Social media reviews as a supplement to traditional quality survey in the Canadian context




Talusan, Christopher

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Measurement for quality improvement in health care can be difficult. Measuring patientcentred care ensures both patient, health care professionals and health system perspectives are accounted for. Unfortunately, obtaining meaningful data is challenging as traditional surveys, while necessary for longitudinal comparison, often fail to capture the changing perspectives of patients. The use of natural language processing to mine free-text reviews can supplement data obtained from traditional quality surveys and identify new areas of concern that patients find important. This work used natural language processing of Google user reviews of hospitals in British Columbia to identify topics relevant to the Canadian Patient Experience Survey – Inpatient Care (CPES-IC) and topics that the CPES-IC did not contain. The results also compared the output from computer-coded topics to ones that were manually identified. Of the 23 topics in the CPES-IC, six in the computer-coded and manual analyses were not found. Seventeen topics not in the CPES-IC were found in the computer-coded analysis, whereas 23 topics were identified in the manual coding. Of the newly identified topics, 12 were shared between the manual and computer-coded analyses. The implications of utilizing computers to make data readily accessible can improve decision-makers' ability to access data.