dr.ir. W. d'Hollosy (Wendy)

Assistant Professor
EEMCS-EE-BSS
w.dhollosy@utwente.nl
+31534892766
Business card

Wendy works in the field of health informatics, with as main topics data science, personalized eHealth technologies, clinical decision supports systems, and the application of interoperability and machine learning to optimize the referral and treatment of patients in healthcare.

She was involved at RRD (www.rrd.nl) as researcher in the H2020 Back-UP project (http://backup-project.eu/), which aims to create a predictive model to support effective and efficient treatment of neck and lower back pain. She also led the Pioneers in HealthCare (PIHC) PReferral project on the optimization of existing care pathways and referrals of patients with chronic musculoskeletal pain in 1st, 2nd and 3rd care based on artificial intelligence. Currently, she is involved in the RE-SAMPLE project that aims to develop AI-powered care for patients with COPD and other chronic illnesses (https://www.re-sample.eu/)

Wendy works 1 day a week at the University of Twente in the Biomedical Signals and System (BSS) group (https://www.utwente.nl/en/eemcs/bss) in the personalized eHealth team and 4 days a week as internal auditor at Univé (www.unive.nl), a cooperative nonprofit insurance company in the Netherlands.

Expertise

  • Medicine and Dentistry

    • Patient
    • Primary Health Care
    • Patient Referral

  • Nursing and Health Professions

    • Low Back Pain

  • Psychology

    • Treatment
    • Chronic Disorder
    • Behavior
    • Diabetes

Organisations

Wendy works in the field of health informatics, with as main topics data science, personalized eHealth technologies, Clinical Decision Supports Systems (CDSS), and the application of interoperability and machine learning to optimize the referral and treatment of patients in healthcare. Currently, this research is mainly focused on patients with chronic musculoskeletal pain.

She was involved at RRD (www.rrd.nl) as researcher in the H2020 Back-UP project (http://backup-project.eu/), which aims to create a predictive model to support effective and efficient treatment of neck and lower back pain. Currently, she leads the Pioneers in HealthCare (PIHC) PReferral project on the optimization of existing care pathways and referrals of patients with chronic musculoskeletal pain in 1st, 2nd and 3rd care based on artificial intelligence.

Currently, she work 1 day a week at the University of Twente in the biomedical signals and system group (https://www.utwente.nl/en/eemcs/bss) within the Personalized eHealth Technology Program (https://www.utwente.nl/en/techmed/research/research-programmes/ehealth/) and 4 days a week as senior data scientist at Univé, a cooperative nonprofit insurance company in the Netherlands.

Publications

2024
Technology-supported shared decision-making in chronic conditions: a systematic review of randomized controlled trials, Article 108267. Vaseur, R. M. E., te Braake, E., Beinema, T., Oude Nijeweme-d'Hollosy, W. & Tabak, M.https://doi.org/10.1016/j.pec.2024.108267Game-based design for eHealth in practice (Accepted/In press). De Vette, F., Ruiz-Rodriguez, A., Tabak, M., Nijeweme-D'Hollosy, W. O., Hermens, H. & Vollenbroek, M.https://doi.org/10.2196/13723
2023
A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing, Article e45294. Hietbrink, E. A. G., Oude Nijeweme-d’Hollosy, W., Middelweerd, A., Konijnendijk, A. A. J., Schrijver, L. K., ten Voorde, A. S., Fokkema, E. M. S., Laverman, G. D. & Vollenbroek-Hutten, M. M. R.https://doi.org/10.2196/45294A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study, Article e40017. Hietbrink, E. A. G., Middelweerd, A., Empelen, P. v., Preuhs, K., Konijnendijk, A. A. J., Nijeweme-d’Hollosy, W. O., Schrijver, L. K., Laverman, G. D. & Vollenbroek-Hutten, M. M. R.https://doi.org/10.2196/40017
2022
A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing. JMIR Publications. Hietbrink, E. A. G., Nijeweme-d’Hollosy, W. O., Middelweerd, A., Konijnendijk, A. A. J., Schrijver, L. K., ten Voorde, A. S., Fokkema, E. M. S., Laverman, G. D. & Vollenbroek-Hutten, M. M. R.https://doi.org/10.2196/preprints.45294Factors used by general practitioners for referring patients with chronic musculoskeletal pain: A qualitative study, Article 126. Slatman, E. S., Mossink, A., Jansen, D. J., Broeks, J., van der Lugt, P., Prosman, G.-J. & d'Hollosy, W.https://doi.org/10.1186/s12875-022-01743-6Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes, Article 227. Verma, D., Jansen, D., Bach, K., Poel, M., Mork, P. J. & d'Hollosy, W.https://doi.org/10.1186/s12911-022-01973-9Technology-supported shared decision-making in chronic conditions: preliminary results of a systematic review. Vaseur, R., te Braake, E., Beinema, T., d'Hollosy, W. & Tabak, M.https://doi.org/10.1183/13993003.congress-2022.2987Technology-supported shared decision making in the treatment and management of chronic conditions: a systematic review protocol. Vaseur, R., d'Hollosy, W. & Tabak, M.https://www.bme2022.nl/bme-2022-programme/
2021
Night to night variability of pulse oximetry features in children at home and at the hospital, Article 104003. Hoppenbrouwer, X., Rollinson, A. U., Dunsmuir, D., Ansermino, J. M., Dumont, G., d'Hollosy, W., Veltink, P. H. & Garde Martinez, A.https://doi.org/10.1088/1361-6579/ac278eThe usage of patient-reported data in machine learning to predict pain rehabilitation: possible or not?. d'Hollosy, W., Bekmann, A., Jansen, D. J. & Prosman, G.-J.Towards optimized personalized referral advice and patient satisfaction in chronic musculoskeletal pain. d'Hollosy, W., Konijnendijk, A., Bessembinder, R., Leeferink, F. & Broeks, J.Appropriate healthcare referrals of patients with chronic musculoskeletal pain? We can do better: a multimodal artificial intelligence approach. d'Hollosy, W., Jansen, D. J. & Poel, M.
2020
Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot StudyIn Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) (pp. 117-124). SCITEPRESS. Oude Nijeweme - d'Hollosy, W., van Velsen, L., Poel, M., Groothuis-Oudshoorn, C., Soer, R., Stegeman, P. & Hermens, H.https://doi.org/10.5220/0008962101170124

Research profiles

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