Three Computational Techniques and One Tool to Bring the Patient Voice into Care

Chris Sidey-Gibbons

Chris Sidey-Gibbons, PhD, is the co-founder and co-director of the Brigham’s Patient-Reported Outcomes, Value, and Experience (PROVE) center. Using computational tools, Sidey-Gibbons aims to assess patient outcomes including quality of life, fatigue, and depression to ultimately transform the collection, analysis, and reporting of such variables in surgery. His recent work focuses on using natural language processing algorithms to parse unstructured patient reports.

“I’m passionate about patient reported data.”

  • Patient-reported data is currently collected on an electronic screen or on a piece of paper screen, much as it was in the 1960s.
  • To improve the nuance and quality of patient-reporting interventions, Sidey-Gibbons’ lab has aimed to reduce response burden through computer adaptive assessment, a platform which mimics an intelligent doctor and allows for shortening of the questionnaire, and increased accuracy.
  • Supported by funding from the UK and the NIH, the team designed a program to draw insights from open, unstructured data.
  • His team has been developing software, Concerto, which is an open-source software which allows clinicians to use CAT, machine learning and feedback to improve communication. They are currently working to develop two additional systems, imPROVE and INSPiRES.

Patient-reported data is usually collected and used to improve communication, identification and treatment, quality of life, mental health, satisfaction, and survival.

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