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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.

OBJECTIVE: 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.
CONCLUSIONS: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).

BACKGROUND: Tobacco cessation remains a critical challenge in healthcare, with evidence-based interventions often under-utilized due to misaligned economic incentives and inadequate training. This study aims to quantify the economic impact of missed billing opportunities for tobacco cessation in a healthcare system, thereby assessing potential revenue loss and evaluating the effectiveness of systems-based approaches to enhancing tobacco cessation efforts.
METHODS: A retrospective cohort study utilized aggregated de-identified patient health data from an 8-hospital regional health system across Pennsylvania and Maryland, from 1/1/21 to 12/31/23. The analysis focused on primary care encounters eligible for tobacco cessation counseling (CPT codes 99406 or 99407), with potential revenue calculated based on the Medicare reimbursement rate.
RESULTS: Over three years, and 507,656 office visits, only 1,557 (0.3%) of encounters with persons using tobacco were billed for cessation services. The estimated total potential revenue gained if each person who was identified as using tobacco was billed consistently for tobacco cessation counseling was $5,947,018.13, and $1,982,339.38 annually.
CONCLUSIONS: The study reveals a significant gap between the potential and actual billing for tobacco cessation services, highlighting not only the financial implications of missed opportunities but also a validation of a health system\'s public health impact. Underbilling contributes to considerable annual revenue loss and undermines primary prevention efforts against tobacco-related diseases. The findings illuminate the need for enhanced billing practices and systemic changes, including policy improvements that influence proper billing to promote public health benefits through improved tobacco cessation interventions.

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.

BACKGROUND: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies.
OBJECTIVE: 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.

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.

All patient refined-diagnosis related groups (APR-DRGs) includes severity of illness (SOI) and risk of mortality (ROM) subclasses. For predictions, both subscales are used together or interchangeably. We aimed to compare SOI and ROM by evaluating the reliability and agreement between both. We performed a retrospective observational study using mainland Portuguese public hospitalisations of adult patients from 2011 to 2016. Reliability (quadratic weighted kappa) and agreement (proportion of agreement) between SOI and ROM were analysed overall and by APR-DRG. While overall reliability and agreement between SOI and ROM were high (weighted kappa: 0.717, 95% CI 0.717-0.718; proportion of agreement: 69.0%, 95% CI 69.0-69.0) there was high heterogeneity across APR-DRGs, ranging from 0.016 to 0.846 on reliability and from 23.1% to 94.8% on agreement. Most of APR-DRGs (263 out of 284) showed a higher proportion of episodes with ROM level above the SOI level than the opposite. In conclusion, SOI and Risk of Mortality measures must be clearly distinguished and are \'two scales of different concepts\' rather than \'two sides of the same coin\'. However, this is more evident for some APR-DRGs than for others.

UNASSIGNED: Health Information Managers (HIMs) play a crucial role in the management and governance of health information ensuring the accuracy, confidentiality and accessibility of health data for clinical care and business operational purposes. This role also extends to education and training in the workplace.
UNASSIGNED: The aim of this scoping review was to explore and elucidate the role played by HIMs when they undertake a health workplace-based (healthcare organisation or service) educational role and/or functions as evidenced in the existing body of literature.
UNASSIGNED: A scoping review of the literature to investigated the importance of the educator role for HIM health workplace-based educators. A three-step search strategy was designed to ensure a comprehensive exploration of relevant research.
UNASSIGNED: Of 63 articles assess for eligibility, 14 were included in the final analysis. All included articles acknowledged the importance of the HIM-educator workplace-based role. Half of the included articles had been published within the last 7 years. Only 8 of the 14 articles provided some description of HIM-educator attributes, suggesting that these characteristics remain unexplored.
UNASSIGNED: Findings from this scoping review have shed light on the limitations within the current available literature concerning the attributes of HIM health workplace-based educators. The findings also highlight an important gap in knowledge concerning the qualities of these HIM-educators.
UNASSIGNED: This identified gap in the literature signals a need for further exploration and investigation into the specific attributes, skills, and characteristics that define effective HIM-educators undertaking a health workplace-based educational role.

The term long COVID (LC) effectively describes the broad long-term disease burden of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections, encompassing individual suffering and significant socioeconomic impacts. However, its general use hampers precise epidemiological research, diagnostics and therapeutic strategies. Misinterpretations occur, for example, when population surveys are compared to studies using health record data, because both refer to these data as LC. This also emphasizes the need for different terminology. The National Institute for Health and Care Excellence (NICE) rapid guideline differentiates ongoing symptomatic COVID-19 from post-COVID conditions, yet real-world observations challenge these two subgroup definitions. We propose refining the term LC into three subgroups: ongoing symptomatic COVID-19, SARS-CoV-2 induced or exacerbated diseases and post-acute COVID condition. This stratification aids targeted diagnostics, treatment and epidemiological research. Subgroup-specific documentation using the International Classification of Diseases, Tenth Revision (ICD-10) codes ensures accurate tracking and understanding of long-term effects. The subgroup of post-acute COVID condition again includes various symptoms, syndromes and diseases like post-exertional malaise (PEM), dysautonomia or cognitive dysfunctions. In this regard, differentiation, especially considering PEM, is crucial for effective diagnostics and treatment.