Document Type

Conference Proceeding

Conference Track

Panels and Special Sessions

Publication Date

2014

Abstract

Mistakes of diagnoses are often topics in medical schools, hospitals and medical practices. These errors can range from the minor and inexpensive to the harmful and costly. According to Johns Hopkins University researchers, devastating errors can lead to permanent damage or death for as many as 160,000 patients yearly. Diagnostic errors, however, may be becoming more preventable as many health-care providers are turning to a number of innovative strategies that are addressing the complicated web of errors, biases, and oversights. Included in innovative changes in medicine are the methods of managing patients’ records through electronic means. These modernizations are often touted as part of an overall plan to provide better health care delivery which in turn will lead to more efficient and effective health outcomes. But with any automation, there are potential downsides, such as information overload. In comparison, business analytics has become a very popular and growing field of operational research with application to many areas of business. Just as in medicine, it is the mission of “big data” to provide targeted and precise information to enhance the probability of making more effective and efficient decisions. Medicine and business have many overlapping concerns around the collection, analysis, and distribution of data. Bringing these professionals together, albeit they work in different contexts, has the possibility for advancing interdisciplinary insights around major decision-making issues that are paramount to both groups. Additionally, many medical professionals are in an ongoing effort to cut down on errors of misdiagnoses through the improvements in communication. Common biases have been identified that can exacerbate physicians making an incorrect diagnosis. For example, last year Dalhousie University in Halifax, Nova Scotia established a Critical Thinking Program that aims to help identify and analyze critical thinking biases. Physician Pat Crosberry, a researcher on the role of cognitive error in diagnosis, developed a list of 50 different types of biases that lead to diagnostic error. Businesses are often involved in strategic diagnostic research and misdiagnoses in this context are always a concern too. An interdisciplinary discussion of these issues will be discussed by panelists.

About the Authors

Mary F. Mobley received her Ph.D. from the University of South Carolina, teaches at Georgia Regents University, and has written primarily in strategic marketing management. She will introduce the panel and frame the discussion which will follow. mmobley99@gru.edu

Michael C. Mobley, M.D. graduated from the Medical College of Georgia, He is a clinical professor at Georgia Regents University and has a private psychiatric practice in Savannah. He has written in the area of behavioral science with an emphasis on work-place well-being. He will discuss how common biases in communications can lead to misdiagnoses in medicine. Also, he will briefly discuss medical record automation, along with devices, new tests and online services that may be part of a solution relative to accurate diagnoses. In addition, he will discuss negative possibilities of data management as they relate to the field of medicine. mcmobley@aol.com

James M. Grayson graduated from University of North Texas with a Ph.D., teaches at Georgia Regents University and has written in the area of operations management and data analytics. He will discuss how “big data” and the access to information is revolutionizing business decision-making. Also, he will address possible negative implications of mass data collection and the implications it has for business strategy. jgrayson@gru.edu

Peter Basciano graduated with a Ph.D. from Kent State University and is MBA Director at Georgia Regents University where he also teaches finance. He will relate how the common biases that prevent physicians from making accurate diagnoses have financial meaning for

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