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.

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.

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).

Hierarchical label-wise attention transformer model for explainable ICD coding.

International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT + ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

Using a Simple Prescription Gap to Determine Warfarin Discontinuation Can Lead to Substantial Misclassification.

BACKGROUND: Warfarin remains widely used and a key comparator in studies of other direct oral anticoagulants. As longer-than-needed warfarin prescriptions are often provided to allow for dosing adjustments according to international normalized ratios (INRs), the common practice of using a short allowable gap between dispensings to define warfarin discontinuation may lead to substantial misclassification of warfarin exposure. We aimed to quantify such misclassification and determine the optimal algorithm to define warfarin discontinuation. METHODS: We linked Medicare claims data from 2007 to 2014 with a multicenter electronic health records system. The study cohort comprised patients ≥65 years with atrial fibrillation and venous thromboembolism initiating warfarin. We compared results when defining warfarin discontinuation by (1) different gaps (3, 7, 14, 30, and 60 days) between dispensings and (2) having a gap ≤60 days or bridging larger gaps if there was INR ordering at least every 42 days (60_INR). Discontinuation was considered misclassified if there was an INR ≥2 within 7 days after the discontinuation date. RESULTS: Among 3,229 patients, a shorter gap resulted in a shorter mean follow-up time (82, 95, 117, 159, 196, and 259 days for gaps of 3, 7, 14, 30, 60, and 60_INR, respectively; p < 0.001). Incorporating INR (60_INR) can reduce misclassification of warfarin discontinuation from 68 to 4% (p < 0.001). The on-treatment risk estimation of clinical endpoints varied significantly by discontinuation definitions. CONCLUSION: Using a short gap between warfarin dispensings to define discontinuation may lead to substantial misclassification, which can be improved by incorporating intervening INR codes.

Prevention and Management of Pseudoverrucous Lesions: A Review and Case Scenarios.

GENERAL PURPOSE: To present the associated risk factors, prevention measures, and assessment and management of pseudoverrucous lesions specific to a surgically created ileal conduit, as well as three clinical scenarios illustrating this condition. TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant will:1. Define pseudoverrucous lesions.2. Identify the risk factors for stoma complications such as pseudoverrucous lesions.3. Select the appropriate routine care procedures to teach patients following stoma creation to help prevent pseudoverrucous lesions.4. Choose the recommended treatment options for patients who develop pseudoverrucous lesions.

Pseudoverrucous lesions are a late peristomal complication that occurs most commonly in people with urinary stomas. Impairment of the peristomal skin can result in pouching system leaks that can translate into odor, embarrassment, and diminished quality of life. Prevention is key to maintaining smooth, dry skin and intact psyche. Treatment revolves around outpatient postoperative follow-up, refitting the pouching system to eliminate moisture impacting the peristomal area, modification of pouching system wear time, acidification of the urine, and intensive education. This review includes three case scenarios to support early, intermediate, and late-stage intervention guidelines. Some interventions were successful; one case remains unresolved.

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)

Comparison of ICD-9-CM to ICD-10-CM Crosswalks Derived by Physician and Clinical Coder vs. Automated Methods.

Purpose: To evaluate whether automated methods are sufficient for deriving ICD-10-CM algorithms by comparing ICD-9-CM to ICD-10-CM crosswalks from general equivalence mappings (GEMs) with physician/clinical coder-derived crosswalks. Patients and methods: Forward mapping was used to derive ICD-10-CM crosswalks for 10 conditions. As a sensitivity analysis, forward-backward mapping (FBM) was also conducted for three clinical conditions. The physician/coder independently developed crosswalks for the same conditions. Differences between the crosswalks were summarized using the Jaccard similarity coefficient (JSC). Results: Physician/coder crosswalks were typically far more inclusive than GEMs crosswalks. Crosswalks for peripheral artery disease were most dissimilar (JSC: 0.06), while crosswalks for mild cognitive impairment (JSC: 1) and congestive heart failure (0.85) were most similar. FBM added ICD-10-CM codes for all three conditions but did not consistently increase similarity between crosswalks. Conclusion: The GEMs and physician/coder algorithms rarely aligned fully; human review is still required for ICD-9-CM to ICD-10-CM crosswalk development.

Coding practice in national and regional kidney biopsy registries.

BACKGROUND: Kidney biopsy registries all over the world benefit research, teaching and health policy. Comparison, aggregation and exchange of data is however greatly dependent on how registration and coding of kidney biopsy diagnoses are performed. This paper gives an overview over kidney biopsy registries, explores how these registries code kidney disease and identifies needs for improvement of coding practice. METHODS: A literature search was undertaken to identify biopsy registries for medical kidney diseases. These data were supplemented with information from personal contacts and from registry websites. A questionnaire was sent to all identified registries, investigating age of registries, scope, method of coding, possible mapping to international terminologies as well as self-reported problems and suggestions for improvement. RESULTS: Sixteen regional or national kidney biopsy registries were identified, of which 11 were older than 10 years. Most registries were located either in Europe (10/16) or in Asia (4/16). Registries most often use a proprietary coding system (12/16). Only a few of these coding systems were mapped to SNOMED CT (1), older SNOMED versions (2) or ERA-EDTA PRD (3). Lack of maintenance and updates of the coding system was the most commonly reported problem. CONCLUSIONS: There were large gaps in the global coverage of kidney biopsy registries. Limited use of international coding systems among existing registries hampers interoperability and exchange of data. The study underlines that the use of a common and uniform coding system is necessary to fully realize the potential of kidney biopsy registries.

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.

Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised machine learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 and March 2019 (n = 2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n = 187). Here, we show that regarding area under the curve (AUC) and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 (0.92-0.94) and F-measure of 0.72 (0.68-0.75). This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information.

Coding of Sexual Assault by Emergency Physicians: A Nationally Representative Study.

INTRODUCTION: Sexual assault is a public health problem that affects many Americans and has multiple long-lasting effects on victims. Medical evaluation after sexual assault frequently occurs in the emergency department, and documentation of the visit plays a significant role in decisions regarding prosecution and outcomes of legal cases against perpetrators. The American College of Emergency Physicians recommends coding such visits as sexual assault rather than adding modifiers such as "alleged." METHODS: This study reviews factors associated with coding of visits as sexual assault compared to suspected sexual assault using the 2016 Nationwide Emergency Department Sample. RESULTS: Younger age, female gender, a larger number of procedure codes, urban hospital location, and lack of concurrent alcohol use are associated with coding for confirmed sexual assault. CONCLUSION: Implications of this coding are discussed.