Co-creation of a toolkit to assist risk communication and clinical decision-making in severe preeclampsia: SPOT-Impact study design.

Globally, the incidence of hypertensive disorders of pregnancy, especially preeclampsia, remains high, particularly in low- and middle-income countries. The burden of adverse maternal and perinatal outcomes is particularly high for women who develop a hypertensive disorder remote from term (<34 weeks). In parallel, many women have a suboptimal experience of care. To improve the quality of care in terms of provision and experience, there is a need to support the communication of risks and making of treatment decision in ways that promote respectful maternity care. Our study objective is to co-create a tool(kit) to support clinical decision-making, communication of risks and shared decision-making in preeclampsia with relevant stakeholders, incorporating respectful maternity care, justice, and equity principles. This qualitative study detailing the exploratory phase of co-creation takes place over 17 months (Nov 2021-March 2024) in the Greater Accra and Eastern Regions of Ghana. Informed by ethnographic observations of care interactions, in-depth interviews and focus group and group discussions, the tool(kit) will be developed with survivors and women with hypertensive disorders of pregnancy and their families, health professionals, policy makers, and researchers. The tool(kit) will consist of three components: quantitative predicted risk (based on external validated risk models or absolute risk of adverse outcomes), risk communication, and shared decision-making support. We expect to co-create a user-friendly tool(kit) to improve the quality of care for women with preeclampsia remote from term which will contribute to better maternal and perinatal health outcomes as well as better maternity care experience for women in Ghana.

Adverse maternal and perinatal outcomes is high for women who develop preeclampsia remote from term (<34 weeks). To improve the quality of provision and experience of care, there is a need to support communication of risks and treatment decisions that promotes respectful maternity care.This article describes the methodology deployed to cocreate a user-friendly tool(kit) to support risk communication and shared decision-making in the context of severe preeclampsia in a low resource setting.

Clinical decision making for VA ECMO weaning in patients with cardiogenic shock A formative qualitative study.

Weaning and liberation from VA ECMO in cardiogenic shock patients comprises a complex process requiring a continuous trade off between multiple clinical parameters. In the absence of dedicated international guidelines, we hypothesized a great heterogeneity in weaning practices among ECMO centers due to a variety in local preferences, logistics, case load and individual professional experience. This qualitative study focused on the appraisal of clinicians' preferences in decision processes towards liberation from VA ECMO after cardiogenic shock while using focus group interviews in 4 large hospitals. The goal was to provide novel and unique insights in daily clinical weaning practices. As expected, we found we a great heterogeneity of weaning strategies among centers and professionals, although participants appeared to find common ground in a clinically straightforward approach to assess the feasibility of ECMO liberation at the bedside. This was shown in a preference for robust, easily accessible parameters such as arterial pulse pressure, stable cardiac index ≥2.1 L/min, VTI LVOT and 'eyeballing' LVEF.

Effects of explainable artificial intelligence in neurology decision support.

OBJECTIVE: Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. METHODS: We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. RESULTS: We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. INTERPRETATION: xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.

High throughput screening aids clinical decision-making in refractory acute myeloid leukaemia.

BACKGROUND: Despite advances in therapeutics for adverse-risk acute myeloid leukaemia (AML), overall survival remains poor, especially in refractory disease. Comprehensive tumour profiling and pre-clinical drug testing can identify effective personalised therapies. CASE: We describe a case of ETV6-MECOM fusion-positive refractory AML, where molecular analysis and in vitro high throughput drug screening identified a tolerable, novel targeted therapy and provided rationale for avoiding what could have been a toxic treatment regimen. Ruxolitinib combined with hydroxyurea led to disease control and enhanced quality-of-life in a patient unsuitable for intensified chemotherapy or allogeneic stem cell transplantation. CONCLUSION: This case report demonstrates the feasibility and role of combination pre-clinical high throughput screening to aid decision making in high-risk leukaemia. It also demonstrates the role a JAK1/2 inhibitor can have in the palliative setting in select patients with AML.

Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases.

BACKGROUND: Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team. METHODS: All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion. RESULTS: Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT. CONCLUSIONS: The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Adolescents with Rett syndrome at critical care pathway junctures: Examining clinicians' decision to initiate invasive long-term ventilation.

BACKGROUND: The initiation of invasive long-term ventilation (I-LTV) for an adolescent with Rett Syndrome (RTT) involves many serious bioethical considerations. In moving towards a more inclusive model of patient participation, transparency surrounding the main influencing factors around this decision is important. OBJECTIVE: We aimed to identify the main drivers influencing a clinician's decision to support initiation of I-LTV for an adolescent with RTT. METHOD: We used an anonymous online vignette-based factorial survey. The survey was distributed internationally through eight professional multi-disciplinary organisations to reach clinicians working in paediatrics. RESULTS: We analysed 504 RTT vignettes completed by 246 clinicians using mixed effect regression modelling. The main three significant influencing factors identified were: parental agreement with the decision to support initiation, the family's support network, and proximity to a tertiary care centre. Additional comments from participants focused on family support, and the importance of on-going communication with the family. CONCLUSION: As the rights of those with disabilities improve and participation of adolescents in decision-making becomes more established, effective communications with the family around goals of care and particular sensitivity and reflective practice around methods of consensus building will likely contribute to a positive decision-making process at this difficult time.

Understanding Social, Cultural, and Religious Factors Influencing Medical Decision-Making on BRCA1/2 Genetic Testing in the Orthodox Jewish Community.

INTRODUCTION: Although the prevalence of a pathogenic variant in the BRCA1 and BRCA2 genes is about 1:400 (0.25%) in the general population, the prevalence is as high as 1:40 (2.5%) among the Ashkenazi Jewish population. Despite cost-effective preventive measures for mutation carriers, Orthodox Jews constitute a cultural and religious group that requires different approaches to BRCA1 and BRCA2 genetic testing relative to other groups. This study analyzed a dialog of key stakeholders and community members to explore factors that influence decision-making about BRCA1 and BRCA2 genetic testing in the New York Orthodox Jewish community. METHODS: Qualitative research methods, based on Grounded Theory and Narrative Research, were utilized to analyze the narrative data collected from 49 key stakeholders and community members. A content analysis was conducted to identify themes; inter-rater reliability was 71%. RESULTS: Facilitators of genetic testing were a desire for preventive interventions and education, while barriers to genetic testing included negative emotions, feared impact on family/romantic relationships, cost, and stigma. Views differed on the role of religious leaders and healthcare professionals in medical decision-making. Education, health, and community were discussed as influential factors, and concerns were expressed about disclosure, implementation, and information needs. CONCLUSION: This study elicited the opinions of Orthodox Jewish women (decision-makers) and key stakeholders (influencers) who play critical roles in the medical decision-making process. The findings have broad implications for engaging community stakeholders within faith-based or culturally distinct groups to ensure better utilization of healthcare services for cancer screening and prevention designed to improve population health.

3D analysis of the preoperative deformity in AIS can be used to guide surgical treatment decisions for selective thoracic fusion.

PURPOSE: To identify 3D measures of scoliosis from preoperative imaging that are associated with optimal radiographic outcomes after selective thoracic fusion (STF) for adolescent idiopathic scoliosis (AIS). METHODS: Subjects with primary thoracic curves (Lenke 1-4, B or C modifiers) fused selectively (L1 or above) who had preoperative 3D reconstructions and minimum 2 years of follow-up were included. An optimal outcome at 2 years was defined as having 4 of 5 parameters previously defined in the literature: (1) lumbar curve < 26º, (2) deformity flexibility quotient < 4, (3) C7-CSVL < 2 cm, (4) lumbar prominence < 5º and (5) trunk shift < 1.5 cm. Univariate and CART analyses were performed to identify preoperative variables associated with achieving an optimal outcome 2 years postoperatively. RESULTS: Ninety-nine (88F, 11 M) patients met inclusion. Mean age was 15 ± 2 years. Fifty-one subjects (52%) had an optimal outcome. Seven preoperative deformity measures representing smaller thoracolumbar/lumbar deformity in the optimal group were found to be significant on univariate analysis. CART analysis identified the following variables associated with optimal outcomes: difference in apical rotation > 30° = 27% optimal outcomes, difference in apical rotation ≤ 30° and coronal vertebral wedging of lumbar apex > 3° = 46% optimal outcomes, and difference in apical rotation ≤ 30° and coronal vertebral wedging of lumbar apex ≤ 3° = 80% optimal outcomes (p < 0.05). CONCLUSION: Optimal outcomes after STF were associated with a preoperative difference in apical vertebral rotation in the axial plane less than 30° between thoracic and lumbar curves as well as coronal plane vertebral wedging of the lumbar apical vertebra less than 3°.

Diagnostic uncertainty and decision-making in home-based primary care: A qualitative study of antibiotic prescribing.

BACKGROUND: Evaluating infection in home-based primary care is challenging, and these challenges may impact antibiotic prescribing. A refined understanding of antibiotic decision-making in this setting can inform strategies to promote antibiotic stewardship. This study investigated antibiotic decision-making by exploring the perspectives of clinicians in home-based primary care. METHODS: Clinicians from the Department of Veterans Affairs Home-Based Primary Care Program were recruited. Semi-structured interviews were conducted from June 2022 through September 2022 using a discussion guide. Transcripts were analyzed using grounded theory. The constant comparative method was used to develop a coding structure and to identify themes. RESULTS: Theoretical saturation was reached after 22 clinicians (physicians, n = 7; physician assistants, n = 2, advanced practice registered nurses, n = 13) from 19 programs were interviewed. Mean age was 48.5 ± 9.3 years, 91% were female, and 59% had ≥6 years of experience in home-based primary care. Participants reported uncertainty about the diagnosis of infection due to the characteristics of homebound patients (atypical presentations of disease, presence of multiple chronic conditions, presence of cognitive impairment) and the challenges of delivering medical care in the home (limited access to diagnostic testing, suboptimal quality of microbiological specimens, barriers to establishing remote access to the electronic health record). When faced with diagnostic uncertainty about infection, participants described many factors that influenced the decision to prescribe antibiotics, including those that promoted prescribing (desire to avoid hospitalization, pressure from caregivers, unreliable plans for follow-up) and those that inhibited prescribing (perceptions of antibiotic-associated harms, willingness to trial non-pharmacological interventions first, presence of caregivers who were trusted by clinicians to monitor symptoms). CONCLUSIONS: Clinicians face the difficult task of balancing diagnostic uncertainty with many competing considerations during the treatment of infection in home-based primary care. Recognizing these issues provides insight into strategies to promote antibiotic stewardship in home care settings.

Tracheostomy and long-term invasive ventilation decision-making in children: A scoping review.

An increasing number of children are surviving critical illnesses requiring tracheostomy/long-term ventilation (LTV). This scoping review seeks to collate the available evidence on decision-making for tracheostomy/LTV in children. Systematic searches of electronic databases and websites were conducted for articles and reports. Inclusion criteria included: (1) children 0-18 years old; (2) described use of tracheostomy or tracheostomy/LTV; and (3) information on recommendations for tracheostomy decision-making or decision-making experiences of family-caregivers or health care providers. Articles not written in English were excluded. Of the 4463 records identified through database search and other methods, a total of 84 articles, 2 dissertations, 1 book chapter, 3 consensus statement/society guidelines, and 8 pieces of grey literature were included. Main thematic domains identified were: (1) legal and moral standards for decision-making; (2) decision-making models, roles of decision-makers, and decisional aids towards a shared decision-making model; (3) experiences and perspectives of decision-makers; (4) health system and society considerations; and (5) conflict resolution and legal considerations. A high degree of uncertainty and complexity is involved in tracheostomy/LTV decision-making. There is a need for a standardized decision-support process that is consistent with a child's best interests and shared decision-making. Strategies for optimizing communication and mechanism for managing disputes are needed.

Evaluation of ChatGPT and Google Bard Using Prompt Engineering in Cancer Screening Algorithms.

Large language models (LLMs) such as ChatGPT and Bard have emerged as powerful tools in medicine, showcasing strong results in tasks such as radiology report translations and research paper drafting. While their implementation in clinical practice holds promise, their response accuracy remains variable. This study aimed to evaluate the accuracy of ChatGPT and Bard in clinical decision-making based on the American College of Radiology Appropriateness Criteria for various cancers. Both LLMs were evaluated in terms of their responses to open-ended (OE) and select-all-that-apply (SATA) prompts. Furthermore, the study incorporated prompt engineering (PE) techniques to enhance the accuracy of LLM outputs. The results revealed similar performances between ChatGPT and Bard on OE prompts, with ChatGPT exhibiting marginally higher accuracy in SATA scenarios. The introduction of PE also marginally improved LLM outputs in OE prompts but did not enhance SATA responses. The results highlight the potential of LLMs in aiding clinical decision-making processes, especially when guided by optimally engineered prompts. Future studies in diverse clinical situations are imperative to better understand the impact of LLMs in radiology.

Ultra-fast deep-learned CNS tumour classification during surgery.

Central nervous system tumours represent one of the most lethal cancer types, particularly among children1. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity2,3. However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery4. Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries.

Rapid Whole-Genomic Sequencing and a Targeted Neonatal Gene Panel in Infants With a Suspected Genetic Disorder.

Importance: Genomic testing in infancy guides medical decisions and can improve health outcomes. However, it is unclear whether genomic sequencing or a targeted neonatal gene-sequencing test provides comparable molecular diagnostic yields and times to return of results. Objective: To compare outcomes of genomic sequencing with those of a targeted neonatal gene-sequencing test. Design, Setting, and Participants: The Genomic Medicine for Ill Neonates and Infants (GEMINI) study was a prospective, comparative, multicenter study of 400 hospitalized infants younger than 1 year of age (proband) and their parents, when available, suspected of having a genetic disorder. The study was conducted at 6 US hospitals from June 2019 to November 2021. Exposure: Enrolled participants underwent simultaneous testing with genomic sequencing and a targeted neonatal gene-sequencing test. Each laboratory performed an independent interpretation of variants guided by knowledge of the patient's phenotype and returned results to the clinical care team. Change in clinical management, therapies offered, and redirection of care was provided to families based on genetic findings from either platform. Main Outcomes and Measures: Primary end points were molecular diagnostic yield (participants with ≥1 pathogenic variant or variant of unknown significance), time to return of results, and clinical utility (changes in patient care). Results: A molecular diagnostic variant was identified in 51% of participants (n = 204; 297 variants identified with 134 being novel). Molecular diagnostic yield of genomic sequencing was 49% (95% CI, 44%-54%) vs 27% (95% CI, 23%-32%) with the targeted gene-sequencing test. Genomic sequencing did not report 19 variants found by the targeted neonatal gene-sequencing test; the targeted gene-sequencing test did not report 164 variants identified by genomic sequencing as diagnostic. Variants unidentified by the targeted genomic-sequencing test included structural variants longer than 1 kilobase (25.1%) and genes excluded from the test (24.6%) (McNemar odds ratio, 8.6 [95% CI, 5.4-14.7]). Variant interpretation by laboratories differed by 43%. Median time to return of results was 6.1 days for genomic sequencing and 4.2 days for the targeted genomic-sequencing test; for urgent cases (n = 107) the time was 3.3 days for genomic sequencing and 4.0 days for the targeted gene-sequencing test. Changes in clinical care affected 19% of participants, and 76% of clinicians viewed genomic testing as useful or very useful in clinical decision-making, irrespective of a diagnosis. Conclusions and Relevance: The molecular diagnostic yield for genomic sequencing was higher than a targeted neonatal gene-sequencing test, but the time to return of routine results was slower. Interlaboratory variant interpretation contributes to differences in molecular diagnostic yield and may have important consequences for clinical management.

Practical recommendations for using ctDNA in clinical decision making.

The continuous improvement in cancer care over the past decade has led to a gradual decrease in cancer-related deaths. This is largely attributed to improved treatment and disease management strategies. Early detection of recurrence using blood-based biomarkers such as circulating tumour DNA (ctDNA) is being increasingly used in clinical practice. Emerging real-world data shows the utility of ctDNA in detecting molecular residual disease and in treatment-response monitoring, helping clinicians to optimize treatment and surveillance strategies. Many studies have indicated ctDNA to be a sensitive and specific biomarker for recurrence. However, most of these studies are largely observational or anecdotal in nature, and peer-reviewed data regarding the use of ctDNA are mainly indication-specific. Here we provide general recommendations on the clinical utility of ctDNA and how to interpret ctDNA analysis in different treatment settings, especially in patients with solid tumours. Specifically, we provide an understanding around the implications, strengths and limitations of this novel biomarker and how to best apply the results in clinical practice.

Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

The therapeutic threshold in clinical decision-making for TB.

Because TB control is still hampered by the limitations of diagnostic tools, diagnostic uncertainty is common. The decision to offer treatment is based on clinical decision-making. The therapeutic threshold, test threshold and test-treatment threshold can guide in making these decisions. This review summarizes the literature on methods to estimate the therapeutic threshold that have been applied for TB. Only five studies estimated the threshold for the diagnosis of TB. The therapeutic threshold can be estimated by prescriptive methods, based on calculations, and by descriptive methods, deriving the threshold from observing clinical practice. Test and test-treatment thresholds can be calculated using the therapeutic threshold and the characteristics of an available diagnostic test. Estimates of the therapeutic threshold for pulmonary TB from intuitive descriptive approaches (20%-50%) are higher than theoretical prescriptive calculations (2%-3%). In conclusion, estimates of the therapeutic threshold for pulmonary TB depend on the method used. Other methods exist within the field of decision-making that have yet to be implemented or adapted as tools to estimate the TB therapeutic threshold. Because clinical decision-making is a core element of TB management, it is necessary to find a new, clinician-friendly way to unbiasedly estimate context-specific, agreed upon therapeutic thresholds.

Artificial intelligence in clinical decision support systems for oncology.

Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

Clinical decision-making and adaptive expertise in residency: a think-aloud study.

Clinical decision-making (CDM) is the ability to make clinical choices based on the knowledge and information available to the physician. It often refers to individual cognitive processes that becomes more dependent with the acquisition of experience and knowledge. Previous research has used dual-process theory to explain the cognitive processes involved in how physicians acquire experiences that help them develop CDM. However, less is known about how CDM is shaped by the physicians' situated cognition in the clinical environment. This is especially challenging for novice physicians, as they need to be adaptive to compensate for the lack of experience. The adaptive expert framework has been used to explain how novice physicians learn, but it has not yet been explored, how adaptive expertise is linked to clinical decision-making amongst novice physicians.This study aimed to analyse how residents utilize and develop adaptive expert cognition in a natural setting. By describing cognitive processes through verbalization of thought processes, we sought to explore their CDM strategies considering the adaptive expert framework.We used concurrent and retrospective think-aloud interviews in a natural setting of an emergency department (ED) at a university hospital, to query residents about their reasoning during a patient encounter. We analysed data using protocol analysis to map cognitive strategies from these verbalizations. Subsequently in a narrative analysis, we compared these strategies with the literature on adaptive expertise.Fourteen interviews were audio recorded over the course for 17 h of observation. We coded 78 informational concepts and 46 cognitive processes. The narrative analysis demonstrated how epistemic distance was prevalent in the initial CDM process and self-regulating processes occurred during hypothesis testing. However, residents who too quickly moved on to hypothesis testing tended to have to redirect their hypothesis more often, and thus be more laborious in their CDM. Uncertainty affected physicians' CDM when they did not reconcile their professional role with being allowed to be uncertain. This allowance is an important feature of orientation to new knowledge as it facilitates the evaluation of what the physician does not know.For the resident to learn to act as an adaptive decision-maker, she relied on contextual support. The professional role was crucial in decisional competency. This supports current literature, which argues that role clarification helps decisional competency. This study adds that promoting professional development by tolerating uncertainty may improve adaptive decisional competency.

Examining the effectiveness and implementation of patient treatment decision-aid tools for men with localised prostate cancer: A systematic review.

OBJECTIVE: Men diagnosed with localised prostate cancer (LPC) often face a difficult process deciding on a treatment choice that suits their personal preferences. This systematic review examines the impact of patient treatment decision-aids (DAs) on decisional outcomes and treatment choice for men diagnosed with LPC. Our secondary aim was to examine how DAs have been implemented into routine clinical practice. METHODS: A systematic search was conducted up to June 2022 using the following databases: Medline, Embase, PsycINFO, CINAHL, Cochrane, Scopus, and Web of Science. Articles were included if they evaluated the effectiveness of treatment DAs for LPC patients on various decisional outcomes and treatment choice. The Mixed-Method Appraisal Tool was used to assess methodological quality and risk of bias. Data on implementation outcomes were also extracted if reported. RESULTS: Twenty-four articles were included for the analysis (seven non-randomised studies, 16 randomised control trials, and one qualitative study). Results showed DAs have the potential to improve patient knowledge but revealed no effects on decisional regret or preparedness in decision-making. Due to the variability in methodology among studies, results varied widely for treatment choice, decision-making involvement, decisional conflict, and treatment decision satisfaction. At least one implementation outcome was reported in 11 of the included studies, with the most commonly assessed outcomes being acceptability and appropriateness. CONCLUSIONS: While DAs appear to improve knowledge, further qualitative evaluations and standardised assessments are needed to better understand men's experiences using DAs and to determine advantages and optimal ways to implement DAs into the treatment decision-making pathway.