The Quality of Insurance and Non-Insurance Patient Services and Their Effect on Loyalty Mediated by Patient Satisfaction
Bibliographic record
Abstract
The world is currently navigating challenges in reaching UHC by 2030. In Indonesia, the Health Services Coverage Index (SCI) recovered significantly however, its downturn in 2021 highlighted a prominent challenge. Eka Hospital, as a reputable referral hospital, has developed its services through innovations and technology; however, little has been discussed regarding the differences in service quality between insured and uninsured patients. This research aims to investigate the variance in service quality between insurance and non-insurance services, as well as its impact on customer satisfaction and loyalty. You'll also discover which service attributes need to be upgraded to meet the needs of patients. Research employed a cross-sectional study design. Regarding its design, this research utilized SERVQUAL, IPA, and Partial Least Squares Multigroup Analysis (PLS-MA) to test the hypothesis. The population consisted of outpatients of Eka Hospital in 2024. At least 110 samples are known, according to the consecutive sampling method/service and the 10-times rule SEM analysis. After excluding non-eligible participants, 415 participants were deemed valid. Results indicated no difference in services between insured and non-insured patients, indicating equivalent service. Some elements that require improvement include better medical equipment, trust in doctors' diagnoses, fair medical fees when treating patients, convenience in the procedure process, procedural effectiveness, and the enhancement or even remission of symptoms after treatment. Quality of service is closely linked to patient satisfaction, which in turn promotes patient loyalty. Hospitals should consider both the technical and emotional aspects of care to create a satisfying experience, which is crucial for UHC success in Indonesia.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".