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Prenatal diagnosis of recurrent Kagami-Ogata syndrome inherited from a mother affected by Temple syndrome: a case report and literature review.

BACKGROUND: Kagami-Ogata syndrome (KOS) and Temple syndrome (TS) are two imprinting disorders characterized by the absence or reduced expression of maternal or paternal genes in the chromosome 14q32 region, respectively. We present a rare prenatally diagnosed case of recurrent KOS inherited from a mother affected by TS. CASE PRESENTATION: The woman's two affected pregnancies exhibited recurrent manifestations of prenatal overgrowth, polyhydramnios, and omphalocele, as well as a small bell-shaped thorax with coat-hanger ribs postnatally. Prenatal genetic testing using a single-nucleotide polymorphism array detected a 268.2-kb deletion in the chromosome 14q32 imprinted region inherited from the mother, leading to the diagnosis of KOS. Additionally, the woman carried a de novo deletion in the paternal chromosome 14q32 imprinted region and presented with short stature and small hands and feet, indicating a diagnosis of TS. CONCLUSIONS: Given the rarity of KOS as an imprinting disorder, accurate prenatal diagnosis of this rare imprinting disorder depends on two factors: (1) increasing clinician recognition of the clinical phenotype and related genetic mechanism, and (2) emphasizing the importance of imprinted regions in the CMA workflow for laboratory analysis.

Humans↗

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗

FACS-Proteomics strategy toward extracellular vesicles single-phenotype characterization in biological fluids: exploring the role of leukocyte-derived EVs in multiple sclerosis.

BACKGROUND: The isolation and proteomics characterization of extracellular vesicles (EVs) from body fluids is challenging due to their vast heterogeneity. We have recently demonstrated that Fluorescence-activated Cell Sorting (FACS) efficiently isolates the whole EV circulating compartment directly from untouched body fluids enabling a comprehensive EV proteomics analysis. RESULTS: Here, we characterized, for the first time, a single-phenotype EV subset by sorting leukocyte-derived EVs (Leuko EVs) from peripheral blood and tears of healthy volunteers. Using an optimized and patented staining protocol of the whole EV compartment we identified and excluded non-EV particles, debris and damaged EVs. We further isolated, using an anti-CD45 antibody, Leuko EVs (CD45+ EVs), reaching a high level of purity (> 90%). Purified Leuko EVs were characterized using atomic force microscopy, nanoparticle tracking, and shotgun proteomics analysis revealing a similar coded protein cargo in both biological fluids. Subsequently, the same workflow was applied to tears from Relapsing-Remitting Multiple Sclerosis (RRMS) patients, revealing a Leuko EVs protein cargo enrichment that reflects the neuroinflammatory condition characteristics of RRMS. This enrichment was evidenced by the activation of upstream regulators TGFB1 and NFE2L2, which are associated with inflammatory responses. Additionally, the analysis identified markers indicative of endothelial cell proliferation and the development of enhanced vascular networks, with AGNPT2 and VEGF emerging as activated upstream regulators. These findings indicate the complex interplay between inflammation and angiogenesis in RRMS. CONCLUSIONS: In conclusion, our combined FACS-Proteomics strategy offers a promising approach for biomarker discovery, analysing cell-specific EV phenotypes directly from untouched body fluids, advancing the clinical value of tears EVs and improving the understanding of EV-mediated processes in vivo. Data are available via ProteomeXchange with the identifier PXD049036 and in EV-TRACK knowledgebase with ID: EV240150.

Humans↗

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗

Clinically actionable stratification of uncommon MET fusions: a precision oncology framework.

BACKGROUND: MET fusions represent emerging therapeutic targets in solid tumors; however, functional interpretation of non-canonical variants remains poorly understood, posing a major challenge for precision oncology. METHODS: We conducted a multicenter, pan-cancer study analyzing 23,299 clinical samples using DNA-based next-generation sequencing (NGS) to profile MET fusions. Transcriptional validation was performed using RNA-based NGS on available samples. Preliminary clinical outcomes were assessed in four patients with advanced malignancies harboring uncommon MET fusions who received MET tyrosine kinase inhibitor therapy. RESULTS: We identified 116 MET fusions (incidence: 0.5%), with 55.2% (64/116) classified as uncommon fusions. These uncommon fusions were stratified into: Group A (5’-retained, n = 12), Group B (intergenic/exonic breakpoints, n = 19), Group C (rare partners, n = 23), and Group D (dual fusions, n = 10). RNA validation revealed an overall low transcriptional consistency of 43.8% (14/32) for uncommon fusions, versus 100% for canonical fusions (PTPRZ1::MET, CAPZA2::MET). Notably, most 5’-retained fusions were transcriptionally silent, while some intergenic fusions resolved into expressed canonical partners (e.g. PTPRZ1::MET). Therapeutically, all four MET inhibitor-treated patients achieved partial responses, including pediatric diffuse midline gliomas (DMG) (median OS: 11.2 months) and lung adenocarcinoma (median OS: 34 months), demonstrating preliminary clinical activity. CONCLUSIONS: uncommon MET fusions are heterogeneous at genomic and transcriptional levels. DNA-level findings often do not predict functional transcripts, underscoring the necessity of RNA-based confirmation for clinical interpretation. Despite low overall consistency, a subset retains therapeutic potential. We propose a refined diagnostic framework integrating DNA-based stratification and RNA validation to guide the management of MET-altered cancers in precision oncology workflows.

Humans↗

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗

Haplotype-aware long-read error correction.

Error correction of long reads is an important initial step in genome assembly workflows. For organisms with ploidy greater than one, it is important to preserve haplotype-specific variation during read correction. This challenge has driven the development of several haplotype-aware correction methods. However, existing methods are based on either ad-hoc heuristics or deep learning approaches. In this paper, we introduce a rigorous formulation for this problem. Our approach builds on the minimum error correction framework used in reference-based haplotype phasing. We prove that the proposed formulation for error correction of reads in de novo context, i.e., without using a reference genome, is NP-hard. To make our exact algorithm scale to large datasets, we introduce practical heuristics. Experiments using PacBio HiFi sequencing datasets from human and plant genomes show that our approach achieves accuracy comparable to state-of-the-art methods. Implementation: https://github.com/at-cg/HALE .

Clustering↗

Spatiotemporal transcriptomic analysis during cold ischemic injury to the murine kidney reveals compartment-specific changes.

BACKGROUND: Kidney transplantation is the preferred treatment strategy for end-stage kidney disease. Deceased donor kidneys usually undergo cold storage until kidney transplantation, leading to cold ischemia injury that may contribute to poor graft outcomes. However, the molecular characterization of potential mechanisms of cold ischemia injury remains incomplete. RESULTS: To bridge this knowledge gap, we leverage 10x Visium spatial transcriptomic technology to perform full transcriptome profiling of murine kidneys subject to varying durations of cold ischemia typical in a deceased donor kidney transplant setting. We develop a computational workflow to identify and compare spatiotemporal transcriptomic changes that accompany the injury pathophysiology in a tissue compartment-specific manner. We identify proportional enrichment of oxidative phosphorylation (OXPHOS) genes with increasing duration of cold ischemia injury within the oxygen-lean inner medulla region, suggestive of atypical metabolic presentation. This is distinct in cold ischemia injury tissue compared to warm ischemia-reperfusion kidney injury tissue. Spatiotemporal trends are validated by qPCR and immunofluorescence in a larger cohort of mice. CONCLUSIONS: Altogether, our spatiotemporal transcriptomic analysis identifies coordinated molecular changes within metabolic pathways such as OXPHOS deep within the cold ischemic kidney, highlighting the need for increased attention to the inner medulla and potential opportunities for new insights beyond those available from superficial biopsy-focused tissue examination.

Animals↗

Computational strategies for copy number variation detection, disease association, and beyond.

Copy number variations (CNVs) are key structural variations that contribute to human genetic diversity, evolution, and disease susceptibility. Advances in sequencing technologies and computational methods have improved CNV detection, yet association studies remain challenged by methodological limitations and a lack of standardisation. This review provides an overview of computational strategies for germline CNV detection and disease association. We highlight the value of CNV analysis for uncovering genetic contributions to complex traits and disease risk and outline an analysis workflow including key benchmarking methods. We also discuss current challenges and future directions for advancing CNV detection and association analysis.

Humans↗

Long-read transcriptomics corrects Trichomonas vaginalis intron annotations and refines transcript-end features.

BACKGROUND: Trichomonas vaginalis causes the most prevalent non-viral sexually transmitted infection worldwide. Despite its large genome (181.5 Mb; 36,310 predicted protein-coding genes in NYU_TvagG3_2), intron annotations remain limited and inconsistently validated. A recent short-read RNA-seq study reported 63 putative active introns, but short reads can misassign splice boundaries and cannot resolve complete transcript structures. METHODS: We integrated Oxford Nanopore direct RNA sequencing (DRS), ONT cDNA long-read sequencing, and Illumina RNA-seq to refine intron annotations, transcript-end features, and UTR boundaries in T. vaginalis. Candidate introns were validated by targeted PCR and Sanger sequencing, and representative splicing events were further assessed using public SRA datasets. RESULTS: Starting from 31 historically annotated introns, motif-guided long-read screening and orthogonal validation identified 17 additional validated introns, increasing the curated set to 48 confirmed introns. Among these 17 events, three were previously unrecognized in the current NYU_TvagG3_2 reference annotation. We also corrected five reported loci, including two false-positive introns, two splice-coordinate misannotations, and one gene-sequence error. DRS further supported transcript termination site mapping, UAAA polyadenylation-signal profiling relative to poly(A) addition sites, and single-molecule poly(A)-tail estimation. StringTie mixed-mode assemblies provided updated UTR boundaries for intron-bearing transcripts and transcripts without curated introns. CONCLUSIONS: This study provides a rigorously validated, long-read-refined resource of intron annotations, UTR boundaries, and UAAA-guided transcript-end features for T. vaginalis, together with a reproducible workflow for non-model protists. These refinements improve the current reference annotation and support future studies of functional genomics, parasite biology, pathogenesis, and diagnostic development.

Trichomonas vaginalis↗

DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.

DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intelligence, analyzes large datasets to identify patterns and make predictions. Over the past two decades, advances in bioinformatics technologies for arrays and sequencing have generated vast amounts of data, leading to the widespread adoption of machine learning methods for analyzing complex biological information for medical problems. This review explores recent advancements in DNA methylation studies that leverage emerging machine learning techniques for more precise, comprehensive, and rapid patient diagnostics based on DNA methylation markers. We present a general workflow for researchers, from clinical research questions to result interpretation and monitoring. Additionally, we showcase successful examples in diagnosing cancer, neurodevelopmental disorders, and multifactorial diseases. Some of these studies have led to the development of diagnostic platforms that have entered the global healthcare market, highlighting the promising future of this field.

Humans↗

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗

Computerized physician order entry and online decision support.

Computerized physician order entry (CPOE) and decision support systems (DSS) can reduce certain types of error but often slow clinicians and may increase other types of error. The net effect of these systems on an emergency department (ED) is unknown. The consensus participants combined published evidence with expert opinion to outline recommendations for success. These include seamless integration of CPOE and DSS into systems and workflow; ensuring access to Internet-based and other online support material in the clinical arena; designing systems specifically for the ED and measuring their impact to ensure an overall benefit; ensuring that CPOE systems provide error and interaction checking and facilitate weight- and physiology-based dosing; using interruptive alerts only for the highest-severity events; providing a simple, vendor-independent interface for institutional customization of CPOE alert thresholds; maximizing the use of automated systems and passive data capture; and ensuring the widespread availability of CPOE and DSS using secure wireless and portable technologies where appropriate. Decisions regarding CPOE and DSS in the ED should be guided by the ED chair or designee. Much of what is believed to be true regarding CPOE and DSS has not been adequately studied. Additional CPOE and DSS research is needed quickly, and this research should receive funding priority. DSS and CPOE hold great promise to improve patient care, but not all systems are equal. Evidence must guide these efforts, and the measured outcomes must consider the many factors of quality care.

Decision Support Systems, Clinical↗

Emergency department digital radiology: moving from photos to pixels.

Emergency department (ED) patient care relies heavily on radiologic imaging. As advances in technologic innovation continue to present opportunities to streamline and simplify the delivery of care, emergency medicine (EM) practitioners face the challenge of transitioning from a system of primarily film-based radiography to one that utilizes digitized images. The move to digital radiology can result in enhanced quality of patient care, reduction of errors, and increased ED efficiency; however, making this transition will necessarily involve changes in EM practice. As the technology evolves, digital radiology will gradually become ingrained into everyday practice because of these and other notable benefits; however, EM practitioners will need to overcome several challenges to make the transition smoothly and consider the potential impacts that this change will have on ED workflow. The authors discuss the benefits, challenges, and other operational considerations involved with the ED implementation of digital radiology and close by presenting guiding principles for current and future users. Despite the unresolved issues, digital radiology will mature as a technology and improve EM practice, making it one of the great information technology advances in EM.

Diagnostic Imaging↗

Clinician use of a palmtop drug reference guide.

OBJECTIVE: Problems involving drug knowledge are one of the most common causes of serious medication errors. Although the information that clinicians need is often available somewhere, retrieving it expeditiously has been problematic. At the same time, clinicians are faced with an ever-expanding pharmacology knowledge base. Recently, point-of-care technology has become more widely available and more practical with the advent of handheld, or palmtop, computing. Therefore, the authors evaluated the clinical contribution of a drug database developed for the handheld computer. ePocrates Rx (formerly known as qRx; ePocrates, San Carlos, California) is a comprehensive drug information guide that is downloadable free from the Internet and designed for the Palm OS platform align="right". DESIGN: A seven-day online survey of 3,000 randomly selected ePocrates Rx users was conducted during March 2000. MEASUREMENTS: User technology experience, product evaluation and usage patterns, and the effects of the drug reference database on information-seeking behavior, practice efficiency, decision making, and patient care. RESULTS: The survey response rate was 32 percent (n=946). Physicians reported that ePocrates Rx saves time during information retrieval, is easily incorporated into their usual workflow, and improves drug-related decision making. They also felt that it reduced the rate of preventable adverse drug events. CONCLUSIONS: Self-reported perceptions by responding clinicians endorse improved access to drug information and improved practice efficiency associated with the use of handheld devices. The clinical and practical value of using these devices in clinical settings will clearly grow further as wireless communication becomes more ubiquitous and as more applications become available.

Attitude to Computers↗

Immediate benefits realized following implementation of physician order entry at an academic medical center.

OBJECTIVE: To evaluate the benefits of computerized physician order entry (POE) and electronic medication administration record (eMAR) on the delivery of health care. DESIGN: Inpatient nursing units in an academic health system were the setting for the study. The study comprised before-and-after comparisons between phase 1, pre-implementation of POE (pre-POE) and phase 2, post-implementation of POE (post-POE) and, within phase 2, a comparison of POE and the combination of POE plus eMAR. Length of stay and cost were compared pre- and post-POE for a period of 10 to 12 months across all services in the respective hospitals. MEASUREMENTS: Comparisons were made pre- and post-POE for the time intervals between initiation and completion of pharmacy (pre-POE, n=46; post-POE, n=70), radiology (pre-POE, n=11; post-POE, n=54), and laboratory orders (without POE, n=683; with POE, n=1,142); timeliness of countersignature of verbal order (University Hospitals [OSUH]: pre-POE, n=605; post-POE, n=19,225; James Cancer Hospital (James): pre-POE, n=478; post-POE, n=10,771); volume of nursing transcription errors (POE with manual MAR, n=888; POE with eMAR, n=396); length of stay and total cost (OSUH: pre-POE, n=8,228; post-POE, n=8,154; James: (pre-POE, n=6,471; post-POE, n=6,045). RESULTS: Statistically significant reductions were seen following the implementation of POE for medication turn-around times (64 percent, from 5:28 hr to 1:51 hr; p<0.001), radiology procedure completion times (43 percent, from 7:37 hr to 4:21 hr; p<0.05), and laboratory result reporting times (25 percent, from 31:3 min to 23:4 min; p=0.001). In addition, POE combined with eMAR eliminated all physician and nursing transcription errors. There were 43 and 26 percent improvements in order countersignature by physicians in OSUH and James, respectively. Severity-adjusted length of stay decreased in OSUH (pre-POE, 3.91 days; post-POE, 3.71 days; p=0.002), but not significantly in James (pre-POE, 3.68 days; post-POE, 3.61 days; p=0.356). Although total cost per admission decreased significantly in selected services, it did not change significantly across either institution (OSUH: pre-POE, 5,697 dollars; post-POE, 5,661 dollars; p=0.687; James: pre-POE, 6,427 dollars; post-POE, 6,518 dollars; p=0.502). CONCLUSION: Physician order entry and eMAR provided the framework for improvements in patient safety and in the timeliness of care. The significant cultural and workflow changes that accompany the implementation of POE did not adversely affect acuity-adjusted length of stay or total cost. The reductions in transcription errors, medication turn-around times, and timely reporting of results supports the view that POE and eMAR provide a good return on investment.

Academic Medical Centers↗

Factors affecting and affected by user acceptance of computer-based nursing documentation: results of a two-year study.

OBJECTIVES: The documentation of the nursing process is an important but often neglected part of clinical documentation. Paper-based systems have been introduced to support nursing process documentation. Frequently, however, problems such as low quality of documentation are reported. It is unclear whether computer-based documentation systems can reduce these problems and which factors influence their acceptance by users. DESIGN: We introduced a computer-based nursing documentation system on four wards of the University Hospitals of Heidelberg and systematically evaluated its preconditions and its effects in a pretest-posttest intervention study. For the analysis of user acceptance, we concentrated on subjective data drawn from questionnaires and interviews. MEASUREMENTS: A questionnaire was developed using items from published questionnaires and items that had to be developed for the special purpose of this study. RESULTS: The quantitative results point to two factors influencing the acceptance of a new computer-based documentation system: the previous acceptance of the nursing process and the previous amount of self-confidence when using computers. On one ward, the diverse acceptance scores heavily declined after the introduction of the nursing documentation system. Explorative qualitative analysis on this ward points to further success factors of computer-based nursing documentation systems. CONCLUSION: Our results can be used to assist the planning and introduction of computer-based nursing documentation systems. They demonstrate the importance of computer experience and acceptance of the nursing process on a ward but also point to other factors such as the fit between nursing workflow and the functionality of a nursing documentation system.

Attitude of Health Personnel↗