We Are Not Speaking the Same Language: Current Procedural Terminology Coding and Provision of Care in Lymphatic Reconstruction.

INTRODUCTION: Current Procedural Terminology (CPT) codes provide a uniform language for medical billing, but specific codes have not been assigned for lymphatic reconstruction techniques. The authors hypothesized that inadequate codes would contribute to heterogeneous coding practices and reimbursement challenges, ultimately limiting surgeons' ability to treat patients. METHODS: A 22-item virtual questionnaire was offered to 959 members of the American Society of Reconstructive Microsurgeons to assess the volume of lymphatic reconstruction procedures performed, CPT codes used for each procedure, and challenges related to coding and providing care. RESULTS: The survey was completed by 66 board-certified/board-eligible plastic surgeons (6.9%), who unanimously agreed that lymphatic surgery is integral to cancer care, with 86.4% indicating that immediate lymphatic reconstruction should be offered after lymphadenectomy. Most performed lymphovenous bypass, immediate lymphatic reconstruction, liposuction, and vascularized lymph node transfer.Respondents reported that available CPT codes failed to reflect procedural scope. A wide variety of CPT codes was used to report each type of procedure. Insurance coverage problems led to 69.7% of respondents forgoing operations and 32% reducing treatment offerings. Insurance coverage and CPT codes were identified as significant barriers to care by 98.5% and 95.5% of respondents, respectively. CONCLUSIONS: Respondents unanimously agreed on the importance of lymphatic reconstruction in cancer care, and most identified inadequate CPT codes as causing billing issues, which hindered their ability to offer surgical treatment. Appropriate and specific CPT codes are necessary to ensure accuracy and consistency of reporting and ultimately to improve patient access to care.

Pediatric Physical Restraint Coding in US Hospitals: A 2019 Kids Inpatient Database Study.

BACKGROUND: Reduction of physical restraint utilization is a goal of high-quality hospital care, but there is little nationally-representative data about physical restraint utilization in hospitalized children in the United States. This study reports the rate of physical restraint coding among hospitalizations for patients aged 1 to 18 years old in the United States and explores associated demographic and diagnostic factors. METHODS: The Kids' Inpatient Database, an all-payors database of community hospital discharges in the United States, was queried for hospitalizations with a diagnosis of physical restraint status in 2019. Logistic regression using patient sociodemographic characteristics was used to characterize factors associated with physical restraint coding. RESULTS: A coded diagnosis of physical restraint status was present for 8893 (95% confidence interval [CI]: 8227-9560) hospitalizations among individuals aged 1 to 18 years old, or 0.63% of hospitalizations. Diagnoses associated with physical restraint varied by age, with mental health diagnoses overall the most frequent in an adjusted model, male sex (adjusted odds ratio [aOR] 1.56; 95% CI: 1.47-1.65), Black race (aOR 1.43; 95% CI: 1.33-1.55), a primary mental health or substance diagnosis (aOR 7.13; 95% CI: 6.42-7.90), Medicare or Medicaid insurance (aOR 1.33; 95% CI: 1.24-1.43), and more severe illness (aOR 2.83; 95% CI: 2.73-2.94) were associated with higher odds of a hospitalization involving a physical restraint code. CONCLUSIONS: Physical restraint coding varied by age, sex, race, region, and disease severity. These results highlight potential disparities in physical restraint utilization, which may have consequences for equity.

Capture, Coding, and Reporting of Health Care and Medicine Information in Australian Workers' Compensation Systems.

OBJECTIVE: This study aims to characterize the approaches to collecting, coding, and reporting health care and medicines data within Australian workers' compensation schemes. METHODS: We conducted a cross-sectional survey of data and information professionals in major Australian workers' compensation jurisdictions. Questionnaires were developed with input from key informants and a review of existing documentation. RESULTS: Twenty-five participants representing regulators (40%) and insurers (60%) with representation from all Australian jurisdictions were included. Health care and medicines data sources, depth, coding standards, and reporting practices exhibited significant variability across the Australian workers' compensation schemes. CONCLUSIONS: Substantial variability exists in the capture, coding, and reporting of health care and medicine data in Australian workers' compensation jurisdictions. There are opportunities to advance understanding of medicines and health service delivery in these schemes through greater harmonization of data collection, data coding, and reporting.

Methodologies and key considerations for implementing the International Classification of Diseases-11th revision morbidity coding: insights from a national pilot study in China.

OBJECTIVE: The aim of this study was to disseminate insights from a nationwide pilot of the International Classification of Diseases-11th revision (ICD-11). MATERIALS AND METHODS: The strategies and methodologies employed to implement the ICD-11 morbidity coding in 59 hospitals in China are described. The key considerations for the ICD-11 implementation were summarized based on feedback obtained from the pilot hospitals. Coding accuracy and Krippendorff's alpha reliability were computed based on the coding results in the ICD-11 exam. RESULTS: Among the 59 pilot hospitals, 58 integrated ICD-11 Coding Software into their health information management systems and 56 implemented the ICD-11 in morbidity coding, resulting in 3 723 959 diagnoses for 873 425 patients being coded over a 2-month pilot coding phase. The key considerations in the transition to the ICD-11 in morbidity coding encompassed the enrichment of ICD-11 content, refinement of tools, provision of systematic and tailored training, improvement of clinical documentation, promotion of downstream data utilization, and the establishment of a national process and mechanism for implementation. The overall coding accuracy was 82.9% when considering the entire coding field (including postcoordination) and 92.2% when only one stem code was considered. Krippendorff's alpha was 0.792 (95% CI, 0.788-0.796) and 0.799 (95% CI, 0.795-0.803) with and without consideration of the code sequence, respectively. CONCLUSION: This nationwide pilot study has enhanced national technical readiness for the ICD-11 implementation in morbidity, elucidating key factors warranting careful consideration in future endeavors. The good accuracy and intercoder reliability of the ICD-11 coding achieved following a brief training program underscore the potential for the ICD-11 to reduce training costs and provide high-quality health data. Experiences and lessons learned from this study have contributed to WHO's work on the ICD-11 and can inform other countries when formulating their transition plan.

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.

The mechanics of risk adjustment and incentives for coding intensity in Medicare.

OBJECTIVE: To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024. DATA SOURCES AND STUDY SETTING: Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees. STUDY DESIGN: We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records. DATA COLLECTION/EXTRACTION METHODS: We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018. PRINCIPAL FINDINGS: Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change. CONCLUSIONS: Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.

Evaluation of the accuracy of diagnostic coding and clinical documentation for traumatic heterotopic ossification diagnoses in Western Australian hospitals.

BACKGROUND: Traumatic heterotopic ossification (tHO) refers to the pathological formation of ectopic bone in soft tissues that can occur following burn, neurological ororthopaedic trauma. As completeness and accuracy of medical diagnostic coding can vary based on coding practices and depend on the institutional culture of clinical documentation, it is important to assess diagnostic coding in that local context. To the authors' knowledge, there is no prior study evaluating the accuracy of medical diagnostic coding or specificity of clinical documentation for tHO diagnoses across Western Australia (WA) trauma centres or across the full range of inciting injury and surgical events. OBJECTIVE: To evaluate and compare the clinical documentation and the diagnostic accuracy of ICD-10-AM coding for tHO in trauma populations across 4 WA hospitals. METHODS: A retrospective data search of the WA trauma database was conducted to identify patients with tHO admitted to WA hospitals following burn, neurological or orthopaedic trauma. Patient demographic and tHO diagnostic characteristics were assessed for all inpatient and outpatient tHO diagnoses. The frequency and distribution of M61 (HO-specific) and broader, musculoskeletal (non-specific) ICD-10-AM codes were evaluated for tHO cases in each trauma population. RESULTS: HO-specific M61 ICD-10-AM codes failed to identify more than a third of true tHO cases, with a high prevalence of non-specific HO codes (19.4 %) and cases identified via manual chart review (25.4 %). The sensitivity of M61 codes for correctly diagnosing tHO after burn injury was 50 %. ROC analysis showed that M61 ICD-10-AM codes as a predictor of a true positive tHO diagnosis were a less than favourable method (AUC=0.731, 95 % CI=0.561-0.902, p = 0.012). Marked variability in clinical documentation for tHO was identified across the hospital network. CONCLUSION: Coding inaccuracies may, in part, be influenced by insufficiencies in clinical documentation for tHO diagnoses, which may have implications for future research and patient care. Clinicians should consistently employ standardised clinical terminology from the point of care to increase the likelihood of accurate medical diagnostic coding for tHO diagnoses.

The applications of Australian-coded ICD-10 and ICD-10-AM data in research: A scoping review of the literature.

Background: Australia uses the International Classification of Diseases (ICD-10) for mortality coding and its Australian Modification, ICD-10-AM, for morbidity coding. The ICD underpins surveillance (population health, mortality), health planning and research (clinical, epidemiological and others). ICD-10-AM also supports activity-based funding, thereby propelling realignment of the foci of clinical coding and, potentially, coded data's research utility. Objective: To conduct a scoping review of the literature exploring the use of ICD-10 and ICD-10-AM Australian-coded data in research. Research questions addressed herein: (1) What were the applications of ICD-10(-AM) Australian-coded data in published peer-reviewed research, 2012-2022? (2) What were the purposes of ICD-10(-AM) coded data within this context, as classified per a taxonomy of data use framework? Method: Following systematic Medline, Scopus and Cumulative Index to Nursing and Allied Health Literature database searches, a scoping literature review was conducted using PRISMA Extension for Scoping Reviews guidelines. References of a random 5% sample of within-scope articles were searched manually. Results were summarised using descriptive analyses. Results: Multi-stage screening of 2103 imported articles produced 636, including 25 from the references, for extraction and analysis; 54% were published 2019-2022; 50% within the largest five categories were published post-2019; 22% fell within the "Mental health and behavioural" category; 60.3% relied upon an ICD-10 modification. Articles were grouped by: research foci; relevant ICD chapter; themes per the taxonomy; purposes of the coded data. Observational study designs predominated: descriptive (50.6%) and cohort (34.6%). Conclusion: Researchers' use of coded data is extensive, robust and growing. Increasing demand is foreshadowed for ICD-10(-AM) coded data, and HIM-Coders' and Clinical Coders' expert advice to medical researchers.

Toward Efficient, Sustainable, and Scalable Methods of Treatment Characterization: An Investigation of Coding Clinical Practice from Chart Notes.

PURPOSE: Chart notes provide a low-cost data source that could help characterize what occurs in treatment with sufficient precision to improve management of care. This study assessed the interrater reliability of treatment content coded from chart notes and evaluated its concordance with content coded from transcribed treatment sessions. METHOD: Fifty randomly selected and digitally recorded treatment events were transcribed and coded for practice content. Independent coders then applied the same code system to chart notes for these same treatment events. ANALYSIS: We measured reliability and concordance of practice occurrence and extensiveness at two levels of specificity: practices (full procedures) and steps (subcomponents of those procedures). RESULTS: For chart notes, practices had moderate interrater reliability (M k = 0.50, M ICC = 0.56) and steps had moderate (M ICC = 0.74) to substantial interrater reliability (M k = 0.78). On average, 2.54 practices and 5.64 steps were coded per chart note and 4.53 practices and 13.10 steps per transcript. Across sources, ratings for 64% of practices and 41% of steps correlated significantly, with those with significant correlations generally demonstrating moderate concordance (practice M r = 0.48; step M r = 0.47). Forty one percent of practices and 34% of steps from transcripts were also identified in the corresponding chart notes. CONCLUSION: Chart notes provide an accessible data source for evaluating treatment content, with different levels of specificity posing tradeoffs for validity and reliability, which in turn may have implications for chart note interfaces, training, and new metrics to support accurate, reliable, and efficient measurement of clinical practice.

Assessment of ethnic inequalities in diagnostic coding of familial hypercholesterolaemia (FH): A cross-sectional database study in Lambeth, South London.

BACKGROUND AND AIMS: Differences in the perceived prevalence of familial hypercholesterolemia (FH) by ethnicity are unclear. In this study, we aimed to assess the prevalence, determinants and management of diagnostically-coded FH in an ethnically diverse population in South London. METHODS: A cross-sectional analysis of 40 practices in 332,357 adult patients in Lambeth was undertaken. Factors affecting a (clinically coded) diagnosis of FH were investigated by multi-level logistic regression adjusted for socio-demographic and lifestyle factors, co-morbidities, and medications. RESULTS: The age-adjusted FH % prevalence rate (OR, 95%CI) ranged from 0.10 to 1.11, 0.00-1.31. Lower rates of FH coding were associated with age (0.96, 0.96-0.97) and male gender (0.75, 0.65-0.87), p < 0.001. Compared to a White British reference group, a higher likelihood of coded FH was noted in Other Asians (1.33, 1.01-1.76), p = 0.05, with lower rates in Black Africans (0.50, 0.37-0.68), p < 0.001, Indians (0.55, 0.34-0.89) p = 0.02, and in Black Caribbeans (0.60, 0.44-0.81), p = 0.001. The overall prevalence using Simon Broome criteria was 0.1%; we were unable to provide ethnic specific estimates due to low numbers. Lower likelihoods of FH coding (OR, 95%CI) were seen in non-native English speakers (0.66, 0.53-0.81), most deprived income quintile (0.68, 0.52-0.88), smokers (0.68,0.55-0.85), hypertension (0.62, 0.52-0.74), chronic kidney disease (0.64, 0.41-0.99), obesity (0.80, 0.67-0.95), diabetes (0.31, 0.25-0.39) and CVD (0.47, 0.36-0.63). 20% of FH coded patients were not prescribed lipid-lowering medications, p < 0.001. CONCLUSIONS: Inequalities in diagnostic coding of FH patients exist. Lower likelihoods of diagnosed FH were seen in Black African, Black Caribbean and Indian ethnic groups, in contrast to higher diagnoses in White and Other Asian ethnic groups. Hypercholesterolaemia requiring statin therapy was associated with FH diagnosis, however, the presence of cardiovascular disease (CVD) risk factors lowered the diagnosis rate for FH.

Interpreting and coding causal relationships for quality and safety using ICD-11.

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of "connecting terms," key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.

How Can Transformer Models Shape Future Healthcare: A Qualitative Study.

Transformer models have been successfully applied to various natural language processing and machine translation tasks in recent years, e.g. automatic language understanding. With the advent of more efficient and reliable models (e.g. GPT-3), there is a growing potential for automating time-consuming tasks that could be of particular benefit in healthcare to improve clinical outcomes. This paper aims at summarizing potential use cases of transformer models for future healthcare applications. Precisely, we conducted a survey asking experts on their ideas and reflections for future use cases. We received 28 responses, analyzed using an adapted thematic analysis. Overall, 8 use case categories were identified including documentation and clinical coding, workflow and healthcare services, decision support, knowledge management, interaction support, patient education, health management, and public health monitoring. Future research should consider developing and testing the application of transformer models for such use cases.

Are we Getting It Right First Time? An exploration of clinical coding practices within a UK paediatric dental hospital department.

Introduction Issues arising from the current coding system in dentistry have been highlighted. Available codes are considered to lack clarity and fail to reflect all dental specialties. There are no paediatric-specific codes, which means codes from other specialties are used, which may not accurately reflect the work carried out.Aim This paper aims to explore the range of codes and the consistency and accuracy of current coding practices within the paediatric dentistry department at Newcastle Dental Hospital, and explore the potential impact of introducing new speciality-specific codes for the aforementioned procedures.Method Data were retrospectively collected to determine whether the following treatments had been undertaken, and if so, which procedure code had been used for the treatment: inhalation sedation; dietary advice; acclimatisation; preformed metal crowns, silver diamine fluoride application; and apexification. All codes used within the department for a six-month period were also reviewed retrospectively and the frequency in which procedures relating to the potential new codes would be undertaken within the department was estimated to facilitate consideration of potential financial impact of the introduction of new codes.Results Codes utilised for the aforementioned procedures did not accurately reflect work carried out despite being relatively consistent. The potential new codes corresponded to procedures that were commonly undertaken within the department.Discussion This study highlights shortcomings in the coding system relating to a lack of applicable codes for paediatric dentistry procedures. Introduction of new speciality-specific codes should help to address this deficit to ensure a more accurate representation of the needs of the community to help commissioning and workforce planning.

Application of specialized word embeddings and named entity and attribute recognition to the problem of unsupervised automated clinical coding.

Notes documented by clinicians, such as patient histories, hospital courses, lab reports and others are often annotated with standardized clinical codes by medical coders to facilitate a variety of secondary processing applications such as billing and statistical analyses. Clinical coding, traditionally manual and labor-intensive, has seen a surge in research interest by deep learning researchers pursuing to automate it. However, deep learning methods require large volumes of annotated clinical data for training and offer little to explain why codes were assigned to pieces of text. In this paper, we propose an unsupervised method which does not need annotated clinical text and is fully interpretable, by using Named Entity and Attribute Recognition and word embeddings specialized for the clinical domain. These methods successfully glean important information from large volumes of clinical notes and encode them effectively in order to perform automatic clinical coding.

An assessment of completeness and medical coding of Medicare Advantage hospitalizations in two national data sets.

OBJECTIVE: To compare the Encounter Data System (EDS) and Medicare Provider Analysis and Review (MedPAR) completeness and medical coding of Medicare Advantage hospitalizations. DATA SOURCES: FY 2016-FY 2019 data limited to hospitals paid under Medicare's Inpatient Prospective Payment System. STUDY DESIGN: Secondary data analysis. DATA COLLECTION/EXTRACTION METHODS: Completeness of EDS and MedPAR data was estimated using the total number of unique hospitalizations in both data sources as denominator. Deriving this denominator involved matching cases in the EDS and MedPAR by MA enrollee, discharge date, and hospital. The higher the match rate, the more informative the comparison of EDS and MedPAR medical coding of the same hospitalization. EDS and MedPAR codes were assessed for similarity on six measures of Medicare Severity Diagnosis-Related Group (MS-DRG) assignment and identical diagnosis and procedure codes. PRINCIPAL FINDINGS: EDS hospitalizations' completeness increased steadily each year from 90% to 93%, driven by the 23 largest Medicare Advantage Organizations, which account for 83% of total cases. MedPAR completeness was relatively stable (89%) and benefited from 91% completeness among the largest hospitals, which are often teaching hospitals and account for 63% of MedPAR cases. By 2019, 97% of medical cases were assigned the same MS-DRG, indicating the high consistency of the severity level coding, since 98% were assigned the same base MS-DRGs, which include all severity levels for the same condition. Without chart reviews, medical cases with identical diagnosis codes increased from 87% to 92%. CONCLUSIONS: The EDS has a completeness advantage over MedPAR for studies of non-teaching disproportionate share (DSH) hospitals and individual hospitals generally. MedPAR is only slightly less complete for hospitalizations of teaching DSH hospitals and large hospitals in general. A highly consistent EDS and MedPAR medical coding of matched cases is an important finding since the matched cases are 88% of EDS and 90% of MedPAR cases.

Coding the "Spot Check": Part 1.

The updated outpatient evaluation and management (E/M) coding paradigm went into effect in January 2021, with level of visit being based on time or medical decision making (MDM). This article discusses how to effectively utilize this coding structure to correctly document for the "spot check," a common encounter within dermatology.

A practical strategy to use the ICD-11 for morbidity coding in the United States without a clinical modification.

OBJECTIVE: The aim of this study was to derive and evaluate a practical strategy of replacing ICD-10-CM codes by ICD-11 for morbidity coding in the United States, without the creation of a Clinical Modification. MATERIALS AND METHODS: A stepwise strategy is described, using first the ICD-11 stem codes from the Mortality and Morbidity Statistics (MMS) linearization, followed by exposing Foundation entities, then adding postcoordination (with existing codes and adding new stem codes if necessary), with creating new stem codes as the last resort. The strategy was evaluated by recoding 2 samples of ICD-10-CM codes comprised of frequently used codes and all codes from the digestive diseases chapter. RESULTS: Among the 1725 ICD-10-CM codes examined, the cumulative coverage at the stem code, Foundation, and postcoordination levels are 35.2%, 46.5% and 89.4% respectively. 7.1% of codes require new extension codes and 3.5% require new stem codes. Among the new extension codes, severity scale values and anatomy are the most common categories. 5.5% of codes are not one-to-one matches (1 ICD-10-CM code matched to 1 ICD-11 stem code or Foundation entity) which could be potentially challenging. CONCLUSION: Existing ICD-11 content can achieve full representation of almost 90% of ICD-10-CM codes, provided that postcoordination can be used and the coding guidelines and hierarchical structures of ICD-10-CM and ICD-11 can be harmonized. The various options examined in this study should be carefully considered before embarking on the traditional approach of a full-fledged ICD-11-CM.

Multigranularity Label Prediction Model for Automatic International Classification of Diseases Coding in Clinical Text.

International Classification of Diseases (ICD) serves as the foundation for generating comparable global disease statistics across regions and over time. The process of ICD coding involves assigning codes to diseases based on clinical notes, which can describe a patient's condition in a standard way. However, this process is complicated by the vast number of codes and the intricate taxonomy of ICD codes, which are hierarchically organized into various levels, including chapter, category, subcategory, and its subdivisions. Many existing studies focus solely on predicting subcategory codes, ignoring the hierarchical relationships among codes. To address this limitation, we propose a multitask learning model that trains multiple classifiers for different code levels, while also capturing the relations between coarser and finer-grained labels through a reinforcement mechanism. Our approach is evaluated on both English and Chinese benchmark dataset, and we demonstrate that our method achieves competitive performance with baseline models, particularly in terms of macro-F1 results. These findings suggest that our approach effectively leverages the hierarchical structure of ICD codes to improve disease code prediction accuracy. Analysis of attention mechanism shows that multigranularity attention of our model captures crucial feature of input text on different granularity levels, which can provide reasonable explanations for the prediction results.