Appropriate healthcare referrals of patients with chronic musculoskeletal pain? We can do better: a multimodal artificial intelligence approach

Wendy d'Hollosy, Duncan Jurriaan Jansen, Mannes Poel

Research output: Contribution to conferenceAbstractAcademic


At least 25% of all healthcare referrals are considered inappropriate, leading to extra burdens on patients by, for example, re-referrals, excessive waiting times, uncoordinated care, and duplicated testing [1]. Physicians can make better informed choices to refer patients by also including relevant information from multimodal data sources, such as electronic health records, clinical images, and sensor data. However, these available data are too voluminous to be interpreted by single physicians themselves. Therefore, Clinical Decision Support Systems (CDSSs) that support physicians during referral decisions are indispensable. CDSSs can be knowledge-driven, i.e. based on guidelines and expert knowledge, or data-driven, i.e. based on Machine Learning (ML) models trained by data. In our research line, we combine both approaches in a multimodal Artificial Intelligence (AI) methodology for building CDSSs on healthcare referral. Several obstacles need to be overcome to develop integrated CDSSs that capitalize on the strengths of both multimodal data-driven and knowledge-driven approaches. The first challenge is to combine multimodal datasets for ML on the same task, because different types of inputs complicate ML on one task. The second challenge is to merge data-driven and knowledge-driven approaches in developing CDSSs with continuous learning loops, e.g. learning from the quality of previous patient referral cases. The methodology will be validated and demonstrated in different projects focused on the referral of chronic musculoskeletal pain. PReferral (Personalised Referral) is the first pioneering project in this research line and has been started in September 2020. When validity of combining both knowledge-driven and data-driven approaches in a multimodal AI methodology is confirmed, this can help to develop multimodal AI referral CDSSs in other healthcare domains as well. If these type of CDSSs can support referrers to reduce inappropriate referrals from 25% to possibly 10%, this will not only significantly diminish the burden of incorrect referrals for patients but also lead to substantial annual healthcare cost reductions (> €150 million in the Netherlands). [1] M. Naseriasl, D. Adham, and A. Janati, “E-referral Solutions: Successful Experiences, Key Features and Challenges- a Systematic Review,” Mater. Socio Medica, vol. 27, no. 3, p. 195, 2015, doi: 10.5455/msm.2015.27.195-199.
Original languageEnglish
Publication statusPublished - 29 Jan 2021
Event8th Dutch Bio-Medical Engineering Conference, BME 2021 - Virtual Conference
Duration: 28 Jan 202129 Jan 2021
Conference number: 8


Conference8th Dutch Bio-Medical Engineering Conference, BME 2021
Abbreviated titleBME 2021
CityVirtual Conference
Internet address


  • chronic musculoskeletal pain
  • Machine learning
  • Artificial intelligence
  • Data Science
  • Patient referral


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