Big Idea: Digital Leadership

Big Data and IT Talent Drive Improved Patient Outcomes at Schumacher Clinical Partners

An evolving care system and the influx of patient data from electronic health records has led health care companies to rethink how they leverage digital tools to better serve patients and providers.

Chris Cotteleer (Schumacher Clinical Partners), interviewed by Gerald C. Kane Reading Time: 8 min 

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Digital Leadership

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Chris Cotteleer, chief information officer, Schumacher Clinical Partners
Chris Cotteleer, chief information officer, Schumacher Clinical Partners

Schumacher Clinical Partners (SCP) staffs and operates more than 400 emergency departments and hospital medicine programs throughout the United States, treating some 8 million patients annually. The company, based in Lafayette, Louisiana, manages electronic medical record, coding, billing, and back-end reimbursements on its platform. Like many health care organizations, changing consumer expectations, new regulations, and an influx of patient data has created a perfect storm for SCP to rethink how it leverages digital tools to better serve patients and providers.

MIT Sloan Management Review guest editor Gerald C. Kane spoke with SCP’s chief information officer Chris Cotteleer about how digital transformation through data and analytics makes his organization more efficient, improves patient outcomes, and offers attractive work environments for health care providers.

MIT Sloan Management Review: How do you use data and analytics insight to change the way you make decisions?

Cotteleer: Our goal is to get the right doctor or clinician into the right facility at the right time for the patient to walk through the door and be treated well. We take a very operation-centered approach to information; it’s not unlike a supply chain. We’ve got a supply of patients coming in — an infinite queue with spikes in demand. They need to be served, and we’ve got to get providers on the ground to do that.

To meet patient demand, we spend a lot of time trying to predict what’s going to happen — accounting for changes like surges and seasonality — and for that, having patient chart information is very important, so we can tell that a Triage Acuity 5 [lower level of support needed] takes a little bit of time, or a Triage Acuity 1 [a higher level of support] will take more. Depending on the blend of what’s occurring in that emergency department, we might staff an extra NP/PA [nurse practitioner or physician’s assistant] or an extra doctor, and all of that goes to cost and quality of care.

I read somewhere that you’re working on something called “syndromic surveillance.” Can you tell me a little bit about that?

Cotteleer: We’re on the precipice of that. It’s a real driver for us.

Topics

Digital Leadership

As organizations rely increasingly on digital technologies, how should they cultivate opportunities and address taking risks in a fast-moving digital market environment?
More in this series

About the Authors

Gerald C. (Jerry) Kane is a professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Digital Business Initiative. He can be reached at gerald.kane@bc.edu and on Twitter at @profkane.

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Comment (1)
Muhammad Moroojo
Predictive analytic is needed in health care industry more than any other. Here we are talking about the welfare of human being which is more essential than profit maximization. However, it will not be only patient but the doctors and clinics are going to reduce cost through efficient and smart processes. According to Gerald C. Kane (2017) Schumacher Clinical Partners (SCP) staffs and operates more than 400 emergency departments and hospital medicine programs throughout the United States, treating some 8 million patients annually. Chris Cotteleer (Schumacher Clinical Partners) said in an interview “to meet patient demand, we spend a lot of time trying to predict what’s going to happen — accounting for changes like surges and seasonality — and for that, having patient chart information is very important, so we can tell that a Triage Acuity 5 [lower level of support needed] takes a little bit of time, or a Triage Acuity 1 [a higher level of support] will take more.” (Kane, 2017)
Linda A. Winters a PhD student thinks that not only can predictive analytics help with predictions, but it can also reveal surprising associations in data that our human brains would never suspect. She has illustrated seven benefits of predictive analytics to medicines as follows,
•	Predictive analytics increase the accuracy of diagnoses.
•	Predictive analytics will help preventive medicine and public health.
•	Predictive analytics provides physicians with answers they are seeking for individual patients.
•	Predictive analytics can provide employers and hospitals with predictions concerning insurance product costs.
•	Predictive analytics allow researchers to develop prediction models that do not require thousands of cases and that can become more accurate over time.
•	Pharmaceutical companies can use predictive analytics to best meet the needs of the public for medications.
•	Patients have the potential benefit of better outcomes due to predictive analytics.

Amin

References
Kane, G. C. (august, 2017). Big Data and IT Talent Drive Improved Patient Outcomes at Schumacher Clinical Partners . MIT Sloan Management Review. Retrieved October 2, 2017, from https://sloanreview.mit.edu/article/big-data-and-it-talent-drive-improved-patient-outcomes-at-schumacher-clinical-partners/

Winters, L. A. (2014, October). Seven ways predictive analytics can improve healthcare. Retrieved October 2, 2017, from https://www.elsevier.com/connect/seven-ways-predictive-analytics-can-improve-healthcare