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PubMed2026

Deep learning techniques in predicting BRAF mutation status in cutaneous melanoma from histopathologic images.

AIMS: To develop and validate a deep learning framework for discriminating BRAF mutation status in cutaneous melanoma from routine H&E whole-slide images (WSIs) as a proof-of-concept complementary approach alongside molecular testing. METHODS: We built a two-stage pipeline comprising U-Net-based tumour segmentation followed by an Inception v3 classifier. In total, 272 institutional melanoma cases with confirmed BRAF status were used for model development (training and internal validation). Generalisability was assessed in an external test set of 76 cutaneous melanoma cases from the Cancer Genome Atlas (TCGA). Dermatopathologist-defined tumour-rich regions of interest were used to train and evaluate segmentation. WSIs were processed at 20×magnification using 512×512 tiles; slide-level mutation probabilities were obtained by averaging the predicted probabilities across all tumour-enriched tiles. RESULTS: Inception v3 achieved area under the receiver operating characteristic curve values of 0.973 (training), 0.954 (validation) and 0.915 (TCGA testing) and outperformed a ResNet50 baseline, showing stable external generalisation. Performance remained robust in advanced pathological T-category primary tumours (pT3-T4). Tumour probability heatmaps supported spatial interpretability by localising regions contributing most strongly to predicted mutation status. CONCLUSIONS: Deep learning applied to routine H&E WSIs can infer BRAF mutation status in cutaneous melanoma with consistent performance across institutional and external cohorts. Given the observed external sensitivity and negative predictive value, the model is not suitable for rule-out use or for deferring/omitting molecular testing. Any workflow integration is future work and would require prospective validation and calibration of probability outputs in real-world clinical series.

Artificial Intelligence↗

PubMed2025

Novel antibodies for identification, selection, and manipulation of T cells expressing Whitlow linker-containing CARs.

BACKGROUND: The translational study of chimeric antigen receptor (CAR) T-cell function, persistence, immunophenotype, and spatial localization after infusion is crucial for understanding factors that influence clinical outcomes. However, research has been limited by a lack of optimized tools to reliably detect CAR-engineered cells. To address this, we developed a novel platform to generate monoclonal antibodies (mAbs) targeting a linker peptide incorporated in single-chain variable fragments (scFvs) of most CAR constructs. METHODS: Using recombinant proteins and scFv linker peptides as immunogens, we generated murine mAbs against the Whitlow linker peptide, capable of binding cells expressing Whitlow linker-containing CARs in both fresh and formalin-fixed paraffin-embedded (FFPE) tissues. We evaluated these antibodies in multiple in vitro translational applications relevant to CAR T-cell research and manufacturing. RESULTS: We identified five unique mAbs reactive against the Whitlow linker and characterized their binding properties and three-dimensional structural conformation. One clone was evaluated in depth, demonstrating comparable capacity to identify CAR T cells in peripheral blood relative to other methods using anti-idiotype antibodies or recombinant CAR-target proteins. In contrast to these reagents, the anti-Whitlow mAb detects cells expressing Whitlow linker-containing CARs with different antigen specificities, including those harboring the widely employed anti-CD19 FMC63-derived scFv as well as other scFvs, such as those targeting B-cell maturation antigen (BCMA) or CD33. Importantly, the anti-Whitlow mAb identified CAR T cells in situ in archival FFPE tissues, and a DNA-barcoded format enabled their spatial characterization and immunophenotyping in highly multiplexed immunohistochemistry. We also assessed the functional consequences of antibody binding on CAR T cells in vitro and demonstrated the feasibility of anti-Whitlow mAb-mediated selective enrichment of CAR-expressing T cells for potential utility in manufacturing workflows. CONCLUSIONS: Anti-Whitlow mAb clones exhibited distinct structural and functional properties that can be leveraged for multiple applications, providing versatile tools for detection, selection and manipulation of a broad range of clinical and preclinical CAR T-cell products.

Humans↗

PubMed2004

Design of a safer approach to intravenous drug infusions: failure mode effects analysis.

OBJECTIVES: A set of standard processes was developed for delivering continuous drug infusions in order to improve (1) patient safety; (2) efficiency in staff workflow; (3) hemodynamic stability during infusion changes, and (4) efficient use of resources. Failure modes effects analysis (FMEA) was used to examine the impact of process changes on the reliability of delivering drug infusions. SETTING: An 11 bed multidisciplinary pediatric ICU in the children's hospital of an academic medical center staffed by board certified pediatric intensivists. The hospital uses computerized physician order entry for all medication orders. METHODS: A multidisciplinary team characterized key elements of the drug infusion process. The process was enhanced to increase overall reliability and the original and revised processes were compared using FMEA. Resource consumption was estimated by reviewing purchasing and pharmacy records for the calendar year after full implementation of the revised process. Staff satisfaction was evaluated using an anonymous questionnaire administered to staff nurses in the ICU and pediatric residents who had rotated through the ICU. RESULTS: The original process was characterized by six elements: selecting the drug; selecting a dose; selecting an infusion rate; calculating and ordering the infusion; preparing the infusion; programming the infusion pump and delivering the infusion. The following practice changes were introduced: standardizing formulations for all infusions; developing database driven calculators; extending infusion hang times from 24 to 72 hours; changing from bedside preparation by nurses to pharmacy prepared or premanufactured solutions. FMEA showed that the last three elements of the original process had high risk priority numbers (RPNs) of >225 whereas the revised process had no elements with RPNs >100. The combined effect of prolonging infusion hang times, preparation in the pharmacy, and purchasing premanufactured solutions resulted in 1500 fewer infusions prepared by nurses per year. Nursing staff expressed a significant preference and pediatric residents unanimously expressed a strong preference for the revised process. CONCLUSIONS: Standardization of infusion delivery reduced the frequency for completing the most unreliable elements of the process and reduced the riskiness of the individual elements. Both contribute to a safer system.

Academic Medical Centers↗

PubMed2004

Beyond usability: designing effective technology implementation systems to promote patient safety.

Evidence is emerging that certain technologies such as computerized provider order entry may reduce the likelihood of patient harm. However, many technologies that should reduce medical errors have been abandoned because of problems with their design, their impact on workflow, and general dissatisfaction with them by end users. Patient safety researchers have therefore looked to human factors engineering for guidance on how to design technologies to be usable (easy to use) and useful (improving job performance, efficiency, and/or quality). While this is a necessary step towards improving the likelihood of end user satisfaction, it is still not sufficient. Human factors engineering research has shown that the manner in which technologies are implemented also needs to be designed carefully if benefits are to be realized. This paper reviews the theoretical knowledge on what leads to successful technology implementation and how this can be translated into specifically designed processes for successful technology change. The literature on diffusion of innovations, technology acceptance, organisational justice, participative decision making, and organisational change is reviewed and strategies for promoting successful implementation are provided. Given the rapid and ever increasing pace of technology implementation in health care, it is critical for the science of technology implementation to be understood and incorporated into efforts to improve patient safety.

Diffusion of Innovation↗

PubMed2005

Applications of DNA microarrays in biology.

DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included.

Animals↗

PubMed2004

Intraoperative high-field-strength MR imaging: implementation and experience in 200 patients.

PURPOSE: To review the initial clinical experience with intraoperative high-field-strength magnetic resonance (MR) imaging of brain lesions in 200 patients. MATERIALS AND METHODS: Two hundred patients (mean age, 46.1 years; range, 7-84 years), most of whom had glioma or pituitary adenoma, were examined with a 1.5-T MR imager equipped with a rotating operating table and located in a radiofrequency-shielded operating theater. A navigation microscope placed inside the 0.5-mT zone and used in combination with a ceiling-mounted navigation system enabled integrated microscope-based neuronavigation. The extent of resection depicted at intraoperative imaging, the surgical consequences of intraoperative imaging, and the clinical practicability of the operating room setup were analyzed. RESULTS: Seventy-seven resections with a transsphenoidal approach, 100 craniotomies, and 23 burr-hole procedures were performed. In 55 (27.5%) of 200 patients, intraoperative MR imaging had immediate surgical consequences (eg, extension of resection in 39% of patients with pituitary adenoma or glioma). In 108 patients the navigation system was used, and for 37 of those patients, functional imaging data were integrated into the navigation system. There was nearly no difference in quality between pre- and intraoperative images. Intraoperative workflow with intraoperative patient transport for imaging was straightforward, and imaging in most cases began less than 2 minutes after sterile covering of the surgical site. No complications resulted from high-field-strength MR imaging. CONCLUSION: The high-field-strength MR imager was successfully adapted for intraoperative use with the integrated neuronavigation system. Intraoperative MR imaging provided valuable information that allowed intraoperative modification of the surgical strategy.

Adenoma↗

PubMed2026

Drug repurposing using transcriptomics: principles and unmet needs in cardiovascular disease.

Although cardiovascular disease is the leading cause of death globally, therapeutic development in this field is slow. Given the high cost of developing new drugs and running clinical trials for cardiovascular disease, repurposing of drugs with approved safety profiles is an attractive strategy for therapeutic development that can significantly reduce the time and cost investment before phase II clinical trials. In the era of "Omics," various new methods and several large databases have been developed to enable the use of transcriptomics data for drug repurposing. This review summarizes the principles and workflow of signature mapping, which forms the foundation of statistical models used for transcriptome-based drug repurposing. We highlight the features of different analysis pipelines and databases that have been developed for signature mapping. These analysis pipelines prioritize genes that are statistically important, an approach that fundamentally differs from the pharmacological approach of identifying disease-driving and therapeutically targetable pathways. Outcomes of signature mapping pipelines are sensitive to the quality of input data, and results are not always reproducible. Moreover, all widely used RNA-seq databases are derived from cancer research and lack high-quality molecular data for cardiovascular disease. These unmet needs call for interdisciplinary collaboration and large networks of cardiovascular research-oriented biobanks to create the databases needed for transcriptomic-based signature mapping for drug repurposing efforts.

Drug Repositioning↗

PubMed2000

Diagnostic errors in interactive telepathology.

Telepathology (TP) as a service in pathology at a distance is now widely used. It is integrated in the daily workflow of numerous pathologists. Meanwhile, in Germany 15 departments of pathology are using the telepathology technique for frozen section service; however, a common recognised quality standard in diagnostic accuracy is still missing. In a first step, the working group Aurich uses a TP system for frozen section service in order to analyse the frequency and sources of errors in TP frozen section diagnoses for evaluating the quality of frozen section slides, the important components of image quality and their influences an diagnostic accuracy. The authors point to the necessity of an optimal training program for all participants in this service in order to reduce the risk of diagnostic errors. In addition, there is need for optimal cooperation of all partners involved in TP service.

Breast Neoplasms↗

PubMed2026

Clinical and Genetic Spectrum of Large AIP Deletions.

Familial isolated pituitary adenoma (FIPA) accounts for approximately 2%-5% of all pituitary adenomas, with inactivating variants of the aryl hydrocarbon receptor-interacting protein (AIP) gene representing the most frequent known genetic cause. Clinically, patients with AIP variants often have young-onset macroadenomas with growth hormone hypersecretion, although disease severity and penetrance are variable. Most reported AIP variants are point mutations, whereas large deletions are rare and potentially underdiagnosed. Accurate detection of AIP copy-number variants requires methods such as multiplex ligation-dependent probe amplification or validated copy-number analysis of next-generation sequencing data, as Sanger sequencing alone may fail to identify these alterations. Due to the rarity of the disease, it is unknown whether large deletions in the ubiquitously expressed AIP gene are associated with potentially more severe phenotype. Available data suggest that large deletions may occur in 8%-10% of AIP mutation-positive pedigrees, highlighting the importance of incorporating copy-number variant detection into AIP testing workflows. We analysed data from all published patients with large AIP deletions (n = 25) and report here two novel large AIP deletions (Exons 3-4 and Exons 2-6 deletions) and three additional three families, including an Albanian kindred associated with metastatic Hürthle cell thyroid carcinoma. No major differences compared with other AIP variants were found in age at diagnosis, tumour size, hormonal profile, sex distribution or presence of other tumours. A role for AIP variants in thyroid carcinogenesis is unlikely.

Humans↗

PubMed2004

[Electronic data capture. Impact on the quality of the clinical research].

Electronic data capture helps improving the quality of clinical research; mainly yielding a more reliable data collection process, but also introducing new resources for controlling the execution of clinical trials and facilitating the monitoring tasks. The protocol can be implemented rather than followed, and tools are at hand to help minimising the human error. The principal difficulty in the use of the electronic data capture lays in a more complex set up. This forces the adaptation of the people involved in the execution of clinical trials to a different workflow. In the future, most of the clinical research will use electronic data capture systems, and the paper shall be left for a few particular cases.

Biomedical Research↗

PubMed2026

A Clinically Integrated Pediatric Patient-Derived Xenograft Program Enables Evaluation of Cohort and Patient-Specific Biology and Therapeutic Strategies.

UNLABELLED: Preclinical translational research has increasingly utilized patient-derived xenograft (PDX) models for mechanistic and experimental therapeutic studies, yet most existing models have been developed from adult cancer types. We describe the establishment of a PDX program to expand the availability of pediatric-specific PDXs for preclinical research and enable studies of pediatric cancer histologies, including ultrarare diseases. Processes for PDX generation were integrated into established clinical workflows to facilitate universal model generation. Methodologies for tissue procurement, processing, and cryopreservation were optimized to enable intra- and interinstitutional PDX model generation. Over a 6-year span, 388 PDX tumor models representing more than 40 diagnoses were generated, including ultrarare tumors and longitudinal models established from pretherapy, posttherapy, and relapse tumors from the same patient. Genomic characterization of these PDXs demonstrates excellent concordance and recapitulation of molecular alterations of the source tumor. Successful PDX generation was enhanced from relapsed samples, was higher in sarcomas compared with other solid tumor types, and was a negative prognosticator for clinical outcome. With a broad portfolio of molecularly annotated models, we demonstrate utility for validating cross-histology biomarker-driven therapeutic strategies by demonstrating antitumor activity of an MAT2A inhibitor in MTAP-deficient PDXs. Universal model creation also allows for experimental validation of therapeutic hypotheses on a patient-specific basis, as we describe the characterization of a novel RAF1 fusion (EPB41L2::RAF1) in an osteosarcoma PDX. Development of a diverse collection of pediatric PDX models enables hypothesis-driven and cross-histology studies that expand our understanding of cancer biology and aid ongoing drug prioritization efforts in rare tumors. SIGNIFICANCE: A clinically integrated, genomically annotated pediatric PDX portfolio supported by systematic benchmarking of model generation facilitates exploratory biomarker-driven and patient-specific translational studies.

Humans↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗

PubMed2026

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

UNLABELLED: Breast and prostate cancers are both hormone-driven adenocarcinomas that undergo analogous invasion programs. Using lightsheet microscopy on intact tumors, we identified transitional junctions between precancerous and invasive regions. We then developed a multimodal serial-section workflow integrating volumetric reconstruction with spatial transcriptomics. Analysis of 319 spatial assays from 51 cases revealed gene expression features and novel structural insights defining the shift from precancer to invasive disease. In breast cancer, loss of MGP and PLAT was associated with invasive transition and promoted tumorigenesis in functional assays. In prostate cancer, GDF15, ALDH1A3, ANPEP, and FASN were upregulated along invasive progression, and their knockdown in PC-3 cells suppressed proliferation and migration. Enrichment of tumor-associated macrophages (SPP1+ and MS4A6A+) along non-triple-negative breast cancer breast cancer transitions highlights immune involvement as a potential driver of invasiveness. SIGNIFICANCE: Our method of defining precise spatial locations of invasive transition allows for the direct interrogation of transition drivers, presenting new therapeutic targets for the two most prevalent cancers and providing a framework for studying spatially defined mechanisms of tumor progression. See related commentary by Jing and Li, p. 1720.

Humans↗

PubMed2006

Proteomics of the heart: unraveling disease.

Heart diseases resulting in heart failure are among the leading causes of morbidity and mortality in developed countries. Underlying molecular causes of cardiac dysfunction in most heart diseases are still largely unknown but are expected to result from causal alterations in gene and protein expression. Proteomic technology now allows us to examine global alterations in protein expression in the diseased heart and can provide new insights into cellular mechanisms involved in cardiac dysfunction. The majority of proteomic investigations still use 2D gel electrophoresis (2-DE) with immobilized pH gradients to separate the proteins in a sample and combine this with mass spectrometry (MS) technologies to identify proteins. In spite of the development of novel gel-free technologies, 2-DE remains the only technique that can be routinely applied to parallel quantitative expression profiling of large sets of complex protein mixtures such as whole cell lysates. It can resolve >5000 proteins simultaneously (approximately 2000 proteins routinely) and can detect <1 ng of protein per spot. Furthermore, 2-DE delivers a map of intact proteins, which reflects changes in protein expression level, isoforms, or post-translational modifications. The use of proteomics to investigate heart disease should result in the generation of new diagnostic and therapeutic markers. In this article, we review the current status of proteomic technologies, describing the 2-DE proteomics workflow, with an overview of protein identification by MS and how these technologies are being applied to studies of human heart disease.

Developed Countries↗

PubMed2026

LDLR Variant Classification Through Activity-Normalized Prime Editing Screening.

BACKGROUND: Inherited variants in the LDL (low-density lipoprotein) receptor (LDLR) gene are the most common cause of familial hypercholesterolemia, significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt lipid-lowering therapy and cascade testing of at-risk relatives; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, leaving many patients without actionable information. METHODS: We developed the first activity-normalized prime editing screening pipeline to measure the impact of 5184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Each prime editing guide RNA is paired with a genotypic outcome reporter to correct for variable editing efficiency, overcoming a key limitation of previous pooled genome editing screens. A statistical framework further improves variant effect estimates by jointly analyzing all missense variants at each amino acid position. RESULTS: We show that prime editing of the reporter construct correlates with endogenous variant installation frequency, validating the activity normalization approach. The resulting scores capture a continuous spectrum of functional effects, robustly separate pathogenic versus benign ClinVar variants, and show concordance with LDL-C levels in UK Biobank participants. We calibrate functional evidence strengths to the ACMG/AMP variant interpretation framework, enabling integration into a clinical variant classification workflow. By combining functional, computational, population, and contextual evidence, 322 of 434 LDLR variants currently classified as variants of uncertain significance, conflicting, or absent from ClinVar appear to meet evidence thresholds for reclassification and can be prioritized for expert review, substantially expanding the pool of actionable variant classifications. The screen also reveals a cluster of gain-of-function variants in LDLR class A repeat 5, at least some of which enhance LDL-C uptake through increased apolipoprotein B interaction, with implications for therapeutic genome editing. Last, prime editing uniquely detects splice-altering coding variants missed by cDNA-based screens and pathogenicity predictors, revealing an advantage of endogenous variant installation. CONCLUSIONS: Altogether, activity-normalized prime editing provides a scalable framework for LDLR variant classification that substantially expands the proportion of variants with evidence for genetic diagnosis and reveals novel biology with therapeutic relevance.

CRISPR screening↗

PubMed2024

Metagenomic Study of the MESA: Detection of Gemella Morbillorum and Association With Coronary Heart Disease.

BACKGROUND: Inflammation is a feature of coronary heart disease (CHD), but the role of proinflammatory microbial infection in CHD remains understudied. METHODS AND RESULTS: CHD was defined in the MESA (Multi-Ethnic Study of Atherosclerosis) as myocardial infarction (251 participants), resuscitated arrest (2 participants), and CHD death (80 participants). We analyzed sequencing reads from 4421 MESA participants in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program using the PathSeq workflow of the Genome Analysis Tool Kit and a 65-gigabase microbial reference. Paired reads aligning to 840 microbes were detected in >1% of participants. The association of the presence of microbe reads with incident CHD (follow-up, ~18 years) was examined. First, important variables were ascertained using a single regularized Cox proportional hazard model, examining change of risk as a function of presence of microbe with age, sex, education level, Life's Simple 7, and inflammation. For variables of importance, the hazard ratio (HR) was estimated in separate (unregularized) Cox proportional hazard models including the same covariates (significance threshold Bonferroni corrected P<6×10-5, 0.05/840). Reads from 2 microbes were significantly associated with CHD: Gemella morbillorum (HR, 3.14 [95% CI, 1.92-5.12]; P=4.86×10-6) and Pseudomonas species NFACC19-2 (HR, 3.22 [95% CI, 2.03-5.41]; P=1.58×10-6). CONCLUSIONS: Metagenomics of whole-genome sequence reads opens a possible frontier for detection of pathogens for chronic diseases. The association of G morbillorum and Pseudomonas species reads with CHD raises the possibilities that microbes may drive atherosclerotic inflammation and that treatments for specific pathogens may provide clinical utility for CHD reduction.

Humans↗

PubMedDate not supplied

Electronic medical records and their impact on resident and medical student education.

OBJECTIVE: Electronic medical records (EMRs) are becoming prevalent and integral tools for residents and medical students. EMRs can integrate point-of-service information delivery within the context of patient care. Though it may be an educational tool, little is known about how EMR technology is currently used for medical learners. METHOD: The authors reviewed the available published literature about the impact of EMRs on learners, including learners' attitudes about EMRs, educational uses of EMRs, and the potential effects of EMRs on learners' daily work. RESULTS: Research on EMRs for education is in its infancy. The authors found fewer than 50 articles with evidence on their use in medical education. The applications to education included point-of-care knowledge delivery, computerized clinical decision support systems, profiling of learner experiences, and daily workflow management. The evidence was mainly derived from single institution studies and occasionally across disciplines. CONCLUSIONS: EMRs have great potential as an educational tool, but thus far, strong data to support their use for this are lacking. As the usage of EMRs rises, educators must continue to study how best to use technology as an educational tool and as a tool to improve the daily work of residents and medical students.

Education, Medical↗

PubMed2004

A computerized clinical decision support system as a means of implementing depression guidelines.

The authors describe the history and current use of computerized systems for implementing treatment guidelines in general medicine as well as the development, testing, and early use of a computerized decision support system for depression treatment among "real-world" clinical settings in Texas. In 1999 health care experts from Europe and the United States met to confront the well-documented challenges of implementing treatment guidelines and to identify strategies for improvement. They suggested the integration of guidelines into computer systems that is incorporated into clinical workflow. Several studies have demonstrated improvements in physicians' adherence to guidelines when such guidelines are provided in a computerized format. Although computerized decision support systems are being used in many areas of medicine and have demonstrated improved patient outcomes, their use in psychiatric illness is limited. The authors designed and developed a computerized decision support system for the treatment of major depressive disorder by using evidence-based guidelines, transferring the knowledge gained from the Texas Medication Algorithm Project (TMAP). This computerized decision support system (CompTMAP) provides support in diagnosis, treatment, follow-up, and preventive care and can be incorporated into the clinical setting. CompTMAP has gone through extensive testing to ensure accuracy and reliability. Physician surveys have indicated a positive response to CompTMAP, although the sample was insufficient for statistical testing. CompTMAP is part of a new era of comprehensive computerized decision support systems that take advantage of advances in automation and provide more complete clinical support to physicians in clinical practice.

Algorithms↗