Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

Historical Field Records Reveal Habitat as an Ecological Correlate of Locomotor Phenotypic Diversity in the Radiation of Neotropical Geophagini Fishes.

AbstractPhenotypic macroevolutionary studies provide insight into how ecological processes shape biodiversity. However, the complexity of phenotype-ecology relationships underscores the importance of also validating phenotype-based ecological inference with direct evidence of resource use. Unfortunately, macroevolutionary-scale ecological studies are often hindered by the challenges of acquiring taxonomically and spatially representative ecological data for large and widely distributed clades. The South American cichlid fish tribe Geophagini represents a continentally distributed radiation whose early locomotor morphological divergence suggests habitat as one ecological correlate of diversification, but an association between locomotor traits and habitat preference has not been corroborated. Field notes accumulated over decades of collecting across South America provide firsthand environmental records that can be mined for habitat data in support of macroevolutionary ecological research. In this study, we applied a newly developed method to transform descriptive field note information into quantitative habitat data and used it to assess habitat preference and its relationship to locomotor morphology in Geophagini. Field note-derived data shed light on geophagine habitat use patterns and reinforced habitat as an ecological correlate of locomotor morphological diversity. Our work emphasizes the rich data potential of museum collections, including often-overlooked material such as field notes, for evolutionary and ecological research.

Opening the Museum's Vault: Historical Field Records Preserve Reliable Ecological Data.

AbstractMuseum specimens have long served as foundational data sources for ecological, evolutionary, and environmental research. Continued reimagining of museum collections is now also generating new types of data associated with but beyond physical specimens, a concept known as "extended specimens." Field notes penned by generations of naturalists contain firsthand ecological observations associated with museum collections and comprise a form of extended specimens with the potential to provide novel ecological data spanning broad geographic and temporal scales. Despite their data-yielding potential, however, field notes remain underutilized in research because of their heterogeneous, unstandardized, and qualitative nature. We introduce an approach for transforming descriptive ecological notes into quantitative data suitable for statistical analysis. Tests with simulated and real-world published data show that field notes and our transformation approach retain reliable quantitative ecological information under a range of sample sizes and evolutionary scenarios. Unlocking the wealth of data contained within field records could facilitate investigations into the ecology of clades whose diversity, distribution, or other demographic features present challenges to traditional ecological studies, improve our understanding of long-term environmental and evolutionary change, and enhance predictions of future change.

Nestling Begging Calls Resemble Maternal Vocal Signatures When Mothers Call Slowly to Embryos.

AbstractVocal production learning (the capacity to learn to produce vocalizations) is a multidimensional trait that involves different learning mechanisms during different temporal and socioecological contexts. Key outstanding questions are whether vocal production learning begins during the embryonic stage and whether mothers play an active role in this through pupil-directed vocalization behaviors. We examined variation in vocal copy similarity (an indicator of learning) in eight species from the songbird family Maluridae, using comparative and experimental approaches. We found that (1) incubating females from all species vocalized inside the nest and produced call types including a signature "B element" that was structurally similar to their nestlings' begging call; (2) in a prenatal playback experiment using superb fairy wrens (Malurus cyaneus), embryos showed a stronger heart rate response to playbacks of the B element than to another call element (A); and (3) mothers that produced slower calls had offspring with greater similarity between their begging call and the mother's B element vocalization. We conclude that malurid mothers display behaviors concordant with pupil-directed vocalizations and may actively influence their offspring's early life through sound learning shaped by maternal call tempo.

Children's Oncology Group's 2023 blueprint for research: Surgery.

Surgery plays a crucial role in the treatment of children with solid malignancies. A well-conducted operation is often essential for cure. Collaboration with the primary care team is important for determining if and when surgery should be performed, and if performed, an operation must be done in accordance with well-established standards. The long-term consequences of surgery also need to be considered. Indications and objectives for a procedure vary. Providing education and developing and analyzing new research protocols that include aims relevant to surgery are key objectives of the Surgery Discipline of the Children's Oncology Group. The critical evaluation of emerging technologies to ensure safe, effective procedures is another key objective. Through research, education, and advancing technologies, the role of the pediatric surgeon in the multidisciplinary care of children with solid malignancies will continue to evolve.

Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.

BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information. METHODOLOGY: We utilized Llama 2 13B model with zero-shot prompting. The dataset comprises unstructured and structured EHRs related to malnutrition management in 40 Australian RACFs. We employed zero-shot learning to the model alone first, then combined it with RAG to accomplish two tasks: generate structured summaries about the nutritional status of a client and extract key information about malnutrition risk factors. We utilized 25 notes in the first task and 1,399 in the second task. We evaluated the model's output of each task manually against a gold standard dataset. RESULT: The evaluation outcomes indicated that zero-shot learning applied to generative AI model is highly effective in summarizing and extracting information about nutritional status of RACFs' clients. The generated summaries provided concise and accurate representation of the original data with an overall accuracy of 93.25%. The addition of RAG improved the summarization process, leading to a 6% increase and achieving an accuracy of 99.25%. The model also proved its capability in extracting risk factors with an accuracy of 90%. However, adding RAG did not further improve accuracy in this task. Overall, the model has shown a robust performance when information was explicitly stated in the notes; however, it could encounter hallucination limitations, particularly when details were not explicitly provided. CONCLUSION: This study demonstrates the high performance and limitations of applying zero-shot learning to generative AI models to automatic generation of structured summarization of EHRs data and extracting key clinical information. The inclusion of the RAG approach improved the model performance and mitigated the hallucination problem.

Psychiatrists' perceptions of conditions and consequences associated with the implementation of open notes: qualitative investigation.

OBJECTIVE: In a growing list of countries, patients are granted access to their clinical notes ("open notes") as part of their online record access. Especially in the field of mental health, open notes remain controversial with some clinicians perceiving open notes as a tool for improving therapeutic outcomes by increasing patient involvement, while others fear that patients might experience psychological distress and perceived stigmatization, particularly when reading clinicians' notes. More research is needed to optimize the benefits and mitigate the risks. METHODS: Using a qualitative research design, we conducted semi-structured interviews with psychiatrists practicing in Germany, to explore what conditions they believe need to be in place to ensure successful implementation of open notes in psychiatric practice as well as expected subsequent changes to their workload and treatment outcomes. Data were analyzed using thematic analysis. RESULTS: We interviewed 18 psychiatrists; interviewees believed four key conditions needed to be in place prior to implementation of open notes including careful consideration of (1) diagnoses and symptom severity, (2) the availability of additional time for writing clinical notes and discussing them with patients, (3) available resources and system compatibility, and (4) legal and data protection aspects. As a result of introducing open notes, interviewees expected changes in documentation, treatment processes, and doctor-physician interaction. While open notes were expected to improve transparency and trust, participants anticipated negative unintended consequences including the risk of deteriorating therapeutic relationships due to note access-related misunderstandings and conflicts. CONCLUSION: Psychiatrists practiced in Germany where open notes have not yet been established as part of the healthcare data infrastructure. Interviewees were supportive of open notes but had some reservations. They found open notes to be generally beneficial but anticipated effects to vary depending on patient characteristics. Clear guidelines for managing access, time constraints, usability, and privacy are crucial. Open notes were perceived to increase transparency and patient involvement but were also believed to raise issues of stigmatization and conflicts.

Feasibility and complications after transoral endoscopic thyroidectomy via vestibular approach (TOETVA) - a single-center first experience case series.

BACKGROUND: This paper reports on the first experience after implementation of a transoral endoscopic thyroidectomy via vestibular approach (TOETVA) as an alternative to (partial) thyroidectomy or isthmusectomy in a single center. Feasibility, implementation and specific complications are addressed. METHODS: All patients who underwent a TOETVA procedure in our center between November 2019 and March 2023 were included. The surgical technique was performed as described by Anuwong et al. All procedures were performed by two dedicated head- and neck surgeons. RESULTS: A total of 20 patients were included. All patients underwent TOETVA surgery as planned and no conversions were needed. Observed complications were post-operative wound infections (POWI) (2/20; 10%), clinically significant seroma (1/20, 5%) and unilateral hemiparesis of the larynx (3/20; 15%). Permanent mental nerve damage was seen in 3/20 patients (15%), and 4 other patients (20%) experienced transient neuropraxia. CONCLUSIONS: TOETVA is a feasible alternative to (partial) thyroidectomy or isthmusectomy in selected patients. Special care should be taken when placing the trocars in the oral vestibulum to prevent mental nerve damage. Experience and training are essential for implementing the TOETVA procedure. TRIAL REGISTRATION: This study was registered to ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT05396703.

Developing comprehensive woman hand-held case notes to improve quality of antenatal care in low-income settings: participatory approach with maternal health stakeholders in Malawi.

BACKGROUND: In the quest for quality antenatal care (ANC) and positive pregnancy experience, the value of comprehensive woman hand-held case notes cannot be emphasised enough. However, the woman's health passport book in Malawi presents gaps which hinder provision of quality care, especially during pregnancy. We aimed to develop a compressive updated woman hand-held case notes tool (health passport book) which reflects WHO 2016 ANC guidelines in Malawi. METHODS: From July 2022 to August 2022, we applied a co-creative participatory approach in 3 workshops with key stakeholders to compare the current ANC tool contents to the WHO 2016 ANC guidelines, decide on key elements to be changed to improve adherence and change in practice, and redesign the woman's health passport tool to reflect the changes. Within-group discussions led to whole-group discussions and consensus, guided by a modified nominal group technique. Facilitators guided the discussions while ensuring autonomy of the group members in their deliberations. Discussions were recorded and transcribed. Data was analysed through thematic analysis, and reduction and summaries in affinity diagrams. The developed tool was endorsed for implementation within Malawi's healthcare system by the national safe motherhood technical working group (TWG) in July 2023. RESULTS: Five themes were identified in the analysis. These were (i) critical components in the current tool missed, (ii) reimagining the current ANC tool, (iii) opportunity for ultrasound scanning conduct and documentation, (iv) anticipated barriers related to implementation of the newly developed tool and (v) cultivating successful implementation. Participants further recommended strengthening of already existing policies and investments in health, strengthening public private partnerships, and continued capacity building of healthcare providers to ensure that their skill sets are up to date. CONCLUSION: Achieving goals of quality ANC and universality of healthcare are possible if tools in practice reflect the guidelines set out. Our efforts reflect a pioneering attempt in Malawi to improve women's hand-held case notes, which we know help in enhancing quality of care and improve overall women's satisfaction with their healthcare system.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

A Nordic Perspective on Patient Online Record Access and the European Health Data Space.

The Nordic countries are, together with the United States, forerunners in online record access (ORA), which has now become widespread. The importance of accessible and structured health data has also been highlighted by policy makers internationally. To ensure the full realization of ORA's potential in the short and long term, there is a pressing need to study ORA from a cross-disciplinary, clinical, humanistic, and social sciences perspective that looks beyond strictly technical aspects. In this viewpoint paper, we explore the policy changes in the European Health Data Space (EHDS) proposal to advance ORA across the European Union, informed by our research in a Nordic-led project that carries out the first of its kind, large-scale international investigation of patients' ORA-NORDeHEALTH (Nordic eHealth for Patients: Benchmarking and Developing for the Future). We argue that the EHDS proposal will pave the way for patients to access and control third-party access to their electronic health records. In our analysis of the proposal, we have identified five key principles for ORA: (1) the right to access, (2) proxy access, (3) patient input of their own data, (4) error and omission rectification, and (5) access control. ORA implementation today is fragmented throughout Europe, and the EHDS proposal aims to ensure all European citizens have equal online access to their health data. However, we argue that in order to implement the EHDS, we need more research evidence on the key ORA principles we have identified in our analysis. Results from the NORDeHEALTH project provide some of that evidence, but we have also identified important knowledge gaps that still need further exploration.

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study.

BACKGROUND: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. OBJECTIVE: This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model's performance across different categories. METHODS: We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. RESULTS: Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the "Objective" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). CONCLUSIONS: Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model's effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. METHODS: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. RESULTS: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. CONCLUSIONS: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare.

INTRODUCTION: Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation. DESIGN: Retrospective data analysis. METHODS: We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models. RESULTS: Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC. CONCLUSION: As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided. CLINICAL RELEVANCE: The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

Adoption of vaginally assisted natural orifice transluminal endoscopic surgery for hysterectomy: A single tertiary experience.

BACKGROUND: Vaginal hysterectomy (VH) rate is declining despite being considered as the optimal minimally invasive option for hysterectomy with reduced operative time and length of stay compared with laparoscopic hysterectomy (LH). Vaginal assisted natural orifice transluminal endoscopic surgery hysterectomy (VANH) combines the advantages of both vaginal and endoscopic approach to surgery. AIMS: To report feasibility and early experience of a single surgeon adopting VANH at a tertiary Australian hospital. MATERIALS AND METHODS: Prospective review of the first 20 VANH cases with complete data set collected retrospectively including patient demographics, indication for surgery and perioperative outcomes. RESULTS: The median age of the first 20 participants was 51.5 years (47-57 years of age) and the median body mass index was 33.5 kg/m2 (27.8-38.3 kg/m2). The predominant indication was complex hyperplasia with atypia (12/20, 60%). The median parity was two (1-3) where four patients were nulliparous. The median blood loss was 125 mL (100-200 mL) with an operative time of 149 min (138-198 min) and median weight of the specimen of 181.5 g (66.5-219 g). The mean length of stay was 1.4 days (1-2 days). Five cases had conversion to laparoscopy and the majority (80%) occurred within the first ten cases. CONCLUSIONS: VANH is feasible but there is a learning curve to achieve competence in this technique, which requires adequate training in the early stages of adoption with careful case selection. Until further robust data is available to determine the clinical benefit and safety profile of VANH, patients should be carefully counselled and the decision on mode of hysterectomy be individualised.

Human Action Recognition and Note Recognition: A Deep Learning Approach Using STA-GCN.

Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being played. This paper proposes a deep learning-based method for simultaneous HAR and musical note recognition in music performances. We conducted experiments on Morin khuur performances, a traditional Mongolian instrument. The proposed method consists of two stages. First, we created a new dataset of Morin khuur performances. We used motion capture systems and depth sensors to collect data that includes hand keypoints, instrument segmentation information, and detailed movement information. We then analyzed RGB images, depth images, and motion data to determine which type of data provides the most valuable features for recognizing actions and notes in music performances. The second stage utilizes a Spatial Temporal Attention Graph Convolutional Network (STA-GCN) to recognize musical notes as continuous gestures. The STA-GCN model is designed to learn the relationships between hand keypoints and instrument segmentation information, which are crucial for accurate recognition. Evaluation on our dataset demonstrates that our model outperforms the traditional ST-GCN model, achieving an accuracy of 81.4%.

Getting More Out of Clinical Documentation: Can Clinical Dashboards Yield Clinically Useful Information?

This study investigated coded data retrieved from clinical dashboards, which are decision-support tools that include a graphical display of clinical progress and clinical activities. Data were extracted from clinical dashboards representing 256 youth (M age = 11.9) from 128 practitioners who were trained in the Managing and Adapting Practice (MAP) system (Chorpita & Daleiden in BF Chorpita EL Daleiden 2014 Structuring the collaboration of science and service in pursuit of a shared vision. 43(2):323 338. 2014, Chorpita & Daleiden in BF Chorpita EL Daleiden 2018 Coordinated strategic action: Aspiring to wisdom in mental health service systems. 25(4):e12264. 2018) in 55 agencies across 5 regional mental health systems. Practitioners labeled up to 35 fields (i.e., descriptions of clinical activities), with the options of drawing from a controlled vocabulary or writing in a client-specific activity. Practitioners then noted when certain activities occurred during the episode of care. Fields from the extracted data were coded and reliability was assessed for Field Type, Practice Element Type, Target Area, and Audience (e.g., Caregiver Psychoeducation: Anxiety would be coded as Field Type = Practice Element; Practice Element Type = Psychoeducation; Target Area = Anxiety; Audience = Caregiver). Coders demonstrated moderate to almost perfect interrater reliability. On average, practitioners recorded two activities per session, and clients had 10 unique activities across all their sessions. Results from multilevel models showed that clinical activity characteristics and sessions accounted for the most variance in the occurrence, recurrence, and co-occurrence of clinical activities, with relatively less variance accounted for by practitioners, clients, and regional systems. Findings are consistent with patterns of practice reported in other studies and suggest that clinical dashboards may be a useful source of clinical information. More generally, the use of a controlled vocabulary for clinical activities appears to increase the retrievability and actionability of healthcare information and thus sets the stage for advancing the utility of clinical documentation.

Total vNOTES hysterectomy versus conventional total laparoscopic hysterectomy in virgin transgender men.

BACKGROUND AND AIM: Comparison of the applicability, safety, and surgical outcomes of total vaginal NOTES hysterectomy (TVNH) using natural orifice transluminal endoscopic surgery, which is considered a natural orifice surgery for hysterectomy with bilateral salpingo-oophorectomy (HBSO) in virgin transgender men, with conventional total laparoscopic hysterectomy (TLH). MATERIAL AND METHODS: A retrospective cohort study was conducted between 2019 and 2021. The results of transgender male individuals who underwent HBSO operations using TVNH (n = 21) were compared with those who underwent operations using TLH (n = 62). RESULTS: TVNH was performed in 21 individuals, while TLH was performed in 62 individuals. Patients in the TVNH approach group had a longer operation duration than those in the TLH group (p = .001). Patients in the TVNH group experienced less pain at two hours (5 ± 1.56), six hours (4 ± 1.57), 12 h (2 ± 0.91), and 24 h (1 ± 0.62) postoperatively (p = .001). The postoperative hospitalization duration was shorter in the TVNH group (1.6 ± 1.01) than in the TLH group (2.9 ± 0.5) (p = .001). CONCLUSIONS: For the HBSO operation of female-to-male transgender individuals, TVNH, which is completely endoscopically performed, can be preferred and safely conducted as an alternative surgical method to conventional laparoscopy.

The vaginal route for minimally invasive surgery: a practical guide for general surgeons.

INTRODUCTION: Vaginal approaches have become routine in the field of gynecologic surgery, whereas in general surgery vaginal wall transection is an infrequent practice typically reserved for extensive tumor resections. Approximately two decades ago, natural orifice transluminal endoscopic surgery (NOTES) revolutionized conventional boundaries by accessing the peritoneal cavity transorally, transrectally, or transvaginally, enabling general surgery without visible scars. Although transvaginal approaches have been successfully used for various abdominal procedures by general surgeons, a gap remains in comprehensive training to fully exploit the potential of this route. MATERIAL AND METHODS: PubMed, Google Scholar, and Scopus databases were searched to retrieve relevant articles illustrating how general surgeons can adeptly manage vaginal approaches. RESULTS: The article presents a practical framework for general surgeons to execute a complete vaginal approach, addressing the management of vaginal specimen extraction and vaginal cuff closure, even in the absence of an experienced gynecologist. CONCLUSION: The evolution of abdominal surgery is moving towards less invasive techniques, emphasizing the importance of understanding the nuances and challenges associated with the vaginal route. This approach is linked to minimal oncological, sexual, and infective complications, and to the absence of pregnancy-related complications. Such knowledge becomes increasingly crucial, particularly with the renewed demand for transvaginal access in robot-assisted NOTES procedures.