Diagnostic delay in Hidradenitis suppurativa investigated using data from social media and from statutory health insurances.

BACKGROUND: The chronic inflammatory skin disease hidradenitis suppurativa (HS) leads to severe pain and reduced quality of life. Nonetheless, it often takes years until a correct diagnosis is made. In this analysis, disease-related experiences and pathways of patients with HS were investigated and compared with the physicians' perspective. METHODS: Public posts on forums and social media as well as results of a survey conducted among dermatologists and their patients on the actual medical care reality of HS in Germany were analysed. Furthermore, claims data from German health insurance companies were evaluated. RESULTS: Patients with HS suffer from a 43.3% reduction in working ability. Dermatology (26.5%) was the most frequently consulted specialty, with HS diagnosed predominantly in the inpatient setting (43.8%). Abscesses were described as the most frequent alternative diagnosis in HS patients (53.2%). Patient-reported changes of physicians in dermatology (34.1%) and surgery (42.4%) occurred predominantly within the specialty. Dermatology received most referrals from general practitioners (67.1%), but only 12.1% from surgeons. CONCLUSION: There is an urgent need to reduce the delay in diagnosis and the prolonged burden of disease in patients with HS. Therefore, awareness of the disease, its detection and treatment which goes beyond dermatology should be promoted, if possible as part of medical studies.

Five points to check when comparing visual perception in humans and machines.

With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed toward comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct, and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception. We demonstrate and apply these ideas through three case studies. The first case study shows how human bias can affect the interpretation of results and that several analytic tools can help to overcome this human reference point. In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks. Thereby, we show that contrary to previous suggestions, feedback mechanisms might not be necessary for the tasks in question. The third case study highlights the importance of aligning experimental conditions. We find that a previously observed difference in object recognition does not hold when adapting the experiment to make conditions more equitable between humans and machines. In presenting a checklist for comparative studies of visual reasoning in humans and machines, we hope to highlight how to overcome potential pitfalls in design and inference.