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Empowering patients in decision-making in radiation oncology - can we do better?

ABSTRACT: The decision as to whether or not a patient should receive radiation therapy as part of their cancer treatment is based on evidence-based practice and on recommended international consensus treatment guidelines. However, the merit of involving the patients' individual preferences and values in the treatment decision is frequently overlooked. Here, we review the current literature pertaining to shared decision-making (SDM) in the field of radiation oncology, including discussion of the patient's perception of radiation therapy as a treatment option and patient involvement in clinical trials. The merit of decision aids during the SDM process in radiation oncology is considered, as are patient preferences for active or passive involvement in decisions about their treatment. Clarity of terminology, a better understanding of effective strategies and increased resources will be needed to ensure SDM in radiation oncology becomes a reality.

SUBMITTER: Leech M

PROVIDER: S-EPMC7332211 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Empowering patients in decision-making in radiation oncology - can we do better?

Leech Michelle M Katz Matthew S MS Kazmierska Joanna J McCrossin Julie J Turner Sandra S

Molecular oncology 20200413 7

The decision as to whether or not a patient should receive radiation therapy as part of their cancer treatment is based on evidence-based practice and on recommended international consensus treatment guidelines. However, the merit of involving the patients' individual preferences and values in the treatment decision is frequently overlooked. Here, we review the current literature pertaining to shared decision-making (SDM) in the field of radiation oncology, including discussion of the patient's ...[more]

PMID: 32198967

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Project description:BackgroundTherapeutic decision-making in oncology is a complex process because physicians must consider many forms of medical data and protocols. Another challenge for physicians is to clearly communicate their decision-making process to patients to ensure informed consent. Computer-based decision tools have the potential to play a valuable role in supporting this process.ObjectiveThis systematic review aims to investigate the extent to which computer-based decision tools have been successfully adopted in oncology consultations to improve patient-physician joint therapeutic decision-making.MethodsThis review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist and guidelines. A literature search was conducted on February 4, 2021, across the Cochrane Database of Systematic Reviews (from 2005 to January 28, 2021), the Cochrane Central Register of Controlled Trials (December 2020), MEDLINE (from 1946 to February 4, 2021), Embase (from 1947 to February 4, 2021), Web of Science (from 1900 to 2021), Scopus (from 1969 to 2021), and PubMed (from 1991 to 2021). We used a snowball approach to identify additional studies by searching the reference lists of the studies included for full-text review. Additional supplementary searches of relevant journals and gray literature websites were conducted. The reviewers screened the articles eligible for review for quality and inclusion before data extraction.ResultsThere are relatively few studies looking at the use of computer-based decision tools in oncology consultations. Of the 4431 unique articles obtained from the searches, only 10 (0.22%) satisfied the selection criteria. From the 10 selected studies, 8 computer-based decision tools were identified. Of the 10 studies, 6 (60%) were conducted in the United States. Communication and information-sharing were improved between physicians and patients. However, physicians did not change their habits to take advantage of computer-assisted decision-making tools or the information they provide. On average, the use of these computer-based decision tools added approximately 5 minutes to the total length of consultations. In addition, some physicians felt that the technology increased patients' anxiety.ConclusionsOf the 10 selected studies, 6 (60%) demonstrated positive outcomes, 1 (10%) showed negative results, and 3 (30%) were neutral. Adoption of computer-based decision tools during oncology consultations continues to be low. This review shows that information-sharing and communication between physicians and patients can be improved with the assistance of technology. However, the lack of integration with electronic health records is a barrier. This review provides key requirements for enhancing the chance of success of future computer-based decision tools. However, it does not show the effects of health care policies, regulations, or business administration on physicians' propensity to adopt the technology. Nevertheless, it is important that future research address the influence of these higher-level factors as well.Trial registrationPROSPERO International Prospective Register of Systematic Reviews CRD42021226087; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021226087.

Project description:PurposeThe potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.MethodsThe 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases.ResultsFor the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts.ConclusionBoth evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.

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Empowering patients in decision-making in radiation oncology - can we do better?

Publications

Empowering patients in decision-making in radiation oncology - can we do better?

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