scholarly journals Informed Decision-making for Health Insurance Enrollment: Survey Study (Preprint)

2021 ◽  
Author(s):  
Coralys M Colón-Morales ◽  
Wayne C W Giang ◽  
Michelle Alvarado
Keyword(s):  
Decision Making ◽  
Health Insurance ◽  
Data Analysis ◽  
Primary Sources ◽  
Full Time ◽  
Sources Of Information ◽  
Insurance Plan ◽  
Health Insurance Plan ◽  
Health Insurance Literacy ◽  
Insurance Enrollment

BACKGROUND Health insurance enrollment is a difficult financial decision with large health impacts. Challenges such as low health insurance literacy and lack of knowledge about choosing a plan further complicate this decision-making process. Therefore, to support consumers in their choice of a health insurance plan, it is essential to understand how individuals go about making this decision. OBJECTIVE This study aims to understand the sources of information used by individuals to support their employer-provided health insurance enrollment decisions. It seeks to describe how individual descriptive factors lead to choosing a particular type of information source. METHODS An introduction was presented on health insurance plan selection and the sources of information used to support these decisions from the 1980s to the present. Subsequently, an electronic survey of 151 full-time faculty and staff members was conducted. The survey consisted of four sections: <i>demographics, sources of information, health insurance literacy,</i> and <i>technology acceptance</i>. Descriptive statistics were used to show the demographic characteristics of the 126 eligible respondents and to study the response behaviors in the remaining survey sections. Proportion data analysis was performed using the Cochran-Armitage trend test to understand the strength of the association between our variables and the types of sources used by the respondents. RESULTS In terms of demographics, most of the respondents were women (103/126, 81.7%), represented a small household (1-2 persons; 87/126, 69%), and used their insurance 3-12 times a year (52/126, 41.3%). They assessed themselves as having moderate to high health insurance literacy and high acceptance of technology. The most selected and top-ranked sources were <i>Official employer or state websites</i> and <i>Official Human Resources Virtual Benefits Counselor Alex</i>. From our data analysis, we found that the use of official primary sources was constant across age groups and health insurance use groups. Meanwhile, the use of friends or family as a primary source slightly decreased as age and use increased. CONCLUSIONS In this exploratory study, we identified the main sources of health insurance information among full-time employees from a large state university and found that most of the respondents needed 2-3 sources to gather all the information that they desired. We also studied and identified the relationships between individual factors (such as age, gender, and literacy) and 2 dependent variables on the types of primary sources of information. We encountered several limitations, which will be addressed in future studies. CLINICALTRIAL

Uncertainty and health insurance

2017 ◽  
Author(s):  
Jan Abel Olsen
Keyword(s):  
Health Insurance ◽  
Excess Demand ◽  
Financial Protection ◽  
Insurance Plan ◽  
Welfare Gain ◽  
Health Insurance Plan ◽  
Insurance Contracts ◽  
Efficiency Losses ◽  
Potential Efficiency

This chapter seeks to explain why most people prefer to have a health insurance plan. Two types of uncertainty give rise to the demand for financial protection: people do not know if they will ever come to need healthcare, and they do not know the full financial implications of illness. Health insurance would take away—or at least reduce—such financial uncertainties associated with future illnesses. A model is presented to show the so-called welfare gain from health insurance. This is followed by an investigation into the potential efficiency losses of health insurance, due to excess demand for services. In the last section, a different efficiency problem is discussed: when people have an incentive to signal ‘false risks’, this can lead to there being no market for insurance contracts which reflect ‘true risks’.

Household Evacuation Decision Making and the Benefits of Improved Hurricane Forecasting: Developing a Framework for Assessment

2010 ◽  
Vol 25 (1) ◽  
pp. 207-219 ◽  
Author(s):  
Jeffrey K. Lazo ◽  
Donald M. Waldman ◽  
Betty Hearn Morrow ◽  
Jennifer A. Thacher
Keyword(s):  
Decision Making ◽  
Economic Value ◽  
Warning System ◽  
Primary Sources ◽  
Sources Of Information ◽  
The Public ◽  
Public Investments ◽  
Improvement Programs ◽  
Hurricane Forecasting ◽  
Survey Research Methods

Abstract Hurricane warnings are the primary sources of information that enable the public to assess the risk and develop responses to threats from hurricanes. These warnings have significantly reduced the number of hurricane-related fatalities in the last several decades. Further investment in the science and implementation of the warning system is a primary mission of the National Weather Service and its partners. It is important that the weather community understand the public’s preferences and values for such investments; yet, there is little empirical information on the use of forecasts in evacuation decision making, the economic value of current forecasts, or the potential use or value for improvements in hurricane forecasts. Such information is needed to evaluate whether improved forecast provision and dissemination offer more benefit to society than alternative public investments. Fundamental aspects of households’ perceptions of hurricane forecasts and warnings and their potential uses of and values for improved hurricane forecast information are examined. The study was designed in part to examine the viability of survey research methods for exploring evacuation decision making and for eliciting values for improved hurricane forecasts and warnings. First, aspects that affect households’ stated likelihood of evacuation are explored, because informing such decisions is one of the primary purposes of hurricane forecasts and warnings. Then, stated-choice valuation methods are used to analyze choices between potential forecast-improvement programs and the accuracy of existing forecasts. From this, the willingness to pay (WTP) for improved forecasts is derived from survey respondents.

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

2018 ◽  
Vol 3 (1) ◽  
pp. 238146831878109 ◽  
Author(s):  
Mary C. Politi ◽  
Enbal Shacham ◽  
Abigail R. Barker ◽  
Nerissa George ◽  
Nageen Mir ◽  
...  
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Health Care Utilization ◽  
Subjective Health ◽  
Care Utilization ◽  
Insurance Plan ◽  
Health Insurance Plan ◽  
Subjective Health Status ◽  
Reported Health ◽  
Nationally Representative

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.

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