Health insurance coverage and its impact on out-of-pocket expenditures at a public sector hospital in Kerala, India
Bibliographic record
Abstract
Background: Health insurance coverage ensures protection from catastrophic health-care expenditure, especially to the underprivileged sections of society. Health insurance schemes such as Ayushman Bharat are coming up in addition to the existing schemes such as Rashtriya Swasthya Bima Yojana in India. The objectives are to find the health insurance coverage and its impact on out-of-pocket (OOP) expenditure for public sector tertiary health-care hospitalization. Methods: A cross-sectional study was conducted at a tertiary care hospital in Kerala. Insurance coverage was assessed among patients seeking inpatient care in various medical and surgical departments. OOP expenses incurred for those receiving and not receiving insurance coverage were compared. In addition, factors influencing enrolment and availing of insurance schemes were determined. Results: The coverage of health insurance was found to be 74%. Awareness campaigns and activities of local self-government (LSG) departments were the important reasons for enrolment and availing, respectively. Significantly lower OOP expenditures occurred in insured persons with regard to expenses incurred for treatment procedures ( P = 0.019), investigations ( P = 0.004), and medicines ( P = 0.001). Among the enrolled patients, 45% expressed dissatisfaction regarding available services. Conclusion: A quarter of patients still remain out of insurance coverage. All patients are incurring OOP expenditures, though the insured patients have significantly lower OOP expenses. The role of primary care providers and LSG is pivotal in creating awareness and ensuring enrolment. Availing services depend on the availability of resources at the respective institution. Improvements in enrolment and use of health insurance should ultimately result in improved patient satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".