Pegged Software Uses Big Data To Improve Diversification in Health Care Hiring

MedicalResearch.com Interview with:

Mike Rosenbaum Founder and CEO Pegged Software

Pegged Software

Myra Norton
President and COO of Pegged Software

MedicalResearch.com editor’s note: As part of an ongoing series on changes in the health care landscape, we interviewed Ms. Myra Norton, President and COO of Pegged Software. Pegged Software uses an advanced “analytics engine to selecting job candidates based on the actual determinants of high performance”, specifically in the health care field. Ms. Norton has a special interest in gender and hospital hiring practices.

MedicalResearch.com: Given that women earn 78 cents to the dollar in regards to men, can big data improve this pay inequity? If so, how does this happen?

Myra Norton

Myra Norton

Ms. Norton: Big data and predictive analytics alone will not solve the problem of pay inequality. What these tools can do is illuminate talent in a way that removes the biases that undermine equality across gender, ethnicity, socio-economic status and other dimensions. For example, predictive analytics allows organizations to identify candidates with the highest likelihood of improving patient experience, being retained, remaining an engaged employee, lowering thirty day readmissions, and positively impacting other organizational outcomes.

MedicalResearch.com: Can Pegged use big data to identify if a company has a gender diversity problem or a pay inequality problem? How does this work?

Ms. Norton: You do not need big data to identify gender diversity or pay inequality issues in terms of current employees. Organizations can assess this for themselves by analyzing payroll, performance and promotion data. The place where big data can have an impact is in ensuring that hiring is done in a manner that does not discriminate; that individuals are hired because of their predicted impact on organizational objectives rather than gender, background, the content of their resumes, or the ability to present well in an interview.

MedicalResearch.com: Are there other areas of inherent bias that big data could tackle?

Ms. Norton: Absolutely. There are so many inherent biases in the hiring process. Beliefs about the amount of experience required for a particular role, the type of degree necessary, what employment gaps or job changes say about an individual – these types of beliefs are often unsubstantiated when you look at the data. It may be that a particular candidate with less than two years of experience in a particular job was unsuccessful. That instance does not extrapolate to all individuals with less than two years of experience. Data allows us to isolate the patterns and signals that actually correlate with improved organizational outcomes.

MedicalResearch.com: Is there any downside to using big data in the hiring process or in determining retention probabilities?

Ms. Norton: In the current environment, I think the largest challenge is one of education; helping both job seekers and employers understand how best to leverage predictive analytics. The more we understand about big data and predictive analytics, the more we can see that for job seekers, these tools generate greater opportunity; and for employers, these tools enable significant improvements in organizational performance by deploying the right talent throughout the organization.

MedicalResearch.com: Why do you feel that more hospitals will adopt this data analytics model for recruitment going forward?

Ms. Norton: The stakes are too high in healthcare not to find better tools for deploying human capital. This is an industry that is facing massive disruptions from regulatory, demographic, and economic shifts, and it is already facing a shortage of workers. In addition, the pressures to shift from a “fee for service” model to value-based economics are forcing healthcare organizations to re-imagine how they care for patients. Developing a workforce that can make this transition is a requirement for any organization that intends to lead in this space. The tools of the past are not sufficient. The clinical side of healthcare has been deploying predictive analytics for some time now. The business side of healthcare must catch up.

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Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

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