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PubMedDate not supplied

Designing computer support for daily hospital staffing decisions.

This paper relates issues encountered in extending a centralized computer-based nurse scheduling system to support daily staffing decisions in a hospital environment. When calculating the daily staffing needs on each nursing unit, the conceptually simple interactive staffing system incurred considerable costs in categorizing patients according to level of care required. Two years of historical data collected daily in a 568 bed St. Louis hospital were used to test the sensitivity of relative staffing requirements to daily fluctuations in patient-care distributions. A periodic sampling plan appears adequate for updating distributions which can then be applied to current census in estimating daily staffing needs. Recommendations for implementing such a system are offered.

Computers↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2026

Temporal Patterns in the Occurrence of Principal Salmonella Serotypes for Raw Meat and Poultry Products in the United States: 1998-2024.

Microbial foodborne pathogens, such as Salmonella, have the characteristic that only some subgroups of these bacteria are highly pathogenic to humans. For the Salmonella species enterica, there are more than 2,600 serotypes. These serotypes can be host-adapted so that for any animal reservoir, such as a particular food animal species, fewer than 10 serotypes make up more than fifty percent of isolates found in the reservoir. The highly concentrated nature of serotypes within a reservoir is a key to the findings of many studies and the cornerstone of some food safety programs. Nevertheless, a key limitation is that the dominant serotypes for a reservoir are likely to change over time. This is problematic because the risk of illness associated with a reservoir can increase dramatically if a more pathogenic serotype replaces one or more less pathogenic serotypes, and vice versa. In the United States, meat and poultry have been sampled continuously by the United States Department of Agriculture Food Safety and Inspection Service (USDA FSIS) for nearly 30 years. For this study, these data are used to describe how the occurrence of the most common serotypes for each sampled commodity has changed over time. Insight into how often and how rapidly the most common serotypes change is important for designing effective mitigation strategies. To aid the development of such a mitigation strategy, we developed a classification system to categorize serotypes based on the direction and magnitude of change in each serotype over time. This information is combined with a ranking system that describes the relative virulence of each serotype using genomic and epidemiological virulence patterns. We then developed a heuristic system to support risk management decisions regarding the potential risks and benefits of applying serotype-specific interventions.

Compositional data↗

PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗

PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMedDate not supplied

Application of information systems to AIDS risk reduction.

Acquired immune deficiency syndrome (AIDS) is a specific group of diseases which are indicative of severe immunosuppression related to infection with the human immunodeficiency virus (HIV). In response to the epidemic, a variety of intervention and prevention has been instituted. In such intervention and prevention activities, the role played by information systems becomes more and more important. This paper describes the design and implementation of an information system for AIDS intervention and prevention.

Acquired Immunodeficiency Syndrome↗

PubMed1977

Special living arrangements: a model for decision-making.

Many chronically disabled patients need special support systems to help them meet material needs, personal-care needs, and psychosocial needs. The authors propose two guidelines that must be considered simultaneously when deciding what special support system is most appropriate for a particular client. The first is that the system be adequate to meet the client's unmet needs, and the second is that the system not meet needs the client can meet hiself. The authors feel that one should look first to support systems other than special living arrangements, which can easily overprovide services to clients. If a special living arrangement is considered appropriate, the one selected should provide only for those needs the client cannot meet himself.

Community Mental Health Services↗

PubMed2025

Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.

Background/Objectives: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. Methods: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. Results: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). Conclusions: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.

artificial intelligence↗

PubMed1992

Expert systems and expert behavior.

Iliad 4.0 and QMR 2.03 are computer-based diagnostic knowledge bases that can play many roles in decision support and other areas of medical practice, but neither appears ready to assume the role of an expert diagnostic consultant. In contrast to human experts, these programs have problems related to recognition of their own limitations, interpretation of continuous data, recognition of dependent findings, selection of tests, and description of the impact of certain tests. Suggestions to improve these aspects of knowledge bases are offered.

Artificial Intelligence↗

PubMed1978

Computer use in diagnosis, prognosis, and therapy.

Computers are used to influence diagnostic and therapeutic decisions. The computer's information-handling capabilities allow it to serve as a reliable extension of the physician's memory and expander of the physician's information and synthesized knowledge resources. Computers have been used to facilitate decisions through organization of patient data, improved classification of patients, decision analysis in clinical settings, and simulation of expert clinical reasoning. Computer programs are more successful in narrow, constrained, single arenas of medicine with much underlying pathophysiologic understanding and where decisions are based largely on hard laboratory data. New models of synthetic reasoning that simulate expert clinical behavior show promise of supporting complicated decisions concerning problems of multiple diseases. All systems are confronted by problems of consensus and authority of the underlying information used.

Computers↗

PubMed2026

Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.

CRISPR-Cas Systems↗

PubMed1992

Computer-assisted test interpretation: considerations in patient care.

Computer-assisted test interpretation (CATI) is a set of developing technologies designed to support medical decision-making. This paper develops a taxonomy of computer-assisted test interpretation, giving specific consideration to the characteristics of the data that are to be interpreted, the nature of the interpretive task, the expected involvement of the health professional in the generation of the interpretation, the inference mechanism used for the interpretation, and the broader context of the interpretation. We go on to examine potential benefits and disadvantages of CATI systems in terms of accuracy, information management, interpretation time, patient management, medical communication, and expense. Finally, we examine electrocardiogram interpretation systems from the perspective of this taxonomy, and offer suggestions regarding areas of further inquiry into the effects of CATI on medical care.

Classification↗

PubMed2026

A MAGIBU-based model for pediatric and juvenile CNS tumors: an in-house epigenetic decision-support framework compared with online DNA methylation classifiers.

Background: DNA methylation profiling is a tool that provides key support for central nervous system (CNS) tumor classification. However, diagnostically ambiguous pediatric cases may result in discordant outputs across classifiers. We developed MAGIBU, a cross-platform, projection-based framework that embeds individual methylomes into a fixed CNS reference landscape, ranking diagnostic entities by local epigenetic proximity to support clinician-led integrative diagnosis. Methods: As a proof-of-concept, we evaluated MAGIBU in eight morphologically challenging pediatric/juvenile CNS tumors with unresolved diagnoses after institutional and central pathology review. To establish a benchmark in the absence of a definitive histopathological ground truth, a consensus epigenetic reference was defined a priori for cases showing concordant results between the Heidelberg CNS Tumor Methylation Classifier and Methylscape Analysis. Comparisons were also performed with Epigenomic Digital Pathology (EpiDiP). To validate MAGIBU beyond this discovery cohort, performance was assessed at the family level across the CNS methylation spectrum (n = 678, 28 methylation families), on non-array platforms (whole-genome bisulfite sequencing and Oxford Nanopore), and in a focused analysis of the low-grade glioma and diffuse midline glioma compartment across four independent cohorts (n = 670). Results: In the discovery cohort, MAGIBU achieved high concordance with the consensus reference (Cohen's κ = 0.855), outperforming EpiDiP (κ = 0.278), which frequently placed low-grade tumors in proximity to higher-grade reference regions. Conclusions: MAGIBU provides a stable, quantitative differential diagnosis framework that mitigates the limitations of rigid categorical assignments. By leveraging a distance-based proximity metric, it offers a transparent decision-support tool that integrates effectively with clinical, radiological, and molecular data. While performance is inherently dependent on reference atlas composition, MAGIBU represents a robust complementary approach for the diagnostic workup of ambiguous CNS tumors.

Brain↗

PubMed1975

A computer-assisted psychiatric assessment unit.

The authors discuss the rationale and functional design for an innovative approach to psychiatric intake decision making, stressing the crucial role of an on-line computer support system. The description of the prototype computer-assisted psychiatric assessment process includes an outline of computer and staffing requirements. The authors discuss the initial impact of this psychiatric assessment unit on the hospital's mental health care delivery system.

Diagnosis, Computer-Assisted↗

PubMed1992

Development of a diagnostic and therapeutic simulation system based on patient data and specialist's knowledge. I. Simulation of diagnostic process.

A new simulation system of diagnostic and therapeutic processes is developed. The aim is to train medical students for the practical use of their knowledge, utilizing patient data in a total hospital information system. The knowledge in the system is presented by the specialists for every case. In medical school there are many specialists in various fields. With their cooperation the system can grow up to a comprehensive CAI system for clinical education. The system is designed to work on the mainframe for easiness of development, maintenance and extensions of the system. The present framework has been applied to the simulation of diagnostic process. The usefulness of the present system has been confirmed by specialists and students.

Computer-Assisted Instruction↗

PubMed2026

The Moral of the Story-Perception of Leadership With Moral Distress in Registered Nurses: A Qualitative Systematic Review.

AIM: To understand how Registered Nurses perceive the impact of nursing leadership on managing moral distress and mitigating burnout. BACKGROUND: Moral distress and burnout are pervasive issues in nursing, compromising well-being, patient safety and workforce sustainability. Leadership is a critical factor in shaping workplace culture and mitigating these challenges, yet evidence remains limited. DESIGN: Qualitative systematic review. METHODS: A qualitative systematic review was conducted following JBI methodology and PRISMA guidelines. Comprehensive searches across MEDLINE, PsycINFO, Embase, CINAHL and Scopus identified 5927 articles, with two studies meeting the inclusion criteria. Data were appraised using the JBI Critical Appraisal Checklist and synthesised via meta-aggregation. Confidence in findings was assessed using the ConQual approach. RESULTS: Four major themes emerged: (1) Behind the barriers, (2) Breaking point, (3) Weathering the storm and (4) Leadership for lasting change. Leadership influenced nurses' psychological safety, ethical decision-making and resilience. Inadequate support amplified moral distress, and effective strategies included authentic communication, team solidarity and systemic interventions. CONCLUSIONS: Leadership plays a pivotal role in mitigating moral distress and burnout. Evidence highlights the need for structural changes and support to sustain registered nurses' well-being and retention. RELATIVE TO CLINICAL PRACTICE: Findings offer direction for leadership strategies that promote ethical workplaces, shared decision-making and mental health supports to enhance resilience and patient care. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Strengthening leadership capability is vital for workforce sustainability, care quality and nurse retention. REPORTING METHOD: Authors have adhered to relevant EQUATOR guidelines. PATIENT OR PUBLIC CONTRIBUTION: This study did not involve patients or the public in its design, conduct or reporting.

Leadership↗