Show Me My Health Plans

Politi, Mary C.; Kuzemchak, Marie D.; Liu, Jingxia; Barker, Abigail; Peters, Ellen; Ubel, Peter A.; Kaphingst, Kimberly A.; McBride, Timothy D.; Kreuter, Matthew W.; Shacham, Enbal; Philpott, Susan

doi:10.1177/2381468316679998

Cited by 31 publications

(36 citation statements)

References 27 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 9 , 10 Most prior studies on this topic have focused on consumers’ ability to select a health insurance plan. 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 Less is known about how patients navigate and use health insurance after obtaining insurance. Understanding insurance cost-sharing features, including deductibles, copayments, and coinsurance, may facilitate appropriate care-seeking and may help to prevent individuals from delaying or avoiding needed care owing to costs.…”

Section: Introductionmentioning

confidence: 99%

Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost

et al. 2018

Self Cite

View full text Add to dashboard Cite

Key Points Question What is the association between health insurance literacy and avoidance of health care services owing to cost? Findings In this US national survey study of 506 insured adults, 29.6% reported having delayed or foregone care because of cost. Higher health insurance literacy was associated with a lower likelihood of delayed or foregone care owing to cost for both preventive and nonpreventive care. Meaning These findings suggest that to improve appropriate use of recommended health care services, including preventive health services, clinicians and policymakers may need to adopt communication strategies that make health insurance concepts accessible to individuals regardless of health insurance literacy and improve consumers’ understanding of services exempt from out-of-pocket costs.

show abstract

Section: Introductionmentioning

confidence: 99%

Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…27,28 Health conditions were reported by selecting all that apply from a list of the most common health conditions as described in the Medical Expenditure Panel Survey and modified from our previous work. 29,30 They could also list other health conditions in an open text response field, if applicable.…”

Section: Methodsmentioning

confidence: 99%

How Do Subjective Health Literacy Measures Work in Young Adults? Specifying “Online” or “Paper-Based” Forms Impacts Results

Politi

Goodwin

Kaphingst

et al. 2020

MDM Policy & Practice

Self Cite

View full text Add to dashboard Cite

Purpose. There is no gold-standard health literacy measure. The Single Item Literacy Screener (SILS) and Subjective Literacy Screener (SLS) ask people to self-report ability to understand health information. They were developed in older adults, before common use of electronic health information. This study explored whether the SILS and SLS related to objective literacy, numeracy, and comprehension among young adults, and whether specifying “online” or “paper-based” wording affected these relationships. Methods. Eligible individuals (18–35 years of age, English-speaking, US residents) from an online survey company were randomized to 1) original measures; 2) measures adding “paper-based” to describe health information/forms; or 3) measures adding “online” to describe health information/forms. We examined how each measure related to e-Health Literacy (eHEALS), subjective numeracy (SNS), objective numeracy (ONS), and comprehension of a short passage. Results. A total of 848/1342 respondents correctly answered attention-checks and were analyzed. The validated SILS related to comprehension ( P = 0.003), eHEALS ( P = 0.04), and ONS ( P < 0.001) but not SNS ( P = 0.44). When adding “paper-based,” SILS related to eHEALS ( P < 0.001) and ONS ( P = 0.003) but did not relate to comprehension ( P = 0.25) or SNS ( P = 0.35). When adding “online,” SILS related to comprehension ( P < 0.001), eHEALS ( P < 0.001), ONS ( P = 0.005), and SNS ( P = 0.03). The validated SLS related to comprehension ( P < 0.001), eHEALS ( P < 0.001), ONS ( P < 0.001), and SNS ( P < 0.001). When adding “paper-based,” the SLS only related to eHEALS ( P = <0.001) and comprehension ( P = 0.03) but did not relate to ONS ( P = 0.13) or SNS ( P = 0.33). When adding “online,” the SLS related to comprehension ( P < 0.001), eHEALS ( P < 0.001), and SNS ( P = 0.03) but not ONS ( P = 0.06). Conclusions. Young adults might interpret subjective health literacy measures differently when prompted to think about electronic or paper-based information. Researchers should consider clearer instructions or modified wording when using these measures in this population.

show abstract

“…Participants were part of a larger randomized study evaluating a web-based decision tool that supported individuals' health insurance choices through the ACA marketplace. 10 The study was approved by the Human Research Protection Office of Washington University. Participants were recruited from community events, online advertisements, social service organizations, and the recruitment/retention arm of the Center for Community-Engaged Research at Washington University.…”

Section: Participantsmentioning

confidence: 99%

“…The website then showed cost estimates for groups of plans by metal level (gold, silver, or bronze) before showing specific plans within metal level category. A more complex tool, 9,10 like some other tools used to facilitate health insurance decisions, [11][12][13] uses data from the Medical Expenditure Panel Survey (MEPS) 14 to estimate costs to the consumer for each available plan in their region, after insurance is applied. These objective estimates are based on national data about health services that Americans use, how frequently they use them, and the cost of these services across demographic characteristics and health conditions.…”

mentioning

confidence: 99%

“…Available plans are then sorted from lowest to highest expected annual cost to an individual or family, and ''good fit'' plans are highlighted based on an algorithmic prediction of costs and the risk of incurring more costs than expected. 9,10 In each of these available tools, consumers can learn about available health insurance plans and the specific costs required to cover themselves and/or their family. However, the best way to estimate personalized costs is unknown.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers’ Health Insurance Plan Choice

Politi

Shacham

Barker

et al. 2018

MDM Policy & Practice

Self Cite

View full text Add to dashboard Cite

Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.

show abstract

Show Me My Health Plans

Cited by 31 publications

References 27 publications

Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost

Association Between Health Insurance Literacy and Avoidance of Health Care Services Owing to Cost

How Do Subjective Health Literacy Measures Work in Young Adults? Specifying “Online” or “Paper-Based” Forms Impacts Results

A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers’ Health Insurance Plan Choice

Contact Info

Product

Resources

About