Introduction
Scholarly writers often strive to be objective by eliminating personal bias in their publications. However, some of them fail in this regard. To demonstrate this fact, this paper analyzes the work of White, Reschovsky, and Bond (2014) in their peer-reviewed article titled, Understanding Differences between High- and Low-Price Hospitals: Implications for Efforts to Rein in Costs.
From a scholarly perspective, this paper shows how the writers show contextual bias in their work and proposes a different strategy for reducing it by proposing the use of alternative analytical frameworks for formulating their findings. However, before delving into the details of this paper, it is important to understand what the article talks about.
Article Summary
White et al. (2014) explored the strategies for managing high health care costs by investigating cost disparities between highly-priced and lowly priced hospitals. Their findings revealed that high-priced hospitals used their market power to charge high health care costs. Reputation also emerged as a distinct differentiating factor that defined how both types of hospitals charged their patients for medical services (White et al., 2014).
Highly-priced hospitals had a better reputation and commanded a greater market share than lowly priced hospitals did. Since these hospitals also offered specialized medical services, White et al. (2014) said it would be difficult to steer the market focus away from the high-priced hospitals. Based on this fact, it would also be difficult to manage the high health care costs experienced today.
Why the Article is Biased
White et al. (2014) acknowledged that reputation played a huge role in understanding how the highly-priced hospitals charged their patients for medical services. They used this measure (reputation) to formulate their findings after investigating the professional standing of such medical facilities in the national ranks of health care facilities. They showed bias in this regard because many lowly priced hospitals operate within small geographical regions.
Therefore, people who do not live in their designated service areas do not know them. Therefore, using reputation as the main measure for determining cost outcomes gave a lot of attention to hospitals that had a rich history (or known on the national front), while sidelining those that may offer quality medical services, but are unknown in the national medical circles. SBO (2014) says this is a context bias because White et al. (2014) failed to consider the contextual factors that affected the reputations of the highly and lowly priced hospitals.
How to Reduce the Bias
Pannucci and Wilkins (2010) explain that contextual bias creates external validity issues in research. To eliminate this bias, they propose that the authors should consider the differences that underlie the sample population when formulating their findings (Pannucci and Wilkins, 2010). Using this framework, this paper proposes that White et al. (2014) should have used a different measure (besides reputation) to assess the performance of different hospitals.
Pannucci and Wilkins (2010) have used this strategy (using objective assessment measures) to improve the outcomes of past clinical trials. Therefore, White et al. (2014) could have used objective measures of health care quality, such as market share, to formulate their findings. Reputation is not an objective assessment of assessing health care pricing models because it favors “popular” health care facilities and ignores hospital facilities that have a narrow service area.
Conclusion
Personal bias affects the credibility of an article’s findings. This paper shows that White et al. (2014) showed contextual bias in their research by using reputation as a measure for determining health care costs. This study also shows that reputation was not an objective assessment of hospital performance because service scopes limited the reputation of highly-priced and lowly priced hospitals. Based on this assessment, this paper proposes using a more objective assessment, such as market share, to measure health care cost valuations.
References
Pannucci, C., & Wilkins, E. (2010). Identifying and Avoiding Bias in Research. Plast Reconstr Surg, 126(2), 619–625.
SBO. (2014). Context Bias. Web.
White, C., Reschovsky, J., & Bond, A. (2014). Understanding Differences between High- And Low-Price Hospitals: Implications for Efforts to Rein In Costs. Health Aff, 33(2), 324-331.