John C. Hershey

• John C. Hershey (Working), Asymmetry in Price and Risk Elasticity for Term Life Insurance.
• Raina M. Merchant, Heather Griffis, Yoonhee P. Ha, Austin Kilaru, Allison Sellers, John C. Hershey, Shawndra Hill, Emily Kramer-Golinkoff, Lindsay Nadkarni, Margaret Debski, Kevin Padrez, Lance B. Becker, David A. Asch (2014), Hidden in Plain Sight: A Crowdsourced Public Art Contest to Make Automated External Defibrillators More Visible, American Journal of Public Health,, 104 (), pp. 2306-2312.
• Heather Griffis, Austin Kilaru, Rachel M. Werner, David Asch, John C. Hershey, Shawndra Hill, Yoonhee P. Ha, Allison Sellers, Kevin Mahoney, Charlene Wong, Raina M. Merchant (2014), Use of Social Media Across US Hospitals: Descriptive Analysis of Adoption and Utilization, Journal of Medical Internet Research, 16 (11), pp. 1-11.
• AM Chang, Alison Leung, Olivia Saynisch, Heather Griffis, Shawndra Hill, John C. Hershey, Lance B. Becker, David Asch, A Seidman, Raina M. Merchant (2013), Using a Mobile App and Mobile Workforce to Validate Data About Emergency Public Health Resources, Emergency Medicine Journal, 3 (), pp. 545-548.
• Raina M. Merchant, David Asch, John C. Hershey, Heather Griffis, Shawndra Hill, Olivia Saynisch, Alison Leung, Jeremy Asch, Kirk Lozada, Lindsay Nadkarni, Austin Kilaru, Charles Branas, Larry Starr, Fran Shofer, Graham Nichol, Lance B. Becker (2013), A Crowdsourcing Innovation Challenge To Locate and Map Automated External Defibrillators, Circulation: Cardiovascular Quality and Outcomes, 6 (), pp. 229-236.
• John C. Hershey, “Biases in Clinical Reasoning”. In Evidence into Practice: Integrating Judgment, Values, and Research, edited by Laurita Hack and Jan Gwyer, (Philadelphia: FA Davis, 2013), pp. 47-58
• Alison Leung, David Asch, Kirkland Lozada, Olivia Saynisch, Jeremy Asch, Nora Becker, Heather Griffis, Frances Shofer, John C. Hershey, Shawndra Hill, Charles Branas, Graham Nichol, Lance B. Becker, Raina M. Merchant (2013), Where Are Lifesaving Automated External Defibrillators Located and How Hard is it to Find Them in a Large Urban City?, Resuscitation, 84 (), pp. 910-914.
• A. Gurmankin-Levy and John C. Hershey (2008), Value-Induced Bias in Medical Decision Making, Medical Decision Making, 28, 269-276. Abstract

  Background. People who exhibit value-induced bias— distorting relevant probabilities to justify medical decisions— may make suboptimal decisions. Objective. The authors examined whether and in what conditions people exhibit value-induced bias. Design. Volunteers on the Web imagined having a serious illness with 2 possible diagnoses and a treatment with the same “small probability” of success for each diagnosis. The more serious diagnosis was designed as a clear-cut decision to motivate most subjects to choose treatment; the less serious diagnosis was designed to make the treatment a close-call choice. Subjects were randomized to estimate the probability of treatment success before or after learning their diagnosis. The “after group” had the motivation and ability to distort the probability of treatment success to justify their treatment preference. In study 1, subjects learned they had the more serious disease. Consistent with value-induced bias, the after group was expected to give higher probability judgments than the “before group.” In study 2, subjects learned they had the less serious disease, and the after group was expected to inflate the probability if they desired treatment and to reduce it if they did not, relative to the before group. Results. In study 1, there was no difference in the mean probability judgment between groups, suggesting no distortion of probability. In study 2, the slope of probability judgment regressed on desire for treatment was steeper for the after group, indicating that distortion of probability did occur. Conclusion. In close-call but not clear-cut medical decisions, people may distort relevant probabilities to justify their preferred choices.

• K. Viswanathan, J. Lemaire, K. Withers, K. Armstrong, A. Baumritter, John C. Hershey, M. Pauly, David A. Asch (2007), Adverse Selection in Term Life Insurance Purchasing Due to the BRCA Genetic Test and Elastic Demand, Journal of Risk and Insurance, 74, 65-86. Abstract

  Consumer groups fear that the use of genetic testing information in insurance underwriting might lead to the creation of an underclass of individuals who cannot obtain insurance; thus, these groups want to ban insurance companies from accessing genetic test results. Insurers contend that such a ban might lead to adverse selection that could threaten their financial solvency. To investigate the potential effect of adverse selection in a term life insurance market, a discrete-time, discrete-state, Markov chain is used to track the evolution of twelve closed cohorts of women, differentiated by family history of breast and ovarian cancer and age at issue of a 20-year annually renewable term life insurance policy. The insurance demand behavior of these women is tracked, incorporating elastic demand for insurance. During the 20-year period, women may get tested for BRCA1/2 mutations. Each year, the insurer calculates the expected premiums and expected future benefit payouts which determine the following year’s premium schedule. At the end of each policy year, women can change their life insurance benefit, influenced by their testing status and premium changes. Adverse selection could result from (i) differentiated benefits following test results; (ii) differentiated lapse rates according to test results; and (iii) differentiated reactions to price increases. It is concluded that with realistic estimates of behavioral parameters, adverse selection could be a manageable problem for insurers.

• M. L. Dekay, John C. Hershey, M. D. Spranca, P. A. Ubel, D. A. Asch (2006), Are Medical Treatments for Individuals and Groups Like Single-Play and Multiple-Play Gambles?, Judgment and Decision Making, 1, 134-145. Abstract

  People are often more likely to accept risky monetary gambles with positive expected values when the gambles will be played more than once. We investigated whether this distinction between single-play and multiple-play gambles extends to medical treatments for individual patients and groups of patients. Resident physicians and medical students (n = 69) and undergraduates (n = 99) ranked 9 different flu shots and a no-flu-shot option in 1 of 4 combinations of perspective (individual patient vs. group of 1000 patients) and uncertainty frame (probability vs. frequency). The rank of the no flu-shot option (a measure of preference for treatment vs. no treatment) was not significantly related to perspective or participant population. The main effect of uncertainty frame and the interaction between perspective and uncertainty frame approached significance (0.1 > p > 0.05), with the no-flu-shot option faring particularly poorly (treatment faring particularly well) when decisions about many patients were based on frequency information. Undergraduate participants believed that the no-flu-shot option would be less attractive (treatment would be more attractive) in decisions about many patients, but these intuitions were inconsistent with the actual ranks. These results and those of other studies suggest that medical treatments for individuals and groups are not analogous to single-play and multiple-play monetary gambles, perhaps because many people are unwilling to aggregate treatment outcomes over patients in the same way that they would compute net gains or losses over monetary gambles.

• All Research from John C. Hershey »

• Excellence in Teaching Award, Wharton Undergraduate Division, 2013
• Excellence in Teaching Award, Wharton Undergraduate Division, 2010
• Named one of top 50 management science professors (in 225 American Business Schools), 2007
• Named a “Favorite Course” by MBA Program for Executives, 2005
• Core Teaching Award for MBA Program for Executives, 2004
• Distinguished Lifetime Service Award, Society for Medical Decision Making, 2003
• Graduate Division Core Teaching Award, The Wharton School, 2001
• Graduate Division Core Teaching Award, The Wharton School, 2000
• Best Paper Award, Journal of Risk and Insurance, 1999
• Telecon Award for Best Continuing Education Program, awarded for Wharton's Program in Building a Business Case, 1999
• Graduate Division Core Teaching Award, The Wharton School, 1999
• Graduate Division Core Teaching Award, The Wharton School, 1998
• Graduate Division Core Teaching Award, The Wharton School, 1998
• Graduate Division Excellence in Teaching Award, 1992
• Graduate Division Excellence in Teaching Award, The Wharton School, 1991
• Graduate Division Core Teaching Award, The Wharton School, 1990
• Graduate Division Excellence in Teaching Awrd, The Wharton School, 1990
• Graduate Division Core Teaching Award, The Wharton School, 1989
• Graduate Division Excellence in Teaching Award, The Wharton School, 1989
• Graduate Division Excellence in Teaching Award, The Wharton School, 1988
• Graduate Division Class of 1984 Teaching Award, The Wharton School, 1988
• Helen Kardon Moss Anvil Award for Teaching Excellence, The Wharton School, 1987
• Award winner, "Society for Management Information Systems" Awards Paper Competition, 1973

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