Mobile Health Insurance System and Associated Costs: A Cross-Sectional Survey of Primary Health Centers in Abuja, Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria contributes only 2% to the world's population, accounts for 10% of the global maternal death burden. Health care at primary health centers, the lowest level of public health care, is far below optimal in quality and grossly inadequate in coverage. Private primary health facilities attempt to fill this gap but at additional costs to the client. More than 65% Nigerians still pay out of pocket for health services. Meanwhile, the use of mobile phones and related services has risen geometrically in recent years in Nigeria, and their adoption into health care is an enterprise worth exploring. OBJECTIVE: The purpose of this study was to document costs associated with a mobile technology-supported, community-based health insurance scheme. METHODS: This analytic cross-sectional survey used a hybrid of mixed methods stakeholder interviews coupled with prototype throw-away software development to gather data from 50 public primary health facilities and 50 private primary care centers in Abuja, Nigeria. Data gathered documents costs relevant for a reliable and sustainable mobile-supported health insurance system. Clients and health workers were interviewed using structured questionnaires on services provided and cost of those services. Trained interviewers conducted the structured interviews, and 1 client and 1 health worker were interviewed per health facility. Clinic expenditure was analyzed to include personnel, fixed equipment, medical consumables, and operation costs. Key informant interviews included a midmanagement staff of a health-management organization, an officer-level staff member of a mobile network operator, and a mobile money agent. RESULTS: All the 200 respondents indicated willingness to use the proposed system. Differences in the cost of services between public and private facilities were analyzed at 95% confidence level (P<.001). This indicates that average out-of-pocket cost of services at private health care facilities is significantly higher than at public primary health care facilities. Key informant interviews with a health management organizations and a telecom operator revealed high investment interests. Cost documentation analysis of income versus expenditure for the major maternal and child health service areas-antenatal care, routine immunization, and birth attendance for 1 year-showed that primary health facilities would still profit if technology-supported, health insurance schemes were adopted. CONCLUSIONS: This study demonstrates a case for the implementation of enrolment, encounter management, treatment verification, claims management and reimbursement using mobile technology for health insurance in Abuja, Nigeria. Available data show that the introduction of an electronic job aid improved efficiency. Although it is difficult to make a concrete statement on profitability of this venture but the interest of the health maintenance organizations and telecom experts in this endeavor provides a positive lead.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".