Data and Analytics in Health Care
March 25-26, 2022
Abstract Submisssions are closed
The era of Big Data and analytics is upon us. Recent advances in data capture, real time analysis, and data-driven decision making have given rise to a rapid proliferation of data analytics across various business functions and sectors. The health care sector is one such industry in the economy which is ripe for innovations in the research and application of analytical methodologies. Key developments over the past few years have paved the way toward applying analytical methodologies in health care. First, progress in the collection and digitization of health care data is happening at a rapid pace. Second, there is renewed managerial and policy focus on delivering quality health care at affordable costs, wearable technology, drug compliance, etc. Given the increased focus of data and analytics in health care, there is clear need for research in understanding the role and boundaries of data-driven decision-making in health care. We invite you to submit your most impactful and unpublished research for presentation at the Neeley Analytics Conference.
We also welcome participation from firms operating in the health care space. Industry participants will have the opportunity to hear cutting edge analytics and research from leading researchers in the field and also have the opportunity to participate in a round table discussion to address critical questions facing them.
The conference is hosted by Neeley Analytics Initiative at the TCU Neeley School of Business. We are bringing together the best minds in analytics to tackle the topic of Data and Analytics in Health Care, all within our inviting campus in the thriving city of Fort Worth. From our world-renowned Cultural District and Stockyards National Historic District to the 35-block Sundance Square, Fort Worth buzzes day and night with people, energy and opportunity. The perfect place to soak up the excitement and friendly ambiance of Fort Worth.
The Neeley Analytics Conference of 2022 has no specific preference for methodological approach or business function. All methods that use primary or secondary data, as well as applications across the business discipline (operations, marketing, finance, accounting, information systems, economics, statistics) are welcome.