Evidence map›Paper›PMID 34698647›Full record

ArticleJMIR human factors2021

User Interactions With Health Insurance Decision Aids: User Study With Retrospective Think-Aloud Interviews.

Wayne C W Giang, Emma Bland, Jeffrey Chen, Coralys M Colón-Morales, Michelle M Alvarado

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 15% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

5 authors at 1 institution in 1 country.

Wayne C W GiangDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-4022-7130
Emma BlandDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0003-1157-0888
Jeffrey ChenDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0002-1305-8530
Coralys M Colón-MoralesDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0002-2879-125X
Michelle M AlvaradoDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.ORCID https://orcid.org/0000-0001-9649-214X
University of Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTwo barriers to effective enrollment decisions are low health insurance literacy and lack of knowledge about how to choose a plan. To remedy these issues, digital decision aids have been used to increase the knowledge of plan options and to guide the decision process. Previous research has shown that the way information is presented in a decision aid can impact consumer choice, and existing health insurance decision aids vary in their design, content, and layout. Commercial virtual benefits counselors (VBCs) are digital decision aids that provide decision support by mimicking the guidance provided by an in-person human resources (HR) counselor, whereas more traditional HR websites provide information that requires self-directed navigation through the system. However, few studies have compared how decision processes are impacted by these different methods of providing information.

objectiveThis study aims to examine how individuals interact with two different types of health insurance decision aids (guided VBCs that mimic conversations with a real HR counselor and self-directed HR websites that provide a broad range of detailed information) to make employer-provided health insurance decisions.

methodsIn total, 16 employees from a local state university completed a user study in which they made mock employer-provided health insurance decisions using 1 of 2 systems (VBC vs HR website). Participants took part in a retrospective think-aloud interview, cued using eye-tracking data to understand decision aid interactions. In addition, pre- and postexperiment measures of literacy and knowledge and decision conflict and usability of the system were also examined.

resultsBoth the VBC and HR website had positive benefits for health insurance knowledge and literacy. Previous health insurance knowledge also impacted how individuals used decision aids. Individuals who scored lower on the pre-experiment knowledge test focused on different decision factors and were more conflicted about their final enrollment decisions than those with higher knowledge test scores. Although both decision aids resulted in similar changes in the Health Insurance Literacy Measure and knowledge test scores, perceived usability differed. Website navigation was not intuitive, and it took longer to locate information, although users appreciated that it had more details; the VBC website was easier to use but had limited information. Lower knowledge participants, in particular, found the website to be less useful and harder to use than those with higher health insurance knowledge. Finally, out-of-pocket cost estimation tools can lead to confusion when they do not highlight the factors that contribute to the cost estimate.

conclusionsThis study showed that health insurance decision aids help individuals improve their confidence in selecting and using health insurance plans. However, previous health insurance knowledge plays a significant role in how users interact with and benefit from decision aids, even when information is presented in different formats.

Indexed as

cognitive ergonomicsdecision aidsemployeehealth benefits planshuman factors engineeringinsurance, health

Identifiers

PMID34698647
PMCPMC8579212
OpenAlexW3208734747

What OpenQuestion holds

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LicenceCC BY
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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.