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.

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.

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.

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.

Variables Associated with Emotional Symptom Severity in Primary Care Patients: The Usefulness of a Logistic Regression Equation to Help Clinical Assessment and Treatment Decisions

The aim of this study is to contribute to the evidence regarding variables related to emotional symptom severity and to use them to exemplify the potential usefulness of logistic regression for clinical assessment at primary care, where most of these disorders are treated. Cross-sectional data related to depression and anxiety symptoms, sociodemographic characteristics, quality of life (QoL), and emotion-regulation processes were collected from 1,704 primary care patients. Correlation and analysis of variance (ANOVA) tests were conducted to identify those variables associated with both depression and anxiety. Participants were then divided into severe and nonsevere emotional symptoms, and binomial logistic regression was used to identify the variables that contributed the most to classify the severity. The final adjusted model included psychological QoL (p < .001, odds ratio [OR] = .426, 95% CI [.318, .569]), negative metacognitions (p < .001, OR = 1.083, 95% CI [1.045, 1.122]), physical QoL (p < .001, OR = .870, 95% CI [.841, .900]), brooding rumination (p < .001, OR = 1.087, 95% CI [1.042, 1.133]), worry (p < .001, OR = 1.047, 95% CI [1.025, 1.070]), and employment status (p = .022, OR [.397, 2.039]) as independent variables, ρ2 = .326, area under the curve (AUC) = .857. Moreover, rumination and psychological QoL emerged as the best predictors to form a simplified equation to determine the emotional symptom severity (ρ2 = .259, AUC = .822). The use of statistical models like this could accelerate the assessment and treatment-decision process, depending less on the subjective point of view of clinicians and optimizing health care resources. (AU)

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.

The Factors Influencing Nurses' Clinical Decision-Making in Emergency Department.

In an emergency, making the correct decision is vital. It is a necessary element of professional nursing care, and the ability of nurses to make successful clinical decisions is the most critical element influencing care quality. The purpose of this study was to assess the factors influencing nurses' clinical decision-making in the emergency department of Palestinan hospitals. A cross-sectional study was targeted at all nurses working in emergency departments at the Palestinian hospitals. The study was completed with 227 nurses, and collecting data was performed with the Clinical Decision Making in Nursing Scale. Results of the study revealed that the average score for the total clinical decision-making score was 3.3 (SD = 0.23). The subscales of clinical decision making were "search for alternatives or options," "canvassing of objectives and values," "evaluation and reevaluation of consequences," and "search for information and unbiased assimilation of new information." Furthermore, multiple linear regression analysis revealed that degree and work hours accounted for 11.7% of the variance in clinical decision-making. The study confirmed the average score for clinical decision-making was slightly higher than the average score. Also, it approved that nursing degree and work hours were predictors of clinical decision-making among nurses in emergency departments.

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.

Shared Medical Decision Making Reconsidered: Challenging an Overly Cognitivist Perspective with a Linguistic Approach.

This article critically examines the four patterns of shared medical decision making (physician-dominated; physician-defined, patient-made; patient-defined, physician-made; and patient-dominated) suggested by Lippa et al. (2017). The aim of the study is to challenge these patterns with a new data set of conversations between physicians and cancer patients in a hospital ward. We recorded 13 physician-patient-conversations during the medical round in an Austrian hospital, which in total lasted about 1.5 h (language: German). We then categorized the medical decisions found in the data following Lippa et al.'s instructions and further analyzed them with a fine-grained linguistic approach. The study revealed no patient-dominated decisions and one decision, which could not be categorized with one of the patterns. Results from the linguistic approach call into question the generalizability, distinctiveness and validity of the patterns. Finally, the relationship between shared decision making and clinical distributed cognition is discussed.

Randomized Clinical Trial to Evaluate an Atrial Fibrillation Stroke Prevention Shared Decision-Making Pathway.

Background Oral anticoagulation reduces stroke and disability in atrial fibrillation (AF) but is underused. We evaluated the effects of a novel patient-clinician shared decision-making (SDM) tool in reducing oral anticoagulation patient's decisional conflict as compared with usual care. Methods and Results We designed and evaluated a new digital decision aid in a multicenter, randomized, comparative effectiveness trial, ENHANCE-AF (Engaging Patients to Help Achieve Increased Patient Choice and Engagement for AF Stroke Prevention). The digital AF shared decision-making toolkit was developed using patient-centered design with clear health communication principles (eg, meaningful images, limited text). Available in English and Spanish, the toolkit included the following: (1) a brief animated video; (2) interactive questions with answers; (3) a quiz to check on understanding; (4) a worksheet to be used by the patient during the encounter; and (5) an online guide for clinicians. The study population included English or Spanish speakers with nonvalvular AF and a CHA2DS2-VASc stroke score ≥1 for men or ≥2 for women. Participants were randomized in a 1:1 ratio to either usual care or the shared decision-making toolkit. The primary end point was the validated 16-item Decision Conflict Scale at 1 month. Secondary outcomes included Decision Conflict Scale at 6 months and the 10-item Decision Regret Scale at 1 and 6 months as well as a weighted average of Mann-Whitney U-statistics for both the Decision Conflict Scale and the Decision Regret Scale. A total of 1001 participants were enrolled and followed at 5 different sites in the United States between December 18, 2019, and August 17, 2022. The mean patient age was 69±10 years (40% women, 16.9% Black, 4.5% Hispanic, 3.6% Asian), and 50% of participants had CHA2DS2-VASc scores ≥3 (men) or ≥4 (women). The primary end point at 1 month showed a clinically meaningful reduction in decisional conflict: a 7-point difference in median scores between the 2 arms (16.4 versus 9.4; Mann-Whitney U-statistics=0.550; P=0.007). For the secondary end point of 1-month Decision Regret Scale, the difference in median scores between arms was 5 points in the direction of less decisional regret (P=0.078). The treatment effects lessened over time: at 6 months the difference in medians was 4.7 points for Decision Conflict Scale (P=0.060) and 0 points for Decision Regret Scale (P=0.35). Conclusions Implementation of a novel shared decision-making toolkit (afibguide.com; afibguide.com/clinician) achieved significantly lower decisional conflict compared with usual care in patients with AF. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04096781.

Inteligencia artificial en medicina y procedimientos quirúrgicos: impacto en la toma de decisiones y la salud / Artificial Intelligence in Medicine and Surgical Procedures: Impact on Decision Making and Health

Introducción: Las revisiones sistemáticas de la literatura constituyen una herramienta metodológica práctica para la búsqueda de información sobre investigaciones clínicas, aplicaciones tecnológicas y la toma de decisiones de impacto en la salud. Objetivo: Describir cómo influye la inteligencia artificial en la toma de decisiones médicas según el grado de concordancia entre estas evidencias y los sistemas expertos aplicados en las especialidades clínicas y quirúrgicas de impacto en la salud, según reportes entre 2010 y 2019. Métodos: Se realizó una revisión sistemática con el uso de un modelo de bases de datos relacional y un modelo de entidad relación para garantizar la entidad referencial de la que hacen parte las bases de datos y los artículos, así como la calidad de cada uno de los artículos mediante clasificación por grados de concordancia entre muy concordante o no concordante con la temática de interés y la toma de decisiones de impacto en la salud. Conclusiones: Las aplicaciones como los sistemas expertos, los aprendizajes de máquinas y la robótica aportan innovación a las instituciones y un cambio revolucionario en lo académico, clínico y epidemiológico(AU)

Introduction: Systematic reviews of the literature constitute a practical methodological tool for the search of information on clinical research, technological applications and health impact decision-making. Objectives: To describe how artificial intelligence influences medical decision-making according to the degree of agreement between this evidence and the expert systems applied in clinical and surgical specialties with an impact on health, according to reports from 2010 to 2019. Methods: A systematic review was conducted with the use of a relational database model and a relationship entity model to guarantee the referential entity of which the databases and articles are part, as well as the quality of each of the articles classified by degrees of agreement between very concordant or not concordant with the topic of interest and the decision making of impact on health. Conclusions: Applications such as expert systems, machine learning and robotics bring innovation to institutions and a revolutionary change in academic, clinical and epidemiological areas(AU)

Clinical Decision-Making for Appendectomy in Kosovo: A Conjoint Analysis.

Objective: The objective was to investigate the association of clinical attributes with decision making for performing appendectomy and making preoperative preparations for appendectomy. Method: A conjoint analysis with 17 clinical scenarios was executed with surgeons employed at public hospitals in Kosovo. Setting: The study was conducted at two public hospitals in Kosovo that have benefited from quality-improvement interventions. Participants: The participants included 22 surgeons. Outcome measures: The primary outcome was the overall effect of clinical attributes on the decision to perform appendectomy and make the preoperative preparations for appendectomy. Results: In the regression analyses, several attributes demonstrated statistically significant effects on the clinical decision to perform appendectomy and on the practice of preoperative preparation. Conclusions: We found that several factors influenced the decision to perform appendectomy and the practices for preoperative preparation. Nevertheless, the small sample size limited our efforts to interpret the results. These findings could assist Kosovo in the design and implementation of future similar studies and in fostering quality improvement measures that address clinical decision making and the lack of process standardization in the delivery of surgical care.

Genomics to select treatment for patients with metastatic breast cancer.

Cancer progression is driven in part by genomic alterations1. The genomic characterization of cancers has shown interpatient heterogeneity regarding driver alterations2, leading to the concept that generation of genomic profiling in patients with cancer could allow the selection of effective therapies3,4. Although DNA sequencing has been implemented in practice, it remains unclear how to use its results. A total of 1,462 patients with HER2-non-overexpressing metastatic breast cancer were enroled to receive genomic profiling in the SAFIR02-BREAST trial. Two hundred and thirty-eight of these patients were randomized in two trials (nos. NCT02299999 and NCT03386162) comparing the efficacy of maintenance treatment5 with a targeted therapy matched to genomic alteration. Targeted therapies matched to genomics improves progression-free survival when genomic alterations are classified as level I/II according to the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT)6 (adjusted hazards ratio (HR): 0.41, 90% confidence interval (CI): 0.27-0.61, P < 0.001), but not when alterations are unselected using ESCAT (adjusted HR: 0.77, 95% CI: 0.56-1.06, P = 0.109). No improvement in progression-free survival was observed in the targeted therapies arm (unadjusted HR: 1.15, 95% CI: 0.76-1.75) for patients presenting with ESCAT alteration beyond level I/II. Patients with germline BRCA1/2 mutations (n = 49) derived high benefit from olaparib (gBRCA1: HR = 0.36, 90% CI: 0.14-0.89; gBRCA2: HR = 0.37, 90% CI: 0.17-0.78). This trial provides evidence that the treatment decision led by genomics should be driven by a framework of target actionability in patients with metastatic breast cancer.

Laypeople's Online Health Information Search Strategies and Use for Health-Related Problems: Cross-sectional Survey.

BACKGROUND: With the increase in the use of the internet to search for health information about health-related problems, there is a need for health care professionals to better understand how their patients search for and use the online health information that may influence their medical decision making. OBJECTIVE: The aims of this study are to explore laypeople's online health information search strategies and examine the relationships between their search strategies and utilization behavior of online health information. METHODS: Two scales, namely match and elaboration, were used to measure patients' basic search strategies (ie, simple approach) and advanced search strategies (ie, integrative approach), respectively. In addition, the consultation scale was used to evaluate the participants' use of online health information to consult doctors and others. A total of 253 outpatients without university education were purposely selected and surveyed. The participants were outpatients at a university-affiliated teaching hospital. Partial least squares-structural equation modeling (PLS-SEM) was performed to analyze the measurement model to specify the measurement validation. In addition, the structure model of PLS-SEM was evaluated to examine the path correlations between variables and to execute interaction effect and curvilinear relationship analyses. RESULTS: The results of the path correlation analysis by PLS-SEM showed that both elaboration strategy (path coefficient=0.55, P<.001) and match strategy (path coefficient=0.36, P<.001) were positively correlated with consultation on online health information with doctors and others. In addition, interaction effect and curvilinear relationship analyses indicated that there was a significant interaction effect between elaboration and match on consultation (path coefficient=-0.34, P<.001) and a significant curvilinear relationship between match and consultation (path coefficient=-0.09, P=.046). CONCLUSIONS: Increasing patients' exposure to online health information through both a simple search approach (ie, match strategy) and a complex search approach (ie, elaboration strategy) may lead them to appropriately use the information to consult doctors and others. However, the results of interaction effect and curvilinear relationship analyses highlighted the essential role of the elaboration strategy to properly locate, evaluate, and apply online health information. The findings of this study may help health care professionals better understand how to communicate with their patients through the health information on the internet.

A Concept Analysis of Nurses' Clinical Decision Making: Implications for Korea.

The study's purpose was to identify the meaning and the attributes of Korean nurses' clinical decision making. A sequential and systematic literature review with reflection according to the conceptual analysis method of Walker and Avant was used in this study. Data sources included the National Assembly Library, the National Digital Science Library, ProQuest, PubMed, MEDLINE, and CINAHL. Finally, twenty-six articles were included in this concept analysis. The concept of Korean nurses' clinical decision making consisted of the following attributes: clinical reasoning, choosing and applying challenging alternatives, and professional assessment and resetting. Antecedents consisted of: recognizing complex and diverse patient situations with high uncertainty, the need to solve problems according to priority, prior experience in clinical decision making, and interrelationships with fellow medical staff. Consequences consisted of: providing high-quality nursing services, improving the patient's safety, and increased satisfaction with clinical decision making. Based on these results, the conceptual attributes of Korean nurses' clinical decision making had slightly different characteristics but were organically interrelated. The results of analyzing the concept of Korean nurses' clinical decision making provide a better understanding of it and contribute to expanding nursing knowledge and developing a valid and reliable measurement.