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.

BACKGROUND: People with dementia are less in focus of palliative care research than other patient groups even though the awareness of their palliative and end-of-life care needs is rising. Empirical data analyses on people with dementia in palliative care services are lacking.
OBJECTIVE: To explore the prevalence of dementia diagnoses as per the ICD criteria among users of various palliative care settings and to compare use of palliative services, care pathways, and outcomes in people with and without a dementia diagnosis.
METHODS: We conducted retrospective analysis of dementia diagnoses as per ICD (F00-F03/G30) in the German National Hospice and Palliative Care Register between 2009 and 2021. The analysis used methods of descriptive and inferential statistics, including the Bonferroni correction for alpha error inflation.
METHODS: We limited the analysis to the subsample of people aged over 64.
RESULTS: The prevalence of dementia in the different settings of palliative care was lower than in the age-comparable population: Of the 69,116 data sets included in the analysis, a small minority (3.3%) was coded with dementia as the principal diagnosis. Among patients on inpatient palliative care wards, 0.8% (148 of 19,161) had a dementia diagnosis, as did 2.2% (52 of 2,380) of those under hospital palliative care support teams and 4.3% (2,014 of 46,803) of those receiving specialized palliative care at home.
CONCLUSIONS: The records of the German National Hospice and Palliative Care Register suggest that the prevalence of dementia is lower than one might expect from general population data, though numbers are in line with international studies on proportion of dementia patients receiving palliative care. Future research could usefully examine whether this discrepancy stems either from omissions in coding dementia as patients\' principal diagnosis respectively from lapses in documentation of a dementia diagnosis previously made, or from barriers to accessing palliative care services or even displays being excluded from palliative care when trying to access it.
BACKGROUND: No registration.

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.

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.
CONCLUSIONS: 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 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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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).
METHODS: 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.
METHODS: 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.

UNASSIGNED: 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.

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.