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Background The World Health Organization (WHO) International Classification of Diseases and Related Health Problems (ICD) is used globally by 194 WHO member nations. It is used for assigning clinical diagnoses, providing the framework for reporting public health data, and to inform the organization and reimbursement of health services. Guided by overarching principles of increasing clinical utility and global applicability, the 11th revision of the ICD proposes major changes that incorporate empirical advances since the previous revision in 1992. To test recommended changes in the Mental, Behavioral, and Neurodevelopmental Disorders chapter, multiple vignette-based case-controlled field studies have been conducted which examine clinicians’ ability to accurately and consistently use the new guidelines and assess their overall clinical utility. This manuscript reports on the results from the study of the proposed ICD-11 guidelines for feeding and eating disorders (FEDs). Method Participants were 2288 mental health professionals registered with WHO’s Global Clinical Practice Network. The study was conducted in Chinese, English, French, Japanese, and Spanish. Clinicians were randomly assigned to apply either the ICD-11 or ICD-10 diagnostic guidelines for FEDs to a pair of case vignettes designed to test specific clinical questions. Clinicians selected the diagnosis they thought was correct for each vignette, evaluated the presence of each essential feature of the selected diagnosis, and the clinical utility of the diagnostic guidelines. Results The proposed ICD-11 diagnostic guidelines significantly improved accuracy for all FEDs tested relative to ICD-10 and attained higher clinical utility ratings; similar results were obtained across all five languages. The inclusion of binge eating disorder and avoidant-restrictive food intake disorder reduced the use of residual diagnoses. Areas needing further refinement were identified. Conclusions The proposed ICD-11 diagnostic guidelines consistently outperformed ICD-10 in distinguishing cases of eating disorders and showed global applicability and appropriate clinical utility. These results suggest that the proposed ICD-11 guidelines for FEDs will help increase accuracy of public health data, improve clinical diagnosis, and enhance health service organization and provision. This is the first time in the revision of the ICD that data from large-scale, empirical research examining proposed guidelines is completed in time to inform the final diagnostic guidelines.

Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text. This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count. An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period. The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI -34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool's suggestions on longer clinical notes. This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

Administrative code data (ACD), such as International Classifications of Diseases, Ninth Revision, Clinical Modification codes, are widely used in surveillance and public reporting programs that seek to identify healthcare-associated infections (HAIs); however, little is known about their accuracy. This systematic review summarizes evidence for the accuracy of ACD for the detection of selected HAIs, including catheter-associated urinary tract infection, Clostridium difficile infection (CDI), central line–associated bloodstream infection, ventilator-associated pneumonia/events, postprocedure pneumonia, methicillin-resistant Staphylococcus aureus, and surgical site infections (SSIs). We conducted meta-analysis for SSIs and CDIs, where acceptable numbers of primary studies were available. For these 2 conditions, ACD have moderate sensitivity and high specificity, but evidence for detection of other HAIs is limited. With current low prevalence of HAIs, the positive predictive value of ACD algorithms would be low. ACD may be inaccurate for detection of many HAIs and should be used cautiously for surveillance and reporting purposes.

On October 1, 2015, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) was incorporated into the US public health system. Because of significant opposition and reservations expressed by stakeholders, while the proposed rule for ICD-10-CM adoption was issued in 2009, the transition did not occur until October 2015. The purpose of this study was to identify conversion initiatives used by a public health institution during the initial and subsequent stages of ICD-10-CM implementation, to help similar institutions address future unfunded healthcare data infrastructure mandates. The data collection for this study occurred from 2015 to 2018, encompassing 20 semistructured interviews with 13 department heads, managers, physicians, and coders. Research findings from this study identified several trends, disruptions, challenges, and lessons learned that might support the industry with strategies to foster success for the transition to future coding revisions (i.e., ICD-11).

The forced implementation of ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes that are specific to the United States, scheduled for implementation October 1, 2015, which is vastly different from ICD-10 (International Classification of Diseases, Tenth Revision), implemented worldwide, which has 14,400 codes, compared to ICD-10-CM with 144,000 codes to be implemented in the United States is a major concern to practicing U.S. physicians and a bonanza for health IT and hospital industry. This implementation is based on a liberal interpretation of the Health Insurance Portability and Accountability Act (HIPAA), which requires an update to ICD-9- CM (International Classification of Diseases, Ninth Revision, Clinical Modification) and says nothing about ICD-10 or beyond. On June 29, 2015, the Supreme Court ruled that the Environmental Protection Agency unreasonably interpreted the Clean Air Act when it decided to set limits on the emissions of toxic pollutants from power plants, without first considering the costs on the industry. Thus, to do so is applicable to the medical industry with the Centers for Medicare and Medicaid Services (CMS) unreasonably interpreting HIPAA and imposing existent extensive regulations without considering the cost. In the United States, ICD-10-CM with a 10-fold increase in the number of codes has resulted in a system which has become so complicated that it no longer compares with any other country. Moreover, most WHO members use the ICD-10 system (not ICD-10-CM) only to record mortality in 138 countries or morbidity in 99 countries. Currently, only 10 countries employ ICD-10 (not ICD-10-CM) in the reimbursement process, 6 of which have a single payer health care system. Development of ICD-10-CM is managed by 4 non-physician groups, known as cooperating parties. They include the Centers for Disease Control and Prevention (CDC), CMS, the American Hospital Association (AHA), and the American Health Information Management Association (AHIMA). The AHIMA has taken the lead with the AHA just behind, both with escalating profits and influence, essentially creating a statutory monopoly for their own benefit. Further, the ICD-10-CM coalition includes 3M which will boost its revenues and profits substantially with its implementation and Blue Cross Blue Shield which has its own agenda. Physician groups are not a party to these cooperating parties or coalitions, having only a peripheral involvement. ICD-10-CM creates numerous deficiencies with 500 codes that are more specific in ICD-9-CM than ICD-10-CM. The costs of an implementation are enormous, along with maintenance costs, productivity, and cash disruptions. Key words: ICD-10-CM, ICD-10, ICD-9-CM (International Classification of Diseases, 10th Revision, Ninth revision, Clinical Modification), Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology (HIT), costs of implementation

Background The International Classification of Functioning, Disability and Health (ICF) was introduced by the World Health Organization as a common taxonomy to describe the burden of health conditions. This study focuses on the development of a scale for staging basic mobility and walking functions based on the ICF. Methods Thirty-three ICF codes were selected to test their fit to the Rasch model and their location. Of these ICF items, four were used to develop a Guttman- type scale of “basic mobility” and another four to develop a“walking” scale to stage functional performance in the elderly. The content validity and differential item functioning of the scales were assessed. The participants, chosen at random, were Japanese over 65 years old using the services of public long-term care insurance, and whose functional assessments were used for scale development and scale validation. Results There were 1164 elderly persons who were eligible for scale development. To stage the functional performance of elderly persons, two Guttman-type scales of “basic mobility” and “walking” were constructed. The order of item difficulty was validated using 3260 elderly persons. There is no differential item functioning about study location, sex and age-group in the newly developed scales. These results suggested the newly developed scales have content validity. Conclusions These scales divided functional performance into five stages according to four ICF codes, making the measurements simple and less time-consuming and enable clear descriptions of elderly functioning level. This was achieved by hierarchically rearranging the ICF items and constructing Guttman-type scales according to item difficulty using the Rasch model. In addition, each functional level might require similar resources and therefore enable standardization of care and rehabilitation. Illustrations facilitate the sharing of patient images among health care providers. By using the ICF as a common taxonomy, these scales could be used internationally as assessment scales in geriatric care settings. However these scales require further validity and reliability studies for international application.

Objective: The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries. Methods: Data from Australia (N = 2000-2001, max 25 diagnosis codes), Canada (N = 2002-2003, max 16 diagnosis codes), Switzerland (N = 1999-2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of ≥2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance. Results: Overall, within each population's data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not. Conclusions: Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70-0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets.

THE ORIGINS OF ICHD-II The first proposals for the classification of headache disorders were put forward in the 1960s, one from an ad hoc committee of the US National Institutes of Health 1 and another, quite similar, from the Research Group on Migraine and Headache of the World Federation of Neurology. 2 Both proposals merely listed the few headache disorders that were accepted at that time, and gave short descriptions of them rather than diagnostic criteria. [...]the advent of criteria for many headache disorders in 1988 made it possible to undertake research that would confirm, dispute, or occasionally reject the existence of, and the validity of the criteria coupled with, each defined disorder.

Accurately measuring the health care needs of patients with different diseases remains a public health challenge for health care management worldwide. There is a need for new computational methods to be able to assess the health care resources required by patients with different diseases to avoid wasting resources. This study aimed to assessing dissatisfaction with allocation of health care resources from the perspective of patients with different diseases that can help optimize resource allocation and better achieve several of the Sustainable Development Goals (SDGs), such as SDG 3 (\"Good Health and Well-being\"). Our goal was to show the effectiveness and practicality of large language models (LLMs) in assessing the distribution of health care resources. We used aspect-based sentiment analysis (ABSA), which can divide textual data into several aspects for sentiment analysis. In this study, we used Chat Generative Pretrained Transformer (ChatGPT) to perform ABSA of patient reviews based on 3 aspects (patient experience, physician skills and efficiency, and infrastructure and administration)00 in which we embedded chain-of-thought (CoT) prompting and compared the performance of Chinese and English LLMs on a Chinese dataset. Additionally, we used the International Classification of Diseases 11th Revision (ICD-11) application programming interface (API) to classify the sentiment analysis results into different disease categories. We evaluated the performance of the models by comparing predicted sentiments (either positive or negative) with the labels judged by human evaluators in terms of the aforementioned 3 aspects. The results showed that ChatGPT 3.5 is superior in a combination of stability, expense, and runtime considerations compared to ChatGPT-4o and Qwen-7b. The weighted total precision of our method based on the ABSA of patient reviews was 0.907, while the average accuracy of all 3 sampling methods was 0.893. Both values suggested that the model was able to achieve our objective. Using our approach, we identified that dissatisfaction is highest for sex-related diseases and lowest for circulatory diseases and that the need for better infrastructure and administration is much higher for blood-related diseases than for other diseases in China. The results prove that our method with LLMs can use patient reviews and the ICD-11 classification to assess the health care needs of patients with different diseases, which can assist with resource allocation rationally.