Measuring patient-perceived quality of care in US hospitals using Twitter

Abstract

Background: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches.

Objective: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures.

Design: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients.

Key results: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003).

Conclusions: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.

Keywords: Healthcare quality improvement; Patient satisfaction; Performance measures; Quality improvement methodologies; Quality measurement.

Figure 1

Geographical distribution of all US hospitals on Twitter (n=2349). Hospitals are coloured by mean sentiment, and sized by the number of patient experience tweets received in the 1-year study period. Sentiment ranges from −1 (negative) to 1 (positive).

Figure 2

Sentiment correlated with 30-day hospital readmission rates. 30-day hospital readmission rates are plotted against average sentiment, for hospitals that have ≥50 patient experience tweets (n=297). This association displays a weak negative correlation (r=−0.215, p<0.001).