Clinicians' roles and necessary levels of understanding in the use of artificial intelligence: A qualitative interview study with German medical students.

BACKGROUND: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders' viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the one hand, investigating the requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice. METHODS: Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees' perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians' side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz). RESULTS: In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees' statements: the first type, the clinician as a systemic trustee (or "the one relying"), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to "the one relying", clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or "the one controlling"), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician's necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees' different conceptions of the clinician's role and responsibilities cause them to have different requirements regarding the clinician's understanding and explicability of an AI-CDSS beyond the proof of benefit. CONCLUSIONS: The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions.

Implementation of a Clinical Decision Support System for Antimicrobial Prescribing in Sub-Saharan Africa: Multisectoral Qualitative Study.

BACKGROUND: Suboptimal use of antimicrobials is a driver of antimicrobial resistance in West Africa. Clinical decision support systems (CDSSs) can facilitate access to updated and reliable recommendations. OBJECTIVE: This study aimed to assess contextual factors that could facilitate the implementation of a CDSS for antimicrobial prescribing in West Africa and Central Africa and to identify tailored implementation strategies. METHODS: This qualitative study was conducted through 21 semistructured individual interviews via videoconference with health care professionals between September and December 2020. Participants were recruited using purposive sampling in a transnational capacity-building network for hospital preparedness in West Africa. The interview guide included multiple constructs derived from the Consolidated Framework for Implementation Research. Interviews were transcribed, and data were analyzed using thematic analysis. RESULTS: The panel of participants included health practitioners (12/21, 57%), health actors trained in engineering (2/21, 10%), project managers (3/21, 14%), antimicrobial resistance research experts (2/21, 10%), a clinical microbiologist (1/21, 5%), and an anthropologist (1/21, 5%). Contextual factors influencing the implementation of eHealth tools existed at the individual, health care system, and national levels. At the individual level, the main challenge was to design a user-centered CDSS adapted to the prescriber's clinical routine and structural constraints. Most of the participants stated that the CDSS should not only target physicians in academic hospitals who can use their network to disseminate the tool but also general practitioners, primary care nurses, midwives, and other health care workers who are the main prescribers of antimicrobials in rural areas of West Africa. The heterogeneity in antimicrobial prescribing training among prescribers was a significant challenge to the use of a common CDSS. At the country level, weak pharmaceutical regulations, the lack of official guidelines for antimicrobial prescribing, limited access to clinical microbiology laboratories, self-medication, and disparity in health care coverage lead to inappropriate antimicrobial use and could limit the implementation and diffusion of CDSS for antimicrobial prescribing. Participants emphasized the importance of building a solid eHealth ecosystem in their countries by establishing academic partnerships, developing physician networks, and involving diverse stakeholders to address challenges. Additional implementation strategies included conducting a local needs assessment, identifying early adopters, promoting network weaving, using implementation advisers, and creating a learning collaborative. Participants noted that a CDSS for antimicrobial prescribing could be a powerful tool for the development and dissemination of official guidelines for infectious diseases in West Africa. CONCLUSIONS: These results suggest that a CDSS for antimicrobial prescribing adapted for nonspecialized prescribers could have a role in improving clinical decisions. They also confirm the relevance of adopting a cross-disciplinary approach with participants from different backgrounds to assess contextual factors, including social, political, and economic determinants.

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

BACKGROUND: Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. OBJECTIVE: This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. METHODS: A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. RESULTS: The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. CONCLUSIONS: The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. TRIAL REGISTRATION: PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.

BACKGROUND: In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods. OBJECTIVE: The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians. METHODS: This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study. RESULTS: This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024. CONCLUSIONS: This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58185.

Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae.

BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning for the rapid detection of ceftazidime-avibactam (CZA) resistance in KP to improve clinical decision-making processes. METHODS: Out of 107,721 bacterial samples, 675 specimens of KP with suspected multi-drug resistance were selected. These specimens were collected from a tertiary hospital and four secondary hospitals between 2022 and 2023 to evaluate CZA resistance. We used MALDI-TOF MS and machine learning to develop an AI-CDSS with enhanced speed of resistance detection. RESULTS: Machine learning models, especially light gradient boosting machines (LGBM), exhibited an area under the curve (AUC) of 0.95, indicating high accuracy. The predictive models formed the core of our newly developed AI-CDSS, enabling clinical decisions quicker than traditional methods using culture and antibiotic susceptibility testing by a day. CONCLUSIONS: The study confirms that MALDI-TOF MS, integrated with machine learning, can swiftly detect CZA resistance. Incorporating this insight into an AI-CDSS could transform clinical workflows, giving healthcare professionals immediate, crucial insights for shaping treatment plans. This approach promises to be a template for future anti-resistance strategies, emphasizing the vital importance of advanced diagnostics in enhancing public health outcomes.

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.

BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care. OBJECTIVE: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care. METHODS: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies. RESULTS: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base. CONCLUSIONS: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

The use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians.

Aims: A major challenge of the use of prediction models in clinical care is missing data. Real-time imputation may alleviate this. However, to what extent clinicians accept this solution remains unknown. We aimed to assess acceptance of real-time imputation for missing patient data in a clinical decision support system (CDSS) including 10-year cardiovascular absolute risk for the individual patient. Methods and results: We performed a vignette study extending an existing CDSS with the real-time imputation method joint modelling imputation (JMI). We included 17 clinicians to use the CDSS with three different vignettes, describing potential use cases (missing data, no risk estimate; imputed values, risk estimate based on imputed data; complete information). In each vignette, missing data were introduced to mimic a situation as could occur in clinical practice. Acceptance of end-users was assessed on three different axes: clinical realism, comfortableness, and added clinical value. Overall, the imputed predictor values were found to be clinically reasonable and according to the expectations. However, for binary variables, use of a probability scale to express uncertainty was deemed inconvenient. The perceived comfortableness with imputed risk prediction was low, and confidence intervals were deemed too wide for reliable decision-making. The clinicians acknowledged added value for using JMI in clinical practice when used for educational, research, or informative purposes. Conclusion: Handling missing data in CDSS via JMI is useful, but more accurate imputations are needed to generate comfort in clinicians for use in routine care. Only then can CDSS create clinical value by improving decision-making.

Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition.

Objective: To determine whether the diagnostic ability of the newly designed hierarchical fuzzy diagnosis method is consistent with that of headache experts for probable migraine (PM) and probable tension-type headache (PTTH). Background: Clinical decision support systems (CDSS) are computer systems designed to help doctors to make clinician decisions by information technology, and have proven to be effective in improving headache diagnosis by making medical knowledge readily available to users in some studies. However, one serious drawback is that the CDSS lacks the ability to deal with some fuzzy boundaries of the headache features utilized in diagnostic criteria, which might be caused by patients' recall bias and subjective bias. Methods: A hybrid mechanism of rule-based reasoning and hierarchical fuzzy diagnosis method based on International Classification of Headache Disorders, 3rd edition (ICHD-3) was designed and then validated by a retrospective study with 325 consecutive patients and a prospective study with 380 patients who were clinically diagnosed with migraine and TTH at the headache clinic of Chinese PLA General Hospital. Results: The results of the diagnostic test in the retrospective study indicated that the fuzzy-based CDSS can be used in the diagnosis of migraine without aura (MO) (sensitivity 97.71%, specificity 100%), TTH (sensitivity 98.57%, specificity 100%), PM (sensitivity 91.25%, specificity 98.75%) and PTTH (sensitivity 90.91%, specificity 99.63%). While in the prospective study, the diagnostic performances were MO (sensitivity 91.62%, specificity 96.52%), TTH (sensitivity 92.17%, specificity 95.47%), PM (sensitivity 85.48%, specificity 98.11%) and PTTH (sensitivity 87.50%, specificity 98.60%). Cohen's kappa values for the consistency test were 0.984 ± 0.018 (MO), 0.991 ± 0.018 (TTH), 0.916 ± 0.051 (PM), 0.932 ± 0.059 (PTTH) in the retrospective study and 0.884 ± 0.047 (MO), 0.870 ± 0.055 (TTH), 0.853 ± 0.073 (PM), 0.827 ± 0.118 (PTTH) in the prospective study, which indicated good consistency with the fuzzy-based CDSS and the gold standard (p < 0.001). Conclusion: We developed a fuzzy-based CDSS performs much more similarly to expert diagnosis and performs better than the routine CDSS method in the diagnosis of migraine and TTH, and it could promote the application of artificial intelligence in the area of headache diagnosis.

Comparison of Three Different Disseminated Intravascular Coagulation (DIC) Criteria and Diagnostic and Prognostic Value of Antithrombin Investigation in Patients with Confirmed Sepsis-Induced Coagulopathy (SIC).

A new scoring system termed sepsis-induced coagulopathy (SIC) has been proposed to diagnose early sepsis-induced disseminated intravascular coagulation (DIC). This study performed DIC-related analyses in patients with confirmed SIC. Data from the intensive care unit (ICU) departments of the three hospitals between 2020 and 2022 were retrospectively analyzed. Finally, 125 patients with confirmed SIC were enrolled in the study. The diagnostic value of three widely used DIC criteria was assessed in patients with newly diagnosed SIC. In addition, the diagnostic and prognostic value of antithrombin (AT) was analyzed in patients with SIC. The Japanese Association for Acute Medicine DIC criteria (JAAM) exhibited the highest DIC diagnostic rate, while the mortality risk of SIC patients demonstrated a proportional increase with higher International Society on Thrombosis and Haemostasis (ISTH) and Chinese DIC scoring system (CDSS) scores. Low AT activity (<70%) in septic patients upon SIC diagnosis predicted a very high 28-day mortality rate, almost twice as high as in the normal AT activity (≥70%) group. A decreasing tendency in AT activity after clinical interventions was correlated with increased mortality. The area under the ROC curve (AU-ROC) of AT in DIC diagnosis was statistically significant when CDSS and ISTH were used as diagnostic criteria, but not JAAM. Each of the three DIC diagnostic criteria showed diagnostic and prognostic advantages for SIC. AT could be an independent prognostic indicator for SIC but demonstrated a relatively limited DIC diagnostic value. Adding AT to the SIC scoring system may increase its prognostic power.

Vignette-based comparative analysis of ChatGPT and specialist treatment decisions for rheumatic patients: results of the Rheum2Guide study.

BACKGROUND: The complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support. OBJECTIVE: To compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). DESIGN/METHODS: Fictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans' safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale. RESULTS: 20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB's treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4's plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4's first-line treatment plans. Rheumatologists' plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed. CONCLUSION: GPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.

Impacts of Clinical Decision Support Systems on the Relationship, Communication, and Shared Decision-Making Between Health Care Professionals and Patients: Multistakeholder Interview Study.

BACKGROUND: Clinical decision support systems (CDSSs) are increasingly being introduced into various domains of health care. Little is known so far about the impact of such systems on the health care professional-patient relationship, and there is a lack of agreement about whether and how patients should be informed about the use of CDSSs. OBJECTIVE: This study aims to explore, in an empirically informed manner, the potential implications for the health care professional-patient relationship and to underline the importance of this relationship when using CDSSs for both patients and future professionals. METHODS: Using a methodological triangulation, 15 medical students and 12 trainee nurses were interviewed in semistructured interviews and 18 patients were involved in focus groups between April 2021 and April 2022. All participants came from Germany. Three examples of CDSSs covering different areas of health care (ie, surgery, nephrology, and intensive home care) were used as stimuli in the study to identify similarities and differences regarding the use of CDSSs in different fields of application. The interview and focus group transcripts were analyzed using a structured qualitative content analysis. RESULTS: From the interviews and focus groups analyzed, three topics were identified that interdependently address the interactions between patients and health care professionals: (1) CDSSs and their impact on the roles of and requirements for health care professionals, (2) CDSSs and their impact on the relationship between health care professionals and patients (including communication requirements for shared decision-making), and (3) stakeholders' expectations for patient education and information about CDSSs and their use. CONCLUSIONS: The results indicate that using CDSSs could restructure established power and decision-making relationships between (future) health care professionals and patients. In addition, respondents expected that the use of CDSSs would involve more communication, so they anticipated an increased time commitment. The results shed new light on the existing discourse by demonstrating that the anticipated impact of CDSSs on the health care professional-patient relationship appears to stem less from the function of a CDSS and more from its integration in the relationship. Therefore, the anticipated effects on the relationship between health care professionals and patients could be specifically addressed in patient information about the use of CDSSs.

Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study.

Background: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area. Objective: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories. Methods: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard. Results: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies. Conclusions: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC's performance evaluation highlights the advancements in AI's role in laboratory medicine.

Development of Unified Terminological System Based on UMLS: Experience and Perspectives.

This article presents experience in construction the National Unified Terminological System (NUTS) with an ontological structure based on international Unified Medical Language System (UMLS). UMLS has been adapted and enriched with formulations from national directories, relationships, extracted from the texts of scientific articles and electronic health records, and weight coefficients.

Modelling Events in Biomedical Decision Support Systems Using Ontologies.

Biomedical decision support systems play a crucial role in modern healthcare by assisting clinicians in making informed decisions. Events, such as physiological changes or drug reactions, are integral components of these systems, influencing patient outcomes and treatment strategies. However, effectively modeling events within these systems presents significant challenges due to the complexity and dynamic nature of medical data. Especially the differentiation between events and processes as well as the nature of events is often unclear. This paper explores approaches to modeling events in biomedical decision support systems, considering factors such as ontology-based representation. By addressing these challenges, we strive to provide the means for enhancing the functionality and interpretability of biomedical decision support systems concerning events.

Understanding WHO SMART Guidelines: Narrative Review of an Innovative Global Digital Health Approach.

The growing challenges of healthcare systems pose a unique opportunity to leverage evidence-based digital health interventions. The WHO's SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines represent a significant advancement in this domain. This paper aims to summarize SMART guidelines authoring and implementation process, drawing on a comprehensive literature analysis. Our findings highlight critical success factors for national implementation, including stakeholder engagement, customization to local contexts, and leveraging international standards and digital technologies. We conclude with recommendations for countries aiming to implement WHO SMART guidelines, underscoring the need for a multi-disciplinary approach and the potential challenges to be navigated.

Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care.

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge.

Pan-lineage Mycobacterium tuberculosis reference genome for enhanced molecular diagnosis.

In Mycobacterium tuberculosis (MTB) control, whole genome sequencing-based molecular drug susceptibility testing (molDST-WGS) has emerged as a pivotal tool. However, the current reliance on a single-strain reference limits molDST-WGS's true potential. To address this, we introduce a new pan-lineage reference genome, 'MtbRf'. We assembled 'unmapped' reads from 3,614 MTB genomes (751 L1; 881 L2; 1,700 L3; and 282 L4) into 35 shared, annotated contigs (54 coding sequences [CDSs]). We constructed MtbRf through: (1) searching for contig homologues among genome database that precipitate results uniquely within Mycobacteria genus; (2) comparing genomes with H37Rv ('lift-over') to define 18 insertions; and (3) filling gaps in H37Rv with insertions. MtbRf adds 1.18% sequences to H37rv, salvaging >60% of previously unmapped reads. Transcriptomics confirmed gene expression of new CDSs. The new variants provided a moderate DST predictive value (AUROC 0.60-0.75). MtbRf thus unveils previously hidden genomic information and lays the foundation for lineage-specific molDST-WGS.

Effectiveness of Using Clinical Decision Support System in Nursing Records.

This study evaluates the impact of Clinical Decision Support System (CDSS) integration into the Nursing Information System 2.0 on nursing records and nurse satisfaction. Longitudinal data collection employs questionnaires and nursing records audits. Findings show improved electronic signature integration and nursing problem identification, benefiting real-time patient information access and record completeness. Younger, less experienced, highly educated nurses exhibit higher CDSS usage and acceptance. Overall, 80.2% agreement rate confirms CDSS's positive impact, highlighting the importance of user effectiveness evaluation in system implementation for nursing innovation.

Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment.

OBJECTIVES: The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions. METHODS: We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation. RESULTS: We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use. CONCLUSIONS: MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.