Predicting Consumer Effort in Finding and Paying for Health Care: Expert Interviews and Claims Data Analysis
Bibliographic record
Abstract
BACKGROUND: For consumers to accept and use a health care information system, it must be easy to use, and the consumer must perceive it as being free from effort. Finding health care providers and paying for care are tasks that must be done to access treatment. These tasks require effort on the part of the consumer and can be frustrating when the goal of the consumer is primarily to receive treatments for better health. OBJECTIVE: The aim of this study was to determine the factors that result in consumer effort when finding accessible health care. Having an understanding of these factors will help define requirements when designing health information systems. METHODS: A panel of 12 subject matter experts was consulted and the data from 60 million medical claims were used to determine the factors contributing to effort. RESULTS: Approximately 60 million claims were processed by the health care insurance organization in a 12-month duration with the population defined. Over 292 million diagnoses from claims were used to validate the panel input. The results of the study showed that the number of people in the consumer's household, number of visits to providers outside the consumer's insurance network, number of adjusted and denied medical claims, and number of consumer inquiries are a proxy for the level of effort in finding and paying for care. The effort level, so measured and weighted per expert panel recommendations, differed by diagnosis. CONCLUSIONS: This study provides an understanding of how consumers must put forth effort when engaging with a health care system to access care. For higher satisfaction and acceptance results, health care payers ideally will design and develop systems that facilitate an understanding of how to avoid denied claims, educate on the payment of claims to avoid adjustments, and quickly find providers of affordable care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".