Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management

TY - JOUR

T1 - Connecting systems, data, and people

T2 - A multidisciplinary research roadmap for chronic disease management

AU - Bardhan, Indranil

AU - Chen, Hsinchun

AU - Karahanna, Elena

N1 - Funding Information: Hsinchun Chen would like to acknowledge the National Science Foundation (NSF) for funding his AI Lab’s health IT research over the past 30 years. He also benefitted greatly from his tenure as the Program Director for NSF’s Smart and Connected Health Program from 2014–2015. He would also like to acknowledge the contributions to this editorial from several of his recent Ph.D. graduates, who have been actively involved in health IT and analytics research at the UA AI Lab with NSF support. Indranil Bardhan would like to acknowledge the NSF and the funding support from the Jindal School of Management at the University of Texas at Dallas for hosting the MIS Quarterly special issue workshop, which provided a forum for authors to receive feedback from editors and associate editors. He also acknowledges the support of current and former Ph.D. students at UT Dallas and UT Austin. Elena Karahanna would like to acknowledge funding support from the Terry College of Business at the University of Georgia. She also expresses her appreciation to her collaborators on health IT and analytics research including her colleague Hani Safadi and her former and current Ph.D. students. Funding Information: Many people have contributed to the success of the special issue. First, we would like to thank the authors who contributed submissions to the special issue and diligently worked to respond to feedback and develop their manuscripts. We would also like to express our appreciation to all the members of the editorial board of the special issue for their time, effort, and constructive feedback as well as for their willingness to work with the tight deadlines. A special thanks to all those who have also attended the special issue workshop. We would also like to thank the Jindal School of Management at the University of Texas at Dallas for hosting the MIS Quarterly special issue workshop in January 2018. Finally, a special thanks to the MIS Quarterly editor-in-chief, Arun Rai, who provided us with feedback on the vision for the special issue, its focus, and its inclusive nature of multiple IS perspectives and methods. Hsinchun Chen would like to acknowledge the National Science Foundation (NSF) for funding his AI Lab's health IT research over the past 30 years. He also benefitted greatly from his tenure as the Program Director for NSF's Smart and Connected Health Program from 2014-2015. He would also like to acknowledge the contributions to this editorial from several of his recent Ph.D. graduates, who have been actively involved in health IT and analytics research at the UA AI Lab with NSF support. Indranil Bardhan would like to acknowledge the NSF and the funding support from the Jindal School of Management at the University of Texas at Dallas for hosting the MIS Quarterly special issue workshop, which provided a forum for authors to receive feedback from editors and associate editors. He also acknowledges the support of current and former Ph.D. students at UT Dallas and UT Austin. Elena Karahanna would like to acknowledge funding support from the Terry College of Business at the University of Georgia. She also expresses her appreciation to her collaborators on health IT and analytics research including her colleague Hani Safadi and her former and current Ph.D. students.

PY - 2020/3

Y1 - 2020/3

UR - http://www.scopus.com/inward/record.url?scp=85088472669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85088472669&partnerID=8YFLogxK

U2 - 10.25300/MISQ/2020/14644

DO - 10.25300/MISQ/2020/14644

M3 - Review article

AN - SCOPUS:85088472669

SN - 0276-7783

VL - 44

SP - 185

EP - 200

JO - MIS Quarterly: Management Information Systems

JF - MIS Quarterly: Management Information Systems

IS - 1

ER -