A Strategic Imperative for Promoting Hospital Branding: Analysis of Outcome Indicators
Bibliographic record
Abstract
BACKGROUND: Optimizing the use of social media to promote hospital branding is important in the present digital era. In Taiwan, only 51.1% of hospitals have official Facebook fan pages. The numbers of likes for these hospitals are also relatively low. OBJECTIVE: Our objective was to establish a special branding team for social media operation, led by top administrators of our hospital. Here we present our strategic imperative for promoting hospital branding as well as an analysis of its effectiveness. METHODS: Led by top administrators, the branding team was formed by 11 divisions to create branding strategies. From 2016 to 2018, the team implemented action plans. All information unique to the hospital was posted on Facebook, as well as on the hospital's official website. To determine the plans' efficiencies, we obtained reference data from Google Analytics, and we compared Facebook Insights reports for 2016 with those for 2017 and 2018. RESULTS: One of the branding team's main missions was to establish branding strategies and to integrate segmental branding messages. In each quarter we regularly monitored a total of 52 action plan indicators, including those for process and outcome, and discussed the results at team meetings. We selected 4 main performance outcome indicators to reflect the effectiveness of the branding efforts. Compared with 2016, the numbers of likes posted on the Facebook fan page increased by 61.2% in 2017 and 116.2% in 2018. Similarly, visits to the hospital website increased by 4.8% in 2017 and 33.1% in 2018. Most Facebook fan page and website viewers were in 2 age groups: 25 to 34 years, and 35 to 44 years. Women constituted 60.42% (14,160/23,436) of Facebook fans and 59.39% (778,992/1,311,605) of website viewers. According to the Facebook Insights reports, the number of likes and post sharing both increased in 2017 and 2018, relative to 2016. Comment messages also increased from 2016 to 2018 (P=.02 for the trend). The most common theme of posts varied over time, from media reports in 2016, to innovative services in both 2017 and 2018. Likes for innovative services posts increased from 2016 through 2018 (P=.045 for the trend). By the end of 2018, we recorded 23,436 cumulative likes for posts, the highest number among medical centers in Taiwan. CONCLUSIONS: We achieved the largest number of Facebook fans among all medical centers in Taiwan. We would like to share our experience with other hospitals that might be interested in engaging in social media for future communications and interactions with their patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".