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BACKGROUND: Assigning International Classification of Diseases (ICD) codes to clinical texts is a common and crucial practice in patient classification, hospital management, and further statistics analysis. Current auto-coding methods mainly transfer this task to a multi-label classification problem. Such solutions are suffering from high-dimensional mapping space and excessive redundant information in long clinical texts. To alleviate such a situation, we introduce text summarization methods to the ICD coding regime and apply text matching to select ICD codes. METHOD: We focus on the tenth revision of the ICD (ICD-10) coding and design a novel summarization-based approach (SuM) with an end-to-end strategy to efficiently assign ICD-10 code to clinical texts. In this approach, a knowledge-guided pointer network is purposed to distill and summarize key information in clinical texts precisely. Then a matching model with matching-aggregation architecture follows to align the summary result with code, tuning the one-vs-all scenario to one-vs-one matching so that the large-label-space obstacle laid in classification approaches would be avoided. RESULT: The 12,788 ICD-10 coded discharge summaries from a Chinese hospital were collected to evaluate the proposed approach. Compared with existing methods, the purposed model achieves the greatest coding results with Micro AUC of 0.9548, MRR@10 of 0.7977, Precision@10 of 0.0944, and Recall@10 of 0.9439 for the TOP-50 Dataset. Results on the FULL-Dataset remain consistent. Also, the proposed knowledge encoder and applied end-to-end strategy are proven to facilitate the whole model to gain efficacy in selecting the most suitable code. CONCLUSION: The proposed automatic ICD-10 code assignment approach via text summarization can effectively capture critical messages in long clinical texts and improve the performance of ICD-10 coding of clinical texts.
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Clasificación Internacional de Enfermedades , Humanos , Registros Electrónicos de Salud , Codificación Clínica/métodosRESUMEN
Background: Coding in medical procedures is crucial for patients, and errors made by hospital administration during the coding process can have an impact on both the financial results and the course of therapy. The present study aims to assess the accuracy of diagnostic and procedural codes as recorded by the hospital's coders and to also evaluate their impact on the hospital's revenue. Methods: In a local hospital in Najran, Saudi Arabia, a cross-sectional observational analysis was conducted on patients with a clinical coder. The percentage of precision and error following the re-coding of cases was calculated using a statistical analysis. Results: Primary diagnosis was incorrectly coded in 57 (26 per cent) records, and secondary diagnosis was incorrectly coded in 21 (9.9 per cent) records. Inaccurate medical labelling has been seen in emergency rooms, operating rooms, and gynaecology facilities. Discussion: The percentage of records with the most incorrect coding was found to be 16 (7.5 per cent) in the emergency room, 10 (4.7 per cent) in the surgical clinic, and 5 (2.3 per cent) in the gynaecology/OBS clinic. Six (2.8 per cent) records in the private clinic had inaccurate secondary diagnoses, followed by four (1.9 per cent) and two (1 per cent) records in nephrology. Conclusion: The percentage of inaccurate clinical codes in primary diagnoses reached (26.8 per cent) and the percentage of incorrect clinical codes in secondary diagnoses reached (9.9 per cent).
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Codificación Clínica , Arabia Saudita , Humanos , Codificación Clínica/economía , Codificación Clínica/normas , Estudios Transversales , Hospitales , Femenino , MasculinoRESUMEN
PURPOSE: Sickle cell disease (SCD) affects all organ systems and is characterized by numerous acute and chronic complications and comorbidities. Standardized codes are needed for complications/comorbidities used in real-world evidence (RWE) studies that rely on administrative and medical coding. This systematic literature review was conducted to produce a comprehensive list of complications/comorbidities associated with SCD, along with their diagnosis codes used in RWE studies. METHODS: A search in MEDLINE and Embase identified studies published from 2016 to 2023. Studies were included if they were conducted in US SCD populations and reported complications/comorbidities and respective International Classification of Diseases, Clinical Modification (ICD-CM) codes. All identified complications/comorbidities and codes were reviewed by a certified medical coding expert and hematologist. RESULTS: Of 1851 identified studies, 39 studies were included. The most reported complications/comorbidities were stroke, acute chest syndrome, pulmonary embolism, venous thromboembolism, and vaso-occlusive crisis. Most of the studies used ICD-9-CM codes (n = 21), while some studies used ICD-10-CM codes (n = 3) or both (n = 15), depending on the study period. Most codes reported in literature were heterogeneous across complications/comorbidities. The medical coding expert and hematologist recommended modifications for several conditions. CONCLUSION: While many studies we identified did not report their codes and were excluded from this review, the studies with codes exhibited diverse coding definitions. By providing a standardized set of diagnosis codes that were reported by studies and reviewed by a coding expert and hematologist, our review can serve as a foundation for accurately identifying complications/comorbidities in future research, and may reduce heterogeneity, enhance transparency, and improve reproducibility. Future efforts focused on validating these code lists are needed.
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Anemia de Células Falciformes , Codificación Clínica , Clasificación Internacional de Enfermedades , Humanos , Anemia de Células Falciformes/epidemiología , Anemia de Células Falciformes/complicaciones , Anemia de Células Falciformes/diagnóstico , Codificación Clínica/normas , Comorbilidad , Clasificación Internacional de Enfermedades/normasRESUMEN
The International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) is internationally used for coding diagnoses, with the ICD-10 German Modification (GM) being prescribed for morbidity coding in Germany. ICD-10-GM is subject to annual revisions. This can lead to backward compatibility issues leading to undesirable consequences for cross-version data analysis. A study of annual crosswalk-tables concerning 21 ICD-10-GM versions showed that the ratio of difficult transitions from an older to a newer version (0.89 %) and vice versa (0.48 %) is not particularly significant but should nevertheless not be neglected. In this paper we present two solutions (Neo4J database and FHIR ConceptMaps) for the automated handling of different ICD-10-GM versions.
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Clasificación Internacional de Enfermedades , Alemania , Humanos , Análisis de Datos , Codificación ClínicaRESUMEN
BACKGROUND: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. OBJECTIVES: This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. RESULTS: The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. CONCLUSIONS: Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.
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Systematized Nomenclature of Medicine , Estonia , Angiografía , Codificación Clínica/normas , HumanosRESUMEN
Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Natural Language Processing (NLP) assists by simplifying the analysis of unstructured data from electronic health records, thereby facilitating diagnosis coding. This study investigates the suitability of transformer models for ICD-10 classification, considering both encoder and encoder-decoder architectures. The analysis is performed on clinical discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset, which contains an extensive collection of electronic health records. Pre-trained models such as BioBERT, ClinicalBERT, ClinicalLongformer, and ClinicalBigBird are adapted for the coding task, incorporating specific preprocessing techniques to enhance performance. The findings indicate that increasing context length improves accuracy, and that the difference in accuracy between encoder and encoder-decoder models is negligible.
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Registros Electrónicos de Salud , Clasificación Internacional de Enfermedades , Procesamiento de Lenguaje Natural , Registros Electrónicos de Salud/clasificación , Humanos , Codificación ClínicaRESUMEN
BACKGROUND: Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality. OBJECTIVE: To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs). RESEARCH DESIGN: To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback. SUBJECTS: Participants included international experts with expertise in administrative health data, data quality, and ICD coding. RESULTS: The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement. CONCLUSIONS: This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality.
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Exactitud de los Datos , Técnica Delphi , Clasificación Internacional de Enfermedades , Indicadores de Calidad de la Atención de Salud , Humanos , Hospitales/normas , Hospitales/estadística & datos numéricos , Hospitales/clasificación , Codificación Clínica/normas , Mejoramiento de la CalidadRESUMEN
To date, symptom documentation has mostly relied on clinical notes in electronic health records or patient-reported outcomes using disease-specific symptom inventories. To provide a common and precise language for symptom recording, assessment, and research, a comprehensive list of symptom codes is needed. The International Classification of Diseases, Ninth Revision or its clinical modification ( International Classification of Diseases, Ninth Revision, Clinical Modification ) has a range of codes designated for symptoms, but it does not contain codes for all possible symptoms, and not all codes in that range are symptom related. This study aimed to identify and categorize the first list of International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes for a general population and demonstrate their use to characterize symptoms of patients with type 2 diabetes mellitus in the Cerner database. A list of potential symptom codes was automatically extracted from the Unified Medical Language System Metathesaurus. Two clinical experts in symptom science and diabetes manually reviewed this list to identify and categorize codes as symptoms. A total of 1888 International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes were identified and categorized into 65 categories. The symptom characterization using the newly obtained symptom codes and categories was found to be more reasonable than that using the previous symptom codes and categories on the same Cerner diabetes cohort.
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Registros Electrónicos de Salud , Clasificación Internacional de Enfermedades , Evaluación de Síntomas , Registros Electrónicos de Salud/estadística & datos numéricos , Humanos , Evaluación de Síntomas/métodos , Diabetes Mellitus Tipo 2/diagnóstico , Codificación Clínica/métodos , Codificación Clínica/normas , Unified Medical Language System , Femenino , Masculino , Persona de Mediana EdadRESUMEN
There is widespread agreement that taxpayers pay more when Medicare beneficiaries are enrolled in Medicare Advantage (MA) plans than if those beneficiaries were enrolled in traditional Medicare. MA plans are paid on the basis of submitted diagnoses and thus have a clear incentive to encourage providers to find and report as many diagnoses for their enrollees as possible. Two mechanisms that MA plans use to identify diagnoses that are not available for beneficiaries in traditional Medicare are in-home health risk assessments and chart reviews. Using MA encounter data for 2015-20, I isolated the impact of these two types of encounters on the risk scores used for payments to MA plans during 2016-21. I found that encounter-based risk scores for MA enrollees were higher by 0.091 points, or 7.4 percent, in 2021 when in-home health risk assessments and chart reviews were included than they would have been without the use of these tools.
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Medicare Part C , Humanos , Estados Unidos , Medición de Riesgo , Anciano , Masculino , Femenino , Anciano de 80 o más Años , Codificación Clínica , Servicios de Atención de Salud a Domicilio/economíaRESUMEN
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.
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Codificación Clínica , Registros Electrónicos de Salud , Médicos Generales , Procesamiento de Lenguaje Natural , Registros Electrónicos de Salud/estadística & datos numéricos , Humanos , Reproducibilidad de los Resultados , Codificación Clínica/métodos , Médicos Generales/educación , Suiza/epidemiología , Masculino , Femenino , Adulto , Persona de Mediana Edad , Clasificación Internacional de EnfermedadesRESUMEN
The quality of input data determines the reliability of epidemiological assessments. Thus, the verification of cases reported to the National Cancer Registry is required. The objective of our study was evaluating the reliability of cases diagnosed by lung cancer, exploring the patterns of erroneous reports. The validation of the 11,750 lung cancer cases reported to the Cancer Registry in 2018 was performed with the involvement of the recording hospitals, analyzing the characteristics of reports by gender, age and attributes of the reporting institutions. 81.3 percent of the reported cases was confirmed, in 40.4 percent of the false reports, malignancy was not present at all. Among the erroneous cases women and the elderly age group were overrepresented. The highest deleted rate occurred in Borsod- Abaúj-Zemplén county. As a conclusion, there is a strong need for the improvement of the efficiency in encoding lung cancer. The most common errors: confusion of malignant-benign, cancerous-non-cancerous and primary-metastatic lesions. The reliability is not affected by the role of individual institutions in the hierarchy of health care. The availability of reliable epidemiological data is crucial in the fight against cancer, which requires broad professional cooperation.
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Codificación Clínica , Neoplasias Pulmonares , Sistema de Registros , Humanos , Neoplasias Pulmonares/epidemiología , Neoplasias Pulmonares/patología , Femenino , Masculino , Anciano , Persona de Mediana Edad , Codificación Clínica/normas , Reproducibilidad de los Resultados , Hungría/epidemiología , AdultoRESUMEN
On January 1, 2024, the new add-on complexity code for evaluation and management (E/M) services, G2211, went into effect. Understanding appropriate use of this code and how it can and cannot be utilized is of importance for all physicians. This article discusses the nuances of this code and gives examples of how to effectively incorporate it into practice.
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Codificación Clínica , Humanos , Dermatología , Estados UnidosRESUMEN
ABSTRACT: Clinical practices that provide workers' compensation care and other services related to managing work-related illnesses and injuries have long been challenged in receiving appropriate payment for their professional work. The American College of Occupational and Environmental Medicine (ACOEM) has provided excellent guidelines for coding and billing via its various documents that have been provided over the years. However, despite these guidelines, payors have been slow to adopt occupational specific coding guidelines to justify higher professional payment. With the move to a Centers for Medicare & Medicaid Services (CMS)-sponsored time-based coding option in 2011, the occupational and environmental medicine (OEM) clinics have been able to finally not only document but recoup the value of those services that go beyond the simple patient interface, being able to capture those activities that truly provide high value in the management of workers' medical issues.
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Codificación Clínica , Indemnización para Trabajadores , Indemnización para Trabajadores/economía , Humanos , Estados Unidos , Codificación Clínica/normas , Medicina del Trabajo , Guías de Práctica Clínica como Asunto , Documentación/normas , Enfermedades Profesionales/terapia , Enfermedades Profesionales/economía , Centers for Medicare and Medicaid Services, U.S. , Traumatismos Ocupacionales/terapia , Traumatismos Ocupacionales/economíaRESUMEN
OBJECTIVES: To document how dementia diagnoses appear in hospice claims, and how these different presentations reflect different hospice utilization. The reliance in the hospice literature on primary diagnosis, in addition to the focus on decedents, may underestimate the true presence of dementia in hospice, and little is known about the health care utilization of hospice patients with dementia as a secondary or not present diagnosis. DESIGN: Secondary data analysis of Medicare claims. SETTING AND PARTICIPANTS: Medicare beneficiaries with 2 or more dementia diagnoses from 2016 to 2018 electing hospice in 2018. METHODS: Beneficiaries were classified based on the presence and position of dementia on their subset of hospice claims: primary diagnosis, secondary diagnosis, and not present. We then compared the demographics and utilization of the 3 claim-based categories of dementia beneficiaries in hospice in 2018. RESULTS: Fewer than half of beneficiaries with a dementia diagnosis in all of their Medicare claims have dementia indicated as the primary diagnosis associated with their hospice claims, and 30% of beneficiaries did not have their diagnosed dementia appear at all on their hospice claims. Hospice length of stay and other utilization characteristics varied markedly across the 3 claim-based categories of dementia beneficiaries in hospice in 2018. CONCLUSIONS AND IMPLICATIONS: Collectively, International Classification of Diseases, Tenth Revision (ICD-10) coding and sequencing conventions, coding practices, and research methods related to hospice claim diagnoses may unintentionally underestimate and oversimplify how dementia manifests in hospice utilization.
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Demencia , Cuidados Paliativos al Final de la Vida , Medicare , Humanos , Demencia/diagnóstico , Estados Unidos , Masculino , Femenino , Anciano , Anciano de 80 o más Años , Cuidados Paliativos al Final de la Vida/estadística & datos numéricos , Codificación Clínica , Hospitales para Enfermos Terminales/estadística & datos numéricos , Revisión de Utilización de SegurosRESUMEN
OBJECTIVES: Conducting longitudinal health research about people experiencing homelessness poses unique challenges. Identification through administrative data permits large, cost-effective studies; however, case validity in Ontario is unknown after a 2018 Canada-wide policy change mandating homelessness coding in hospital databases. We validated case definitions for identifying homelessness using Ontario health administrative databases after introduction of this coding mandate. STUDY DESIGN AND SETTING: We assessed 42 case definitions in a representative sample of people experiencing homelessness in Toronto (n = 640) from whom longitudinal housing history (ranging from 2018 to 2022) was obtained, and a randomly selected sample of presumably housed people (n = 128,000) in Toronto. We evaluated sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios to select an optimal definition, and compared the resulting true positives against false positives and false negatives to identify potential causes of misclassification. RESULTS: The optimal case definition included any homelessness indicator during a hospital-based encounter within 180 days of a period of homelessness (sensitivity = 52.9%; specificity = 99.5%). For periods of homelessness with ≥1 hospital-based healthcare encounter, the optimal case definition had greatly improved sensitivity (75.1%) while retaining excellent specificity (98.5%). Review of false positives suggested that homeless status is sometimes erroneously carried forward in healthcare databases after an individual transitioned out of homelessness. CONCLUSION: Case definitions to identify homelessness using Ontario health administrative data exhibit moderate to good sensitivity and excellent specificity. Sensitivity has more than doubled since the implementation of a national coding mandate. Mandatory collection and reporting of homelessness information within administrative data present invaluable opportunities for advancing research on the health and healthcare needs of people experiencing homelessness.
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Personas con Mala Vivienda , Personas con Mala Vivienda/estadística & datos numéricos , Humanos , Ontario , Femenino , Masculino , Adulto , Persona de Mediana Edad , Bases de Datos Factuales/estadística & datos numéricos , Bases de Datos Factuales/normas , Estudios Longitudinales , Codificación Clínica/normas , Codificación Clínica/estadística & datos numéricos , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) uses Current Procedural Terminology (CPT) codes for risk-adjusted calculations. This study evaluates the inter-rater reliability of coding colorectal resections across Canada by ACS-NSQIP surgical clinical nurse reviewers (SCNR) and its impact on risk predictions. METHODS: SCNRs in Canada were asked to code simulated operative reports. Percent agreement and free-marginal kappa correlation were calculated. The ACS-NSQIP risk calculator was utilized to illustrate its impact on risk prediction. RESULTS: Responses from 44 of 150 (29.3 â%) SCNRs revealed 3 to 6 different codes chosen per case, with agreement ranging from 6.7 â% to 62.3 â%. Free-marginal kappa correlation ranged from moderate agreement (0.53) to high disagreement (-0.17). ACS-NSQIP risk calculator predicted large absolute differences in risk for serious complications (0.2 â%-13.7 â%) and mortality (0.2 â%-6.3 â%). CONCLUSION: This study demonstrated low inter-rater reliability in coding ACS-NSQIP colorectal procedures in Canada among SCNRs, impacting risk predictions.
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Mejoramiento de la Calidad , Humanos , Canadá , Reproducibilidad de los Resultados , Codificación Clínica/normas , Current Procedural Terminology , Variaciones Dependientes del Observador , Medición de Riesgo/métodosRESUMEN
BACKGROUND: Alcohol (AC) and nonalcohol-associated cirrhosis (NAC) epidemiology studies are limited by available case definitions. We compared the diagnostic accuracy of previous and newly developed case definitions to identify AC and NAC hospitalizations. METHODS: We randomly selected 700 hospitalizations from the 2008 to 2022 Canadian Discharge Abstract Database with alcohol-associated and cirrhosis-related International Classification of Diseases 10th revision codes. We compared standard approaches for AC (ie, AC code alone and alcohol use disorder and nonspecific cirrhosis codes together) and NAC (ie, NAC codes alone) case identification to newly developed approaches that combine standard approaches with new code combinations. Using electronic medical record review as the reference standard, we calculated case definition positive and negative predictive values, sensitivity, specificity, and AUROC. RESULTS: Electronic medical records were available for 671 admissions; 252 had confirmed AC and 195 NAC. Compared to previous AC definitions, the newly developed algorithm selecting for the AC code, alcohol-associated hepatic failure code, or alcohol use disorder code with a decompensated cirrhosis-related condition or NAC code provided the best overall positive predictive value (91%, 95% CI: 87-95), negative predictive value (89%, CI: 86-92), sensitivity (81%, CI: 76-86), specificity (96%, CI: 93-97), and AUROC (0.88, CI: 0.85-0.91). Comparing all evaluated NAC definitions, high sensitivity (92%, CI: 87-95), specificity (82%, CI: 79-86), negative predictive value (96%, CI: 94-98), AUROC (0.87, CI: 0.84-0.90), but relatively low positive predictive value (68%, CI: 62-74) were obtained by excluding alcohol use disorder codes and using either a NAC code in any diagnostic position or a primary diagnostic code for HCC, unspecified/chronic hepatic failure, esophageal varices without bleeding, or hepatorenal syndrome. CONCLUSIONS: New case definitions show enhanced accuracy for identifying hospitalizations for AC and NAC compared to previously used approaches.
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Algoritmos , Bases de Datos Factuales , Registros Electrónicos de Salud , Hospitalización , Cirrosis Hepática Alcohólica , Cirrosis Hepática , Humanos , Hospitalización/estadística & datos numéricos , Masculino , Femenino , Persona de Mediana Edad , Canadá , Clasificación Internacional de Enfermedades , Anciano , Codificación Clínica , Sensibilidad y Especificidad , AdultoRESUMEN
BACKGROUND: Although administrative claims data have a high degree of completeness, not all medically attended Respiratory Syncytial Virus-associated lower respiratory tract infections (RSV-LRTIs) are tested or coded for their causative agent. We sought to determine the attribution of RSV to LRTI in claims data via modeling of temporal changes in LRTI rates against surveillance data. METHODS: We estimated the weekly incidence of LRTI (inpatient, outpatient, and total) for children 0-4 years using 2011-2019 commercial insurance claims, stratified by HHS region, matched to the corresponding weekly NREVSS RSV and influenza positivity data for each region, and modelled against RSV, influenza positivity rates, and harmonic functions of time assuming negative binomial distribution. LRTI events attributable to RSV were estimated as predicted events from the full model minus predicted events with RSV positivity rate set to 0. RESULTS: Approximately 42% of predicted RSV cases were coded in claims data. Across all regions, the percentage of LRTI attributable to RSV were 15-43%, 10-31%, and 10-31% of inpatient, outpatient, and combined settings, respectively. However, when compared to coded inpatient RSV-LRTI, 9 of 10 regions had improbable corrected inpatient LRTI estimates (predicted RSV/coded RSV ratio < 1). Sensitivity analysis based on separate models for PCR and antigen-based positivity showed similar results. CONCLUSIONS: Underestimation based on coding in claims data may be addressed by NREVSS-based adjustment of claims-based RSV incidence. However, where setting-specific positivity rates is unavailable, we recommend modeling across settings to mirror NREVSS's positivity rates which are similarly aggregated, to avoid inaccurate adjustments.
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Infecciones por Virus Sincitial Respiratorio , Virus Sincitial Respiratorio Humano , Humanos , Infecciones por Virus Sincitial Respiratorio/epidemiología , Infecciones por Virus Sincitial Respiratorio/diagnóstico , Infecciones por Virus Sincitial Respiratorio/virología , Lactante , Incidencia , Preescolar , Recién Nacido , Estados Unidos/epidemiología , Virus Sincitial Respiratorio Humano/genética , Virus Sincitial Respiratorio Humano/aislamiento & purificación , Infecciones del Sistema Respiratorio/epidemiología , Infecciones del Sistema Respiratorio/virología , Infecciones del Sistema Respiratorio/diagnóstico , Masculino , Femenino , Codificación Clínica , Gripe Humana/epidemiología , Gripe Humana/diagnóstico , Gripe Humana/virologíaRESUMEN
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.
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Asma , Causas de Muerte , Codificación Clínica , Certificado de Defunción , Clasificación Internacional de Enfermedades , Humanos , Asma/mortalidad , Asma/diagnóstico , Codificación Clínica/métodos , Codificación Clínica/estadística & datos numéricos , Codificación Clínica/normas , Masculino , Femenino , Escocia/epidemiología , Adulto , Persona de Mediana Edad , AncianoRESUMEN
BACKGROUND: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. CONCLUSION: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.