Evidence-based Decision Support Research Articles

A year of rapid reviews: evidence-based decision support for Austrian social insurances & hospitals

Abstract Background The Austrian Institute for Health Technology Assessment (AIHTA) has supported the decisions of Austrian social insurance funds and hospital providers with rapid reviews since 2023. This summary of rapid reviews aims to provide insights on findings, as well as experiences and challenges of conducting the rapid reviews after one year. Methods We included all rapid reviews that were requested to the AIHTA team in 2023. For research questions that were withdrawn, the reasoning was documented. The completed rapid reviews were grouped into medical specialties, depending on the intervention analysed. The conclusions of the rapid reviews were presented in tabular form, with the strength of the available evidence classified as high, moderate, low or insufficient. Results The rapid reviews on interventions (n = 9) cover a variety of medical specialties (orthopaedics, gynaecology, neurology, physical medicine, ophthalmology, vascular surgery and dermatology). In three rapid reviews, the analysed evidence on the intervention was insufficient for benefit (compared to standard of care). In four rapid reviews, the evidence indicated a potential benefit with some limitations (e.g. indication-specific results, or minimal clinically important difference not clearly achieved). In two rapid reviews, the evidence indicated a potential benefit. For three topics, no rapid review was conducted (topic scope too broad for rapid review, change in prioritisation). Challenges include time constraints, as well as narrowing the research questions to be suitable for rapid reviews. Conclusions The overview of rapid reviews highlights evidence-based findings on medical interventions. Further steps include collecting feedback from inquirers, on the extent to which the evidence from rapid reviews was used to support the decision-making. Rapid reviews are a feasible tool but require continuous evaluation and further adaptation of methods. Key messages • The rapid reviews to inform decision-making processes for Austrian social insurance funds and hospitals spanned multiple specialties and revealed varying levels of evidence. • To improve the implementation of rapid reviews, and to overcome challenges of time constraints and narrowing down research questions, feedback from stakeholders should be collected.

CT Pulmonary Angiogram Clinical Pretest Probability Tool: Impact on Emergency Department Utilization

ObjectiveCurrently, computed tomographic pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in Emergency Departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE. MethodsThe study was performed at EDs in a large healthcare system and included 9 academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPA performed/number of ED visits) and secondary outcome was yield (percentage of CTPA positive for acute PE), comparing 12 months before (6/1/2021-5/31/2022) vs. 12 months after (6/1/2022-5/31/2023) a system-wide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control (SPC) charts were used to assess monthly trends in utilization and yield. ResultsAmong 931,677 visits to Emergency Departments, 28,101 CTPAs were performed on 24,675 patients. 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention; 13,276 among 476,639 visits (2.79%) post-intervention, a 14.51% relative decrease in CTPA utilization (chi-square, p<0.001). CTPA yield remained unchanged (1371/14825=9.25% pre- vs. 1184/13276=8.92% post-intervention; chi-square, p=0.34). Patients with COVID diagnosis prior to CTPA had higher probability of acute PE. SPC charts demonstrated seasonal variation in utilization (Friedman test, p=0.047). DiscussionImplementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months post-intervention.

SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare

There have been impressive advances in the physical and mathematical modelling of complex physiological systems in the last few decades, with the potential to revolutionise personalised healthcare with patient-specific evidence-based diagnosis, risk assessment and treatment decision support using digital twins. However, practical progress and genuine clinical impact hinge on successful model calibration, parameter estimation and uncertainty quantification, which calls for novel innovative adaptions and methodological extensions of contemporary state-of-the-art inference techniques from Statistics and Machine Learning. In the present study, we focus on two computational fluid-dynamics (CFD) models of the blood systemic and pulmonary circulation. We discuss state-of-the-art emulation techniques based on deep learning and Gaussian processes, which are coupled with established inference techniques based on greedy optimisation, simulated annealing, Markov Chain Monte Carlo, History Matching and rejection sampling for computationally fast inference of unknown parameters of the CFD models from blood flow and pressure data. The inference task was set as a competitive challenge which the participants had to conduct within a limited time frame representative of clinical requirements. The performance of the methods was assessed independently and objectively by the challenge organisers, based on a ground truth that was unknown to the method developers. Our results indicate that for the systemic challenge, in which an idealised case of noise-free data was considered, the relative deviation from the ground-truth in parameter space ranges from 10−5% (highest-performing method) to 3% (lowest-performing method). For the pulmonary challenge, for which noisy data was generated, the performance ranges from 0.9% to 7% deviation for the parameter posterior mean, and from 35% to 570% deviation for the parameter posterior variance.

Increasing Screening Rates for Comorbidities in Adolescents with Elevated Body Mass Index in Pediatric Primary Care.

Adolescents with elevated body mass index are at increased risk for comorbidities such as dyslipidemia, diabetes mellitus, and metabolic dysfunction-associated steatotic liver disease. Guideline-based screening can identify impacted patients early, allowing for lifestyle modifications and other treatments to improve long-term health. Unfortunately, only 20% of pediatric patients with obesity receive recommended screening. A multidisciplinary quality improvement team designed and implemented a project to improve comorbidity screening utilizing the Model for Improvement. Provider education and incentive, clinical decision support, and regular performance feedback were chosen as interventions. Screening rates were tracked on a statistical process control chart. From March through December of 2022, 9547 pediatric patients aged 10 years and up with body mass index greater than or equal to the 95th percentile were seen for preventive care visits. Screening rates for comorbidities increased from a baseline of 19.5%-58% and were sustained for over 3 months. Numerous patients at risk for chronic disease were identified. Evidence-based clinical decision support, along with provider education and engagement, can effectively increase screening rates for comorbidities in pediatric patients with obesity.

How debunking biases in research and development decisions could lead to more equitable healthcare?

Decades of research have demonstrated that a variety of cognitive biases can affect our judgment and ability to make rational decisions in personal and professional environments. The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making. Moreover, cognitive biases can play a role in regulatory and clinical decision-making, the latter impacting diagnostic and treatment decisions in the therapeutic use of medicines. These inherent and/or institutionalized biases (e.g., in assumptions, data, or decision-making practices) could conceivably contribute to health inequities. In this mini-review, we provide a broad perspective on how cognitive biases can affect pharmaceutical R&D, regulatory evaluation, and therapeutic decision-making. Example approaches to mitigate the effect of common biases in the development, approval, and use of new therapeutics, such as quantitative decision criteria, multidisciplinary reviews, regulatory and treatment guidelines, and evidence-based clinical decision support systems are illustrated. Mitigating the impact of cognitive biases could increase pharma R&D efficiency, change the perspective and prioritization of unmet medical needs, increase representativeness and quality of evidence generated through clinical trials and real-world research, leading to higher quality insights and more effective medication use, and as such could eventually contribute to more equitable healthcare.

Fuzzy rule based classifier model for evidence based clinical decision support systems

Clinicians benefit from the use of artificial intelligence and machine learning techniques applied to health data within health records, which identify commonalities between them. It enables them to get evidence-based support in recommending shared treatment paths for undiagnosed health records. The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential features, supporting public health experts in their management of population health conditions. This paper presents a novel mapping tool model designed to analyze electronic health records and provide healthcare providers with evidence-based decision support. The work focuses on the analysis of health records from hospital databases, encompassing parameters extracted from routine health checkups. By scrutinizing patterns within examined health records, healthcare providers can seamlessly align with newer health records for diagnosis and treatment recommendations. Core to this approach is the integration of a fuzzy rule-based classifier system within the proposed system. This incorporation facilitates the processing of health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a specially developed distance-measure technique tailored for the proposed fuzzy-based system. Results showcase satisfying performance and robust discriminant capability for accurate recommendations. The alignment of outcomes with expert evaluations underscores the model's efficacy and attainment of benchmarks.

Implementation and Evaluation of Educational Workshops to Increase Primary Care Provider and Medical Student Comfort in Psychiatric Care.

Mental health treatment is often initiated in primary care settings, but many primary care providers (PCPs), residents, and medical students report discomfort in managing psychiatric conditions. This study evaluated the effect of an educational workshop that featured an evidence-based psychopharmacology clinical decision support tool (CDST) on trainee confidence and willingness to treat psychiatric conditions. Participants completed pre- and post-workshop surveys. Nine months after the workshop, a subset of trainees participated in a focus group. Of the participants, 62.5% of the obstetrics-gynecology (OB-GYN) resident physicians (10/16) and 100% of the medical students (18/18) completed both pre- and post-surveys. Following the workshop, OB-GYN resident physicians reported significantly improved confidence in treating psychiatric disorders (p < 0.001), sense of having psychiatric support tools (p < 0.001), and knowledge of treating psychiatric disorders (p = 0.021). Medical students reported significantly improved confidence in treating psychiatric disorders (p < 0.001), willingness to devise treatment plans for psychiatric disorders (p = 0.024), sense of having psychiatric support tools (p < 0.001), knowledge of treating psychiatric disorders (p < 0.001), and comfort in presenting a psychiatric treatment plan to an attending (p = 0.003). Most focus group participants (93.75%; 15/16) reported that they continued to use the CDST, and it increased their confidence in formulating psychiatric treatment plans. These findings suggest that educational workshops that introduce high-quality psychopharmacology CDSTs may be an effective method for improving provider comfort in treating psychiatric disorders.

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typically, funding is distributed by a reactive allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous County, this study can inform the development of evidence-based decision support tools in similar contexts, including lessons about data needs, model design, evaluation, and field validation.

Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.

A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life Implementation Report.

The increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). This paper's objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

Trends and Futuristic Applications of Big Data and Electronic Health Record Data in Empowering Constructive Clinical Decision Support Systems

Modern healthcare benefits significantly from big data, electronic healthcare record data technologies, and artificial intelligence, which provide robust tools for the collection and analysis of vast and diverse datasets originating from various sources, such as clinical care, administration, and research. This advancement enables the creation of information technology infrastructures that facilitate the realization of the "Learning Healthcare System Cycle," wherein healthcare practice and research seamlessly intertwine in a synergistic manner. This review focuses on illustrating how the integration of extensive data collections, empowered by big data, can enhance clinical decision-making and advance biomedical research. Most importantly, electronic health records offer several benefits, including heightened accessibility to patient information, enhanced interdisciplinary communication, improved continuity of care, legible documentation, minimized duplication, and increased efficiency. The incorporation of computerized physician order entry within electronic medical records contributes to patient safety by mitigating medication errors and offering clinical guidance through prompts and alerts during electronic order entry. Furthermore, when evidence-based clinical decision support is integrated with electronic health records, it serves as a valuable tool for guiding healthcare providers and clinicians in aligning their clinical practices with meaningful use standards and compliance with quality metrics. This review also outlines the contemporary application of clinical decision support systems in the field of medicine. Its types, their existing practical applications with documented effectiveness, prevalent challenges, and potential adverse consequences. The review concludes by offering evidence-based guidelines aimed at mitigating risks associated with support systems design, implementation, evaluation, and ongoing maintenance.

Drug Cost Optimization Using Evidence-Based Decision Support Systems

The article presents an overview of specialized medical information systems that perform ABC/VEN analysis. It provides a comparison of specific examples, describes their advantages and disadvantages, as well as the possibility of automating the processes of searching for information on the proven effectiveness of drugs, selecting alternative drugs within one ATC group, selecting alternative drugs within the framework of the current clinical recommendations of the Ministry of Health of the Russian Federation, assessing drug-drug interactions, as well as the reference information from the instructions for use of the drugs.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

PubMed4Hh: A Point-of-Care Mobile App for Evidence-Based Clinical Decision Support for Nurse Residents in Maryland.

Evidence-based clinical decision-making is considered a core competency for professional nurses. However, barriers such as lack of time in clinical practice; information overload; restricted access to resources; lack of evidence appraisal skills, awareness, and knowledge; and unreliable resources prevent them from adopting evidence-based practice. This study examined the usefulness of the PubMed4Hh (PubMed for Handhelds) mobile application for clinical decision-making at the point of care among Maryland newly licensed RNs. Using iOS and Android smartphones, 178 newly licensed RNs participated in a 6-month trial from November 2019 to April 2020. Nurses manually entered free-text queries or used voice inputs on the "Patient, Intervention, Comparison, Outcome" or "askMEDLINE" search engines. The results retrieved were presented as journal article abstracts or short summaries called "the bottom line," designed for quick reading at the point of care. Both Patient, Intervention, Comparison, Outcome and askMEDLINE were rated highly for their usefulness, and participants said they would continue using PubMed4Hh and recommend it to others. Newly licensed RNs had a significantly higher perception of the usefulness of PubMed4Hh when the results of "the bottom line" or abstracts confirmed, led, or modified their nursing skills, knowledge, or the patient's care plan.

QLTI-09. INTEGRATION OF NEURO-ONCOLOGY CLINICAL PATHWAYS IN PROVIDENCE SOUTHERN CALIFORNIA CLINICAL RESEARCH NETWORK: UPDATED INFO AFTER 17 MONTHS OF UTILIZATION

Abstract INTRODUCTION ClinicalPath is an evidence-based oncology decision support and analytics tool for cancer care. ClinicalPath’s treatment recommendations are prioritized based on efficacy, toxicity, and cost, by a nationwide committee of oncologists. The pathways are updated quarterly with the most recent evidenced-based treatment options that healthcare providers choose for adoption to help standardize therapy, improve quality, and decrease cost. The pathway system also allows for local clinical trial mapping to promote clinical trial awareness and help increase enrollment. ClinicalPath provides clinical pathways delivering personalized, evidence-based oncology guidance at the point of care. ClinicalPath’s network in North America serves more than 2,000 cancer physicians, within 54 practices in 31 states (15 academic medical centers, 29 hospital systems, and 9 community practices). Population: Our population is derived from multiple hospitals in Southern California within the Providence Health System. Methodology: Medical oncologists and Advanced Practice Providers received training on ClinicalPath before go-live. ClinicalPath was integrated into the Epic EHR in multiple Southern CA hospitals in a single month in Jan 2022. RESULTS On reviewing the utilization data within our Southern California region from Jan 2022-May 2023, 2681 treatment decisions were made across all cancers, with 86.7% of cancer patients treated on pathway. Of these decisions, 114 were made within the neuro-oncology specialty, and 70.2% of those cancer patients were treated on pathway. CONCLUSION We successfully integrated and initiated ClinicalPath in a multiple hospital-affiliated community oncology clinical trial network which included a strong neuro-oncology presence. We are actively working across our Southern California Region to map locally available clinical trials to promote awareness and increase enrollment. Provider utilization and patient on-pathway rates are actively monitored and will be updated.

Using clinical pathways to identify patient candidates for survivorship in testicular cancer.

492 Background: Dana-Farber Cancer Institute’s (DFCI) Clinical Pathways provide evidence-based decision support for oncologists across a variety of care settings. Data generated by Pathways enable continued cancer care improvement and may also be a tool for identifying patient candidates for survivorship. Here, we explore patients diagnosed with testicular cancer treated with curative intent. Methods: Oncologists at DFCI navigate Clinical Pathways when starting or changing therapies. Each navigation provides discrete clinical data elements about disease (e.g., histology, stage), treatment setting (e.g., line of therapy), and treatment selected. Here, pathway navigation data was used to retrospectively identify patients with testicular cancer who were appropriate for survivorship resources. Eligibility criteria included patients who completed therapy, had not received treatment in 12-24 months since initial therapy navigation, and had a follow-up appointment scheduled. Eligible patients were referred to the testicular cancer survivorship program. Results: Between April 2021 to April 2022, we identified 25 testicular cancer patients treated at DFCI’s primary Longwood campus. Twelve of the 25 patients were identified as eligible for a survivorship referral. Nine of those 12 patients (75%) were successfully identified through the Pathways testicular survival algorithm described above. A Pathways navigation had not been completed by the providers of the 3-remaining survivorship-eligible patients, who were instead identified by EHR treatment plan data. One of the 12 patients screened by the testicular survivorship identification process developed metastatic disease that was not captured by the current algorithm and was then deemed inappropriate for survivorship referral. The remaining 11 patients were contacted and offered survivorship resources. As a reference, the total pathways usage rate between April 2021 to April 2022 for the DFCI genitourinary oncology group was 85% across all GU malignancies, and 74% in testicular cancer. Conclusions: Pathways Navigations can provide robust clinical information about patients, disease, and treatment. This information can be used to identify patients appropriate for survivorship and other clinical resources and trial opportunities in testicular cancer and across other malignancies. The impact of this process requires reliable provider usage of the Clinical Pathways tool.[Table: see text]

Digital CBTI hubs as a treatment augmentation strategy in military clinics: study protocol for a pragmatic randomized clinical trial

BackgroundChronic insomnia is the most prevalent sleep disorder among military service members, and it compromises readiness, performance, and physical and mental health. Cognitive behavioral treatment for insomnia (CBTI) is the standard of care for the treatment of insomnia recommended by the VA/DoD Clinical Practice Guideline, the American Academy of Sleep Medicine, and the American College of Physicians. CBTI is highly effective but has limited scalability. It is often unavailable in clinical settings where service members receive sleep care. Digital technologies offer unique opportunities to scale and broaden the geographic reach of CBTI services and support increased patient access and engagement in behavioral sleep care. This study aims to evaluate the impact and acceptability of digital CBTI hubs to augment military treatment facilities’ capabilities in behavioral sleep medicine.MethodsThis is a multi-site, non-inferiority randomized clinical trial designed to compare the effects of in-person (face-to-face or virtual) insomnia care as usual at three military sleep clinics versus CBTI delivered remotely and asynchronously through digital CBTI hubs. Digital CBTI hubs are led by licensed, certified clinicians who use NOCTEM’s® evidence-based clinical decision support platform COAST™ (Clinician Operated Assistive Sleep Technology). Changes in insomnia severity and daytime symptoms of depression and anxiety will be compared at baseline, at 6–8 weeks, and at 3-month follow-up. Patient satisfaction with insomnia care as usual versus digital CBTI hubs will also be examined. We hypothesize that digital CBTI hubs will be non-inferior to insomnia care as usual for improvements in insomnia and daytime symptoms as well as patient satisfaction with insomnia care.DiscussionDigital technology has a high potential to scale CBTI accessibility and delivery options required to meet the insomnia care needs of military service members. Digital CBTI hubs using COAST offer a novel approach to broaden service members’ access to CBTI and to serve as an augmentation strategy for existing sleep services at military treatment facilities. The pragmatic approach leveraging technology in this trial has the potential to rapidly inform clinical practice within the Defense Health Agency as well as other healthcare systems.Trial registrationClinicalTrials.gov NCT05490550. Registered on 14 July 2023.

Artificial Intelligence in Medicine: Progress, Challenges, and Promises

This article discusses the potential of artificial intelligence (AI) in medicine to address complex challenges, from personalized treatment to difficult diagnoses. The use of AI in medicine has exhibited remarkable performance in various fields, including evidence-based clinical decision support, AI-assisted surgery, and electronic health records analysis. However, challenges remain, such as data silos and potential biases in AI training data. Despite these challenges, AI technology has the potential to transform healthcare in significant ways, and ongoing research and development, collaborations among stakeholders, and innovative initiatives can help ensure its safe and ethical use in medicine and healthcare.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BackgroundAccurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.MethodAn iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.ResultsAccording to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).ConclusionThrough a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Automated clinical knowledge graph generation framework for evidence based medicine

To practice the evidence-based medicine, clinicians are interested to find the most suitable research for the clinical decision making. The use of knowledge graphs (KGs) in evidence-based clinical decision support systems is becoming increasingly popular. However, existing KG construction frameworks are not fully automated and contextualized, thus unable to adapt to new domains and incorporate constantly changing information into their knowledge base, resulting in loss of relevance over time. Furthermore, existing KGs construction frameworks don't generate KG that provide relevant information within an acceptable response time for evidence-based practitioners because the organization of constructed subgraphs is neither topic-specific nor evidence-based PICO (Participants/Problem P, Intervention-I, Comparison C, Outcome O) query-friendly. By employing concept extraction, semantic enrichment, optimized clustering, and state of art Recurrent Neural Networks (RNNs) with BioBERT based encoded representation to categorize PICO elements and predict relationships between concepts using huge corpus of publicly available literature on COVID-19 and cerebral aneurysm, this paper proposes a topic specific, PICO enabled, and fully automated framework to curate information and create KG of different clinical domains. The evaluation shows that the proposed framework achieves significant improvement over baseline models and has 93 %, and 82 % accuracy on aneurysm and COVID data set respectively for PICO classification. Also, the relationship extraction module has an accuracy of 96 % with precision and recall being 92 %, and 90 % respectively.