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Standardized and structured data collection is necessary in the health care sector to advance nursing research, enable the comparison of practice-based data, and optimize the potential of technological innovations and digitalization. This can be supported by nursing minimum datasets (NMDSs). This scoping review aims to present the state of research on NMDSs in long-term care settings. Articles addressing NMDSs in long-term care published in the PubMed, CINAHL, Embase, or Digital Bibliography & Library Project databases up to September 2024 were included. Additionally, forward and backward citation tracking and manual searches of original records were conducted. All types of articles were included, with no time limit, and the articles had to be in English or German. The selected sources were screened and evaluated in a double-review process. Evaluation was carried out using a qualitative content analysis approach, supplemented by inductive and model-based categorization, and concluded with a final narrative synthesis. A total of 36 sources covering 9 NMDS projects or initiatives were included. Most of the included articles (15/36, 42%) were published between 2004 and 2015. The United States accounted for the largest share (16/36, 44%) of the country of origin. The topic of NMDSs has gained more relevance in recent years, with 5 sources from 2022 to 2024 being found. Most publications were overview articles (15/36, 42%), followed by reviews and discussion papers (5/36, 14% each), highlighting the literature's predominantly conceptual and discursive focus. Various types of NMDSs were identified, including country-specific, topic-specific, and international adaptations of the US NMDS systems. The content of the NMDSs could be categorized as patient, interpersonal, or institutional data. The most comprehensive information is available on the US NMDS. Many initiatives were described but few have been developed or are currently in use. The literature included recommendations at the clinical, scientific, and administrative levels, emphasizing standardization, stakeholder involvement, and using NMDS data to improve care practices and policies. NMDS initiatives are becoming increasingly important in the context of digitalization, demographic change, and legislative developments, especially in Europe. Existing NMDSs primarily focus on patient data, and nursing interventions, outcomes, and the perspectives of individuals in need of care have so far received little attention. A lack of standardized descriptions and scientifically usable content hinders comparability and further development, underscoring the need for legal frameworks and stronger involvement from health care practitioners and researchers.

Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrombotic agents. Timely and accurate detection of bleeding events is essential for improving drug safety surveillance and clinical risk management. The study aimed to develop and validate automated algorithms for detecting major bleeding (MB) and clinically relevant nonmajor bleeding (CRNMB) events from electronic medical records (EMRs) by combining structured data-based rule models and a natural language processing (NLP) approach, and to evaluate their performance and generalizability against a manually reviewed gold standard and an external dataset. We conducted a multicenter retrospective study using routinely collected EMR data from 3 Swiss university hospitals. Patients 65 years or older who received at least one antithrombotic agent and were hospitalized between January 2015 and December 2016 were included. To detect MB and CRNMB events, rule-based algorithms were developed using structured data (International Statistical Classification of Diseases, 10th Revision, German Modification [ICD-10-GM] codes, laboratory values, transfusion records, and antihemorrhagic prescriptions), with variables and cutoff values defined according to adapted International Society on Thrombosis and Haemostasis definitions and expert consensus. In parallel, a supervised NLP model was applied to discharge summaries from one hospital. A manual review of 754 EMRs served as the reference standard for internal validation, and the algorithm performance of the structured data algorithms (SDA), NLP, and their combination (SDA+NLP) was evaluated against this manually reviewed gold standard using standard performance metrics. External validation was performed on an independent dataset from the Lausanne University Hospital to assess model robustness and generalizability. Among 36,039 inpatient stays, SDA identified 8.26% (n=2979) as MB and 15.04% (n=5419) as CRNMB cases. ICD-10-GM codes alone detected 28.5% (n=849) of MB and 31.48% (n=1706) of CRNMB cases, while laboratory data contributed most to event detection (n=1994, 66.94% for MB and n=3663, 67.60% for CRNMB). Integrating SDA with NLP improved detection, identifying 12.2% (920/7513) of MB and 27.4% (2062/7513) of CRNMB cases at 1 hospital. The combined model achieved the best performance (sensitivity 0.84, positive predictive value 0.51, F1-score 0.64). External validation on Lausanne University Hospital 2021-2022 data (n=24,054 stays) confirmed the algorithms' reproducibility; the prevalence of MB decreased while CRNMB increased, reflecting evolving clinical practices and antithrombotic use patterns. Our integrated approach, combining SDA with NLP, enhances the detection of hemorrhagic events in older hospitalized patients treated with antithrombotic agents, suggesting its potential usefulness for drug safety monitoring and clinical risk management.

Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms. This qualitative study aims to explore the current challenges and demands in surgical geriatric patient care. Based on these challenges, the study explores the attitude of interviewees toward the introduction of an AI-supported CDSS (AI-CDSS) in geriatric patient care in surgery, focusing on technical and general wishes about an AI-CDSS, as well as ethical considerations. In this study, 15 personal interviews with physicians, nurses, physiotherapists, and social workers, employed in surgical departments at a university hospital in Southern Germany, were conducted in April 2022. Interviews were conducted in person, transcribed, and coded by 2 researchers (AU, LB) using content and thematic analysis. During the analysis, quotes were sorted into the main categories of geriatric patient care, use of an AI-CDSS, and ethical considerations by 2 authors (AU, LB). The main themes of the interviews were subsequently described in a narrative synthesis, citing key quotes. In total, 399 quotes were extracted and categorized from the interviews. Most quotes could be assigned to the primary code challenges in geriatric patient care (111 quotes), with the most frequent subcode being medical challenges (45 quotes). More quotes were assigned to the primary code chances of an AI-CDSS (37 quotes), with its most frequent subcode being holistic patient overview (16 quotes), then to the primary code limits of an AI-CDSS (26 quotes). Regarding the primary code technical wishes (37 quotes), most quotes could be assigned to the subcode intuitive usability (15 quotes), followed by mobile availability and easy access (11 quotes). Regarding the main category ethical aspects of an AI-CDSS, most quotes could be assigned to the subcode critical position toward trust in an AI-CDSS (9 quotes), followed by the subcodes respecting the patient's will and individual situation (8 quotes) and responsibility remaining in the hands of humans (7 quotes). Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care. These findings, together with the wish to preserve the patient-caregiver relationship, will help set the focus for the ongoing development of AI-supported CDSS.

The development and introduction of an artificial intelligence (AI)-based clinical decision support system (CDSS) in surgical departments as part of the \"Supporting Surgery with Geriatric Co-management and AI\" project addresses the challenges of an increasingly aging population. The system enables digital comanagement of older patients by providing evidence-based evaluations of their health status, along with corresponding medical recommendations, with the aim of improving their perioperative care. The use of an AI-based CDSS in patient care raises ethical challenges. Gathering the opinions, expectations, and concerns of older adults (as potential patients) regarding the CDSS enables the identification of ethical opportunities, concerns, and limitations associated with implementing such a system in hospitals. We conducted 5 focus groups with participants aged 65 years or older. The transcripts were evaluated using qualitative content analysis and ethically analyzed. Categories were inductively generated, followed by a thematic classification of participants' statements. We found that technical understanding did not influence the older adults' opinions. Ethical opportunities and concerns were identified. On the one hand, diagnosis and treatment could be accelerated, the patient-AI-physician interaction could enhance medical treatment, and the coordination of hospital processes could be improved. On the other hand, the quality of the CDSS depends on an adequate data foundation and robust cybersecurity. Potential risks included habituation effects, loss of a second medical opinion, and illness severity influencing patients' attitude toward medical recommendations. The risk of overdiagnosis and overtreatment was discussed controversially, and treatment options could be influenced by interests and finances. Additional concerns included challenges with time savings, potential declines in medical skills, and effects on the length of hospital stay. To address the ethical challenges, we recommend allocating sufficient time for use of the CDSS and emphasizing individualized review of the CDSS results. Furthermore, we suggest limiting private financial sponsorship.

The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgical resection is important for cancer treatment; however, predicting postoperative disability and mortality in older patients is crucial for surgical decision-making, considering the quality of life and care burden. Currently, no model directly predicts postoperative functional disability in this population. We aimed to develop and validate machine-learning models to predict postoperative functional disability (≥5-point decrease in the Barthel Index) or in-hospital death in patients with cancer aged ≥ 65 years. This retrospective cohort study included patients aged ≥65 years who underwent surgery for major cancers (lung, stomach, colorectal, liver, pancreatic, breast, or prostate cancer) between April 2016 and March 2023 in 70 Japanese hospitals across 6 regional groups. One group was randomly selected for external validation, while the remaining 5 groups were randomly divided into training (70%) and internal validation (30%) sets. Predictor variables were selected from 37 routinely available preoperative factors through electronic medical records (age, sex, income, comorbidities, laboratory values, and vital signs) using crude odds ratios (P<.1) and the least absolute shrinkage and selection operator method. We developed 6 machine-learning models, including category boosting (CatBoost), extreme gradient boosting (XGBoost), logistic regression, neural networks, random forest, and support vector machine. Model predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) with 95% CI. We used the Shapley additive explanations (SHAP) method to evaluate contribution to the predictive performance for each predictor variable. This study included 33,355 patients in the training, 14,294 in the internal validation, and 6711 in the external validation sets. In the training set, 1406/33,355 (4.2%) patients experienced worse discharge. A total of 24 predictor variables were selected for the final models. CatBoost and XGBoost achieved the largest AUCs among the 6 models: 0.81 (95% CI 0.80-0.82) and 0.81 (95% CI 0.80-0.82), respectively. In the top 15 influential factors based on the mean absolute SHAP value, both models shared the same 14 factors such as dementia, age ≥85 years, and gastrointestinal cancer. The CatBoost model showed the largest AUCs in both internal (0.77, 95% CI 0.75-0.79) and external validation (0.72, 95% CI 0.68-0.75). The CatBoost model demonstrated good performance in predicting postoperative outcomes for older patients with cancer using routinely available preoperative factors. The robustness of these findings was supported by the identical top influential factors between the CatBoost and XGBoost models. This model could support surgical decision-making while considering postoperative quality of life and care burden, with potential for implementation through electronic health records.

Clinical decision support systems (CDSS) have the potential to play a crucial role in enhancing health care quality by providing evidence-based information to clinicians at the point of care. Despite their increasing popularity, there is a lack of comprehensive research exploring their design characterization and trends. This limits our understanding and ability to optimize their functionality, usability, and adoption in health care settings. This systematic review examined the design characteristics of CDSS from a user-centered perspective, focusing on user-centered design (UCD), user experience (UX), and usability, to identify related design challenges and provide insights into the implications for future design of CDSS. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations and used a grounded theory analytical approach to guide the conduct, data analysis, and synthesis. A search of 4 major electronic databases (PubMed, Web of Science, Scopus, and IEEE Xplore) was conducted for papers published between 2013 and 2023, using predefined design-focused keywords (design, UX, implementation, evaluation, usability, and architecture). Papers were included if they focused on a designed CDSS for a health condition and discussed the design and UX aspects (eg, design approach, architecture, or integration). Papers were excluded if they solely covered technical implementation or architecture (eg, machine learning methods) or were editorials, reviews, books, conference abstracts, or study protocols. Out of 1905 initially identified papers, 40 passed screening and eligibility checks for a full review and analysis. Analysis of the studies revealed that UCD is the most widely adopted approach for designing CDSS, with all design processes incorporating functional or usability evaluation mechanisms. The CDSS reported were mainly clinician-facing and mostly stand-alone systems, with their design lacking consideration for integration with existing clinical information systems and workflows. Through a UCD lens, four key categories of challenges relevant to CDSS design were identified: (1) usability and UX, (2) validity and reliability, (3) data quality and assurance, and (4) design and integration complexities. Notably, a subset of studies incorporating Explainable artificial intelligence highlighted its emerging role in addressing key challenges related to validity and reliability by fostering explainability, transparency, and trust in CDSS recommendations, while also supporting collaborative validation with users. While CDSS show promise in enhancing health care delivery, identified challenges have implications for their future design, efficacy, and utilization. Adopting pragmatic UCD design approaches that actively involve users is essential for enhancing usability and addressing identified UX challenges. Integrating with clinical systems is crucial for interoperability and presents opportunities for AI-enabled CDSS that rely on large patient data. Incorporating emerging technologies such as Explainable Artificial Intelligence can boost trust and acceptance. Enabling functionality for CDSS to support both clinicians and patients can create opportunities for effective use in virtual care.

Large language models (LLMs) such as ChatGPT-4, LLaMA-3.1, Gemini-1.5, DeepSeek-R1, and OpenAI-O3 have shown promising potential in health care, particularly for clinical reasoning and decision support. However, their reliability across critical tasks like diagnosis, medical coding, and risk prediction has received mixed reviews, especially in real-world settings without task-specific training. This study aims to evaluate and compare the zero-shot performance of reasoning and nonreasoning LLMs in three essential clinical tasks: (1) primary diagnosis generation, (2) ICD-9 (International Classification of Diseases, Ninth Revision) medical code prediction, and (3) hospital readmission risk stratification. The goal is to assess whether these models can serve as general-purpose clinical decision support tools and to identify gaps in current capabilities. Using the Medical Information Mart for Intensive Care-IV dataset, we selected a random cohort of 300 hospital discharge summaries. Prompts were engineered to include structured clinical content from 5 note sections: chief complaints, past medical history, surgical history, laboratories, and imaging. Prompts were standardized and zero-shot, with no model fine-tuning or repetition across runs. All model interactions were conducted through publicly available web user interfaces, without using application programming interfaces, to simulate real-world accessibility for nontechnical users. We incorporated rationale elicitation into prompts to evaluate model transparency, especially in reasoning models. Ground-truth labels were derived from the primary diagnosis documented in clinical notes, structured ICD-9 codes from diagnosis, and hospital-recorded readmission frequencies for risk stratification. Performance was measured using F1-scores and correctness percentages, and comparative performance was analyzed statistically. Among nonreasoning models, LLaMA-3.1 achieved the highest primary diagnosis accuracy (n=255, 85%), followed by ChatGPT-4 (n=254, 84.7%) and Gemini-1.5 (n=237, 79%). For ICD-9 prediction, correctness dropped significantly across all models: LLaMA-3.1 (n=128, 42.6%), ChatGPT-4 (n=122, 40.6%), and Gemini-1.5 (n=44, 14.6%). Hospital readmission risk prediction showed low performance in nonreasoning models: LLaMA-3.1 (n=124, 41.3%), Gemini-1.5 (n=122, 40.7%), and ChatGPT-4 (n=99, 33%). Among reasoning models, OpenAI-O3 outperformed in diagnosis (n=270, 90%) and ICD-9 coding (n=136, 45.3%), while DeepSeek-R1 performed slightly better in the readmission risk prediction (n=218, 72.6% vs O3's n=212, 70.6%). Despite improved explainability, reasoning models generated verbose responses. None of the models met clinical standards across all tasks, and performance in medical coding remained the weakest area across all models. Current LLMs exhibit moderate success in zero-shot diagnosis and risk prediction but underperform in ICD-9 code generation, reinforcing findings from prior studies. Reasoning models offer marginally better performance and increased interpretability, with limited reliability. Overall, statistical analysis between the models revealed that OpenAI-O3 outperformed the other models. These results highlight the need for task-specific fine-tuning and need human-in-the-loop checking. Future work will explore fine-tuning, stability through repeated trials, and evaluation on a different subset of deidentified real-world data with a larger sample size.

Global digitalization continues to advance, extending its influence into medicine and health care systems worldwide. In recent years, substantial advancements have been made in the research and development of artificial intelligence (AI), raising questions about its potential in medicine. The integration and application of AI in intensive care medicine, particularly in sepsis treatment, presents significant potential for advancing patient outcomes and enhancing patient-relevant benefits. However, a comprehensive and systematic overview of the full spectrum of patient-relevant benefits associated with AI-based clinical decision support systems (CDSS) remains lacking. This scoping review aimed to identify and categorize evidence on patient-relevant benefits of AI-based CDSS in sepsis care. Systematic research was conducted in 4 electronic databases: MEDLINE via PubMed, Embase, the ACM Digital Library, and IEEE Xplore. In addition, a comprehensive search on the websites of relevant international organizations, along with a citation search of the included articles, was conducted. Articles were included if they (1) focused on sepsis and (2) described patient-relevant benefits of AI-based CDSS. Articles published between January 1, 2008, and March 2, 2023, were considered for inclusion. Study selection was performed independently by 2 reviewers. The manuscript was drafted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The analysis of the included articles was conducted using the program MAXQDA (VERBI Software GmbH), with systemization finalized in a consensus workshop. A total of 3368 records were identified across the 4 databases, of which 24 met the inclusion criteria and were included in the scoping review. The additional search on international websites and in reference lists identified 6 more relevant articles, resulting in 30 included studies. Of these, 20 were quantitative, comprising 7 prospective and 13 retrospective designs. In addition, 1 qualitative study, 1 mixed methods study, 6 review articles, and 2 articles from institutional websites were included. Patient-relevant benefits were systematized in six main categories: (1) prediction, (2) earlier treatment and prioritization of high-risk patients, (3) individualized therapy, (4) improved patient outcomes (including improved Sequential Organ Failure Assessment score, reduced length of stay, and reduced mortality), (5) general improvements in care, and (6) reduced readmission rate. This scoping review underscores the potential of AI-based CDSS to positively impact patient-relevant benefits, particularly in sepsis care, where they demonstrate considerable promise for improving intensive care. However, the majority of the identified studies rely on retrospective database analyses. Future research should focus on validating these findings through prospective studies.

Emergency toxicology is a complex field requiring rapid and precise decision-making to manage acute poisonings effectively. Toxic exposures are often unpredictable, and the constraints of time and resources often challenge conventional diagnostic and treatment approaches. Artificial intelligence (AI) has emerged as a valuable tool in emergency medicine, offering the potential to enhance diagnostic accuracy, predict clinical outcomes and improve clinical decision support systems. Despite the increasing focus of AI in medicine, its applications in emergency toxicology are still underexplored. This viewpoint aims to provide perspectives on AI applications in emergency toxicology by highlighting key advancements, challenges, and future directions. While AI has demonstrated significant potential in improving toxicological predictions through various applications, challenges such as data quality, regulatory concerns, and implementation barriers are still hurdles to its use. Further research, regulatory frameworks, and integration strategies are needed to ensure effective and ethical implementation in clinical practice.

Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored. We conducted a randomized controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention. In a 3-way, within-subject randomized design, doctors completed 9 clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (eg, random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and antihypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured. In total, 102 doctors from all 7 geographical regions of Indonesia participated. Most (n=85, 83%) participants were 26-35 years of age, and 57 (56%) were male, with a median of 6 (IQR 4.75) years of clinical experience. AI-based CDS improved risk assessment by 27% (χ22 (n=102)=48.875, P<.001) when compared to unassisted risk assessment, equating to 1 additional correct risk classification for every 3.7 patients where doctors used AI (number needed to treat=3.7, 95% CI 2.9-5.2). The prescription of statins also improved by 29% (χ22 (n=102)=36.608, P<.001). In pairwise comparisons, doctors who were assisted by the AI-based CDS correctly assessed significantly more cases (z=-5.602, n=102, adjusted P<.001) and prescribed the appropriate statin more often (z=-4.936, adjusted P<.001, medium effect size r=0.35) when compared with the control. AI-assisted cases required less time (estimated marginal means 63.6 s vs 72.8 s, F2, 772.8=5.710, P=.003). However, improvements in the prescription of aspirin and antihypertensives did not reach statistical significance (P=.08 and P=.30, respectively). No improvement was observed in referral decisions. Participants generally viewed AI-based CDS positively, with 81 (79%) agreeing or strongly agreeing that they would follow its recommendations and 82 (82%) indicating they would use it if given access. They believed CDS could enhance the efficiency of risk assessment, particularly in high-volume primary care settings, while noting the need to verify AI recommendations against clinical guidelines for each patient. Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of health care resources and doctors' time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.