Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM)
Abstract Background Evaluating patients’ experiences is essential when incorporating the patients’ perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) ca...
Main Authors: | Marieke M. van Buchem, Olaf M. Neve, Ilse M. J. Kant, Ewout W. Steyerberg, Hileen Boosman, Erik F. Hensen |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2022-07-01
|
Series: | BMC Medical Informatics and Decision Making |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12911-022-01923-5 |
Similar Items
-
The added value of the artificial intelligence patient-reported experience measure (AI-PREM tool) in clinical practise: Deployment in a vestibular schwannoma care pathway
by: O.M. Neve, et al.
Published: (2023-12-01) -
Validation of the patient-reported experience measure for care in Chinese hospitals (PREM-CCH)
by: Xuanxuan Wang, et al.
Published: (2021-01-01) -
Short form version of the Quality of Trauma Care Patient-Reported Experience Measure (SF QTAC-PREM)
by: Niklas Bobrovitz, et al.
Published: (2017-12-01) -
The digital scribe in clinical practice: a scoping review and research agenda
by: Marieke M. van Buchem, et al.
Published: (2021-03-01) -
Development and validation of a patient reported experience measure for experimental cancer medicines (PREM-ECM) and their carers (PREM-ECM-Carer)
by: Chelsea S. Sawyer, et al.
Published: (2024-04-01)