Evidence map›Paper›PMID 36719718›Full record

ArticleJMIR formative research2023

Helping Patients With Chronic Conditions Overcome Challenges of High-Deductible Health Plans: Mixed Methods Study.

Tiffany Yung-Shin Hu, Iman Ali, Michele Heisler, Helen Levy, Angela Fagerlin, Jeffrey T Kullgren

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tiffany Yung-Shin HuUniversity of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-2701-1274
Iman AliUniversity of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-9241-9893
Michele HeislerUniversity of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-6889-2063
Helen LevyUniversity of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4316-9143
Angela FagerlinSalt Lake City Veterans Affairs Medical Center, Salt Lake City, UT, United States.ORCID https://orcid.org/0000-0002-9192-2777
Jeffrey T KullgrenUniversity of Michigan, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-6269-5707

Funding

Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
NRSA Training CoreTL1TR002242 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HOLINSTAT, MICHAEL ALLAN · 2017 to 2021
$2.8M
NCATS NIH HHS TL1 TR002242NIDDK NIH HHS P30 DK092926
6 · The paper itself

Abstract

backgroundA 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.

objectiveThis 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.

methodsIn 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.

resultsApproximately 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.

conclusionsRegular 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.

Indexed as

behavioral interventionchronic conditionsconsumer behaviorscost-conscious strategiesHDHPhealth care costshigh-deductible health planmobile phoneOOPout-of-pocket spending

Identifiers

PMID36719718
PMCPMC9929725

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.