Difficulties Coding Dermatological Disorders Using the ICD-10: The DIADERM Study

Epidemiology and Healthcare Expenditure for Skin Disease in Emergency Departments in Alberta, Canada

BACKGROUND

There are limited data on the epidemiology and costs associated with managing dermatologic conditions in emergency departments (EDs).

OBJECTIVE

To assess the incidence and mean cost per case of skin diseases in EDs in Alberta.

METHODS

Alberta Health Services' Interactive Health Data Application was used to determine the epidemiology and costs associated with nonneoplastic dermatologic diseases in EDs in the province of Alberta, Canada, from 2018 to 2022. Skin conditions were identified using the International Classification of Disease 10th edition diagnostic groupings.

RESULTS

Skin disease represented 3.59% of all ED presentations in Alberta in 2022. The total costs associated with managing dermatologic conditions have remained stable over time at approximately 15 million Canadian Dollars (CAD) annually, but the mean cost per case has risen from 188.88 (SD 15.42) in 2018 to 246.25 CAD (SD 27.47) in 2022 (7.59%/year). Infections of skin and subcutaneous tissue were the most expensive diagnostic grouping. The most common dermatologic diagnostic groupings presenting to the ED were infections of skin and subcutaneous tissue [mean age-standardized incidence rate (ASIR) of 143.67 per 100,000 standard population (SD 241.99)], urticaria and erythema [mean ASIR 33.57 per 100,000 standard population (SD 59.13)], and dermatitis and eczema [mean ASIR 18.59 per 100,000 standard population (SD 23.65)]. Cellulitis was both the most common and the costliest individual diagnosis. The majority of patients were triaged as less urgent or nonurgent.

CONCLUSIONS

Skin disease represents a substantial public health burden in EDs. Further research into drivers of cost change and areas for cost savings is essential.

[Translated article] Analysis of Undergraduate Dermatology Syllabi at Spanish Universities: Does the Weight of Theoretical Content Match the Skin Conditions Seen in Primary Care and General Dermatology Practices?

INTRODUCTION

Undergraduate dermatology courses vary in the nearly 50 Spanish medical faculties that teach the subject. This study aimed to describe the characteristics of these courses and to analyze whether the weight assigned to dermatology topics reflects the caseloads of primary care physicians and general dermatologists in the Spanish national health system.

MATERIAL AND METHODS

Cross-sectional study of syllabi used in Spanish medical faculties during the 2021-2022 academic year. We determined the number of teaching hours in public and private university curricula and compared the weight of dermatology topics covered to the dermatology caseloads of primary care physicians and general dermatologists as reported in published studies.

RESULTS

Most medical faculties taught dermatology for one semester. The median number of credits offered was 4.5. On average, lectures covered 24 theoretical topics, and seminars and workshops covered 9 topics. We identified a clear disparity between the percentage of time devoted to dermatology topics in course lectures and the skin conditions usually managed in primary care and general dermatology practices.

DISCUSSION

The skin diseases most commonly treated by primary care physicians and general dermatologists are underrepresented in the curricula of Spanish medical faculties. The topics that should be given more weight in syllabi, or recovered for inclusion in dermatology courses, should be re-examined. Our findings show that the topics that ideally should be emphasized more are types of dermatitis, infectious skin diseases, acne, psoriasis, rashes, and the differential diagnosis of benign and malignant neoplasms. There should be additional support for the theoretical teaching of these topics.

Analysis of Undergraduate Dermatology Syllabi at Spanish Universities: Does the Weight of Theoretical Content Match the Skin Conditions Seen in Primary Care and General Dermatology Practices?

INTRODUCTION

Undergraduate dermatology courses vary in the nearly 50 Spanish medical faculties that teach the subject. This study aimed to describe the characteristics of these courses and to analyze whether the weight assigned to dermatology topics reflect the caseloads of primary care physicians and general dermatologists in the Spanish national health system.

MATERIAL AND METHODS

Cross-sectional study of syllabi used in Spanish medical faculties during the 2021-2022 academic year. We determined the number of teaching hours in public and private university curricula and compared the weight of dermatology topics covered to the dermatology caseloads of primary care physicians and general dermatologists as reported in published studies.

RESULTS

Most medical faculties taught dermatology for one semester. The median number of credits offered was 4.5. On average, lectures covered 24 theoretical topics, and seminars and workshops covered 9 topics. We identified a clear disparity between the percentage of time devoted to dermatology topics in course lectures and the skin conditions usually managed in primary care and general dermatology practices.

DISCUSSION

The skin diseases most commonly treated by primary care physicians and general dermatologists are underrepresented in the curricula of Spanish medical faculties. The topics that should be given more weight in syllabi, or recovered for inclusion in dermatology courses, should be re-examined. Our findings show that the topics that ideally should be emphasized more are types of dermatitis, infectious skin diseases, acne, psoriasis, rashes, and the differential diagnosis of benign and malignant neoplasms. There should be additional support for the theoretical teaching of these topics.

Deep Learning Approaches for Prognosis of Automated Skin Disease

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.

Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method

With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2–4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology.