Abstract
BACKGROUND: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritization when allocating appointments. METHODS: Using a registration form for new patients, standardized symptoms and laboratory results were collated. After reviewing this information by a medical specialist the allocation of appointments was carried out in three categories: a) < 6 weeks, b) 6 weeks up to 3 months and c) > 3 months. The waiting time between the time of registration and the presentation appointment was calculated and compared between patients with and without a diagnosis of an inflammatory rheumatic disease (IRD). In addition a decision tree (DT), a method taken from the field of supervised learning within artificial intelligence (AI), was established and the resulting classification was compared with respect to the accuracy and calculated saving in waiting time. RESULTS: In this study 800 appointments between 2020 and 2023 (including 555 women, 69.4%, median age 53 years, interquartile range, IQR 39-63 years) were analyzed. An IRD could be confirmed in 409 (51.1%) cases with a waiting time of 58 vs. 93 days for non-IRD cases (-38%, p < 0.01). An AI-based stratification resulted in an accuracy of 67% for IRD and a predicted saving of 19% waiting time. The accuracy increased up to 78% with a time saving for IRD cases of up to 31%, when all basic laboratory results were known. Simplified algorithms, e.g., stratification by the use of laboratory findings alone, resulted in a lower accuracy and time savings. CONCLUSION: Manual allocation of appointments by a medical specialist is effective and significantly reduces the waiting times for patients with IRD. An automated categorization can lead to a reduction in waiting times for appointments when taking complete laboratory results and a lower sensitivity into consideration.