ArticleHealth expectations : an international journal of public participation in health care and health policy2022
Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making.
Article in Health expectations : an international journal of public participation in health care and health policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Trust in Artificial Intelligence-Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Real-world effectiveness of pembrolizumab for first-line treatment of metastatic non-small-cell lung cancer: a decade of evidence and experience.Future oncology (London, England) · 2026Review
- Decision-Making Readiness and Its Influencing Factors Among Lung Cancer Patients Receiving Chemotherapy: A Cross-Sectional Study.Nursing open · 2026Observational
- A Systematic Review of Factors Affecting Utilization of Decision Support Systems: The Interplay Between Technology, Users, and the Healthcare Environment.Health science reports · 2026Review
- Understanding Clinicians' Informational Needs for AI-Driven Clinical Decision Support Systems: Qualitative Interview Study.JMIR medical education · 2026Article
- Closing the evidence gap in older adults with extensive-stage small-cell lung cancer: a CGA-guided, risk-adapted treatment framework.Therapeutic advances in medical oncology · 2026Review
- Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.Scientific reports · 2025Article
- Oncologists' preferences for first-line treatment of ALK-positive metastatic NSCLC in the United States: a discrete choice experiment.Future oncology (London, England) · 2025Article
- Improving Patient Engagement in Phase 2 Clinical Trials With a Trial-Specific Patient Decision Aid: Development and Usability Study.Journal of medical Internet research · 2025Article
- Cancer patient preferences for traditional Chinese medicine treatment: a discrete choice experiment study.BMC complementary medicine and therapies · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Quality of interaction between clinicians and artificial intelligence systems. A systematic review.Future healthcare journal · 2024Review
- Artificial intelligence in clinical decision support systems for oncology.International journal of medical sciences · 2023Review
- Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making.Health expectations : an international journal of public participation in health care and health policy · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision-making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it.
objectiveTo explore (1) the extent to which patient preferences are taken into consideration in non-small-cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM.
designMixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations. SETTING AND
participantsNSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics.
resultsWe found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations.
conclusionLung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized. PATIENT OR PUBLIC CONTRIBUTION: This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled 'My Best Treatment' that improves patient-centeredness and the lung cancer patient pathway in the Netherlands.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.