Display options
Cite
Maramba ID, Davey A, Elliott MN, et al. Web-based textual analysis of free-text patient experience comments from a survey in primary care. JMIR Med Inform. 2015;3(2):e20doi: 10.2196/medinform.3783.
Maramba, I. D., Davey, A., Elliott, M. N., Roberts, M., Roland, M., Brown, F., Burt, J., Boiko, O., & Campbell, J. (2015). Web-based textual analysis of free-text patient experience comments from a survey in primary care. JMIR medical informatics, 3(2), e20. https://doi.org/10.2196/medinform.3783
Maramba, Inocencio Daniel, et al. "Web-based textual analysis of free-text patient experience comments from a survey in primary care." JMIR medical informatics vol. 3,2 (2015): e20. doi: https://doi.org/10.2196/medinform.3783
Maramba ID, Davey A, Elliott MN, Roberts M, Roland M, Brown F, Burt J, Boiko O, Campbell J. Web-based textual analysis of free-text patient experience comments from a survey in primary care. JMIR Med Inform. 2015 May 06;3(2):e20. doi: 10.2196/medinform.3783. PMID: 25947632; PMCID: PMC4439523.
Copy Download .nbib Format: NLM
Share it on

JMIR Med Inform. 2015 May 06;3(2):e20. doi: 10.2196/medinform.3783.

Web-based textual analysis of free-text patient experience comments from a survey in primary care.

JMIR medical informatics

Inocencio Daniel Maramba, Antoinette Davey, Marc N Elliott, Martin Roberts, Martin Roland, Finlay Brown, Jenni Burt, Olga Boiko, John Campbell

Affiliations

  1. Primary Care, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom. [email protected].

PMID: 25947632 PMCID: PMC4439523 DOI: 10.2196/medinform.3783

Abstract

BACKGROUND: Open-ended questions eliciting free-text comments have been widely adopted in surveys of patient experience. Analysis of free text comments can provide deeper or new insight, identify areas for action, and initiate further investigation. Also, they may be a promising way to progress from documentation of patient experience to achieving quality improvement. The usual methods of analyzing free-text comments are known to be time and resource intensive. To efficiently deal with a large amount of free-text, new methods of rapidly summarizing and characterizing the text are being explored.

OBJECTIVE: The aim of this study was to investigate the feasibility of using freely available Web-based text processing tools (text clouds, distinctive word extraction, key words in context) for extracting useful information from large amounts of free-text commentary about patient experience, as an alternative to more resource intensive analytic methods.

METHODS: We collected free-text responses to a broad, open-ended question on patients' experience of primary care in a cross-sectional postal survey of patients recently consulting doctors in 25 English general practices. We encoded the responses to text files which were then uploaded to three Web-based textual processing tools. The tools we used were two text cloud creators: TagCrowd for unigrams, and Many Eyes for bigrams; and Voyant Tools, a Web-based reading tool that can extract distinctive words and perform Keyword in Context (KWIC) analysis. The association of patients' experience scores with the occurrence of certain words was tested with logistic regression analysis. KWIC analysis was also performed to gain insight into the use of a significant word.

RESULTS: In total, 3426 free-text responses were received from 7721 patients (comment rate: 44.4%). The five most frequent words in the patients' comments were "doctor", "appointment", "surgery", "practice", and "time". The three most frequent two-word combinations were "reception staff", "excellent service", and "two weeks". The regression analysis showed that the occurrence of the word "excellent" in the comments was significantly associated with a better patient experience (OR=1.96, 95%CI=1.63-2.34), while "rude" was significantly associated with a worse experience (OR=0.53, 95%CI=0.46-0.60). The KWIC results revealed that 49 of the 78 (63%) occurrences of the word "rude" in the comments were related to receptionists and 17(22%) were related to doctors.

CONCLUSIONS: Web-based text processing tools can extract useful information from free-text comments and the output may serve as a springboard for further investigation. Text clouds, distinctive words extraction and KWIC analysis show promise in quick evaluation of unstructured patient feedback. The results are easily understandable, but may require further probing such as KWIC analysis to establish the context. Future research should explore whether more sophisticated methods of textual analysis (eg, sentiment analysis, natural language processing) could add additional levels of understanding.

Keywords: free-text comments; patient experience; patient feedback; quantitative content analysis; textual analysis

References

  1. Arch Dis Child. 2010 May;95(5):330-5 - PubMed
  2. Yearb Med Inform. 2013;8:59-63 - PubMed
  3. J Med Internet Res. 2013 Oct 25;15(10):e230 - PubMed
  4. Value Health. 2008 Jul-Aug;11(4):719-25 - PubMed
  5. Int J Qual Health Care. 2012 Oct;24(5):509-16 - PubMed
  6. J Med Internet Res. 2013 Nov 01;15(11):e239 - PubMed
  7. Isr J Health Policy Res. 2013 Sep 17;2(1):36 - PubMed
  8. BMJ. 2009 Sep 29;339:b3851 - PubMed
  9. J Clin Nurs. 2013 Nov;22(21-22):3170-82 - PubMed
  10. Qual Health Care. 1999 Dec;8(4):212 - PubMed
  11. Ambul Pediatr. 2007 May-Jun;7(3):207-13 - PubMed
  12. BMC Fam Pract. 2009 Aug 22;10 :57 - PubMed
  13. Med Care Res Rev. 2010 Feb;67(1):27-37 - PubMed
  14. Int J Qual Health Care. 2002 Oct;14(5):353-8 - PubMed
  15. J Med Internet Res. 2014 Jan 29;16(1):e25 - PubMed
  16. BMJ. 2014 Nov 11;349:g6034 - PubMed
  17. Nurs Res. 2014 Mar-Apr;63(2):137-42 - PubMed
  18. Med Educ. 2012 Apr;46(4):372-80 - PubMed
  19. AMIA Annu Symp Proc. 2006;:764-8 - PubMed
  20. Br J Gen Pract. 2013 Mar;63(608):e177-84 - PubMed
  21. Soc Sci Med. 1985;20(9):911-21 - PubMed
  22. Br J Gen Pract. 2013 Mar;63(608):e200-8 - PubMed
  23. Int J Pharm Pract. 2012 Feb;20(1):57-64 - PubMed
  24. J Am Med Inform Assoc. 2014 Nov-Dec;21(6):1098-103 - PubMed
  25. J Med Internet Res. 2014 Mar 26;16(3):e94 - PubMed
  26. Med Educ. 2010 Mar;44(3):316-22 - PubMed
  27. J Med Internet Res. 2014 Feb 20;16(2):e56 - PubMed
  28. Med Educ. 2005 Aug;39(8):763-8 - PubMed
  29. J Med Internet Res. 2003 Oct 10;5(4):e24 - PubMed
  30. J Med Internet Res. 2014 Jul 23;16(7):e177 - PubMed
  31. Patient Educ Couns. 2013 Aug;92(2):197-204 - PubMed
  32. Qual Manag Health Care. 2012 Jan-Mar;21(1):9-19 - PubMed
  33. Int J Med Inform. 2012 Jan;81(1):36-44 - PubMed
  34. Acad Med. 2012 Dec;87(12 ):1668-78 - PubMed
  35. Int J Audiol. 2014 Jun;53(6):377-82 - PubMed
  36. PLoS One. 2014 Feb 12;9(2):e82452 - PubMed
  37. Med Educ. 2009 Aug;43(8):757-66 - PubMed
  38. BMJ Qual Saf. 2013 Aug;22(8):698 - PubMed

Publication Types

Grant support