Developing and testing a framework for coding general practitioners' free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing.

BACKGROUND: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim of this study was to develop a framework of research-relevant diagnostic codes, to test the framework using free-text diagnoses from a Swiss primary care database and to generate training data for NLP modelling. METHODS: The framework of diagnostic codes was developed based on input from local stakeholders and consideration of epidemiological data. After pre-testing, the framework contained 105 diagnostic codes, which were then applied by two raters who independently coded randomly drawn lines of free text (LoFT) from diagnosis lists extracted from the electronic medical records of 3000 patients of 27 general practitioners. Coding frequency and mean occurrence rates (n and %) and inter-rater reliability (IRR) of coding were calculated using Cohen's kappa (Κ). RESULTS: The sample consisted of 26,980 LoFT and in 56.3% no code could be assigned because it was not a specific diagnosis. The most common diagnostic codes were, 'dorsopathies' (3.9%, a code covering all types of back problems, including non-specific lower back pain, scoliosis, and others) and 'other diseases of the circulatory system' (3.1%). Raters were in almost perfect agreement (Κ ≥ 0.81) for 69 of the 105 diagnostic codes, and 28 codes showed a substantial agreement (K between 0.61 and 0.80). Both high coding frequency and almost perfect agreement were found in 37 codes, including codes that are particularly difficult to identify from components of the electronic medical record, such as musculoskeletal conditions, cancer or tobacco use. CONCLUSION: The coding framework was characterised by a subset of very frequent and highly reliable diagnostic codes, which will be the most valuable targets for training NLP models for automated disease classification based on free-text diagnoses from Swiss general practice.

Cause of death coding in asthma.

BACKGROUND: While clinical coding is intended to be an objective and standardized practice, it is important to recognize that it is not entirely the case. The clinical and bureaucratic practices from event of death to a case being entered into a research dataset are important context for analysing and interpreting this data. Variation in practices can influence the accuracy of the final coded record in two different stages: the reporting of the death certificate, and the International Classification of Diseases (Version 10; ICD-10) coding of that certificate. METHODS: This study investigated 91,022 deaths recorded in the Scottish Asthma Learning Healthcare System dataset between 2000 and 2017. Asthma-related deaths were identified by the presence of any of ICD-10 codes J45 or J46, in any position. These codes were categorized either as relating to asthma attacks specifically (status asthmatic; J46) or generally to asthma diagnosis (J45). RESULTS: We found that one in every 200 deaths in this were coded as being asthma related. Less than 1% of asthma-related mortality records used both J45 and J46 ICD-10 codes as causes. Infection (predominantly pneumonia) was more commonly reported as a contributing cause of death when J45 was the primary coded cause, compared to J46, which specifically denotes asthma attacks. CONCLUSION: Further inspection of patient history can be essential to validate deaths recorded as caused by asthma, and to identify potentially mis-recorded non-asthma deaths, particularly in those with complex comorbidities.

EHR coding with hybrid attention and features propagation on disease knowledge graph.

And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first construct a disease knowledge graph that focuses on the multi-view disease attributes of ICD codes and the disease relationships between these codes. we also use a long sequence encoder to get EHR document representation. most importantly ►KGENet leverages multi-view disease attributes and structured disease relationships for knowledge enhancement through hybrid attention and graph propagation ►Respectively. furthermore ►The above processes can provide attribute-aware and relationship-augmented explainability for the model prediction results based on our disease knowledge graph. experiments conducted on the MIMIC-III benchmark dataset show that KGENet outperforms state-of-the-art models in both model effectiveness and explainability Electronic health record (EHR) coding assigns International Classification of Diseases (ICD) codes to each EHR document. These standard medical codes represent diagnoses or procedures and play a critical role in medical applications. However, EHR is a long medical text that is difficult to represent, the ICD code label space is large, and the labels have an extremely unbalanced distribution. These factors pose challenges to automatic EHR coding. Previous studies have not explored the disease attributes (e.g., symptoms, tests, medications) of ICD codes and the disease relationships (e.g., causes, risk factors, comorbidities) between them. In addition, the important roles of medical.

Observational Behavioral Coding in the Pediatric Emergency Department: Development of the Emergency Department Child Behavior Coding System.

BACKGROUND: Despite improvements over the past decade, children continue to experience significant pain and distress surrounding invasive procedures in the emergency department (ED). To assess the impact of newly developed interventions, we must create more reliable and valid behavioral assessment tools that have been validated for the unique settings of pediatric EDs. OBJECTIVE: This study aimed to create and test the Emergency Department Child Behavior Coding System (ED-CBCS) for the assessment of child distress and nondistress behaviors surrounding pediatric ED procedures. METHODS: Via an iterative process, a multidisciplinary expert panel developed the ED-CBCS, an advanced time-based behavioral coding measure. Inter-rater reliability and concurrent validity were examined using 38 videos of children aged from 2 to 12 years undergoing laceration procedures. Face, Legs, Activity, Cry, Consolability (FLACC) scale scores were used to examine concurrent validity. RESULTS: The final ED-CBCS included 27 child distress and nondistress behaviors. Time-unit κ values from 0.64 to 0.98 and event alignment κ values from 0.62 to 1.00 indicated good to excellent inter-rater reliability for all but one of the individual codes. ED-CBCS distress (B = 1.26; p < 0.001) and nondistress behaviors (B = -0.69, p = 0.025) were independently significantly associated with FLACC scores, indicating concurrent validity. CONCLUSIONS: We developed a psychometrically sound tool tailored for pediatric ED procedures. Future work could use this measure to better identify behavioral targets and test the effects of interventions to relieve pediatric ED pain and distress.

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019.

OBJECTIVE: For ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team focused on certificates with a special public health interest and those for which DNNs have a low confidence index. METHODS: Supervised seq-to-seq DNNs are trained on previously coded data to ICD-10 code multiple causes and underlying causes of death. The DNNs are then used to target death certificates to be sent to the coding team and to predict multiple causes and underlying causes of death for part of the certificates. Hence, the coding campaign for 2018 and 2019 combines three modes of coding and a loop of interaction between the three. FINDINGS: In this campaign, 62% of the certificates are automatically batch coded by the expert system, 3% by the coding team, and the remainder by DNNs. Compared to a traditional campaign that would have relied on automatic batch coding and manual coding, the present campaign reaches an accuracy of 93.4% for ICD-10 coding of the underlying cause (95.6% at the European shortlist level). Some limitations (risks of under- or overestimation) appear for certain ICD categories, with the advantage of being quantifiable. CONCLUSION: The combination of the three coding methods illustrates how artificial intelligence, automated and human codings are mutually enriching. Quantified limitations on some chapters of ICD codes encourage an increase in the volume of certificates sent for manual coding from 2021 onward.

Validation Study of Cardiovascular International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification (ICD-10-AM) Codes in Administrative Healthcare Databases (ANZACS-QI 77).

BACKGROUND: Administrative healthcare databases can be utilised for research. The accuracy of the International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification (ICD-10-AM) coding of cardiovascular conditions in New Zealand is not known and requires validation. METHOD: International Statistical Classification of Diseases and Related Health Problems, Tenth Edition, Australian Modification coded discharges for acute coronary syndrome (ACS), heart failure (HF) and atrial fibrillation (AF), in both primary and secondary diagnostic positions, were identified from four district health boards between 1 January 2019 and 31 June 2019. A sample was randomly selected for retrospective clinician review for evidence of the coded diagnosis according to contemporary diagnostic criteria. Positive predictive values (PPVs) for ICD-10-AM coding vs clinician review were calculated. This study is also known as All of New Zealand, Acute Coronary Syndrome-Quality Improvement (ANZACS-QI) 77. RESULTS: A total of 600 cases (200 for each diagnosis, 5.0% of total identified cases) were reviewed. The PPV of ACS was 93% (95% confidence interval [CI] 89%-96%), HF was 93% (95% CI 89%-96%) and AF was 96% (95% CI 92%-98%). There were no differences in PPV between district health boards. PPV for ACS were lower in Maori vs non-Maori (72% vs 96%; p=0.004), discharge from non-Cardiology vs Cardiology services (89% vs 96%; p=0.048) and ICD-10-AM coding for unstable angina vs myocardial infarction (81% vs 95%; p=0.011). PPV for HF were higher in the primary vs secondary diagnostic position (100% vs 89%; p=0.001). CONCLUSIONS: The PPVs of ICD-10-AM coding for ACS, HF, and AF were high in this validation study. ICD-10-AM coding can be used to identify these diagnoses in administrative databases for the purposes of healthcare evaluation and research.

Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data.

OBJECTIVE: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text. METHODS: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific International Classification of Primary Care (ICPC-1) codes. Two text representations and three machine learning algorithms were evaluated for the (semi-)supervised models. Additionally, we measured the improvement achieved by the refinement process on all code occurrences in the database. RESULTS: The classification models performed well for most codes. In general, no single classification approach consistently outperformed the others. However, there were variations in the relative performance of the classification approaches within each code and in the use of different text representations and machine learning algorithms. Class imbalance and limited training data affected the performance of the (semi-)supervised models, yet the simple search queries remained particularly effective. Ultimately, the developed models improved the specificity of over half of all the unspecific code occurrences in the database. CONCLUSIONS: Our findings show the feasibility of using information from clinical text to improve the specificity of unspecific condition codes in observational healthcare databases, even with a limited range of machine-learning techniques and modest annotated training sets. Future work could investigate transfer learning, integration of structured data, alternative semi-supervised methods, and validation of models across healthcare settings. The improved level of detail enriches the interpretation of medical information and can benefit observational research and patient care.

Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy.

OBJECTIVE: Machine learning methods hold the promise of leveraging available data and generating higher-quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets. This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture. METHODS: We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks. RESULTS: We introduce a novel metric, , tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval. CONCLUSION: This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation.

Can the electronic medical record provide reliable indicators of primary care behavioral health fidelity? Comparison of accessibility and productivity indicators assessed through observational coding.

INTRODUCTION: The primary care behavioral health (PCBH) model is one of the most widely implemented integrated care approaches. However, research on the model has been limited by inconsistent measurement and reporting of model fidelity. One way of making measurement of PCBH model fidelity more routine is to incorporate fidelity indicators into the electronic medical record (EMR), though research regarding the accuracy of EMR data is mixed. In this study, we aimed to assess the reliability of EMR data as a PCBH fidelity measurement tool by comparing key EMR indicators of PCBH fidelity to those recorded by an observational coder. METHOD: Over an 8-month period (October 2021-May 2022), 12 behavioral health consultants (BHCs; 92% White, 75% female) across five primary care clinics recorded indicators of PCBH fidelity in the EMR as part of their routine charting of behavioral health visits. During that same period, one observational coder completed seven 4-hr visits per clinic to obtain multiple samples of data from each over time and recorded the same variables (i.e., percentage of visits prompted by warm handoffs, number of warm handoffs, and number of patient visits). We used bivariate correlations to test the associations between the EMR variables and the observer-coded variables. RESULTS: Correlations between EMR and observer-coded variables were moderate to strong, ranging from r = .46 to r = .97. DISCUSSION: Leveraging EMR data appears to be a fairly reliable approach to capturing indicators of PCBH model fidelity in the key domains of accessibility and high productivity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

La trazabilidad en el sistema logístico de medicamentos en Cuba y el uso de las tecnologías de auto-identificación / Traceability in the logistics system of medicines in Cuba, use of auto-identification

La trazabilidad es la capacidad para rastrear la historia, aplicación o ubicación de un objeto bajo consideración. En el ámbito farmacéutico, el rastreo y seguimiento de los medicamentos, incluyendo las vacunas y otros medicamentos biológicos, a lo largo de la cadena de suministro constituye un requisito obligatorio establecido por las autoridades sanitarias a nivel internacional, que se exige en mayor o menor magnitud en las reglamentaciones vigentes. En este artículo se analiza el sistema de codificación y clasificación en el sector de la salud y su estado actual en la cadena de suministro de medicamentos de Cuba. Se presenta un procedimiento para la implementación de las tecnologías de auto-identificación e intercambio electrónico de datos, mediante el uso de GS1 en el sistema de codificación y clasificación empleado en el sector de salud, que permita la trazabilidad en toda la cadena de suministro en Cuba(AU)

Traceability is the capability to track the history, application or location of an object under consideration. In the pharmaceutical field, the tracking and monitoring of medicines, including vaccines and other biological medicines, along the supply chain constitutes a mandatory requirement established by the sanitary authorities at an international level, which is demanded to a greater or lesser extent in the regulations in force. This research was carried out involving different links in the drug supply chain in Cuba, ranging from drug suppliers, drug distribution company, to healthcare centers and pharmacies. An analysis is carried out on the current coding and classification system, detecting the ineffectiveness of the identification of the drugs as the main deficiency. A procedure is proposed for the implementation of the auto-identification and electronic data interchange technologies using GS1 in the coding and classification system used in the health sector that allows traceability throughout the supply chain in Cuba(AU)

Estándares de codificación e interoperabilidad en Salud: evaluación del proyecto AmIHEALTH / Coding and interoperability standards in eSalud: projection evaluation AmIHEALTH

En el área de la salud, la interoperabilidad en las tecnologías de la información y la comunicación es la capacidad de los sistemas de información para comunicarse, intercambiar datos y utilizarlos en un sistema de salud. Los estándares de interoperabilidad en el sector salud han creado un auge paralelo al desarrollo de los sistemas de información, las aplicaciones web y los móviles, y han aumentado la calidad de la asistencia, la experiencia y la seguridad del usuario o paciente al permitir el acceso de sus datos personales clínicos desde cualquier punto, sin exposición a riesgos de dicha información. Este artículo describe los estándares de interoperabilidad desde la utilización en la mensajería, la terminología y la documentación como punto fundamental para el desarrollo de los sistemas de información en general. De igual forma, presenta aspectos para la seguridad de los datos de los pacientes, usuarios de dichos sistemas. Se desglosa individualmente cada uno de los estándares de interoperabilidad, de forma tal que se pueda tener conocimiento de cuál usar en un caso respectivo. Finalmente, se realiza una evaluación general en el proyecto AmIHEALTH, el cual permite llevar el control y el monitoreo de datos de salud, utilizando el estándar Fast Healthcare Interoperability Resources como base para el intercambio de datos de los usuarios en una plataforma web junto con una aplicación móvil, sin poner en riesgo la seguridad de estos datos(AU)

In the health area, interoperability in information and communication technologies (TIC´s) is the ability of information systems to communicate, exchange data and use them in a health system. Interoperability standards in the health sector have created a parallel boom to the development of information systems, web and mobile applications, increasing the quality of assistance, experience and safety of the user or patient by allowing access to their clinical personal data from any point without exposure to risks of such information. This article describes the interoperability standards from the use in messaging, terminology and documentation as a fundamental point for the development of information systems in general, similarly presents aspects for the safety of patient data, users of such systems. Each one of the interoperability standards is individually broken down, so that one can know which one to use in a respective case. At the end of the writing, we carried out a general evaluation of the AmIHEALTH project, which allows for the control and monitoring of health data, using the Fast Healthcare Interoperability Resources standard as a basis for the exchange of user data on a Web platform together with a mobile application, without putting such data at risk(AU)

Estandarización de prótesis dentales mediante codificación personal incluida en bases termoácido resistentes con fines de identificación forense / Dental prosthesis standarization by means of thermal-acid resistant-included personal code, for forensic identification purposes

La identificación categórica de un cadáver no sólo es importante por razones humanitarias y emocionales, sino también por sus efectos legales y administrativos. Durante el proceso de identificación humana, toda la información necesaria se obtiene del cuerpo desconocido de la víctima, permitiendo cumplir el objetivo de que su perfil sea reconstruido. Se utilizan sistemas de marcado y etiquetado de prótesis dentales en diferentes situaciones, detallándose métodos directos e indirectos para tal fin. Se propone la incorporación del número del documento nacional de identidad (DNI) en todas las prótesis removibles y fijas, con el fin de adoptar un único y definitivo código de identificación personal con el objetivo de lograr un método uniforme, estandarizado, sencillo y rápido para la identificación forense en pacientes atendidos en la Facultad de Odontología de la Universidad de Buenos Aires.

The categorical identification of a corpse is not only important for humanitarian and emotional reasons, but also for legal andadministrative purposes. During the human identification process, all necessary information is gathered from the unknown body of thevictim and hence that an objective reconstructed profile can be established. Denture marking and labeling systems are being used in varioussituations, and a number of direct and indirect methods are reported. Is proposed that national identity number (DNI) be incorporated inall removable and fixed prostheses, so as to adopt a single and definitive personal identification code with the aim of achieving a uniform,standardized, easy, and fast identification method in patients treated at the Faculty of Dentistry at the University of Buenos Aires forforensic identification.