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In Germany, there is currently no official guideline for the submission of placentas for histopathological examination. Placentas are sent for histological examination by obstetricians according to locally defined indications, which leads to different practices in different centers. In this study, two cohorts of placentas were compared to assess the clinical relevance of placental examination. One cohort consisted of placentas with a clinical indication for histologic examination and the other of placentas with a clinically healthy pregnancy and a healthy infant. In this study, a placenta request form based on established international guidelines was used. Placentas from singleton and twin pregnancies with and without clinical indications were histopathologically examined. Clinical information was extracted from the request form and later correlated with histological findings. A total of 236 placentas were examined, including 127 (53.8%) with clinical indications and 109 (46.2%) without. The concordance between submission reasons and histopathological findings was higher in singleton pregnancies with clinical indications (90.9%) compared to twin pregnancies (62.97%). Placentas from singleton and twin pregnancies with clinical indications exhibited significantly more pathological findings than their respective healthy control groups. Histopathological examination of the placenta can confirm or reveal placenta pathologies and therefore improve the care of the mother, child and future pregnancies. [ABSTRACT FROM AUTHOR]

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care. [ABSTRACT FROM AUTHOR]

The referral is the basis for radiologists' assessment of modality, protocol and urgency, and insufficient information may threaten patient safety. The aim of this study was to assess the completeness of referrals for lower extremity venous duplex ultrasonography (LEVDUS) and computed tomography pulmonary angiography (CTPA), and to investigate associations between the provided clinical information including risk factors, symptoms and lab results in the referrals and positive findings of deep vein thrombosis (DVT) and pulmonary embolism (PE), respectively. Referrals for LEVDUS (801) and CTPA (800) performed from 2016 to 2019 were obtained. Three categories of clinical information from the referrals were recorded: symptoms, risk factors and laboratory results, as well as positive imaging findings of venous thromboembolism (VTE). Referral completeness was rated from zero to three according to how many categories of clinical information the referral provided. Information from all three clinical information categories was provided in 15% and 25% of referrals for LEVDUS and CTPA, respectively, while 2% and 10% of referrals did not contain any clinical information. Symptoms were provided most often (85% for LEVDUS and 94% for CTPA). Provided information about risk factors was significantly associated with positive findings for LEVDUS, (p = 0.02) and CTPA (p < 0.001). A great majority of referrals failed to provide one or more categories of clinical information. Risk factors were associated with a positive finding of VTE on LEVDUS and CTPA. Improving clinical information in referrals may improve justification, patient safety and quality of radiology services. [ABSTRACT FROM AUTHOR]

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of highquality data. [ABSTRACT FROM AUTHOR]

BackgroundThe Kids Intracranial Injury Decision Support tool for Traumatic Brain Injury (KIIDS-TBI) tool is a validated risk prediction model for managing children with mild traumatic brain injuries (mTBI) and intracranial injuries. Electronic clinical decision support (CDS) may facilitate the clinical implementation of this evidence-based guidance.ObjectiveOur objective was to evaluate the acceptability and usability of an electronic CDS tool for managing children with mTBI and intracranial injuries.MethodsEmergency medicine and neurosurgery physicians (10 each) from 10 hospitals in the United States were recruited to participate in usability testing of a novel CDS prototype in a simulated electronic health record environment. Testing included a think-aloud protocol, an acceptability and usability survey, and a semi-structured interview. The prototype was updated twice during testing to reflect user feedback. Usability problems recorded in the videos were categorized using content analysis. Interview transcripts were analyzed using thematic analysis.ResultsAmong the 20 participants, most worked at teaching hospitals (80%), freestanding children's hospitals (95%), and level-1 trauma centers (75%). During the two prototype updates, problems with clarity of terminology and navigating through the CDS interface were identified and corrected. Corresponding to these changes, the number of usability problems decreased from 35 in phase 1 to 8 in phase 3 and the number of mistakes made decreased from 18 (phase 1) to 2 (phase 3). Through the survey, participants found the tool easy to use (90%), useful for determining a patient's level of care (95%), and likely to improve resource use (90%) and patient safety (79%). Interview themes related to the CDS's ability to support evidence-based decision-making and improve clinical workflow proposed implementation strategies and potential pitfalls.ConclusionAfter iterative evaluation and refinement, the KIIDS-TBI CDS tool was found to be highly usable and useful for aiding the management of children with mTBI and intracranial injuries.

Objective: Information contained in request forms for histopathological examinations plays a critical role in the microscopic interpretation of tissue changes. Despite its importance, studies have shown inadequacies in the information communicated by clinicians. This study aimed to determine how well the necessary information is provided on the histopathology request forms and to compare its variability among different departments of a hospital. Material and Method: A retrospective, 3-month, cross-sectional study was conducted to evaluate all consecutive histopathology request forms received from different departments of a tertiary, academic hospital for three months, regarding the documentation of 12 criteria. Results: None of the 2040 requests received had all the required items. Four items of specimen description, laboratory and imaging findings, and physician contact number were available only in less than 12.5% (range between 0.05 to 12.45%) of the requests. However, four other items of patient name and contact number, physician name, and anatomical site of the lesion were documented in more than 90%. The median number of the documented items was the highest in the surgery and orthopedics (9 items) and the lowest in the pulmonology department (7 items). Comparison between departments showed that the documentation of items in the surgery department were significantly better than that of the ENT, urology, and internal medicine departments (p

In Germany, there is currently no official guideline for the submission of placentas for histopathological examination. Placentas are sent for histological examination by obstetricians according to locally defined indications, which leads to different practices in different centers. In this study, two cohorts of placentas were compared to assess the clinical relevance of placental examination. One cohort consisted of placentas with a clinical indication for histologic examination and the other of placentas with a clinically healthy pregnancy and a healthy infant. In this study, a placenta request form based on established international guidelines was used. Placentas from singleton and twin pregnancies with and without clinical indications were histopathologically examined. Clinical information was extracted from the request form and later correlated with histological findings. A total of 236 placentas were examined, including 127 (53.8%) with clinical indications and 109 (46.2%) without. The concordance between submission reasons and histopathological findings was higher in singleton pregnancies with clinical indications (90.9%) compared to twin pregnancies (62.97%). Placentas from singleton and twin pregnancies with clinical indications exhibited significantly more pathological findings than their respective healthy control groups. Histopathological examination of the placenta can confirm or reveal placenta pathologies and therefore improve the care of the mother, child and future pregnancies.

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

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Objective: Information contained in request forms for histopathological examinations plays a critical role in the microscopic interpretation of tissue changes. Despite its importance, studies have shown inadequacies in the information communicated by clinicians. This study aimed to determine how well the necessary information is provided on the histopathology request forms and to compare its variability among different departments of a hospital. Material and Methods: A retrospective, 3-month, cross-sectional study was conducted to evaluate all consecutive histopathology request forms received from different departments of a tertiary, academic hospital for three months, regarding the documentation of 12 criteria. Results: None of the 2040 requests received had all the required items. Four items of specimen description, laboratory and imaging findings, and physician contact number were available only in less than 12.5% (range between 0.05 to 12.45%) of the requests. However, four other items of patient name and contact number, physician name, and anatomical site of the lesion were documented in more than 90%. The median number of the documented items was the highest in the surgery and orthopedics (9 items) and the lowest in the pulmonology department (7 items). Comparison between departments showed that the documentation of items in the surgery department were significantly better than that of the ENT, urology, and internal medicine departments (p<0.001). Also, the internal medicine department was significantly different from all other departments (p<0.001) except neurosurgery (p=0.88). Conclusion: Our results point out a serious gap in the adequacy of pathology request forms, especially clinical items. Given the implication of such information to ensure patient safety, further studies are recommended to evaluate the impact of educational and supportive computerized interventions such as clinician education and barcoding and specimen tracking systems to help fill in the required items completely. [ABSTRACT FROM AUTHOR]

Background: Ectopic pregnancy (EP) occurs when a fertilised embryo implants extra-endometrium. Common symptoms include lower abdominal pain, amenorrhea, and per vaginal (PV) bleeding. Ultrasonography (sonogra- phy) plays a positive role in investigating EP. Aim: The aim of this study was to establish the relationship between the clinicians' provisional diagnosis for EP and the conclusion in the respective diagnostic ultrasound report. Methods: A retrospective descriptive quantitative study was performed to examine 50 files of women aged 12 to 25 years with lower abdominal pain and a provisional diagnosis for EP. Statistical Package for Social Sciences version 22 (SPSS 22.0) of 2013 and Microsoft Excel 2010™ software facilitated data analysis, guided by the sys- tems theory. Results: Files reviewed for completeness of clinical information recorded EP signs or symptoms: lower abdom- inal pain, amenorrhea, pelvic mass, and PV bleeding. The positive laboratory test findings for pregnancy, based on human chorionic gonadotropin (HCG) hormone were also on record in some cases. The files were examined for expressed provisional diagnosis of EP by clinicians and supported by clinical history involving lower abdom- inal pain (98%), amenorrhea (78%), and PV bleeding (76%). Most files were silent on the question of palpable pelvic mass (90%) and laboratory pregnancy tests (74%), which could have reinforced the provisional diagnosis. The adequacy of clinical information for provisional diagnosis was categorised as adequate, fairly adequate, and inadequate. The sonography requisition forms (SRFs) depicted compliance to compelling clinical details for provisional diagnosis (88%) and reference to previous ultrasound examination (2%). The overall finding showed increased compatibility between provisional diagnosis and sonographic findings for EP in cases with adequately recorded information on clinical history. Conclusion: This study established that adequate completion of SRFs by clinicians and provision of appropriate clinical information enhanced diagnosis of EP. [ABSTRACT FROM AUTHOR]

Introduction: Clinical history and examination, radiography and histopathology are the three pillars of accurate diagnosis. The decisive nature of histopathology reporting has made it the cornerstone of modern medical science, thus making it extremely important to render timely and well-elaborated reports with utmost diagnostic accuracy. Inadequate communication between the clinician/surgeon, radiologist and the histopathologist hampers the delivery of correct and complete diagnosis on time. Aim: To assess the awareness among practicing Oral pathologists from Kerala, India about importance of clinical and radiographic details for histopathological reporting. Materials and Methods: A population-based, cross-sectional questionnaire study was conducted from January 2022 to April 2022. Kerala was divided into four zones-North, South, East and West. An online questionnaire containing eight questions was devised and circulated among 75 practicing Oral Pathologist from each zone through digital messenger platforms or e-mail. Descriptive statistics (frequency and percentage) was calculated using Microsoft Excel 2020. Results: A total of 220 responses were received. Among the participants, 100 (45.5.%) stated that their clinician or surgeon did not provide them with adequate clinical and radiographic details. It was found that 140 (63.6%) participants opined that their clinicians or surgeons used to specify the clinical details and in 188 (85.5%) cases the surgeon or clinician mentioned the demographic data. Conclusion: It was concluded that there was a good awareness among practicing Oral Pathologists from Kerala, India, regarding the importance of clinical and radiographic details for histopathological reporting. [ABSTRACT FROM AUTHOR]

Background: Multiparametric magnetic resonance imaging (mpMRI) has been increasingly recognised as an important tool in the diagnosis of prostate cancer. PI-RADSv2 guidelines recommend that important clinical information including prostate-specific antigen (PSA) levels, examination findings, and biopsy information should be included in mpMRI requests. PIRADS score and PSA density (PSAD) are both independent predictors for the presence of a clinically significant prostate cancer. Aims: This study aims to evaluate the quality of mpMRI requests and reports at our institution in accordance with these parameters. Methods: All prostate mpMRIs performed by radiology services in Galway University Hospital between 1st September 2019 and 1st March 2020 were reviewed. Exclusion criteria were applied. Requests and reports were analysed for the presence of the following parameters: PSA-results, examination findings, biopsy information, PI-RADS score, prostate volume, and PSAD. Results: A total of 586 mpMRIs were performed, and of these, 546 were included. PSA value was provided in 497 (91%) of requests, exam findings in 355 (65%), and biopsy information in 452 (82%). PIRADS score was included in 224 (41%) of reports, prostate volume in 178 (32.6%), and PSAD in 106 (19%). Conclusions: Great variation in the quality of information contained in both requests and reports for prostate mpMRIs exists within our service. We aim to improve this by collaborating with our radiology colleagues to develop a proforma for requesting and reporting of mpMRIs for our radiology systems to ensure important clinical and radiological information is provided in future. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To evaluate the performance of a machine learning method to differentiate malignant from benign soft tissue tumors based on textural features on multiparametric magnetic resonance imaging (mpMRI).Materials and Methods: We enrolled 163 patients with soft tissue tumors whose diagnosis was pathologically proven (71 malignant, 92 benign). All patients underwent mpMRI. Twelve histographic and textural parameters were assessed on T1-weighted imaging (T1WI), T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1WI imaging. We compared mean signals of all sequences from the malignant and benign tumors using Welch's t-test. Prediction models were developed via a machine learning technique (support vector machine) using textural features of each sequence, clinical information (sex + age + tumor size), and the combined model incorporating all features. Areas under the receiver operating characteristic curves (AUCs) of these models were calculated using fivefold cross validation.Results: The diagnostic ability of clinical information model (AUC 0.85) was not inferior to the model with textural features of each sequence (AUC 0.79-0.84). The combined model showed the highest diagnostic ability (AUC 0.89). The AUC of the combined model (0.89) was comparable to those of two board-certified radiologists (0.89 and 0.87).Conclusions: Machine learning methods based on textural features on mpMRI and clinical information offer adequate diagnostic performance to differentiate between malignant and benign soft tissue tumors. [ABSTRACT FROM AUTHOR]

Introduction: Clinical history and examination, radiography and histopathology are the three pillars of accurate diagnosis. The decisive nature of histopathology reporting has made it the cornerstone of modern medical science, thus making it extremely important to render timely and well-elaborated reports with utmost diagnostic accuracy. Inadequate communication between the clinician/surgeon, radiologist and the histopathologist hampers the delivery of correct and complete diagnosis on time. Aim: To assess the awareness among practicing Oral pathologists from Kerala, India about importance of clinical and radiographic details for histopathological reporting. Materials and Methods: A population-based, cross-sectional questionnaire study was conducted from January 2022 to April 2022. Kerala was divided into four zones-North, South, East and West. An online questionnaire containing eight questions was devised and circulated among 75 practicing Oral Pathologist from each zone through digital messenger platforms or e-mail. Descriptive statistics (frequency and percentage) was calculated using Microsoft Excel 2020. Results: A total of 220 responses were received. Among the participants, 100 (45.5.%) stated that their clinician or surgeon did not provide them with adequate clinical and radiographic details. It was found that 140 (63.6%) participants opined that their clinicians or surgeons used to specify the clinical details and in 188 (85.5%) cases the surgeon or clinician mentioned the demographic data. Conclusion: It was concluded that there was a good awareness among practicing Oral Pathologists from Kerala, India, regarding the importance of clinical and radiographic details for histopathological reporting.

Background: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence-driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population. Objective: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort. Methods: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the 'Findability, Accessibility, Interoperability, and Reuse' guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large. Results: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients' self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations. Conclusions: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders., Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones., Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines., Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms., Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general., (© Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered care (PCC) has been limited. Principles of PCC planning, a recovery-based approach to service planning that operationalizes PCC, can inform the measurement of person-centeredness within clinical documentation., Objective: This study aims to use the clinical informatics approach of natural language processing (NLP) to examine the impact of CD on person-centeredness in clinic visit notes. Using a dictionary-based approach, this study conducts a textual analysis of clinic notes from a community mental health center before and after staff were trained in CD., Methods: This study used visit notes (n=1981) from 10 providers in a community mental health center 6 months before and after training in CD. LIWC-22 was used to assess all notes using the Linguistic Inquiry and Word Count (LIWC) dictionary, which categorizes over 5000 linguistic and psychological words. Twelve LIWC categories were selected and mapped onto PCC planning principles through the consensus of 3 domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modeling was used to identify differences in notes before and after CD training while accounting for nesting within the provider., Results: Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated PCC. The fixed effects analysis found a significant positive shift toward person-centeredness; this was observed in 6 of the selected LIWC categories post CD. Specifically, there was a notable increase in words associated with achievement (β=.774, P<.001), power (β=.831, P<.001), money (β=.204, P<.001), physical health (β=.427, P=.03), while leisure words decreased (β=-.166, P=.002)., Conclusions: By using a dictionary-based approach, the study identified how CD might influence the integration of PCC principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of CD in enhancing person-centeredness in clinic notes. By leveraging NLP techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in health care settings and emphasize the need for refining algorithms to more accurately measure PCC., (© Victoria Stanhope, Nari Yoo, Elizabeth Matthews, Daniel Baslock, Yuanyuan Hu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

The diagnosis of pulmonary nodules by clinicians depends not only on radiological imaging but also on the patient's own clinical record information and other factors. However, exploring the guiding role of clinical information is a major challenge. In this article, an intelligent personalized diagnosis decision‐making model is proposed, which combines radiology images with patient information. First, the 3D image cube of the pulmonary nodule is constructed. Then, a 3D multi‐level fusion ResNet is designed to extract the features of the nodule by making full use of the spatial context information. Finally, a kind of classification model based on feature‐related analysis was proposed, which fused clinical information features and image features and realized a nonlinear radial basis feature mapping. We tested this method on the public dataset and a cooperation hospital dataset. Experiments show that this method can effectively improve the classification accuracy of unstable nodules at the classification boundary. Our model showed significant improvements in sensitivity, specificity, and accuracy. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis. [ABSTRACT FROM AUTHOR]

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists' experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible. [ABSTRACT FROM AUTHOR]

Purpose: Alzheimer's is the most common irreversible neurodegenerative disease. Its symptoms range from memory impairments to degradation of multiple cognitive abilities and ultimately death. Mild cognitive impairment (MCI) is the earliest detectable stage that happens between normal aging and early dementia, and even though MCI subjects have a chance of changing back to cognitively normal or even staying the same, there is a risk that their condition progresses to Alzheimer's disease (AD) annually. Therefore predicting AD among MCI subjects is pivotal for starting treatments at an opportune time in case of progression, and if staying stable is the case, the need for consistent medical observations would eliminate. Thus, we aim to diagnose possible conversion from MCI to AD by exploiting a class of deep learning (DL) methods called convolutional neural network (CNN). Methods: We proposed a three-dimensional CNN (3D-CNN) to combine and analyze resting-state functional magnetic resonance imaging (rs-fMRI), clinical assessment results, and demographic information to predict conversion from MCI to AD in an average 5-years interval. Initially, a 3D-CNN was developed based on fMRI single volumes of 266 samples from 81 subjects; then, we used neuron layers to combine clinical data with fMRI to improve the results. Results: At first, the CNN model demonstrated an AUC of 87.67% and an accuracy of 85.7%, then after combining clinical and rs-fMRI features, we observed the following improved scores: an AUC of 91.72%, an accuracy of 87.6%, a sensitivity of 75.58% and a specificity of 92.57%. Conclusion: Our developed algorithm managed to predict prognosis from MCI to AD with high levels of accuracy, proving the potential of DL approaches in solving the matter and the efficiency of integrating clinical information with imaging according to the proposed method. [ABSTRACT FROM AUTHOR]

Background: After the Great East Japan Earthquake in 2011, backup systems for clinical information were launched in Japan. The system in Miyagi Prefecture called the Miyagi Medical and Welfare Information Network (MMWIN) is used as a health information exchange network to share clinical information among various medical facilities for patients who have opted in. Hospitals and clinics specializing in chronic renal failure require patients' data and records during hemodialysis to facilitate communication in daily clinical activity and preparedness for disasters. Objective: This study aimed to facilitate the sharing of clinical data of patients undergoing hemodialysis among different hemodialysis facilities. Methods: We introduced a document-sharing system to make hemodialysis reports available on the MMWIN. We also recruited hospitals and clinics to share the hemodialysis reports of their patients and promoted the development of a network between emergency and dialysis clinics. Results: In addition to basic patient information as well as information on diagnosis, prescription, laboratory data, hospitalization, allergy, and image data from different facilities, specific information about hemodialysis is available, as well as a backup of indispensable information in preparation for disasters. As of June 1, 2021, 12 clinics and 10 hospitals of 68 dialysis facilities in Miyagi participated in the MMWIN. The number of patients who underwent hemodialysis in Miyagi increased by more than 40%. Conclusions: Our backup system successfully developed a network of hemodialysis facilities. We have accumulated data that are beneficial to prevent the fragmentation of patient information and would be helpful in transferring patients efficiently during unpredictable disasters. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to determine the information retrieval and information therapy behavior of Asthma and allergy specialists in the country, based on cohonen self-organized neural network model.Methods: The methodology of the present study, which is an applied study in terms of purpose, has been done by descriptive-survey method using neural network technique. The tool of this research is a researcher-made questionnaire that was distributed among a sample of people in the community (149 people). After collecting the data, the neural network was selected for data clustering and using MATLAB software, Asthma and allergy specialists were clustered based on the main components of the research. Then, by removing each of the main sub-components of the research, the most effective and least effective option in their information-seeking behavior in working with information resources in this specialized field was determined.Results: The most effective component in clustering information barriers, was "lack of time due to workload" and the least was "distance of libraries and information centers". About information retrieval skills, the most effective component is " knowing what keywords to use when searching the Internet, and to be familiar with synonyms and terms related to the information I need."Conclusion: By studying the clustering of information behaviors resulting from the information needs of Asthma and allergy specialists, their needs are recognized, and this is one of the measures that provides the basis for effective research, appropriate findings and ,consequently, informational decision-making for those involved in this field.

Abstract Introduction The aim of this study was to investigate the effects of clinical information on the accuracy, timeliness, reporting confidence and clinical relevance of the radiology report. Methods A systematic review of studies that investigated a link between primary communication of clinical information to the radiologist and the resultant report was conducted. Relevant studies were identified by a comprehensive search of electronic databases (PubMed, Scopus and EMBASE). Studies were screened using pre‐defined criteria. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi‐Experimental Studies. Synthesis of findings was narrative. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Results There were 21 studies which met the inclusion criteria, of which 20 were included in our review following quality assessment. Sixteen studies investigated the effect of clinical information on reporting accuracy, three studies investigated the effect of clinical information on reporting confidence, three studies explored the impact of clinical information on clinical relevance, and two studies investigated the impact of clinical information on reporting timeliness. Some studies explored multiple outcomes. Studies concluded that clinical information improved interpretation accuracy, clinical relevance and reporting confidence; however, reporting time was not substantially affected by the addition of clinical information. Conclusion The findings of this review suggest clinical information has a positive impact on the radiology report. It is in the best interests of radiologists to communicate the importance of clinical information to reporting via the creation of criteria standards to guide the requesting practices of medical imaging referrers. Further work is recommended to establish these criteria standards.

Introduction: Clinical governance in radiology is an improved quality assurance program where the patient well-being is the central focus concerning radiologic service using the radiology request form (RRF). Methodology: The study was a 6-month descriptive study conducted from January 2020 to June 2020. A total of 2053 request forms were collated retrospectively and evaluated to ascertain the appropriateness of the clinical information with the investigation requested. A 3 stage Likert scale of appropriate somewhat appropriate and inappropriate was used and the data analyzed using the statistical package for the social sciences (SPSS) IBM Corp. version 23.0 (Armonk, NY: USA). The results were expressed in percentages and frequencies and presented in tables and charts. Results: Of the 2053 request forms, X-rays constitute 32.54% while ultrasound scans and computed tomography (CT) investigations constitute 43.21% and 5.46%, respectively. Clinical information was indicated in 51.39% of the RRF, out of which 75.95% of the clinical information were adequate while 13.93% were not in keeping with the investigation. Ultrasound scan request had the highest inappropriate clinical information (54.68%) followed by plain radiography request (41.73%) whereas all the CT request was in tandem with the investigation. Conclusion: There are occasional clinical information and radiologic request mismatch. This may be due to the filling of the RRF by medical interns or nonmedical personnel such as allied health workers with less knowledge on radiologic imaging modalities. The audit recommends proper supervision of young clinicians and continues medical education concerning the rational use of imaging modality.

The widespread use of electronic health records (EHR) systems in health care provides a large amount of real-world data, leading to new areas for clinical research. Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great amount of valuable clinical information. However, in a free-form text such as electronic health records, many clinical data are still hidden in a clinical narrative format. Therefore, the performance of biomedical NLP techniques is required to unlock the full potential of EHR data to convert a clinical narrative text automatically into structured clinical data. In this way, biomedical NLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease. This review discusses the current literature on the secondary use of electronic health record data for clinical research on chronic diseases and addresses the potential, challenges, and applications of biomedical NLP techniques. We review some of the biomedical NLP methods and systems used over EHRs and give an overview of machine learning and deep learning methodologies used to process EHRs and improve the understanding of the patient’s clinical records and the prediction of chronic diseases risk, providing a great chance to extract previously unknown clinical information. Moreover, this review summarizes the utilizing of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data. Finally, this review presents the future trends and challenges in the biomedical NLP.

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Background: The clinician's role is paramount in surgical pathologist’s evaluation of surgical specimens. The study aims at evaluating clinicians’ role in the surgical pathology process in Nigeria. Materials and Methods: This is a three-year (2016-2018) study of all (1262) routines surgical pathology specimens (SPS) received at the histopathology department of Delta State University Teaching Hospital Oghara. Parameters analyzed include the quality of the containers and fixatives used during specimen submission and the information supplied by the clinician (Specimen nature, patients’ age, gender, address, , hospital number, phone number and clinical information. The information was analyzed using Microsoft Excel 2017 and the result presented in tables. Results: Nine (0.6%) SPS were not stored in appropriate fixative while in 88(8.3%) cases, the quantity of fixatives were inadequate. Forty (3.2%) of sample containers were inadequate while 86(6.8%) of the sample labels were wrongly placed. Fifty-two containers accommodated more than one SPS. The gender, age, address, hospital number, time of collection in day and hour were omitted in 21(1.7%), 18(1.4%), 627(49.7%), 921(73.0%) and 58(4.6%) of the cases respectively. The clinical request form, clinical findings, previous investigations, previous treatment history and presumptive or definitive diagnosis were missing in 288(22.8%), 192(15.2%), 282(22.4%), 424(33.6%), 340(27.0%) and 98(7.7%) cases respectively. Their names, contact telephone numbers and signature were omitted in the request in 58(4.6%), 1230(97.5%) and 143(11.3%) of the cases respectively. Conclusions: Among the parameters evaluated, Clinicians are mostly non-compliant in providing clinical information and their phone numbers. Continuous education and interaction with clinicians, computerized information management system and introduction of non-conformity registrar are possible ways of closing this gap.

Background Due to the current high demand for transplant tissue, an increasing proportion of kidney donors are considered extended criteria donors, which results in a higher incidence of delayed graft function (DGF) in organ recipients. Therefore, it is important to fully investigate the risk factors of DGF, and establish a prediction system to assess donor kidney quality before transplantation. Methods A total of 333 donation after cardiac death kidney transplant recipients were included in this retrospective study. Both univariate and multivariate analyses were used to analyze the risk factors of DGF occurrence. Receiver operating characteristic (ROC) curves were used to analyze the predictive value of variables on DGF posttransplant. Results The donor clinical scores, kidney histopathologic Remuzzi scores and hypothermic mechanical perfusion (HMP) parameters (flow and resistance index) were all correlated. 46 recipients developed DGF postoperatively, with an incidence of 13.8% (46/333). Multivariate logistic regression analysis of the kidney transplants revealed that the independent risk factors of DGF occurrence post-transplantation included donor score (OR = 1.12, 95% CI 1.06–1.19, p

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.

Background: Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes., Objective: The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students' free-text history and physical notes., Methods: This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students' notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct., Results: The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0%, and the standardized patient incorrect scoring rate was 7.2%. The ChatGPT error rate was 86%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002)., Conclusions: ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students' standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice., (© Harry B Burke, Albert Hoang, Joseph O Lopreiato, Heidi King, Paul Hemmer, Michael Montgomery, Viktoria Gagarin. Originally published in JMIR Medical Education (https://mededu.jmir.org).)

Background/Objectives: Increased rates of histopathological misdiagnosis of melanoma have been associated with incisional punch more so than shave biopsy when compared with complete excisional biopsy. It is unknown how the increasing utilisation of shave biopsy may impact melanoma diagnosis. The extent to which the provision of clinical information to the pathologist may improve diagnostic accuracy remains unclear. This study assessed the impact of both initial biopsy technique and provision of adequate clinical information to pathologists on the accuracy of histopathological diagnosis of melanoma and disease progression. Methods: We conducted a retrospective cohort with nested case‐control study of all histopathological false‐negative and false‐positive melanoma diagnoses from January 2014 to May 2019 from the Victorian Melanoma Service electronic database. Cases were assessed for the initial biopsy type, provision of clinical information on pathology request forms and disease progression associated with false‐negative diagnosis. Results: Partial shave biopsy had higher odds of false‐negative (OR 5.19, 95% CI 2.89–9.32; P < 0.001) and false‐positive diagnoses (OR 1.95, 95% CI 1.45–2.63; P < 0.001) of melanoma when compared with elliptical excisional biopsy. These odds ratios were comparable with those found with incisional punch biopsy. Providing the suspected clinical diagnosis to pathologists also reduced the odds of false‐negative diagnosis with melanoma progression by 3.8‐fold (P = 0.02). Conclusion: The choice of initial biopsy technique and providing the suspected clinical diagnosis to pathologists are important for correct histopathological diagnosis of cutaneous melanoma and prevention of further disease progression. [ABSTRACT FROM AUTHOR]

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists’ experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.

Background: Laboratory request form is an important means of interaction between clinicians and laboratory service providers. The omission of information on the request form may result in laboratory errors which may have a negative impact on patients’ outcome. Objective: To assess the pattern of completion of laboratory request forms in a tertiary facility. Methods: Two thousand, two hundred and forty-one laboratory request forms sent to the laboratory over a period of two months were assessed for their level of completeness. Results: Out of 2241 laboratory request forms, only 5 (0.2%) was fully completed. The most complete information on the forms included types of investigation required (98.9%), the gender of the patient (97.8%), the identity of consultant-in-charge of the patient (95.3%) and the referring physician's name and signature (93.8%). The least provided information was the time of collection of the specimen (0.7%). Conclusion: This study shows that laboratory request forms are frequently incompletely and inadequately completed. Continuous medical education of clinicians on the need for adequate completion of request forms is required.

Objectives: To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods: A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results: Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ217 = 29.4, P = 0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic = 0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions: The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic?: Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add?: This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners?: The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to investigate the effects of clinical information on the accuracy, timeliness, reporting confidence and clinical relevance of the radiology report. Methods: A systematic review of studies that investigated a link between primary communication of clinical information to the radiologist and the resultant report was conducted. Relevant studies were identified by a comprehensive search of electronic databases (PubMed, Scopus and EMBASE). Studies were screened using pre‐defined criteria. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi‐Experimental Studies. Synthesis of findings was narrative. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Results: There were 21 studies which met the inclusion criteria, of which 20 were included in our review following quality assessment. Sixteen studies investigated the effect of clinical information on reporting accuracy, three studies investigated the effect of clinical information on reporting confidence, three studies explored the impact of clinical information on clinical relevance, and two studies investigated the impact of clinical information on reporting timeliness. Some studies explored multiple outcomes. Studies concluded that clinical information improved interpretation accuracy, clinical relevance and reporting confidence; however, reporting time was not substantially affected by the addition of clinical information. Conclusion: The findings of this review suggest clinical information has a positive impact on the radiology report. It is in the best interests of radiologists to communicate the importance of clinical information to reporting via the creation of criteria standards to guide the requesting practices of medical imaging referrers. Further work is recommended to establish these criteria standards. [ABSTRACT FROM AUTHOR]

Introduction: Clinical governance in radiology is an improved quality assurance program where the patient well-being is the central focus concerning radiologic service using the radiology request form (RRF). Methodology: The study was a 6-month descriptive study conducted from January 2020 to June 2020. A total of 2053 request forms were collated retrospectively and evaluated to ascertain the appropriateness of the clinical information with the investigation requested. A 3 stage Likert scale of appropriate somewhat appropriate and inappropriate was used and the data analyzed using the statistical package for the social sciences (SPSS) IBM Corp. version 23.0 (Armonk, NY: USA). The results were expressed in percentages and frequencies and presented in tables and charts. Results: Of the 2053 request forms, X-rays constitute 32.54% while ultrasound scans and computed tomography (CT) investigations constitute 43.21% and 5.46%, respectively. Clinical information was indicated in 51.39% of the RRF, out of which 75.95% of the clinical information were adequate while 13.93% were not in keeping with the investigation. Ultrasound scan request had the highest inappropriate clinical information (54.68%) followed by plain radiography request (41.73%) whereas all the CT request was in tandem with the investigation. Conclusion: There are occasional clinical information and radiologic request mismatch. This may be due to the filling of the RRF by medical interns or nonmedical personnel such as allied health workers with less knowledge on radiologic imaging modalities. The audit recommends proper supervision of young clinicians and continues medical education concerning the rational use of imaging modality. [ABSTRACT FROM AUTHOR]

Background: Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches., Objective: The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types-heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models., Methods: This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches., Results: The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types., Conclusions: This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area., (©Sonish Sivarajkumar, Mark Kelley, Alyssa Samolyk-Mazzanti, Shyam Visweswaran, Yanshan Wang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.04.2024.)

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank p value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as PROM1 (HR = 2.4120, p = 0.0071) and CXCR4 (HR = 1.5578, p < 0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp.

Due to the current high demand for transplant tissue, an increasing proportion of kidney donors are considered extended criteria donors, which results in a higher incidence of delayed graft function (DGF) in organ recipients. Therefore, it is important to fully investigate the risk factors of DGF, and establish a prediction system to assess donor kidney quality before transplantation. A total of 333 donation after cardiac death kidney transplant recipients were included in this retrospective study. Both univariate and multivariate analyses were used to analyze the risk factors of DGF occurrence. Receiver operating characteristic (ROC) curves were used to analyze the predictive value of variables on DGF posttransplant. The donor clinical scores, kidney histopathologic Remuzzi scores and hypothermic mechanical perfusion (HMP) parameters (flow and resistance index) were all correlated. 46 recipients developed DGF postoperatively, with an incidence of 13.8% (46/333). Multivariate logistic regression analysis of the kidney transplants revealed that the independent risk factors of DGF occurrence post-transplantation included donor score (OR = 1.12, 95% CI 1.06–1.19, p < 0.001), Remuzzi score (OR = 1.21, 95% CI 1.02–1.43, p = 0.029) and acute tubular injury (ATI) score (OR = 4.72, 95% CI 2.32–9.60, p < 0.001). Prediction of DGF with ROC curve showed that the area under the curve was increased to 0.89 when all variables (donor score, Remuzzi score, ATI score and HMP resistance index) were considered together. Combination of donor clinical information, kidney pre-implant histopathology and HMP parameters provide a more accurate prediction of DGF occurrence post-transplantation than any of the measures alone. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is one of the leading causes of death in women. Therefore, the accuracy and speed of diagnosis are crucial in the treatment procedure. In this regard, the BI-RADS classification system has been used for the standardization of mammography reports. However, there is much disagreement among physicians about the BI-RADS values. The aim of this paper is to diagnose BI-RADS by natural language processing of mammography reports and clinical information from the electronic health records and combining them to identify molecular subtypes and help patient follow-up. Methods: In this study, 1200 mammography reports and electronic health records obtained from Namazi Educational and Medical Center for years between 2015-2017. After text processing, the vector with 160 features was obtained, then 18 features were extracted by referring to the electronic health records. Finally, 178 features were used by SVM and naïve Bayesian to predict BI-RADS and molecular subtypes, respectively. Results: The values of Accuracy, Positive Prediction Value, Negative Prediction Value, Sensitivity, and Specificity were calculated to evaluate the results. Accuracy was 85.42% for BI-RADS and 72.31% for molecular subtypes. Conclusion: The proposed decision support system was an appropriate model to help the physician to diagnose breast cancer and categorize patients. It was also found that the combined information, including electronic medical records of patients and designated molecular subtypes along with mammography reports, can be useful in diagnosing the disease and defining the treatment follow-up. [ABSTRACT FROM AUTHOR]

A number of approaches have been proposed in literature to collect and classify patient related information for purpose of better clinical diagnosis for safer treatment and administration of related activities. This type of data collection and classification benefits doctors and the corresponding hospitals. However, no effort is made, as to our knowledge, to classify accumulated data within insurance company databases to facilitate doctors as well as insurance companies for better analysis and cost-effective treatment of patients suffering from chronic (and expensive to treat) diseases such as related to oncology. In this study, a customized self-organized data classification model is applied to an insurance company database to build clusters based on age, patient condition, tests done, etc. These clusters provide integrated analysis to doctors in providing patient- specific, disease-specific, etc., and cost-effective treatment. On the other side, it saves on costs to be incurred on repeated tests to be done on the patient. An experimental setup is developed to train such a network, and testing results are presented. The practical constraints are also discussed. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank p value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as PROM1 (HR = 2.4120, p = 0.0071) and CXCR4 (HR = 1.5578, p < 0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To objectively and subjectively evaluate a large, academic radiology department's transition to electronic health record (EHR) centered workflow.Materials and Methods: Multiple metrics were compared from before and after the move to EHR-driven workflow. Examination ordering and reading priority data were obtained for 30 days both before and after the transition. Sixteen radiologists were observed opening a computed tomography (CT) examination, and time to open, mouse clicks, and keystrokes were recorded. Information available to the radiologist during interpretation was also compared. Additionally, a 12 question survey was sent out to the residents and faculty both before and after the transition.Results: Implementation of an eight-level reading priority system increased worklist granularity and improved identification of more urgent studies to read. Radiologists opened CT studies in picture archiving and communications system-driven workflow in 52.4 ± 16.9 seconds using 9.5 ± 3.9 clicks and 6.3 ± 2.9 keystrokes, compared to 17.3 ± 9.5 seconds, 4.8 ± 1.5 clicks, and 0.1 ± 0.3 keystrokes in EHR-driven workflow (p < 0.001 for each measure). More information was available to the radiologist during examination interpretation, and 54.7% of radiologists rated the ease of use of the new system as good or very good (compared to 4.2% for the old system, p < 0.001).Conclusion: Transitioning to an EHR-driven workflow at a large academic medical center improved efficiency, was favorable to radiologists, and enhanced examination prioritization. [ABSTRACT FROM AUTHOR]