Support Tool Research Articles

A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.

The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the Neonatal Intensive Care Unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased.

Application of the NCAR FastEddy® Microscale Model to a Lake Breeze Front

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy® (FE) model. Motivated by the need for sub-10 m scale decision support tools for uncrewed aerial systems (UAS), the FE model was used to simulate turbulent flows around urban structures at 5 m horizontal resolution with a 9 km × 9 km domain centered on the Salt Lake City International Airport. FE was one-way nested within a 1 km resolution Weather Research and Forecasting (WRF) domain spanning 400 × 400 km. Focused on the late morning lake breeze on 3 June 2022, an FE simulation was compared with WRF outputs and validated using surface and radar observations. The FE simulation revealed low sensible heat flux and cool near-surface temperatures, attributed to a relatively low specification of thermal roughness suitable for previously tested FE applications. Lake breeze characteristics were minimally affected, as FE effectively resolved interactions between the lake breeze and urban-induced turbulent eddies, providing insights into fine-scale boundary layer processes. FE’s GPU acceleration ensured efficient simulations, underscoring its potential for aiding decision support in UAS operations in complex urban environments.

An artificial intelligence-driven support tool for prediction of urine culture test results

Background and aimsWe aimed to develop an easily deployable artificial intelligence (AI)-driven model for rapid prediction of urine culture test results. Material and MethodsWe utilized a training dataset (n = 34,584 urine samples) and two separate, unseen test sets (n = 10,083 and 9,289 samples). Various machine learning models were compared for diagnostic performance. Predictive parameters included urinalysis results (dipstick and flow cytometry), patient demographics (age and gender), and sample collection method. ResultsAlthough more complex models achieved the highest AUCs for predicting positive cultures (highest: multilayer perceptron (MLP) with AUC of 0.884, 95% CI 0.878–0.89), multiple logistic regression (MLR) using only flow cytometry parameters achieved a very good AUC (0.858, 95% CI 0.852–0.865). To aid interpretation, prediction results of the MLP and MLR models were categorized based on likelihood ratio (LR) for positivity: highly unlikely (LR 0.1), unlikely (LR 0.3), grey zone (LR 0.9), likely (LR 5.0), and highly likely (LR 40). This resulted in 17%, 28%, 34%, 9%, and 13% of samples falling into each respective category for the MLR model and 20%, 26%, 31%, 7%, and 16% for the MLP model. ConclusionsIn conclusion, this robust model has the potential to assist clinicians in their decision-making process by providing insights prior to the availability of urine culture results in a significant portion of samples (∼2/3rd).

Prediction of intra-abdominal injury using natural language processing of electronic medical record data

BackgroundThis study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records. MethodsThis was a random-sample retrospective observational cohort study leveraging unstructured data from injured patients taken to one of 9 acute care hospitals in an integrated health system between 2015 and 2021. Patients with International Classification of Diseases External Cause of Morbidity codes were identified. History and physical, consult, progress, and radiology report text from the first 8 hours of care were abstracted. Annotator dyads independently annotated encounters’ text files to establish ground truth regarding whether intra-abdominal injury occurred. Features were extracted from text using natural language processing techniques, bag of words, and principal component analysis. We tested logistic regression, random forests, and gradient boosting machine to determine accuracy, recall, and precision of natural language processing to predict intra-abdominal injury. ResultsA random sample of 7,000 patient encounters of 177,127 was annotated. Only 2,951 had sufficient information to determine whether an intra-abdominal injury was present. Among those, 84 (2.9%) had an intra-abdominal injury. The concordance between annotators was 0.989. Logistic regression of features identified with bag of words and principal component analysis had the best predictive ability, with an area under the receiver operating characteristic curve of 0.9, recall of 0.73, and precision of 0.17. Text features with greatest importance included “abdomen,” “pelvis,” “spleen,” and “hematoma.” ConclusionNatural language processing could be a screening decision support tool, which, if paired with human clinical assessment, can maximize precision of intra-abdominal injury identification.

Concordance Study of Diagnoses and Therapeutic Management for Military Personnel Evacuated for Medico-psychological Reasons: From the Theater of Operations to the Military Training Hospital.

The evolving global landscape has led to increased involvement of the French armed forces, exposing military personnel to operational challenges that can affect their mental well-being. As a result, psychiatry has become the second most common reason for Medical Evacuation (MEDEVAC). In war zones where specialized medico-psychological consultations may not be readily available, medical officers play a vital role in providing initial care. Therefore, there is a growing emphasis on the precise evaluation of these practices. In this retrospective observational study, we analyzed MEDEVAC request files from the Operational Health Headquarters (Patient Medical Request sheets), Aeromedical Evacuation Mission Order sheets, and hospital records from the entire military hospital complex in the Île-de-France region for French military personnel who underwent low-priority MEDEVAC (P3) for medico-psychological reasons from a non-metropolitan area to metropolitan France. The study spanned from January 1, 2013, to December 31, 2016. The primary objective is to evaluate the concordance of diagnoses between general practitioners and psychiatrists. The secondary objective is a detailed description of the introduction of psychotropic drugs, especially benzodiazepines, by the medical officer in the field. In total, our study included 610 patients. Significant differences were observed between diagnoses made by military general practitioners and military psychiatrists, except for "psychotic disorders" and "other diagnoses" categories. During hospitalization, benzodiazepines were prescribed to 26.5% of repatriated patients, antidepressants to 12.7%, hypnotics to 17.6%, neuroleptics to 24.23%, and hydroxyzine to 18.8%. Upon discharge, benzodiazepines were prescribed to 23.5% of patients, antidepressants to 17.8%, hypnotics to 9.9%, neuroleptics to 28.9%, and hydroxyzine to 19.7%. The chi-squared test revealed significant differences in prescription between military operations and hospitalization for all molecules except hydroxyzine. Among patients diagnosed with Psychological Disorder Related to a Traumatic Event (TPRET) (<1 and >1 month) by psychiatrists during hospitalization, 66.2% were prescribed benzodiazepines during operational theaters, 24.3% continued during hospitalization, and 16.8% received a prescription upon discharge.The duration of missions often hinders precise psychiatric diagnoses, leading medical officers to transmit clinical data for optimized specialized care at military training hospitals. Furthermore, significant differences in therapeutic administration between medical officers and psychiatrists, particularly in benzodiazepine prescriptions for patients with TPRET, highlight the importance of prioritizing psychotropic prescription modalities in the training of medical officers on mental disorders. Strengthening operational preparations in recent years could enable more practitioners to benefit from these measures. We suggest several measures to enhance the transmission of medical information between medical officers and military psychiatrists. First, optimizing the drafting of Patient Movement Requests could involve implementing pre-filled drop-down menus or providing an adapted bilingual lexicon, facilitating the optimal transmission of clinical information for repatriated patients. Second, strengthening the training of medical officers before deployment and sharing the "Emergency Psy Kit," a comprehensive support tool developed by French military psychiatrists, would further enhance the tool kit available to field practitioners for judiciously prescribing psychiatric drugs.

Exploring decision-makers’ challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare

BackgroundSystematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare...

Assessing the dynamics of forest ecosystem services to define forest use suitability: a case study of Pinus sylvestris in Spain

Adopting a multi-criteria approach in forest management is essential for preserving or improving specific benefits while minimizing negative environmental impacts. Determining the appropriate long-term management approach for a forest requires considering heterogeneous environmental and social factors, as well as changes in forest characteristics over time. Conducting a strategic assessment of forest use suitability (FUS), namely productive, protective, conservation-oriented, social and multifunctional, at the national level, taking into account the dynamics in the provision of forest ecosystem services and the trade-offs between FUS alternatives, can guide the development of customized management strategies and policies that align with the specific requirements and conditions of the forest. In this study, we evaluate the supply and simulation-based changes over time of diverse ecosystem services of Pinus sylvestris stands in Spain and utilize a decision model to determine the most suitable FUS alternative that enhances the provision of these services. The assignment of the most appropriate FUS alternative aims to help in decision-making processes and in the selection of the most adequate management strategies. To achieve this, we utilize the last version of ecosystem management decision support (EMDS) system, a spatially focused decision support tool capable of generating precise results for multi-criteria assessment. Participatory planning actions based on Delphi principles and Analytic Hierarchy Process (AHP) analysis were applied and combined with geospatial logic-based modelling. According to the results, the dominant FUS is protective, followed by productive alternative, exhibiting high levels of multifunctionality.

Uncovering hidden and complex relations of pandemic dynamics using an AI driven system

The COVID-19 pandemic continues to challenge healthcare systems globally, necessitating advanced tools for clinical decision support. Amidst the complexity of COVID-19 symptomatology and disease severity prediction, there is a critical need for robust decision support systems to aid healthcare professionals in timely and informed decision-making. In response to this pressing demand, we introduce BayesCovid, a novel decision support system integrating Bayesian network models and deep learning techniques. BayesCovid automates data preprocessing and leverages advanced computational methods to unravel intricate patterns in COVID-19 symptom dynamics. By combining Bayesian networks and Bayesian deep learning models, BayesCovid offers a comprehensive solution for uncovering hidden relationships between symptoms and predicting disease severity. Experimental validation demonstrates BayesCovid ’s high prediction accuracy (83.52–98.97%). Our work represents a significant stride in addressing the urgent need for clinical decision support systems tailored to the complexities of managing COVID-19 cases. By providing healthcare professionals with actionable insights derived from sophisticated computational analysis, BayesCovid aims to enhance clinical decision-making, optimise resource allocation, and improve patient outcomes in the ongoing battle against the COVID-19 pandemic.

Construction and validation of a risk prediction model for postoperative ICU admission in patients with colorectal cancer: clinical prediction model study

BackgroundTransfer to the ICU is common following non-cardiac surgeries, including radical colorectal cancer (CRC) resection. Understanding the judicious utilization of costly ICU medical resources and supportive postoperative care is crucial. This study aimed to construct and validate a nomogram for predicting the need for mandatory ICU admission immediately following radical CRC resection.MethodsRetrospective analysis was conducted on data from 1003 patients who underwent radical or palliative surgery for CRC at Ningxia Medical University General Hospital from August 2020 to April 2022. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression in the training cohort to construct the nomogram. An online prediction tool was developed for clinical use. The nomogram's calibration and discriminative performance were assessed in both cohorts, and its clinical utility was evaluated through decision curve analysis (DCA).ResultsThe final predictive model comprised age (P = 0.003, odds ratio [OR] 3.623, 95% confidence interval [CI] 1.535–8.551); nutritional risk screening 2002 (NRS2002) (P = 0.000, OR 6.129, 95% CI 2.920–12.863); serum albumin (ALB) (P = 0.013, OR 0.921, 95% CI 0.863–0.982); atrial fibrillation (P = 0.000, OR 20.017, 95% CI 4.191–95.609); chronic obstructive pulmonary disease (COPD) (P = 0.009, OR 8.151, 95% CI 1.674–39.676); forced expiratory volume in 1 s / Forced vital capacity (FEV1/FVC) (P = 0.040, OR 0.966, 95% CI 0.935–0.998); and surgical method (P = 0.024, OR 0.425, 95% CI 0.202–0.891). The area under the curve was 0.865, and the consistency index was 0.367. The Hosmer–Lemeshow test indicated excellent model fit (P = 0.367). The calibration curve closely approximated the ideal diagonal line. DCA showed a significant net benefit of the predictive model for postoperative ICU admission.ConclusionPredictors of ICU admission following radical CRC resection include age, preoperative serum albumin level, nutritional risk screening, atrial fibrillation, COPD, FEV1/FVC, and surgical route. The predictive nomogram and online tool support clinical decision-making for postoperative ICU admission in patients undergoing radical CRC surgery.Trial registrationDespite the retrospective nature of this study, we have proactively registered it with the Chinese Clinical Trial Registry. The registration number is ChiCTR2200062210, and the date of registration is 29/07/2022.

P-623 Post-market analysis of an AI-powered clinical decision support tool for ovarian stimulation

Abstract Study question How do treatment decisions and patient outcomes change with physician utilization of artificial intelligence (AI) to help determine FSH starting dose and trigger injection timing? Summary answer With physician use of an AI platform, patients were treated with significantly less starting and total FSH without statistically significant changes in outcomes. What is known already Stim Assist is an AI clinical decision support software providing physicians with adjunctive information for the prediction of the number of mature (MII) oocytes that may be retrieved from an ovarian stimulation cycle. The software is intended to assist with the selection of the starting dose of FSH as well as the decision of when to administer the trigger injection. Prior studies have retrospectively estimated the benefit of using AI for treatment decisions during ovarian stimulation, but there is a lack of post-market analyses evaluating the true efficacy of these tools when integrated in clinical practice. Study design, size, duration This study was conducted by multiple physicians at a single IVF clinic in the U.S. The treatment arm consisted of 292 patients seen by physicians using Stim Assist between December 2022 - December 2023. The control arm consisted of 292 matched historical control patients, treated between May 2019 - May 2022 at the same clinic without use of Stim Assist. Participants/materials, setting, methods Matching for each patient was performed for each physician individually, comparing each treatment-arm patient to a historical control-arm patient who was seen by the same physician. Patients were matched 1-to-1 based on age, baseline AMH, and baseline antral follicle count. The primary endpoints were the starting FSH and total FSH prescribed to the patient, as well as MIIs retrieved at the end of stimulation. Main results and the role of chance Comparing the treatment arm to the control arm, the average MIIs were 11.17 vs. 11.45 (p = 0.68), the average starting FSH was 397.09 vs. 443.58 (p &amp;lt; 0.01), and the average total FSH was 4181.77 vs 4601.8 (p &amp;lt; 0.01). The results were stratified by age groups of &amp;lt; 35, 35-40, and &amp;gt;40. For patients in the &amp;lt;35 group (N = 122), the average MIIs were 13.99 vs. 16.06 (p = 0.06), the average starting FSH was 346.72 vs. 380.53 (p &amp;lt; 0.01), and the average total FSH was 3525.84 vs. 3927.25 (p &amp;lt; 0.01). For patients in the 35-40 group (N = 129), the average MIIs were 9.46 vs. 8.71 (p = 0.34), the average starting FSH was 420.35 vs. 486.05 (p &amp;lt; 0.01), and the average total FSH was 4568.6 vs. 5063.37 (p &amp;lt; 0.01). For patients in the &amp;gt;40 group (N = 41), the average MIIs were 8.17 vs. 6.34 (p = 0.12), the average starting FSH was 473.78 vs. 497.56 (p = 0.16), and the average total FSH was 4916.46 vs. 5156.71 (p = 0.34). Limitations, reasons for caution This preliminary post-market analysis was performed at a single clinic in the U.S. We could not calculate live birth outcomes. Due to the limited sample size, differences in outcomes were not statistically significant. Wider implications of the findings These findings suggest that physician use of Stim Assist helped significantly reduce the starting and total FSH prescribed to patients. Additionally, MII outcome differences showed a trend towards an increase in MIIs for patients &amp;gt;35 years old, and a slight reduction in MIIs for younger, better prognosis patients. Trial registration number not applicable

P-595 Increased clinical pregnancy rates using a clinical decision support tool - a retrospective cohort study

Abstract Study question Does using a clinical decision support tool, Opt-IVF, to guide hormone dosing during superovulation yield increased clinical pregnancy rates in routine clinical practice? Summary answer Higher clinical pregnancy rates were obtained using Opt-IVF to guide stimulatory hormone dosing during superovulation. What is known already ART success rates and cost depend heavily on the superovulation stage of the cycle. Two prospective single-center trials and one prospective multi-center trial had shown that using Opt-IVF to guide dosing resulted in increased numbers of high-quality embryos, used lower hormone doses, and required less ultrasound testing. This is a retrospective cohort study of the use of Opt-IVF in clinical practice over 12 months compared with the control cohort from the previous 12 months. Study design, size, duration Design: The study examined patients undergoing an IVF cycle using Opt-IVF in a single center for 12 months. Opt-IVF was used in all patients who presented to one of the investigators at Akanksha Hospital during the study period. As a comparison group, we examined patients from the previous 12 months when Opt-IVF was not used, and standard clinical protocols were followed to guide dosing. Duration: The study was conducted over 24 months Participants/materials, setting, methods 204 women aged 25-45 years underwent superovulation using hormone doses suggested by Opt-IVF (intervention). Of the participants, 19% were poor responders, 74% were below 35 years of age, 19% were 35-39 years of age, and 7% were above 39 years of age. The control group included 207 women, of whom 10% were poor responders. 72% were below 35 years of age, 26% were 35-39 years, and 2% were above 39 years of age. Main results and the role of chance The participants in the intervention group required significantly lower cumulative gonadotropin dosage compared to the control group during their controlled ovarian stimulation cycles (p &amp;lt; 0.0001). The average number of total embryos was 20% higher (p &amp;lt; 0.001), and the average number of good-quality embryos was 50% higher (p &amp;lt; 0.0001) in the Opt-IVF group than in the control group. Only 4.9% of cycles were canceled in the intervention group, compared to 12.9% canceled in the control group. Overall, Clinical pregnancy rates (including canceled cycles) increased from 35 % in the control group to 43% in the intervention group. The clinical pregnancy rate for poor responder patients was 39% in the Opt-IVF group but was only 16% in controls (p &amp;lt; 0.0001). Similarly, the clinical pregnancy rates were 34% in patients ages 35-39 years and 33% in patients above 39 years of age, much higher than the corresponding clinical pregnancy rates in controls (27% and 0%, respectively). Higher clinical pregnancy rates were also noted in the intervention group for normal responders and for younger patients. Limitations, reasons for caution This is a single-center retrospective cohort study. Wider implications of the findings Using Opt-IVF to guide dosing resulted in increased numbers of high-quality embryos and higher clinical pregnancy rates, particularly among older patients and poor responders. The proportions of these patients can be expected to grow in the future, so improving results in such patients is essential. Trial registration number not applicable

P-500 Digital support tools for fertility patients – a systematic review and meta-analysis of the effect of these tools on psychological outcomes and pregnancy rates

Abstract Study question Do digital support tools for fertility patients improve psychological outcomes and pregnancy rates? Summary answer Overall, digital support tools for fertility patients do not improve depression, anxiety or Fertility Problem Index scores but have a beneficial effect on pregnancy rates. What is known already There are numerous mobile and web-based digital health tools in reproductive medicine, but the research evidence supporting the effect on clinical outcomes and to mitigate the psychological effects of infertility is sparse. If existing digital tools have demonstrated effectiveness in their stated aims of providing psychological support to patients during the arduous process of fertility treatment, it is vital to identify this so they can be implemented and further developed to tackle this important problem. In addition, if any digital tools have shown a positive impact on pregnancy rates, this would be of great interest to patients and clinicians alike. Study design, size, duration The study was prospectively registered (PROSPERO-CRD42020156441). An initial systematic review identified digital support tools and their salient features. This study presents meta-analysis of the data from RCT studies of digital support tools identified. Meta-analysis was performed of pregnancy rates, FPI (Fertility Problem Index), Depression and Anxiety scores between the digital support tool user and waiting list control groups. Studies were assessed for risk of bias (Cochrane risk of bias, robvis tool) and graded for quality. Participants/materials, setting, methods All studies were published after 2006. The RCT studies were conducted in the USA, Switzerland, Germany, UK, and The Netherlands. The total number of patients involved was 1268 (1059 women and 209 men). The average age of participants was 34.3 years. All studies reported psychological outcome scores. In 5 of the 10 studies identified pregnancy rates were reported. Meta-analysis was performed using the ‘meta’ package in R using random effects model to generate forest plots. Main results and the role of chance Overall, digital tools have a small positive impact on pregnancy rates, suggesting a beneficial effect for users of digital tools compared to non-user controls. Comparing digital support tools users with non-users, the clinical pregnancy rate (CPR) odds ratio (OR, with confidence interval) was 1.715 (1.042:2.82). The meta-analysis of the effect sizes of digital interventions on psychological outcomes, measured using validated measures of anxiety, depression, or the fertility specific FPI, identified no significant effects of digital interventions on patients’ psychological wellbeing. There was no significant standardized mean difference seen with the FPI analysis, SMD -0.10 (95% CI -0.26:0.05). As a variety of depression and anxiety outcome scales were used, there was insufficient comparable data to perform a quantitative meta-analysis for these outcomes and so this data is presented narratively. No convincing benefit of the digital tools to reduce symptoms of depression during fertility treatment is demonstrated in these studies. The impact of digital support tools on anxiety scores is somewhat mixed, with most studies demonstrating no significant difference in the anxiety scores between intervention and control group and a small number of statistically and clinically significant reductions in anxiety severity. Limitations, reasons for caution Meta-analysis was limited by the range of psychological outcome scales used. There were issues with deviation from intended interventions and missing outcome data. There is a risk the results of this meta-analysis were affected by publication bias, particularly the reporting of pregnancy rate, which was not included in all studies. Wider implications of the findings Establishing an agreed framework or core outcome set for evaluation of tools aiming to ameliorate the psychological distress associated with fertility treatments would be useful. Further research to explore the potential of digital support tools to impact pregnancy rates, particularly within fixed time frames/ treatment cycles, is warranted. Trial registration number N/A

The Impact of ChatGPT on English for Academic Purposes (EAP) Students’ Language Learning Experience: A Self-Determination Theory Perspective

This qualitative study explores the perceptions of English language students regarding the use of the generative AI tool, ChatGPT, as a supportive tool for English for Academic Purposes (EAP) students in a New Zealand university context. Using self-determination theory (SDT) as an explanatory framework, this study explores how ChatGPT impacts students’ basic psychological needs for autonomy, competence, and relatedness in their language-learning experience. Semi-structured interviews are conducted with 24 postgraduate EAP students and the data are analysed using thematic analysis. The findings suggest that ChatGPT has the potential to support students’ needs for autonomy and competence by providing flexibility, personalised feedback and a safe space for practice. However, the impact on relatedness needs is mixed, with some students experiencing a sense of companionship and others expressing concerns about reduced human interaction. While students acknowledge the benefits of ChatGPT, they also emphasise the importance of human-teacher interactivity and empathy. The findings provide theoretical insights and practical recommendations for educators seeking to integrate generative AI tools effectively into language education.

Pengaruh Kompetensi Guru Dalam Penggunaan Teknologi Pembelajaran Untuk Meningkatkan Motivasi Belajar Siswa

This study aims to examine the influence of the relationship between teacher competence in using learning technology and student learning motivation. The study utilized a sample of 30 respondents, collecting data through a questionnaire to measure scores of teacher competence (X) and student learning motivation (Y). Correlation analysis yielded a correlation coefficient (r_xy) of 0.880, indicating a significant positive relationship between the two variables. The findings emphasize the importance of investing in training and developing teachers' abilities to effectively utilize technology as a supportive tool in the learning process. The practical implications of this research underscore the need for ongoing support from schools and governments to facilitate better and adequate integration of technology into the educational curriculum, with the aim of enhancing overall student learning motivation and supporting improved learning outcomes.

Accompaniment in the UGELs of the Cusco Region: A Systematic Review

In recent years, the quality of education has emerged as a globally relevant issue, prompting considerable interest in general primary education. This is because the period of general education is one in which students must be provided with the best possible education. Consequently, the present study has the general objective of investigating the accompaniment in the UGELs of Peru. This research project employed a non-transversal experimental design, in which the variables were not deliberately manipulated and only the phenomena that occurred were observed in their natural context for further analysis. For this purpose, indexed articles pertaining to the subject of study were sourced from specialized repositories such as Scopus, Web of Science (WoS), EBSCO, Proquest, Scielo, Science Direct, Directory of Open Access Journal (DOAJ), DIALNET, and REDALYC. The implementation of a monitoring plan has emerged as a crucial strategy for educational institutions seeking to enhance learning outcomes. In light of the aforementioned findings, it becomes evident that the information obtained from the articles provides insight into the state of pedagogical accompaniment, encompassing various perspectives. This, in turn, prompts reflection and the identification of areas in need of improvement, particularly in the context of education at its various levels within the country. The subsequent conclusions highlight a dearth of monitoring and support tools that facilitate the systematic assessment of academic learning outcomes. The studies observed were predominantly quantitative, descriptive, and correlational, with a comparatively smaller proportion of qualitative and mixed methods.

Supporting Self-Management of Healthy Behaviors in Chronic Kidney Disease and Hypertension: The Supporting Self-Management of Healthy Behaviors Pilot Randomized Trial.

Support programs for self-management are under-utilized among people with chronic kidney disease (CKD). We examined the feasibility of a smartphone-based intervention to support physical activity and blood pressure monitoring, Supporting Self-Management of Healthy Behaviors (SMART-HABITS), for individuals with CKD and hypertension. SMART-HABITS was piloted in a 12-week randomized cross-over trial among people with CKD and hypertension. Participants were asked to monitor blood pressure ≥3-times/week and step counts ≥5-times/week. Participants were randomized to blood pressure communication approach-self-report through text message for six weeks vs. automatic reporting with a smartphone application (app) paired to a Bluetooth enabled blood pressure machine for the alternate six weeks. The approach to monitoring and reporting steps was the same during both phases. Primary outcomes were adoption (retention and use of SMART-HABITS dashboard), adherence (% of transmitted blood pressure and step counts), and acceptability as assessed with surveys and interviews. Secondary outcomes were reach, maintenance, CKD knowledge, digital health literacy, self-management, self-efficacy, quality of life, step counts and blood pressure values. Interviews were conducted at study end. Of the 47 randomized participants, 44 (94%) completed the Text phase and 43 (92%) completed the App phase. Median age was 63 years, 49% were female, and 45% were Black. Retention was 91%. Blood pressure adherence was 87% in the Text phase and 74% in the App phase, and step count adherence was 97%. Acceptability scores were high and interviews largely conveyed acceptance. CKD knowledge increased but remaining survey scores did not change. Mean step counts increased from the pre-study period similarly in both phases. Blood pressure did not change over time. Implementing a smartphone support tool for self-management was feasible among people with CKD and hypertension. The approach can supplement clinic-based care and potentially lead to less cardiovascular disease and CKD progression.

ChatGPT and other Chatbots in Psychiatry

Aim: Artificial intelligence (AI) is making significant inroads into the field of psychiatry, offering new tools and applications. ChatGPT, a specific chatbot, is at the forefront of this digital revolution. AI’s use in psychiatry ranges from identifying psychiatric symptoms, predicting treatment responses, and improving medication adherence to assisting in patient education, monitoring, and bridging gaps in mental health care. Materials and Methods: This review used a literature study method. Results: ChatGPT functions as a clinical decision support tool. It can analyse patient data and provide diagnostic insights, recommend evidence-based treatments, and offer drug information. It has demonstrated proficiency in generating summaries from medical records, saving clinicians time and enabling them to focus on patient care. Additionally, chatbots like ChatGPT serve as therapist assistants, offering emotional support between therapy sessions and potentially conducting psychotherapy. Studies have shown positive outcomes, with chatbots reducing depression, anxiety symptoms, and providing 24/7 availability for crisis situations. Users find them non-judgmental and comfortable for discussing sensitive issues. Despite their potential, chatbots have limitations, such as the risk of incorrect or biased information due to their training data. They lack genuine understanding, creativity, and the ability to clarify user input. Ethical considerations regarding responsibility and data usage are paramount. Conclusion: AI, particularly ChatGPT, holds substantial promise in modern psychiatry, enhancing diagnostics, patient education, monitoring, and therapeutic support. Its integration into everyday psychiatric practice requires careful use, continuous oversight, and ethical considerations. Psychiatrists must become more familiar with AI tools to leverage their benefits in patient care.

DCIS knowledge of women choosing between active surveillance and surgery for low-risk DCIS

BackgroundDuctal carcinoma in situ (DCIS) can progress to invasive breast cancer (IBC), but often never will. As we cannot predict accurately which DCIS-lesions will or will not progress to IBC, almost all women with DCIS undergo breast-conserving surgery supplemented with radiotherapy, or even mastectomy. In some countries, endocrine treatment is prescribed as well. This implies many women with non-progressive DCIS undergo overtreatment. To reduce this, the LORD patient preference trial (LORD-PPT) tests whether mammographic active surveillance (AS) is safe by giving women with low-risk DCIS a choice between treatment and AS. For this, sufficient knowledge about DCIS is crucial. Therefore, we assessed women's DCIS knowledge in association with socio-demographic and clinical characteristics. MethodsLORD-PPT participants (N = 376) completed a questionnaire assessing socio-demographic and clinical characteristics, risk perception, treatment choice and DCIS knowledge after being informed about their diagnosis and treatment options. Results66 % of participants had poor knowledge (i.e., answered ≤3 out of 7 knowledge items correctly). Most incorrect answers involved overestimating the safety of AS and misunderstanding of DCIS prognostic risks. Overall, women with higher DCIS knowledge score perceived their risk of developing IBC as being somewhat higher than women with poorer knowledge (p = 0.049). Women with better DCIS knowledge more often chose surgery whilst most women with poorer knowledge chose active surveillance (p = 0.049). DiscussionOur findings show that there is room for improvement of information provision to patients. Decision support tools for patients and clinicians could help to stimulate effective shared decision-making about DCIS management.

Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored. Purpose To investigate the ability of GPT-4V and Gemini Pro Vision in generating differential diagnoses at different temperatures compared with radiologists using Radiology Diagnosis Please cases. Materials and Methods This retrospective study included Diagnosis Please cases published from January 2008 to October 2023. Input images included original images and captures of the textual patient history and figure legends (without imaging findings) from PDF files of each case. The LLMs were tasked with providing three differential diagnoses, repeated five times at temperatures 0, 0.5, and 1. Eight subspecialty-trained radiologists solved cases. An experienced radiologist compared generated and final diagnoses, considering the result correct if the generated diagnoses included the final diagnosis after five repetitions. Accuracy was assessed across models, temperatures, and radiology subspecialties, with statistical significance set at P < .007 after Bonferroni correction for multiple comparisons across the LLMs at the three temperatures and with radiologists. Results A total of 190 cases were included in neuroradiology (n = 53), multisystem (n = 27), gastrointestinal (n = 25), genitourinary (n = 23), musculoskeletal (n = 17), chest (n = 16), cardiovascular (n = 12), pediatric (n = 12), and breast (n = 5) subspecialties. Overall accuracy improved with increasing temperature settings (0, 0.5, 1) for both GPT-4V (41% [78 of 190 cases], 45% [86 of 190 cases], 49% [93 of 190 cases], respectively) and Gemini Pro Vision (29% [55 of 190 cases], 36% [69 of 190 cases], 39% [74 of 190 cases], respectively), although there was no evidence of a statistically significant difference after Bonferroni adjustment (GPT-4V, P = .12; Gemini Pro Vision, P = .04). The overall accuracy of radiologists (61% [115 of 190 cases]) was higher than that of Gemini Pro Vision at temperature 1 (T1) (P < .001), while no statistically significant difference was observed between radiologists and GPT-4V at T1 after Bonferroni adjustment (P = .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.

Implementing a clinical decision support tool to increase early peanut introduction guidance

BackgroundGeneral pediatric providers are the frontline for early peanut introduction discussions, but many feel ill-equipped to handle such discussions as guidelines have quickly changed. ObjectiveWe hypothesized that a clinical decision support (CDS) tool could improve peanut introduction discussions. MethodsCDS tools were designed by stakeholders, improved through usability testing, and integrated into the current note templates. Based on queries of electronic health record (EHR), we did a pre-post performance evaluation of peanut introduction conversations, barriers for introduction, and percentage of 12-month WCC visits that had successfully introduced peanut. Providers completed surveys before and after intervention to assess awareness of early peanut introduction and comfort using CDS. ResultsProviders’ awareness of early peanut introduction guidelines increased from 17.8% to 66.7% after the CDS tool was implemented. 79.1% were comfortable using the tool. The CDS tool improved peanut introduction conversations at the 4-month well-child (WCC) care visit from 2.4% to 81.2%, at the 6-month WCC visit from 3.0% to 84.2%, and at the 12-month WCC visit from 2.7% to 82.9%. 56.6% of families had a plan to introduce peanut at the 4-month WCC visit. Of those who did not have a plan, the most common barrier was family's unawareness of the benefits of early peanut introduction. At the 12-month visit, 62.8% of families had introduced peanut without concerns. ConclusionA point-of-care CDS tool encouraged more discussions by general pediatric providers on early peanut introduction to all patients. CDS tools should be considered in quality improvement projects as an implementation method for the most up-to-date guidelines.