Utrecht University, Utrecht, Netherlands.
  • Jongsma KR; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • van Solinge WW; Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Bots ML; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Vernooij RWM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Haitjema S; Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands.
J Med Internet Res ; 26: e50853, 2024 May 28.
Article em En | MEDLINE | ID: mdl-38805702
ABSTRACT

BACKGROUND:

Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear.

OBJECTIVE:

We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS.

METHODS:

We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance.

RESULTS:

In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively).

CONCLUSIONS:

We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Relações Médico-Paciente / Inteligência Artificial / Confiança Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Europa Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Relações Médico-Paciente / Inteligência Artificial / Confiança Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Europa Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Holanda