Anticipatory moral distress in machine learning-based clinical decision support tool development
A qualitative analysis
Bibliographic Data
| ID | 21257923 |
|---|---|
| Authors | Cindy Whitney (0000-0003-2219-8911, Stony Brook School, corresponding author), Heidi Preis (0000-0002-0459-290X, Stony Brook School), Alessa Ramos Vargas (Stony Brook School) |
| Year | 2025 |
| Volume | 7 |
| Pages | 100540 |
| Publication date | 2025-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SSM - Qualitative Research in Health (JOURNAL) |
| Journal identifiers | ISSN: 2667-3215 • E-ISSN: 2667-3215 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ssmqr.2025.100540 |
| PMID | 42222666 |
| OpenAlex | W4407731003 |
| Language | EN |
| Citations received | 1 |
| References cited | 26 |
Ongoing interest in machine learning systems include the emerging capability to integrate electronic health records to develop clinical decision support (CDS) tools that improve medical care, diagnostics, and therapy. Such CDS tools, which can handle a large quantity of data sources, can advise clinicians and amplify insights on diverse patient risk factors, from physiological challenges to psychosocial vulnerabilities. Despite a growing interest, there are various challenges that hinder the successful use of CDS tools in clinical practice. Among these, a key challenge is hesitance or resistance among end-users to take up tools and integrate their use into practice. The current inquiry applied a framework of the symbolic interaction of participatory experience-based co-design and used an interpretive descriptive approach to analysis of qualitative data, investigating the ethical issues brought to light by clinicians participating in three participatory experience-based co-design focus groups, as a part of the initial development of a CDS tool for detecting risk factors for adverse health outcomes in outpatient obstetric care at a single academically affiliated medical institution. Findings revealed that participants describe their anticipated symbolic relationship with a ML-based CDS tool as either promising or morally distressing. Anticipatory moral distress includes three separate sub-categories: 1) clinical conflict with clinical assessment and judgment, 2) partial conflict with comprehensive clinical considerations, and 3) resource conflict with structural barriers related to care delivery. Future work should include utilizing participatory experience-based co-design with end users to identify relevant context and institution-specific priorities and concerns from the beginning of CDS tool development and to continue co-design throughout the development process. • Clinician s feel anticipatory moral distress regarding ML-based CDS tool development. • Conflicts between CDS tools and clinical wisdom result in distress. • Even when CDS tools work well, distress emerges if there aren't enough resources
Clinical decision support system · Decision support system · Distress · Psychotherapist · Qualitative analysis · Qualitative research · Social science · Sociology · Computer Science · Ethics in Clinical Research · Ethics in medical practice · Mathematics · Patient Dignity and Privacy · Psychology · Artificial Intelligence
Moral Distress, Moral Residue, and the Crescendo Effect
Experience-based Co-design and Healthcare Improvement
Reduce Prenatal Maternal Stress to Improve Pregnancy and Birth Outcomes
Interpretive Description
The Participatory Zeitgeist
Criteria for assessing the trustworthiness of naturalistic inquiries
Co-design and ethical artificial intelligence for health
Whose Um Voice is it Anyway? Leveraging 'Thick Transcription' to Promote Inclusion in Qualitative Research Through Transcript Alignment
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |