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Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).
van Buchem, Marieke M; Neve, Olaf M; Kant, Ilse M J; Steyerberg, Ewout W; Boosman, Hileen; Hensen, Erik F.
Afiliação
  • van Buchem MM; Information Technology & Digital Innovation Department, Leiden University Medical Center, Leiden, the Netherlands. m.m.van_buchem@lumc.nl.
  • Neve OM; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands. m.m.van_buchem@lumc.nl.
  • Kant IMJ; Clinical Artificial Intelligence Implementation and Research Lab (CAIRELab), Leiden University Medical Center, Leiden, the Netherlands. m.m.van_buchem@lumc.nl.
  • Steyerberg EW; Department of Otorhinolaryngology and Head and Neck Surgery, Leiden University Medical Center, Leiden, the Netherlands.
  • Boosman H; Information Technology & Digital Innovation Department, Leiden University Medical Center, Leiden, the Netherlands.
  • Hensen EF; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
BMC Med Inform Decis Mak ; 22(1): 183, 2022 07 15.
Article em En | MEDLINE | ID: mdl-35840972
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) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness.

METHODS:

We developed the Artificial Intelligence Patient-Reported Experience Measures (AI-PREM) tool, consisting of a new, open-ended questionnaire, an NLP pipeline to analyze the answers using sentiment analysis and topic modeling, and a visualization to guide physicians through the results. The questionnaire and NLP pipeline were iteratively developed and validated in a clinical context.

RESULTS:

The final AI-PREM consisted of five open-ended questions about the provided information, personal approach, collaboration between healthcare professionals, organization of care, and other experiences. The AI-PREM was sent to 867 vestibular schwannoma patients, 534 of which responded. The sentiment analysis model attained an F1 score of 0.97 for positive texts and 0.63 for negative texts. There was a 90% overlap between automatically and manually extracted topics. The visualization was hierarchically structured into three stages the sentiment per question, the topics per sentiment and question, and the original patient responses per topic.

CONCLUSIONS:

The AI-PREM tool is a comprehensive method that combines a validated, open-ended questionnaire with a well-performing NLP pipeline and visualization. Thematically organizing and quantifying patient feedback reduces the time invested by healthcare professionals to evaluate and prioritize patient experiences without being confined to the limited answer options of closed-ended questions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Inteligência Artificial Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Inteligência Artificial Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article