AB1338 FASTER DIAGNOSES OF RARE DISEASES BY USING DIAGNOSTIC DECISION SUPPORT SYSTEMS - IMPACT ON COSTS AND QUALITY OF LIFE
Authors: L. Völkel, L. Willmen, T. Willmen, S. Geyer, A. D. Wagner
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Around 300 million people worldwide suffer from a rare disease (Wakap et al., 2019). A helpful therapy is always based on a diagnosis. For rare diseases this often takes a long time. In the future, diagnostic decision support systems could be important tools for speeding up successful diagnoses. <h3>Objectives:</h3> The aim of this study is to map the overall economic costs of our patient population from the time of initial contact with a doctor to successful diagnosis. Also, the cost-saving potential of an earlier diagnosis by complementing it with a diagnostic decision support system was modelled. In addition, the patient's quality of life at the time of diagnosis is to be considered. <h3>Methods:</h3> In this study, three points in time are relevant for our data collection: firstly, the patient's initial contact with a physician, then the retrospectively modelled time stamp of correct diagnosis by using a diagnostic decision support system (Ronicke et al., 2019). Finally the time of correct diagnosis by the treating specialist was taken into account. In previous studies direct (Willmen et al., 2021) and indirect (Willmen et al., 2023 (submitted)) costs were analysed. The data collection of our patient sample (n = 63) was carried out on their fundamental. Patients with data on all cost factors were selected from this database. This patient population was subdivided into two different disease groups (diseases of muscles and vessels). For the data analysis all direct and indirect costs were calculated. Direct costs were extracted from the evaluation of doctors' letters. Indirect costs were based on WHO's Health and Work Performance Questionnaire (HPQ) data. Income levels, periods of unemployment, reduced working hours and early retirement were assessed using this questionnaire. The costs were standardised to annual costs per patient and 95% confidence intervals were calculated. Based on these results the annual costs for Germany were approximated. The SF-36 was used to assess patients' quality of life at the time of diagnosis. The SF-36 scores were generated by counting physical and mental dimensions together. The resulting means were compared and tested with the norm population for statistical significance. <h3>Results:</h3> The patients in the sample waited on average 5.5 years until a correct diagnosis was established. The longest waiting time was about 30 years. Overall, direct costs amounted to € 1,010,600.26 and indirect costs to € 4,138,413.91 from initial contact to diagnosis (Figure 1). The results suggest that the patient population incurred average annual direct costs of € 6271.02 (95% CI: € 4355.88; € 8186.16) (Figure 2). In addition, annual indirect costs per patient totalled € 12,327.76 (95%CI: € 6396.95; € 18,258.58). In comparison the time span from the initial contact with a physician until the correct prediction of the diagnosis using the diagnostic decision support system was considered. The patient population incurred average annual direct costs of € 2633.25 (95% CI: € 1810.83; € 3455.67) (Figure 2). Moreover, annual indirect costs per patient totalled € 7485.47 (95% CI: € 3840.96; € 11,129.99). Potential cost savings using the diagnostic decision support system showed that direct costs of € 570,835.50 and indirect costs of € 2,128,662.73 could have been saved (Figure 1). Thus potential cost savings of € 60,555.73 per patient might be possible. Patients' quality of life compared to the standard population showed that the patients were mentally healthy (52.7 ± 10; 51.2 ± 2) at the time of diagnosis, but physically more impaired (41.4 ± 12.3; 48,2 ± 5.6). <h3>Conclusion:</h3> Diagnostic decision support systems combined with expert knowledge can speed up successful diagnoses of patients with rare diseases. This might have a positive impact on patients' quality of life and lead to potential savings of direct and indirect costs. <h3>REFERENCES:</h3> <b>NIL.</b> <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> <b>None declared.</b>