%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 19
%N 3
%P e65
%T Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy
%A Gibbons,Chris
%A Richards,Suzanne
%A Valderas,Jose Maria
%A Campbell,John
%+ The Psychometrics Centre, University of Cambridge, 16 Mill Lane, Cambridge, CB2 1RH, United Kingdom, 44 1223 765 203, cg598@cam.ac.uk
%K machine learning
%K surveys and questionnaires
%K feedback
%K data mining
%K work performance
%D 2017
%7 15.03.2017
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods: We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results: Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Conclusions: Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance. 
%M 28298265
%R 10.2196/jmir.6533
%U http://www.jmir.org/2017/3/e65/
%U https://doi.org/10.2196/jmir.6533
%U http://www.ncbi.nlm.nih.gov/pubmed/28298265