A Behavioral Economics Approach: Guiding Consumers in Choosing the Best Healthcare Plan Specific to Personalized Needs

A Behavioral Economics Approach: Guiding Consumers in Choosing the Best Healthcare Plan Specific to Personalized Needs

Americans have more choices regarding health care plans in the present era than ever. With the ability to schedule a date, buy a car, or have gourmet dinner delivered to one’s doorstep all with the swipe of a finger, choice paralysis appears inevitable. This inability to make good decisions ultimately leaves a consumer vulnerable to the capitalism that dominates the health insurance industry. Consumers often resort to letting emotions sway decisions which can become overwhelming. This impulsive pattern of behavior is part of the field now labeled “behavioral economics.” The theory of behavioral economics states that humans are not rational when it comes to making decisions since we value present comfort over future comfort, when it comes to evaluating perceived happiness of a choice (Khullar, The New York Times). Therefore, if  “people don’t always make decisions — even hugely important ones about physical or financial well-being” with objective reasoning, how can consumers ever successfully tackle the ever-involved process of signing with a health care plan catering to a person’s specific needs (Khullar, The New York Times)? It turns out the answer may be more complicated than originally perceived.  
In a 2016 national survey conducted by Financial Capability in the United States Investor Education Foundation (FINRA), only 20% of the study’s participants reported either receiving or being offered basic financial training (Snider, U.S News: Money). In a different 2012 investigation conducted by Consumer Reports, researchers found that participants could not estimate their budget when instructed choose between two different plans. This inability was explained by the participants’ lack of competence in interpreting phrases like “co-insurance”, “annual benefit limit,” “allowed amount,” “out of pocket limit,”and “drug tier.” In addition, many participants were unable to define common phrases such as “primary and preventive care,” “speciality drugs” and “diagnostic testing” (Quincy, Consumer Reports). These terms have ramifications when it comes to the billing aspect of outpatient medical treatments. Without a working understanding of such terms, participants could not identify which of the given options were more preferable given one’s personal circumstances. In fact, some participants refused to choose any plan at all (Quincy, Consumer Reports). In addition to this lack of qualitative literacy, participants also failed to demonstrate the quantitative knowledge of numeracy required to make a rational choice when choosing health insurance. Numeracy, the ability to “reason with numbering and other mathematical concepts,” is crucial when working with values like “deductibles,” “co-insurance levels,” and “benefit maximums” (Quincy, Consumer Reports). These values can help consumers determine if a plan falls within the constrictions of personal budgets and scope out the coverage included in fineprint (Quincy, Consumer Reports).

It is the ethical duty of the U.S Government to equip citizens with the tools necessary to make the best personalized decision when buying health care. All citizens pay taxes; regardless of the amount and income level, the government most certainly uses a percentage of these tax dollars to fund health care for qualifying individuals. Essentially, every average tax-paying citizen does not fully benefit from the funds going toward healthcare since they cannot navigate the system. Primary care issues are being treated in the emergency room since patients are not able to pay for preventative measures often covered by health insurance (Taber). As a result, tax dollars are allocated not towards providing insurance, but funding non-emergent or preventable visits to the emergency room.
Psychiatry and other services not considered immediately life threatening can easily be excluded in insurance plans. Additionally, physicians and prescribers are often ill prepared to confidently recommend treatment plans which effectively balance financial means with the medical needs of a patient. According to Dr. Dhruv Khullar, a resident at Massachusetts General Hospital, physicians will often “prescribe a particular brand of medication not because it has proved to be better, but because it happens to be the default option in my hospital’s electronic ordering system” (Khullar, The New York Times). Default options often do not account for potential money-saving options such as choosing generic over brand name drugs, choosing brands with coupons, or identifying the pharmacies with the best prices. Behavioral economics argues that a patient who is prescribed medication out of budget will not take action unless he/she is dying. According to University of Maryland’s medical school or “UMSOM”, almost one half of medical care is provided by emergency departments in the U.S (UMSOM and Marcozzi). The people who most need care will often end up in the emergency room because they cannot afford preventative measures. This leaves them with an unavoidable expensive surgery bill and no way to pay it.

References:

Khullar, Dhruv. “How Behavioral Economics Can Produce Better Health Care.” The New York Times: The Upshot, The New York Times Company, 13 Apr. 2017.

Marcozzi, David, et al. “Trends in the Contribution of Emergency Departments to the Provision of Hospital-Associated Healthcare in the USA.” International Journal of Health Services, vol. 48, no. 2, Apr. 2018, pp. 267–288, doi 10.1177/0020731417734498

Quincy, Lynn. “What's Behind the Door: Consumer Difficulties Selecting Health Plans.” Consumer Reports , Consumers Union , Jan. 2012.

Snider, Susannah. “5 Scary Facts About Financial Literacy – and How to Avoid Becoming a Statistic.” U.S. News & World Report: Money, U.S. News & World Report L.P., 1 Aug. 2017, 3:09 p.m.

Taber, Jennifer M et al. “Why do people avoid medical care? A Qualitative Study Using National Data” Journal of General Internal Medicine vol. 30, no. 3, 2014, pp 290-297.




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