Abstract
Abstract
Background
About 2% of the German population are affected by psoriasis. A growing number of cost-intensive systemic treatments are available. Surveys have shown high proportions of patients with moderate to severe psoriasis are not adequately treated despite a high disease burden. Digital therapy recommendation systems (TRS) may help implement guideline-based treatment. However, little is known about the acceptance of such clinical decision support systems (CDSSs). Therefore, the aim of the study was to access the acceptance of a prototypical TRS demonstrator.
Methods
Three scenarios (potential test patients with psoriasis but different sociodemographic and clinical characteristics, previous treatments, desire to have children, and multiple comorbidities) were designed in the demonstrator. The TRS demonstrator and test patients were presented to a random sample of 76 dermatologists attending a national dermatology conference in a cross-sectional face-to-face survey with case vignettes. The dermatologist were asked to rate the demonstrator by system usability scale (SUS), whether they would use it for certain patients populations and barriers of usage. Reasons for potential usage of the TRS demonstrator were tested via a Poisson regression with robust standard errors.
Results
Acceptance of the TRS was highest for patients eligible for systemic therapy (82%). 50% of participants accepted the system for patients with additional comorbidities and 43% for patients with special subtypes of psoriasis. Dermatologists in the outpatient sector or with many patients per week were less willing to use the TRS for patients with special psoriasis-subtypes. Dermatologists rated the demonstrator as acceptable with an mean SUS of 76.8. Participants whose SUS was 10 points above average were 27% more likely to use TRS for special psoriasis-subtypes. The main barrier in using the TRS was time demand (47.4%). Participants who perceived time as an obstacle were 22.3% less willing to use TRS with systemic therapy patients. 27.6% of physicians stated that they did not understand exactly how the recommendation was generated by the TRS, with no effect on the preparedness to use the system.
Conclusion
The considerably high acceptance and the preparedness to use the psoriasis CDSS suggests that a TRS appears to be implementable in routine healthcare and may improve clinical care. Main barrier is the additional time demand posed on dermatologists in a busy clinical setting. Therefore, it will be a major challenge to identify a limited set of variables that still allows a valid recommendation with precise prediction of the patient-individual benefits and harms.
Funder
Universitätsklinikum Carl Gustav Carus Dresden an der Technischen Universität Dresden
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
Reference25 articles.
1. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of Artificial Intelligence. JAMA. 2018;320:2199. https://doi.org/10.1001/jama.2018.17163.
2. Gräßer F, Beckert S, Küster D, Schmitt J, Abraham S, Malberg H, et al. Therapy decision support based on Recommender System Methods. J Healthc Eng. 2017;2017:1–11. https://doi.org/10.1155/2017/8659460.
3. Kong G, Xu D-L, Yang J-B. Clinical decision support Systems: a review on knowledge representation and inference under uncertainties. Int J Comput Intell Syst. 2008;1:159–67.
4. Chen JH, Altman RB. Automated physician order recommendations and outcome predictions by data-mining electronic medical records. AMIA Jt Summits Transl Sci Proc AMIA Jt Summits Transl Sci. 2014;2014:206–10.
5. Khairat S, Marc D, Crosby W, Al Sanousi A. Reasons for Physicians not adopting clinical decision support Systems: critical analysis. JMIR Med Inform. 2018;6:e24. https://doi.org/10.2196/medinform.8912.