Dealing with Data
Measuring Workforce Diversity at the Individual Employee Level: Applying Graph Theory to Measure Individual Diversity Experience
Paper Presentation in a Themed Session Paul Beckman
This project constructs a framework by which organizations can measure the diversity level of each individual employee through the experience they have gained working on organizational tasks with colleagues from diverse backgrounds. Prior simplistic workforce diversity measures, such as “% female”, have been limited to values calculated against the entire workforce. Our interdisciplinary project proposes constructing Information Systems using mathematical Graph Theory concepts with data supplied from organizational Human Resources systems. Our method calculates for every employee in the organization the number of hours they have worked on organizational tasks with every other co-worker. From those calculations we can determine which employees have the highest “Diversity Experience Index” (DEI), a value that indicates the diversity level of that individual employee. \For example, an employee who has worked many hours with female colleagues will have a higher “female” DEI value than does an employee who has not. The implication of our work is that organizations that know their employees’ DEI values will function better because psychology research has shown that humans are more open and accepting to others who are different than themselves as they increase the number or time of interactions with those different others.
Exploitation of Physician Prescribing Data as a Health Information Industry Standard: A Case of Big Data Practices and Pitfalls
Paper Presentation in a Themed Session Frederick Langshaw
North American physicians are not afforded the same protection of privacy and control over their information as patients are. Pharmaceutical drug Intermediaries combine datasets to reidentify physicians, link them to their prescribing habits, and sell this data to pharmaceutical marketers. Sales representatives approach unknowing doctors with data-informed sales pitches to influence their prescribing habits. Framed in a big-data, surveillance studies framework, this work explores the philosophical underpinnings, as well as social, technical, and legal issues central to the exploitation of physician data. Its aim is to understand to what degree physicians own their data, how far their data extends them (i.e. for access) and who can legitimately control and act on their data. This work is informed by surveillance, structural-functionalist, and medical sociology literature, legal documents, industry publications, and corporate websites, reports, best practices materials, policies, and white papers. This case reflects problematic big-data practices, consequences, public concern and agency for privacy protection in an increasingly data-driven world. Implications of limiting or prohibiting these practices include improving the state of privacy (and data-trading) law, physician privacy, public trust in medicine and research, and public health, as less physicians will be persuaded to prescribe expensive, inadequately tested, or unsafe brand pharmaceuticals.
Defining Strategies for Technology Transfer: A Technology, Intelligence-based Tool for Risk Assessment
Paper Presentation in a Themed Session Karla G. Cedano, Mauricio Perez
In the technology transfer context, defining strategies for technology project planning arises as a necessary and challenging activity. This challenging activity comprises elements like market uncertainty and the need for timely decision making within an environment of limited resources (financial, time, among others). In this context, technology intelligence (TI) aids in the generation of knowledge for reducing risk in decision making. This work suggests a tool for identifying and qualitatively measuring different risks, detected from previous TI activities. Such risks, their level and alternatives for mitigating them are also discussed. Furthermore, a tool for calculating a risk score for technology projects at early stages of development is also proposed. This tool was developed by the Technology Transfer Office of the National Institute of Genomic Medicine, in collaboration with Cedano Villavicencio, Ph.D.
