A Study in Wad Madani, Sudan: Are We Documenting Operation Notes Effectively?

BACKGROUND: Operative notes represent the critical record of a surgical procedure, encompassing comprehensive details encountered throughout the operation. Recognizing the importance of comprehensive documentation, the Royal College of Surgeons (RCS) developed the Good Surgical Practice guidelines, which emphasize accurately recording every procedure and specifying the necessary parameters for each operative note. These guidelines help maintain high standards of surgical care and patient safety. METHODS: A retrospective review of 88 orthopaedic surgery operative notes for fracture neck of femurs was conducted at Gezira Centre for Orthopedic Surgery and Traumatology (GCOST) from March 12 to May 28, 2022. The review assessed 18 parameters against RCS guidelines. Statistical analysis was performed using Statistical Product and Service Solutions (SPSS, version 25.0; IBM SPSS Statistics for Windows, Armonk, NY), which facilitated comprehensive data examination. RESULTS: In 37 cases (42.05%), the operation notes were written by a medical officer. In 29 cases (32.95%), an orthopaedic resident authored the notes. A specialist documented the notes in 21 cases (23.86%), and a consultant wrote the notes in one case (1.14%). Over 90% of the notes included surgeon and assistant names, procedure names, operative diagnoses, operative procedures, prosthesis details, deep vein thrombosis (DVT) and antibiotic prophylaxis, and signatures. The name of the theatre anaesthetist, elective/emergency details, and additional procedures with reasons were absent in all notes. Less than 50% of the notes documented the time of the procedure, type of incision, operative findings, anticipated blood loss, closure technique specifics, and complications. CONCLUSION: The study emphasizes the shortcomings in the operating notes, underscoring the necessity for training initiatives to enhance the recording by medical officers and orthopaedic trainees. Implementing structured templates that adhere to RCS standards can improve the comprehensiveness and consistency of operating notes, effectively resolving existing discrepancies. Regular audits and feedback sessions are essential for identifying and rectifying persistent issues. It is recommended to arrange workshops and seminars to educate medical officials and trainees on the skills of efficient note-taking and thorough documentation procedures.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. OBJECTIVE: This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. METHODS: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. RESULTS: Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. CONCLUSIONS: Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

The 21st Century Cures Act: More Harm Than Good for Survivors of Intimate Partner Violence?

The 21st Century Cures Act requires that health organizations make all medical records rapidly available to patients through secure online portals. Referred to as "open notes," this approach is intended to improve health outcomes by facilitating easier and more transparent communication between patients and providers. For patients experiencing intimate partner violence (IPV), however, open notes can create serious safety risks to their physical and mental health when not handled carefully. This clinical note aims to raise awareness of how open notes can be harmful in IPV situations, provide a set of evidence-informed recommendations on how healthcare providers and institutions can help to mitigate this harm, and outline areas for future research.

The Effects of the Medium of Notetaking on the Delayed Learning Effect of College Students: A Mediated Moderation Model.

This study systematically probed the relationship between the medium of taking classroom notes (virtual variable, electronic notetaking = 0 vs. traditional notetaking = 1), the word count in each medium, as well as the review process, and the students' delayed learning effect for each notetaking approach. Data were collected from 189 college students, with the influence of gender and prior knowledge being controlled. The conclusions were as follows. (1) The notetaking medium was positively correlated with delayed test scores, irrespective of whether reviews were allowed or not. (2) The mediating role of word count between notetaking medium and delayed test scores was moderated by review. That is, when reviews were allowed, a significant correlation was found between the medium of the notes and the delayed test scores; when reviews were not allowed, the mediating effect of word count was not significant.

The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.

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 (ß=.774, P<.001), power (ß=.831, P<.001), money (ß=.204, P<.001), physical health (ß=.427, P=.03), while leisure words decreased (ß=-.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.

Psychotherapists' views on open notes: An online survey from Germany.

Background: In an increasing number of countries, patients are given online record access (ORA) to their clinical notes ("open notes"). In many places, psychotherapy notes are exempt, even if patients explicitly wish to read them. Previous research suggests that psychotherapists (PTs) have reservations that are not yet fully understood. Objective: To investigate the attitudes and perceived effects of open notes on psychotherapeutic care, patients, and individual psychotherapeutic practice in Germany. Methods: Psychological and medical therapists were invited to participate in a national online survey. Sociodemographic characteristics such as gender, age, professional group, and psychotherapeutic school were gathered. Descriptive statistics were used to analyze the 51-item survey. Results: 129 PTs completed the survey. Only a small proportion of respondents (30 out of 129, 23.3%) suspected that open notes would improve the efficiency of psychotherapeutic care. On the one hand, participants assumed that patients gain more control over their treatment (59 out of 129, 45.7%) and are better able to remember therapy goals (55 out of 129, 42.6%), although this was considered unlikely to lead to greater engagement in the therapy process (94 out of 129, 72.9%). On the other hand, PTs expected patients to misunderstand their notes, feel offended (98 out of 129, 76.0%), and approach them with questions (107 out of 129, 82.9%) or requests for changes (94 out of 129, 72.9%). The respondents also anticipated being less honest when writing (95 out of 129, 73.6%) and reported they needed more time for documentation (99 out of 129, 76.7%). A meaningful use of open notes for working with relatives was envisaged (101 out of 129, 78.3%). Conclusion: PTs in Germany tend to have a negative attitude towards patients' ORA on open notes. Further research on clinical efficacy and feasibility is necessary to demonstrate whether open notes add value in the context of psychotherapy.

Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods.

Purpose: To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. Design: Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a Bidirectional Encoder Representations from Transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-International Classification of Diseases [ICD] NLP System). Subjects: Adults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods: We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome Measures: Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. Results: A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39-0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21-0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System). Conclusions: The prevalence of DR and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the Text-and-ICD NLP System that used information in both diagnosis codes as well as free-text notes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparison of Diagnosis Codes to Clinical Notes in Classifying Patients with Diabetic Retinopathy.

Purpose: Electronic health records (EHRs) contain a vast amount of clinical data. Improved automated classification approaches have the potential to accurately and efficiently identify patient cohorts for research. We evaluated if a rule-based natural language processing (NLP) algorithm using clinical notes performed better for classifying proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) severity compared with International Classification of Diseases, ninth edition (ICD-9) or 10th edition (ICD-10) codes. Design: Cross-sectional study. Subjects: Deidentified EHR data from an academic medical center identified 2366 patients aged ≥18 years, with diabetes mellitus, diabetic retinopathy (DR), and available clinical notes. Methods: From these 2366 patients, 306 random patients (100 training set, 206 test set) underwent chart review by ophthalmologists to establish the gold standard. International Classification of Diseases codes were extracted from the EHR. The notes algorithm identified positive mention of PDR and NPDR severity from clinical notes. Proliferative diabetic retinopathy and NPDR severity classification by ICD codes and the notes algorithm were compared with the gold standard. The entire DR cohort (N = 2366) was then classified as having presence (or absence) of PDR using ICD codes and the notes algorithm. Main Outcome Measures: Sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score for the notes algorithm compared with ICD codes using a gold standard of chart review. Results: For PDR classification of the test set patients, the notes algorithm performed better than ICD codes for all metrics. Specifically, the notes algorithm had significantly higher sensitivity (90.5% [95% confidence interval 85.7, 94.9] vs. 68.4% [60.4, 75.3]), but similar PPV (98.0% [95.4-100] vs. 94.7% [90.3, 98.3]) respectively. The F1 score was 0.941 [0.910, 0.966] for the notes algorithm compared with 0.794 [0.734, 0.842] for ICD codes. For PDR classification, ICD-10 codes performed better than ICD-9 codes (F1 score 0.836 [0.771, 0.878] vs. 0.596 [0.222, 0.692]). For NPDR severity classification, the notes algorithm performed similarly to ICD codes, but performance was limited by small sample size. Conclusions: The notes algorithm outperformed ICD codes for PDR classification. The findings demonstrate the significant potential of applying a rule-based NLP algorithm to clinical notes to increase the efficiency and accuracy of cohort selection for research. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Assessing the quality and standards of operative notes in general surgery; A teaching institute's experience in Pakistan.

Objective: To evaluate the quality and standard of hand-written operative notes in a teaching institute. Methods: This prospective study was carried out in the department of surgery, Fatima Hospital, Baqai Medical University, from January 2023 till May 2023. One hundred fifty operative notes from general surgery domain were considered. These notes were evaluated according to the guidelines of Royal College of Surgeons, with added-on a few variables by the author. Results: All 150 notes were handwritten. Resident surgeon wrote the operative notes under the supervision of primary surgeon. There was a deficiency in mentioning medical record number, procedure starting time and duration of surgery. An important statement about the hemostasis is that it is secured-per-operatively was not documented. The residents were reluctant to explain the surgical procedures diagrammatically. The operative room number was missing in all notes. Post operative instructions lacked the information for nothing per oral, blood pressure, temperature, pulse rate, and input and output charting. Conclusion: It is observed that the operative surgical notes were however explainable about the procedure, but quality and standard was not matchable with that of Royal College of Surgeons notes. Hence, a lack of formal training for the resident surgeons in operative notes writing was observed. This study is a thought provoker to the surgeons and a guide to resident trainees, and hospital management to provide a handful operative notes writing theme in the form of performa provided in the department.

Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.

Objective: This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers. Materials and Methods: We analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these models and identify the predictive risk factors associated with fatigue as an exemplar. Results: The RF model exhibited superior performance with a macro average AUC of 0.755 and an F1-score of 0.729 in predicting a range of cancer-related symptoms. For instance, the RF model achieved an AUC of 0.954 and an F1-score of 0.914 for pain prediction. Key predictive factors identified included clinical history, cancer characteristics, treatment modalities, and patient demographics depending on the symptom. For example, the odds ratio (OR) for fatigue was significantly influenced by allergy (OR = 2.3, 95% CI: 1.8-2.9) and colitis (OR = 1.9, 95% CI: 1.5-2.4). Discussion: Our research emphasizes the critical integration of multimorbidity and patient characteristics in modeling cancer symptoms, revealing the considerable influence of chronic conditions beyond cancer itself. Conclusion: We highlight the potential of ML for predicting cancer symptoms, suggesting a pathway for integrating such models into clinical systems to enhance personalized care and symptom management.

Dementia risk prediction using decision-focused content selection from medical notes.

Several general-purpose language model (LM) architectures have been proposed with demonstrated improvement in text summarization and classification. Adapting these architectures to the medical domain requires additional considerations. For instance, the medical history of the patient is documented in the Electronic Health Record (EHR) which includes many medical notes drafted by healthcare providers. Direct processing of these notes may not be possible because the computational complexity of LMs imposes a limit on the length of input text. Therefore, previous applications resorted to content selection using truncation or summarization of the text. Unfortunately, these text processing techniques may lead to information loss, redundancy or irrelevance. In the present paper, a decision-focused content selection technique is proposed. The objective of this technique is to select a subset of sentences from the medical notes of a patient that are relevant to the target outcome over a predefined observation period. This decision-focused content selection methodology is then used to develop a dementia risk prediction model based on the Longformer LM architecture. The results show that the proposed framework delivers an AUC of 78.43 when the summary is restricted to 1024 tokens, outperforming previously proposed content selection techniques. This performance is notable given that the model estimates dementia risk with a one year prediction horizon, relies on an observation period of only one year and solely uses medical notes without other EHR data modalities. Moreover, the proposed techniques overcome the limitation of machine learning models that use a tabular representation of the text by preserving contextual content, enable feature engineering from raw text and circumvent the computational complexity of language models.

Establishing a Method of Systematic and Reliable Analysis of Psychodynamic Process Notes.

Introduction: Process notes contain unique information concerning core elements of a psychodynamic treatment. These elements may be both conscious and unconscious for the author. One element for study is the tendency to which a therapist writes about providing either supportive or expressive interventions. This study sought to establish a method of systematically and reliably identifying the records of therapists' interventions as supportive or expressive. Methods: Three early-career clinicians were trained in the use of a process note intervention rating scale constructed specifically for this study. Quantitative statistical analyses assessed the scale's reliability and internal consistency. Results: Interrater reliability analysis determined at a p of 0.005 a Fleiss's kappa of 0.24 and an intraclass correlation coefficient of 0.264, suggesting a low but statistically significant reliability between the raters. A Cronbach's alpha of 0.67 and a McDonald's omega of 0.53 suggested questionable internal consistency. Discussion: Early-career clinicians can reliably code the manifestations of interventions in psychodynamic process notes as supportive or expressive. Future studies may improve the reliability and internal consistency of the scale, add measures of interpretation content, and evaluate these data in relation to other core elements of process notes, such as the author's emotional engagement as manifested in language measures and clinical outcome.

Characterizing Myasthenia Gravis Symptoms, Exacerbations, and Crises From Neurologist's Clinical Notes Using Natural Language Processing.

Background Myasthenia gravis (MG) is a rare, autoantibody neuromuscular disorder characterized by fatigable weakness. Real-world evidence based on administrative and structured datasets regarding MG may miss important details related to the clinical encounter. Examination of free-text clinical progress notes has the potential to illuminate aspects of MG care. Objective The primary objective was to examine and characterize neurologist progress notes in the care of individuals with MG regarding the prevalence of documentation of clinical subtypes, antibody status, symptomatology, and MG deteriorations, including exacerbations and crises. The secondary objectives were to categorize MG deteriorations into practical, objective states as well as examine potential sources of clinical inertia in MG care. Methods We performed a retrospective, cross-sectional analysis of de-identified neurologist clinical notes from 2017 to 2022. A qualitative analysis of physician descriptions of MG deteriorations and a discussion of risks in MG care (risk for adverse effects, risk for clinical decompensation, etc.) was performed. Results Of the 3,085 individuals with MG, clinical subtypes and antibody status identified included gMG (n = 400; 13.0%), ocular MG (n = 253; 8.2%), MG unspecified (2,432; 78.8%), seropositivity for acetylcholine receptor antibody (n = 441; 14.3%), and MuSK antibody (n = 29; 0.9%). The most common gMG manifestations were dysphagia (n = 712; 23.0%), dyspnea (n = 626; 20.3%), and dysarthria (n = 514; 16.7%). In MG crisis patients, documentation of difficulties with MG standard therapies was common (n = 62; 45.2%). The qualitative analysis of MG deterioration types includes symptom fluctuation, symptom worsening with treatment intensification, MG deterioration with rescue therapy, and MG crisis. Qualitative analysis of MG-related risks included the toxicity of new therapies and concern for worsening MG because of changing therapies. Conclusions This study of neurologist progress notes demonstrates the potential for real-world evidence generation in the care of individuals with MG. MG patients suffer fluctuating symptomatology and a spectrum of clinical deteriorations. Adverse effects of MG therapies are common, highlighting the need for effective, less toxic treatments.

Bridging the Gap: Can Large Language Models Match Human Expertise in Writing Neurosurgical Operative Notes?

BACKGROUND: Proper documentation is essential for patient care. The popularity of artificial intelligence (AI) offers the potential for improvements in neurosurgical note-writing. The study aimed to assess how AI can optimize documentation in neurosurgical procedures. METHODS: Thirty-six notes were included. All identifiable data were removed. Essential information, such as perioperative data and diagnosis, was sourced from these notes. ChatGPT 4.0 was trained to draft notes from surgical vignettes using each surgeon's note template. One hundred forty-four surveys, with a surgeon or AI note, were shared with three surgeons to evaluate accuracy, content, and organization using a five-point scale. Accuracy was the factual correctness. Content was the comprehensiveness. Organization was the arrangement of the note. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) scores quantified each note's readability. RESULTS: The mean AI accuracy (4.44) was not different from the mean surgeon accuracy (4.33, p = 0.512). The mean AI content (3.73) was lower than the mean surgeon content (4.42, p < 0.001). The mean AI organization (4.54) was greater than the mean surgeon organization (4.24, p = 0.064). The mean AI note's FKGL (13.13) was greater than the mean surgeon FKGL (9.99, p <0.001). The mean AI FRE (21.42) was lower than the mean surgeon FRE (41.70, p <0.001). CONCLUSION: AI notes were on par with surgeon notes in accuracy and organization, but lacked in content. Additionally, AI notes utilized language at an advanced reading level. These findings underscore the potential for ChatGPT to enhance the efficiency of neurosurgery documentation.

A comparison of written case notes and the delivery of care in dementia specialist mental health wards.

Introduction: Stigmatising language concerning people living with dementia can cause potentially harmful and dehumanising consequences. Language used about people living with dementia in mental health wards may focus on medical perspectives and suggest custodial relationships with patients rather than person-centred accounts of individuals. This language could have a devastating impact on the provision of person-centred care. This study investigated the relationship between accounts of people living with dementia written in healthcare case notes and clinical practice at three dementia specialist wards in Wales, UK. Language guidance was provided to ward staff to assess whether stigmatising language could be reduced and whether this influenced the provision of person-centred care.Methodology: Dementia Care Mapping was adapted to analyse case note entries for enhancing and detracting accounts of people living with dementia at three data collection points. These were compared to the results of routine DCM observations of care across the three wards. The healthcare case notes of 117 people living with dementia, encompassing 4, 522 entries over ten months were analysed. DCM observations of 38 people living with dementia within the three wards were compared against the case note results. Person-centred language guidance was shared with care staff following each data collection point.Results: Following the provision of person-centered language guidance, the use of personally enhancing language was observed to increase across all three wards. Non-person-centred case note entries predominantly focussed on Labelling language, whilst language concerning Invalidation and Objectification also occurred frequently compared to other DCM domains. Person centred language typically concerned Acknowledgement. A relationship between case note entries and practice was evident in some domains although findings were inconsistent.Discussion and Implications: The findings highlight the importance of addressing stigmatising language in healthcare and suggest that further studies to support the anti-stigma agenda in dementia care are required.

[Indications and technique for transoral thyroid gland and parathyroid gland surgery]. / Indikation und Technik der transoralen Schilddrüsen­/Nebenschilddrüsenchirurgie.

The conventional Kocher collar incision is the standard access to the thyroid and parathyroid glands. Although the incision length has been significantly shortened in recent years with this approach, there is increasing interest among patients in a surgical technique without visible scars in the décolleté. Transoral endoscopic thyroid gland surgery via the vestibular approach (TOETVA) is a modern technique that can be learned relatively quickly and leaves no visible scars because it is carried out exclusively through a natural orifice (natural orifice transluminal endoscopic surgery, NOTES). For retrieval of larger specimens, the transoral approach can be combined with a retroauricular access and thus covers a larger range of indications. The indications must be strictly followed, analogous to conventional surgery. Once the transoral access has been established, the operation is carried out as in open surgery but strictly from cranial to caudal. The classical complications are comparable to the results of conventional surgery. Specific complications include perioral, mandibular or cervical dysesthesia and hypesthesia.

Vaginal assisted NOTES hysterectomy in The Netherlands; A prospective cohort study.

Objectives: Vaginal assisted Natural Orifice Transluminal Endoscopic Surgery (NOTES) combines the benefits of vaginal and endoscopic surgery. This study presents the results of the first vaginal assisted NOTES hysterectomies (VANH) in The Netherlands. Study design: A prospective cohort study was performed in two non-academic teaching hospitals in The Netherlands. Data was collected from patients who underwent a VANH for benign indications between August 2019 and April 2023. Baseline characteristics and data of intra- and postoperative surgical outcomes were recorded and analysed. The VANHs were performed by four experienced vaginal and endoscopic gynaecological surgeons. Results: A total of 200 patients underwent a VANH. Indications were dysfunctional menstrual bleeding (61 %; n = 122), abnormal cervical cytology (15.5 %; n = 31), abdominal pain (11.5 %; n = 23), post ablation/sterilization pain syndrome (3.5 %; n = 7), uterine fibroids (5.0 %; n = 10), atypical endometrial hyperplasia (2.5 %; n = 5) and Lynch or BRCA gene mutation carriers (1.0 %, n = 2). The mean surgical time was 61.4 min ( ± 22.8 min) with a mean blood loss of 88 mL ( ± 89 mL) and a mean uterine weight of 150 g ( ± 112 g). In 2.0 % (n = 4) of the cases a conversion was necessary. Same day discharge (SDD) was feasible in 80.2 % (n = 105) of the patients planned in day-care. In 2.0 % (n = 4) an intra-operative complication and in 9.0 % (n = 18) a post-operative complication occurred. Conclusion: This study shows vNOTES to be a safe and feasible surgical technique and can be safely implemented with appropriate patient selection and skilled surgeons. It highlights the importance of surgeon awareness of the challenges inherent in the initial stages of the implementation of a new surgical technique when performing their first vNOTES procedures. Additional randomized clinical trials are needed to show superiority of vNOTES compared to traditional surgery.

Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study.

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.

Healthcare Professionals' Documentation in Supported Accommodation for People with Profound Intellectual Disabilities: An Educational Intervention Study.

Good-quality relationships in which individuals with profound intellectual disabilities (intelligence quotient, IQ < 20-25) are recognized by healthcare professionals (HPs) are essential for the quality of healthcare and promoting autonomy. This study examines the impact of an educational intervention on documentation of the interplay between HP and individuals receiving services in supported accommodation in Norway. An educational intervention study was designed to encourage HPs to document their approaches and interplay. The Scale for the Evaluation of Staff-Patient Interactions in Progress Notes (SESPI) was applied to measure documentation before and after the intervention. Journal notes written over a three-month period before the intervention and a three-month period after the intervention were measured. Prior to the intervention, only 23.1% of the journal notes described the resident's experiences, increasing by 5.4% (p = 0.041) post-intervention. Practical solutions to individual experiences increased from 0.9% to 8.5% (p < 0.001). The educational intervention demonstrated a significant increase in the documentation of residents' experiences and the interplay between HPs and residents. Future research should explore the generalizability of these findings. Incomplete documentation of HPs' relational work conceals important aspects of the healthcare provided, potentially resulting in confining autonomy and participation for individuals with intellectual disabilities.

Assessing the Proficiency of LLMs with Various Tasks and Evaluators.

Previous studies have been limited to giving one or two tasks to Large Language Models (LLMs) and involved a small number of evaluators within a single domain to evaluate the LLM's answer. We assessed the proficiency of four LLMs by applying eight tasks and evaluating 32 results with 17 evaluators from diverse domains, demonstrating the significance of various tasks and evaluators on LLMs.