Helping Patients With Chronic Conditions Overcome Challenges of High-Deductible Health Plans: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: A growing number of Americans are enrolled in high-deductible health plans (HDHPs). Enrollees in HDHPs, particularly those with chronic conditions, face high out-of-pocket costs and often delay or forgo needed care owing to cost. These challenges could be mitigated by the use of cost-conscious strategies when seeking health care, such as discussing costs with providers, saving for medical expenses, and using web-based tools to compare prices, but few HDHP enrollees engage in such cost-conscious strategies. A novel behavioral intervention could enable HDHP enrollees with chronic conditions to adopt these strategies, but it is unknown which intervention features would be most valued and used by this patient population. OBJECTIVE: This study aimed to assess preferences among HDHP enrollees with chronic conditions for a novel behavioral intervention that supports the use of cost-conscious strategies when planning for and seeking health care. METHODS: In an exploratory sequential mixed methods study among HDHP enrollees with chronic conditions, we conducted 20 semistructured telephone interviews and then surveyed 432 participants using a national internet survey panel. Participants were adult HDHP enrollees with diabetes, hypertension, coronary artery disease, chronic obstructive pulmonary disease, or asthma. The interviews and survey assessed participants' health care experiences when using HDHPs and their preferences for the content, modality, and frequency of use of a novel intervention that would support their use of cost-conscious strategies when seeking health care. RESULTS: Approximately half (11/20, 55%) of the interview participants reported barriers to using cost-conscious strategies. These included not knowing where to find information and worrying that the use of cost-conscious strategies would be very time consuming. Most (18/20, 90%) interviewees who had discussed costs with providers, saved for medical expenses, or used web-based price comparison tools found these strategies to be helpful for managing their health care costs. Most (17/20, 85%) interviewees expressed interest in an intervention delivered through a website or phone app that would help them compare prices for services at different locations. Survey participants were most interested in learning to compare prices and quality, followed by discussing costs with their providers and putting aside money for care, through a website-based or email-based intervention that they would use a few times a year. CONCLUSIONS: Regular use of cost-conscious strategies could mitigate financial barriers faced by HDHP enrollees with chronic conditions. Interventions to encourage the use of cost-conscious strategies should be delivered through a web-based modality and focus on helping these patients in navigating their HDHPs to better manage their out-of-pocket spending.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".