Historical Analyses of Disordered Handwriting

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter’s research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources

Systematic review of the safety of medication use in inpatient, outpatient and primary care settings in the Gulf Cooperation Council countries

Background Errors in medication use are a patient safety concern globally, with different regions reporting differing error rates, causes of errors and proposed solutions. The objectives of this review were to identify, summarise, review and evaluate published studies on medication errors, drug related problems and adverse drug events in the Gulf Cooperation Council (GCC) countries. Methods A systematic review was carried out using six databases, searching for literature published between January 1990 and August 2016. Research articles focussing on medication errors, drug related problems or adverse drug events within different healthcare settings in the GCC were included. Results Of 2094 records screened, 54 studies met our inclusion criteria. Kuwait was the only GCC country with no studies included. Prescribing errors were reported to be as high as 91% of a sample of primary care prescriptions analysed in one study. Of drug-related admissions evaluated in the emergency department the most common reason was patient non-compliance. In the inpatient care setting, a study of review of patient charts and medication orders identified prescribing errors in 7% of medication orders, another reported prescribing errors present in 56% of medication orders. The majority of drug related problems identified in inpatient paediatric wards were judged to be preventable. Adverse drug events were reported to occur in 8.5–16.9 per 100 admissions with up to 30% judged preventable, with occurrence being highest in the intensive care unit. Dosing errors were common in inpatient, outpatient and primary care settings. Omission of the administered dose as well as omission of prescribed medication at medication reconciliation were common. Studies of pharmacists’ interventions in clinical practice reported a varying level of acceptance, ranging from 53% to 98% of pharmacists’ recommendations. Conclusions Studies of medication errors, drug related problems and adverse drug events are increasing in the GCC. However, variation in methods, definitions and denominators preclude calculation of an overall error rate. Research with more robust methodologies and longer follow up periods is now required.Peer reviewe

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Jefferson Digital Commons quarterly report: October-December 2018

This quarterly report includes: Articles Dissertations From the Archives Grand Rounds and Lectures Industrial Design Capstones Journals and Newsletters LabArchives Launch Masters of Public Health Capstones Posters Reports Videos What People are Saying About the Jefferson Digital Common

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

The quality of discharge summaries completed in the general paediatric wards at the Chris Hani Baragwanath Academic Hospital

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, in partial fulfilment of the requirements for the Degree Masters of Medicine. 17 November 2017.Background: Hospital discharge summaries are deemed to be an essential part of the medical record in South Africa but a formal assessment of the quality of these summaries is rarely undertaken. At the Chris Hani Baragwanath Academic Hospital (CHBAH), medical admission notes (bedletters) are difficult to retrieve from the hospital archives and the discharge summary is often the only readily available medical record that documents details of the hospital admission. Objectives: This study determined the proportion of discharge summaries that are appropriately completed for children admitted to the general paediatric wards at CHBAH in Soweto. Methods: A retrospective review of discharge summaries completed for children admitted from 01 May to 31 July 2016 was undertaken. The completeness of the following demographic and clinical variables was assessed: patient identifiers, hospital outcome, HIV infection status, and anthropometric status. The documentation of correct ICD-10 codes was assessed in children who were diagnosed with any form of lower respiratory tract infection (LRTI), which is the commonest diagnosis recorded in hospitalised children at CHBAH. Results: Discharge summaries were available for 1148 (78.3%) of 1466 children admitted during the study period. For completed discharge summaries, between 80.1% to 93.3% of patient identifiers and 91.4% of patient outcomes were appropriately completed. HIV-exposure was documented in 84.7% of summaries. The anthropometric parameters, including admission weight and length/height, and discharge weight, were appropriately completed in 91.4%, 70.9%, and 50.0% of summaries respectively. The ICD-10 code for children with LRTI was appropriately recorded by medical staff in 338 (67.2%) of 503 cases. ICD-10 codes and anthropometric parameters, which are important clinical parameters in the paediatric followup consultation, were both correctly recorded in only 21.6% of children who required followup clinical consultations at CHBAH. Conclusion: Compared to similar studies, both the rate of completion and the quality of completed discharge summaries were modest in this tertiary academic teaching hospital. As discharge summaries are crucial medical documents, interventions to improve the completeness rate and quality of discharge summaries need to be developed.LG201

A systematic review of the nature of dispensing errors in hospital pharmacies

Background: Dispensing errors are common in hospital pharmacies. Investigating dispensing errors is important for identifying the factors involved and developing strategies to reduce their occurrence. Objectives: To review published studies exploring the incidence and types of dispensing errors in hospital pharmacies and factors contributing to these errors. Methods: Electronic databases including PubMed, Scopus, Ovid, and Web of Science were searched for articles published between January 2000 and January 2015. Inclusion criteria were: studies published in English, and studies investigating type, incidence and factors contributing to dispensing errors in hospital pharmacies. One researcher searched for all relevant published articles, screened all titles and abstracts, and obtained complete articles. A second researcher assessed the titles, abstracts, and complete articles to verify the reliability of the selected articles. Key findings: Fifteen studies met the inclusion criteria all of which were conducted in just four countries. Reviewing incident reports and direct observation were the main methods used to investigate dispensing errors. Dispensing error rates varied between countries (0.015%–33.5%) depending on the dispensing system, research method, and classification of dispensing error types. The most frequent dispensing errors reported were dispensing the wrong medicine, dispensing the wrong drug strength, and dispensing the wrong dosage form. The most common factors associated with dispensing errors were: high workload, low staffing, mix-up of look-alike/sound-alike drugs, lack of knowledge/experience, distractions/interruptions, and communication problems within the dispensary team. Conclusion: Studies relating to dispensing errors in hospital pharmacies are few in number and have been conducted in just four countries. The majority of these studies focused on the investigation of dispensing error types with no mention of contributing factors or strategies for reducing dispensing errors. Others studies are thus needed to investigate dispensing errors in hospital pharmacies, and a combined approach is recommended to investigate contributing factors associated with dispensing errors and explore strategies for reducing these errors.Peer reviewe