EP.TU.79Assessed and Discharged - An Audit of Discharge Summaries Completion During On-Calls

Aim Effective health care provision is heavily dependent on timely, reliable transfer of patient information. Failure of this communication between professionals could result in redundancy of tests, delay in treatment, which may in turn endanger patient safety. The NHS Standard Contract requirements state discharge summaries should be completed within 24 hours of hospital assessment and discharge. Discharge summaries for patients who were reviewed but not admitted have been observed to be poorly completed during on-calls and this audit aims to clarify this. Method On-Call Patient Lists between 1 December to 14 December 2020 were studied retrospectively. Patients who were assessed by the on-call surgical team but not admitted were included in the audit. Patients referred to other specialties were excluded. Hospital electronic system was reviewed for electronic records from the encounter including clinical note or discharge summary. Results In total, 47 patients were identified during the 2 week- period. 40/47 patients were referred from AE and 9 of these patients were discharged from AE directly. 3 of the patients had a clinical note or discharge summary completed on the hospital electronic system. Overall, 18 of the 47 (38.3%) patients had a clinical note or discharge summary on the electronic system, with 6 (12.8%) of them being recorded as discharge summaries. Conclusion The overall completion of discharge summaries for this group of patients was poor. Awareness of this failing and the importance of professional communication should be highlighted with the juniors during surgical meeting to improve compliance.

402 Assessed and Discharged - An Audit of Discharge Summaries Completion During On-Calls

Aim Effective health care provision is heavily dependent on timely, reliable transfer of patient information. Failure of this communication between professionals could result in redundancy of tests, delay in treatment, which may in turn endanger patient safety. The NHS Standard Contract requirements state discharge summaries should be completed within 24 hours of hospital assessment and discharge. Discharge summaries for patients who were reviewed but not admitted have been observed to be poorly completed and this audit aims to clarify this. Method On-Call Patient Lists between 1 December to 14 December 2020 were studied retrospectively. Patients who were assessed by the on-call surgical team but not admitted were included in the audit. Patients referred to other specialties were excluded. Hospital electronic system was reviewed for electronic records frim the encounter including clinical note or discharge summary. Results In total, 47 patients were identified during the 2 week- period. 40/47 patients were referred from AE and 9 of these patients were discharged from AE directly. 3 of the patients had a clinical note or discharge summary completed on the hospital electronic system. Overall, 18 of the 47 (38.3%) patients had a clinical note or discharge summary on the electronic system, with 6 (12.8%) of them being recorded as discharge summaries. Conclusions The overall completion of discharge summaries for this group of patients was poor. Awareness of this failing and the importance of professional communication should be highlighted with the juniors during surgical meeting to improve compliance before re-audit.

Algorithm of management based on treatment goals for calcifying tendonitis of the shoulder: rigidity, pain and size of calcification

Purpose: The objective of the present manuscript is to propose a step-by-step algorithm for the management of calcifying tendonitis (CT) of the shoulder based on treatment goals, from conservative to surgical approaches. Method: A clinical note to present the main treatments for the management of calcifying tendonitis of the shoulder based on pain, rigidity and size of calcification based on the clinical experience and previous publications of the authors has been performed. Arguments: Treatment is conservative and surgical although there is controversy on the most adequate treatment. Kinesiotherapy is the recommended therapy for shoulder rigidity. For pain management after NSAIDs; microwaves, short waves, TENS, ultrasounds and Interferentials are effective. For the management of size of calcification, Iontophoresis, Electroshock wave therapy, Ultrasound needle guided aspiration and arthroscopic surgeries are the recommended alternatives, in that order. Conclusions: CT of the shoulder must be treated based on specific goals, mainly pain, rigidity or size of calcification. The proposed step-by-step algorithm of treatment is suggested based on the effectiveness of available techniques. If rigidity is present, kinesiotherapy is the recommended technique. For pain management, physical therapy such as microwaves, short waves, TENS, Ultrasound and Interferentials are effective techniques. For the treatment of calcification, iontophoresis is the most common, safe and inexpensive technique. If all previous conservative techniques failed, advanced techniques such as ESWT, US guided aspiration and arthroscopy are recommended, although they are not exempt of risk factors and complications.

A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types

In the electronic health record, the majority of clinically relevant information is stored within clinical notes. Most clinical notes follow a set organizational structure composed of canonicalized section headers that facilitate clinical review and information gathering. Standardized section header terminologies such as the SecTag terminology permit the identification and standardization of headers to a canonicalized form. Although the SecTag terminology has been evaluated extensively for history & physical notes, the coverage of canonical section header terms has not been assessed across other note types. For this pilot study, we conducted a coverage study and characterization of canonical section headers across 5 common, clinical note types and a generalizability study of canonical section headers detected within two types of clinical notes from Penn Medicine.

Mononeuropathy of the maxillary nerve branches associated with COVID-19

This clinical note describes a case report where the patient notices that one of the symptoms suffered during COVID-19 infection is a mononeuropathy of the branches of the maxillary nerve, reviewing in the literature the findings of the affinity of the coronavirus to nerve fibers.

Multimodal temporal-clinical note network for mortality prediction

Background Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results. Results We propose a multimodal temporal-clinical note network to model both temporal and clinical notes. Specifically, the clinical text are further processed for differentiating the chronic illness patients in the historical information of clinical notes from non-chronic illness patients. In order to further mine the information related to the mortality in the text, we learn the time series embedding with Long Short Term Memory networks and the clinical notes embedding with a label aware convolutional neural network. We also propose a scoring function to measure the importance of clinical note sections. Our approach achieved a better AUCPR and AUCROC than competing methods and visual explanations for word importance showed the interpretability improvement of the model. Conclusions We have tested our methodology on the MIMIC-III dataset. Contributions of different clinical note sections were uncovered by visualization methods. Our work demonstrates that the introduction of the medical history related information can improve the performance of the mortality prediction. Using label aware convolutional neural networks can further improve the results.

Identification of social determinants of health using multi-label classification of electronic health record clinical notes

Objectives Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department. Methods and Materials We labeled a gold-standard corpus of EHR clinical note sentences (N = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR. We then trained 5 classification models: linear-Support Vector Machine, K-Nearest Neighbors, Random Forest, XGBoost, and bidirectional Long Short-Term Memory (BI-LSTM). We adopted 5 common evaluation measures: accuracy, average precision–recall (AP), area under the curve receiver operating characteristic (AUC-ROC), Hamming loss, and log loss to compare the performance of different methods for MLL classification using the F1 score as the primary evaluation metric. Results Our results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59–1.0). We found that 44.6% of our study population (N = 1119) had at least one positive documentation of SDH. Discussion and Conclusion The proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.