Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.

INTRODUCTION: Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation). METHODS AND ANALYSIS: A parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups. ETHICS AND DISSEMINATION: This study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders. TRIAL REGISTRATION NUMBER: NCT05933694.

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.

How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review.

BACKGROUND: Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care. METHODS: We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool. RESULTS: Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results. CONCLUSIONS: Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions. PROSPERO REGISTRATION: CRD42023464746.

Hybrid electronic record: An error reduction strategy for diverse medical prescription formats.

BACKGROUND:  This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. METHODS:  Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. RESULTS:  There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. CONCLUSION:  Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct.Contribution: Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

Implementation of a pharmacogenetic panel-based test for pharmacotherapy-based supportive care in an adult oncology clinic.

The University of Florida Health conducted a pragmatic implementation of a pharmacogenetics (PGx) panel-based test to guide medications used for supportive care prescribed to patients undergoing chemotherapy. The implementation was in the context of a pragmatic clinical trial for patients with non-hematologic cancers being treated with chemotherapy. Patients were randomized to either the intervention arm or control arm and received PGx testing immediately or at the end of the study, respectively. Patients completed the MD Anderson Symptom Inventory (MDASI) to assess quality of life (QoL). A total of 150 patients received PGx testing and enrolled in the study. Clinical decision support and implementation infrastructure were developed. While the study was originally planned for 500 patients, we were underpowered in our sample of 150 patients to test differences in the patient-reported MDASI scores. We did observed a high completion rate (92%) of the questionnaires; however, there were few medication changes (n = 6 in the intervention arm) based on PGx test results. Despite this, we learned several lessons through this pragmatic implementation of a PGx panel-based test in an outpatient oncology setting. Most notably, patients were less willing to undergo PGx testing if the cost of the test exceeded $100. In addition, to enhance PGx implementation success, reoccurring provider education is necessary, clinical decision support needs to appear in a more conducive way to fit in with oncologists' workflow, and PGx test results need to be available earlier in treatment planning.

Piloting a Clinical Decision Support Tool to Identify Patients With Social Needs and Provide Navigation Services and Referral to Community-Based Organizations: Protocol for a Randomized Controlled Trial.

BACKGROUND: Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs). OBJECTIVE: This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs. METHODS: The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system. RESULTS: This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits. CONCLUSIONS: The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57316.

Pharmacogenetics Clinical Decision Support Systems for Primary Care in England: Co-Design Study.

BACKGROUND: Pharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care. OBJECTIVE: Explore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution. METHODS: We used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team. RESULTS: User research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR. CONCLUSIONS: This study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively.

Learning implementation of a guideline based decision support system to improve hypertension treatment in primary care in China: pragmatic cluster randomised controlled trial.

OBJECTIVE: To evaluate the effectiveness of a clinical decision support system (CDSS) in improving the use of guideline accordant antihypertensive treatment in primary care settings in China. DESIGN: Pragmatic, open label, cluster randomised trial. SETTING: 94 primary care practices in four urban regions of China between August 2019 and July 2022: Luoyang (central China), Jining (east China), and Shenzhen (south China, including two regions). PARTICIPANTS: 94 practices were randomised (46 to CDSS, 48 to usual care). 12 137 participants with hypertension who used up to two classes of antihypertensives and had a systolic blood pressure <180 mm Hg and diastolic blood pressure <110 mm Hg were included. INTERVENTIONS: Primary care practices were randomised to use an electronic health record based CDSS, which recommended a specific guideline accordant regimen for initiation, titration, or switching of antihypertensive (the intervention), or to use the same electronic health record without CDSS and provide treatment as usual (control). MAIN OUTCOME MEASURES: The primary outcome was the proportion of hypertension related visits during which an appropriate (guideline accordant) treatment was provided. Secondary outcomes were the average reduction in systolic blood pressure and proportion of participants with controlled blood pressure (<140/90 mm Hg) at the last scheduled follow-up. Safety outcomes were patient reported antihypertensive treatment related events, including syncope, injurious fall, symptomatic hypotension or systolic blood pressure <90 mm Hg, and bradycardia. RESULTS: 5755 participants with 23 113 visits in the intervention group and 6382 participants with 27 868 visits in the control group were included. Mean age was 61 (standard deviation 13) years and 42.5% were women. During a median 11.6 months of follow-up, the proportion of visits at which appropriate treatment was given was higher in the intervention group than in the control group (77.8% (17 975/23 113) v 62.2% (17 328/27 868); absolute difference 15.2 percentage points (95% confidence interval (CI) 10.7 to 19.8); P<0.001; odds ratio 2.17 (95% CI 1.75 to 2.69); P<0.001). Compared with participants in the control group, those in the intervention group had a 1.6 mm Hg (95% CI -2.7 to -0.5) greater reduction in systolic blood pressure (-1.5 mm Hg v 0.3 mm Hg; P=0.006) and a 4.4 percentage point (95% CI -0.7 to 9.5) improvement in blood pressure control rate (69.0% (3415/4952) v 64.6% (3778/5845); P=0.07). Patient reported antihypertensive treatment related adverse effects were rare in both groups. CONCLUSIONS: Use of a CDSS in primary care in China improved the provision of guideline accordant antihypertensive treatment and led to a modest reduction in blood pressure. The CDSS offers a promising approach to delivering better care for hypertension, both safely and efficiently. TRIAL REGISTRATION: ClinicalTrials.gov NCT03636334.

The application of health information technology for the elderly care in the emergency department: a conceptual model.

INTRODUCTION: In the emergency departments (EDs), usually the longest waiting time for treatment and discharge belongs to the elderly patients. Moreover, the number of the ED admissions for the elderly increases every year. It seems that the use of health information technology in geriatric emergency departments can help to reduce the burden of the healthcare services for this group of patients. This research aimed to develop a conceptual model for using health information technology in the geriatric emergency department. METHODS: This study was conducted in 2021. The initial conceptual model was designed based on the findings derived from the previous research phases (literature review and interview with the experts). Then, the model was examined by an expert panel (n = 7). Finally, using the Delphi technique (two rounds), the components of the conceptual model were reviewed and finalized. To collect data, a questionnaire was used, and data were analyzed using descriptive statistics. RESULTS: The common information technologies appropriate for the elderly care in the emergency departments included emergency department information system, clinical decision support system, electronic health records, telemedicine, personal health records, electronic questionnaires for screening, and other technologies such as picture archiving and communication systems (PACS), electronic vital sign monitoring systems, etc. The participants approved all of the proposed systems and their applications in the geriatric emergency departments. CONCLUSION: The proposed model can help to design and implement the most useful information systems in the geriatric emergency departments. As the application of technology accelerates care processes, investing in this field would help to support the care plans for the elderly and improve quality of care services. Further research is recommended to investigate the efficiency and effectiveness of using these technologies in the EDs.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

A randomized, embedded, pragmatic, Bayesian clinical trial examining clinical decision support for high flow nasal cannula management in children with bronchiolitis: design and statistical analysis plan.

BACKGROUND: High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS: The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION: Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.

De-implementation strategy to reduce unnecessary antibiotic prescriptions for ambulatory HIV-infected patients with upper respiratory tract infections in Mozambique: a study protocol of a cluster randomized controlled trial.

BACKGROUND: Antibiotics are globally overprescribed for the treatment of upper respiratory tract infections (URTI), especially in persons living with HIV. However, most URTIs are caused by viruses, and antibiotics are not indicated. De-implementation is perceived as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excessive or inappropriate antibiotic use for URTI, through the employment of evidence-based interventions to reduce these practices. Research into strategies that lead to successful de-implementation of unnecessary antibiotic prescriptions within the primary health care setting is limited in Mozambique. In this study, we propose a protocol designed to evaluate the use of a clinical decision support algorithm (CDSA) for promoting the de-implementation of unnecessary antibiotic prescriptions for URTI among ambulatory HIV-infected adult patients in primary healthcare settings. METHODS: This study is a multicenter, two-arm, cluster randomized controlled trial, involving six primary health care facilities in Maputo and Matola municipalities in Mozambique, guided by an innovative implementation science framework, the Dynamic Adaption Process. In total, 380 HIV-infected patients with URTI symptoms will be enrolled, with 190 patients assigned to both the intervention and control arms. For intervention sites, the CDSAs will be posted on either the exam room wall or on the clinician´s exam room desk for ease of reference during clinical visits. Our sample size is powered to detect a reduction in antibiotic use by 15%. We will evaluate the effectiveness and implementation outcomes and examine the effect of multi-level (sites and patients) factors in promoting the de-implementation of unnecessary antibiotic prescriptions. The effectiveness and implementation of our antibiotic de-implementation strategy are the primary outcomes, whereas the clinical endpoints are the secondary outcomes. DISCUSSION: This research will provide evidence on the effectiveness of the use of the CDSA in promoting the de-implementation of unnecessary antibiotic prescribing in treating acute URTI, among ambulatory HIV-infected patients. Findings will bring evidence for the need to scale up strategies for the de-implementation of unnecessary antibiotic prescription practices in additional healthcare sites within the country. TRIAL REGISTRATION: ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350.

Study protocol: Collaboration Oriented Approach to Controlling High blood pressure (COACH) in adults - a randomised controlled trial.

INTRODUCTION: Hypertension, the clinical condition of persistent high blood pressure (BP), is preventable yet remains a significant contributor to poor cardiovascular outcomes. Digital self-management support tools can increase patient self-care behaviours to improve BP. We created a patient-facing and provider-facing clinical decision support (CDS) application, called the Collaboration Oriented Approach to Controlling High BP (COACH), to integrate home BP data, guideline recommendations and patient-centred goals with primary care workflows. We leverage social cognitive theory principles to support enhanced engagement, shared decision-making and self-management support. This study aims to measure the effectiveness of the COACH intervention and evaluate its adoption as part of BP management. METHODS AND ANALYSIS: The study design is a multisite, two-arm hybrid type III implementation randomised controlled trial set within primary care practices across three health systems. Randomised participants are adults with high BP for whom home BP monitoring is indicated. The intervention arm will receive COACH, a digital web-based intervention with effectively enhanced alerts and displays intended to drive engagement with BP lowering; the control arm will receive COACH without the alerts and a simple display. Outcome measures include BP lowering (primary) and self-efficacy (secondary). Implementation preplanning and postevaluation use the Consolidated Framework for Implementation Research and Reach-Effectiveness-Adoption-Implementation-Maintenance metrics with iterative cycles for qualitative integration into the trial and its quantitative evaluation. The trial analysis includes logistic regression and constrained longitudinal data analysis. ETHICS AND DISSEMINATION: The trial is approved under a single IRB through the University of Missouri-Columbia, #2091483. Dissemination of the intervention specifications and results will be through open-source mechanisms. TRIAL REGISTRATION NUMBER: NCT06124716.

Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology.

BACKGROUND: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. METHODS: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. RESULTS: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. CONCLUSION: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.

Objectives: The increased utilization of Artificial intelligence (AI) in healthcare changes practice and introduces ethical implications for AI adoption in medicine. We assess medical doctors' ethical stance in situations that arise in adopting an AI-enabled Clinical Decision Support System (AI-CDSS) for antibiotic prescribing decision support in a healthcare institution in Singapore. Methods: We conducted in-depth interviews with 30 doctors of varying medical specialties and designations between October 2022 and January 2023. Our interview guide was anchored on the four pillars of medical ethics. We used clinical vignettes with the following hypothetical scenarios: (1) Using an antibiotic AI-enabled CDSS's recommendations for a tourist, (2) Uncertainty about the AI-CDSS's recommendation of a narrow-spectrum antibiotic vs. concerns about antimicrobial resistance, (3) Patient refusing the "best treatment" recommended by the AI-CDSS, (4) Data breach. Results: More than half of the participants only realized that the AI-enabled CDSS could have misrepresented non-local populations after being probed to think about the AI-CDSS's data source. Regarding prescribing a broad- or narrow-spectrum antibiotic, most participants preferred to exercise their clinical judgment over the AI-enabled CDSS's recommendations in their patients' best interest. Two-thirds of participants prioritized beneficence over patient autonomy by convincing patients who refused the best practice treatment to accept it. Many were unaware of the implications of data breaches. Conclusion: The current position on the legal liability concerning the use of AI-enabled CDSS is unclear in relation to doctors, hospitals and CDSS providers. Having a comprehensive ethical legal and regulatory framework, perceived organizational support, and adequate knowledge of AI and ethics are essential for successfully implementing AI in healthcare.

Medical-informed machine learning: integrating prior knowledge into medical decision systems.

BACKGROUND: Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. METHODS: The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. RESULTS: The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. CONCLUSIONS: By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Optimizing Advanced Imaging of the Pediatric Patient in the Emergency Department: Technical Report.

Advanced diagnostic imaging modalities, including ultrasonography, computed tomography, and magnetic resonance imaging, are key components in the evaluation and management of pediatric patients presenting to the emergency department. Advances in imaging technology have led to the availability of faster and more accurate tools to improve patient care. Notwithstanding these advances, it is important for physicians, physician assistants, and nurse practitioners to understand the risks and limitations associated with advanced imaging in children and to limit imaging studies that are considered low value, when possible. This technical report provides a summary of imaging strategies for specific conditions where advanced imaging is commonly considered in the emergency department. As an accompaniment to the policy statement, this document provides resources and strategies to optimize advanced imaging, including clinical decision support mechanisms, teleradiology, shared decision-making, and rationale for deferred imaging for patients who will be transferred for definitive care.