The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE).

PURPOSE: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Introducing a Framework for Surgical Discharge Summaries to Improve Accuracy and Succinctness in a Busy District General Hospital.

Background Discharge summaries (TTOs) are essential documents in the effective communication between primary and secondary care, particularly in conveying critical post-discharge instructions to patients. Inconsistencies and omissions in TTOs can significantly undermine patient outcomes and disrupt continuity of care. This is particularly relevant to surgical patients, who often require specific follow-up care such as the removal of clips or drains shortly after discharge. Following the recent transition from paper-based to electronic records at a busy district general hospital (Royal Blackburn Hospital, Blackburn), the quality of TTOs was noted to be substandard. This quality improvement project aimed to enhance the accuracy and clarity of surgical TTOs.  Methods A targeted framework was developed in collaboration with local consultants and the consideration of national guidelines to guide the content of surgical TTOs, focusing on five essential components: Reason for Admission, Intervention, Surgical Details, Discharge Plan, and Follow-up Instructions. Initial retrospective data analysis included TTOs (n=60) across five surgical wards, evaluating their quality against the framework. The framework was then introduced via educational initiatives and integrated into the hospital's electronic patient record (EPR) system (CERNER). The impact of these interventions was assessed through data collection after two Plan-Do-Study-Act (PDSA) cycles.  Results Baseline data highlighted significant deficiencies; 42 (70%) of TTOs were missing at least one key element, with missing follow-up details most often the reason. Many TTOs also included excessive or inappropriate information, and their format varied greatly depending on individual writing styles. Following the first PDSA cycle and the introduction of the framework, the number of TTOs containing all essential data points increased by 14 (30% increase), while those missing two or more elements decreased by 16 (48% decrease). After the second cycle, further improvements were observed, with the number of TTOs missing one or more data points decreased by 6 (21%). Despite the overall progress, follow-up information continued to be the most frequently omitted element. Feedback from resident doctors was positive and the unanimous opinion was that the framework improved not only the quality of TTOs, but also how efficiently they were written.  Conclusions Implementing a standardised framework significantly improved the quality of surgical TTOs, particularly by increasing the inclusion of critical information. These results are encouraging. However, anecdotal evidence suggests there is a lack of training in writing TTOs at both undergraduate and foundation levels. Ongoing efforts are required to address these areas to ensure a sustained improvement in quality.

Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes.

Objective: We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing to derive insights from social worker documentation. Materials and Methods: We developed and employed a Bidirectional Encoder Representations from Transformers (BERT) based approach, using a hierarchical multi-step BERT model (BERT-MS), to predict the prescription of targeted cancer therapy to patients based solely on documentation by clinical social workers. Our corpus included free-text clinical social work notes, combined with medication prescription information, for all patients treated for breast cancer at UCSF between 2012 and 2021. We conducted a feature importance analysis to identify the specific social circumstances that impact cancer therapy regimen. Results: Using only social work notes, we consistently predicted the administration of targeted therapies, suggesting systematic differences in treatment selection exist due to non-clinical factors. The findings were confirmed by several language models, with GatorTron achieving the best performance with an area under the receiver operating characteristic curve (AUROC) of 0.721 and a Macro F1 score of 0.616. The UCSF BERT-MS model, capable of leveraging multiple pieces of notes, surpassed the UCSF-BERT model in both AUROC and Macro-F1. Our feature importance analysis identified several clinically intuitive social determinants of health that potentially contribute to disparities in treatment. Discussion: Leveraging social work notes can be instrumental in identifying disparities in clinical decision-making. Hypotheses generated in an automated way could be used to guide patient-specific quality improvement interventions. Further validation with diverse clinical outcomes and prospective studies is essential. Conclusions: Our findings indicate that significant disparities exist among breast cancer patients receiving different types of therapies based on social determinants of health. Social work reports play a crucial role in understanding these disparities in clinical decision-making.

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models.

BACKGROUND: Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR. METHODS: Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes. RESULTS: LSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥ 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0. CONCLUSIONS: These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

Epigastric Ventral Hernia Repair Through Vaginal Natural Orifice Transluminal Endoscopic Surgery.

OBJECTIVE: Ventral hernia repair is a commonly performed operation and can be executed by open or laparoscopic approach. The search for even less invasive techniques continues. Natural orifice transluminal endoscopic surgery (NOTES) is a known method of minimally invasive surgery. METHODS: We performed an epigastric ventral hernia repair through vaginal NOTES during a concurrent hysterectomy and bilateral salpingectomy. We used the access to do a synchronous hernia repair with mesh augmentation. The technique of repair was identical to the laparoscopic intraperitoneal onlay mesh repair (Lap. IPOM). RESULTS: We reported a sufficient hernia repair without intra-operative complications. Also, post-operatively, no problems were encountered. Follow-up after 4 weeks showed a good and strong hernia repair. The complaints of the patient were relieved. CT scan 10 months after operation showed no recurrence nor signs of mesh infection. CONCLUSIONS: Ventral hernia repair through vaginal NOTES can be considered a possible new and minimal invasive (scarless) technique for ventral hernia repair but further investigations on a larger scale are needed to confirm feasibility & safety.

Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.

BACKGROUND: Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. OBJECTIVE: This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. METHODS: Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. RESULTS: The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R2 of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. CONCLUSIONS: To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.

Early hospital readmission refers to unplanned emergency admission of patients within 30 days of discharge. Predicting early readmission risk before discharge can help to reduce the cost of readmissions for hospitals and decrease the death rate for Intensive Care Unit patients. In this paper, we propose a novel approach for prediction of unplanned hospital readmissions using discharge notes from the MIMIC-III database. This approach is based on first extracting relevant information from clinical reports using a pretrained Named Entity Recognition model called BioMedical-NER, which is built on Bidirectional Encoder Representations from Transformers architecture, with the extracted features then used to train machine learning models to predict unplanned readmissions. Our proposed approach achieves better results on clinical reports compared to the state-of-the-art methods, with an average precision of 88.4% achieved by the Gradient Boosting algorithm. In addition, explainable Artificial Intelligence techniques are applied to provide deeper comprehension of the predictive results.

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.

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.

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.

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.

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.

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.

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.

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.