Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.

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.

In-Home Health Risk Assessments And Chart Reviews Contribute To Coding Intensity In Medicare Advantage.

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.

Practical Applications and Observational Implications of Time-Based Coding for Workers' Compensation Clinical Encounters-A Follow-up to Best Practices in Documenting and Coding High-Value Care in Workers' Compensation Encounters-ACOEM Guidance Statement.

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.

Understanding the Evaluation and Management Add-on Complexity Code.

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.

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.

[The pitfalls of lung cancer coding practices based on the evaluation of the National Cancer Registry]. / A tüdorákhoz kötodo kódolási gyakorlat buktatói a Nemzeti Rákregiszter felmérése alapján.

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.

Czech medical coding assistant based on transformer networks.

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes. The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability. The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based "Four-headed" model incorporating four distinct classification heads. This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.

Model correction of diagnostic coding-based RSV incidence for children 0-4 years in the US.

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.

Pediatric Primary Care Billing Trends After the 2021 Evaluation and Management Coding Changes.

This cross-sectional study examines the differences in billing trends for pediatric patient care compared with adult care after the 2021 evaluation and management (E/M) policy changes.

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.

Inter-rater reliability of ACS-NSQIP colorectal procedure coding in Canada.

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.

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.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.

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.

Validating new coding algorithms to improve identification of alcohol-associated and nonalcohol-associated cirrhosis hospitalizations in administrative databases.

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.

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.

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.

Best Practices in Documenting and Coding High-Value Care in Workers' Compensation Encounters-ACOEM Guidance Statement.

ABSTRACT: Workers' compensation outpatient care requires attention to causation, functional assessment, work disability prevention, and return-to-work planning, elements not usually addressed in other types of outpatient encounters. Because these elements of care deviate from the usual pattern of ambulatory services, providers of workers' compensation care have faced challenges in billing and auditing practices resulting in underpayment when providing high-value care based on evidence-based guidelines. Recent changes in Centers for Medicare & Medicaid Services rules on documentation requirements for coding outpatient evaluation and management encounters offer an opportunity for occupational health clinicians to be paid appropriately for care that follows occupational medicine practice guidelines. There remains a need to define the elements of documentation that should be expected in delivering high-value workers' compensation care. This article provides guidance for documenting high-value workers' compensation care.

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes. It leverages a unique architecture featuring a training layer for model development and a knowledge base that captures relationships between symptoms and diseases. Experiments using data from a large medical research center demonstrated the system's effectiveness in disease classification prediction. Logistic regression emerged as the optimal model due to its superior processing speed, achieving an accuracy of 81.07% with acceptable error rates during high-load testing. This approach offers a promising solution to improve healthcare informatics by overcoming coding standard incompatibility and automating code prediction from unstructured medical text.