Category Archives: Fall 2015

Comparing success and sustainability in two health systems

A recent CHOT study investigating how to successfully implement an innovative care management model such as Studer Group’s Evidence-based Leadership (EBL) tool found that culture (accountability, buy-in and communication) is most important for sustainability of initiative (long-term) and leadership seems most important initially during kick-off and early implementation stage. This research study analyzed the implementation of EBL in two large, US health systems by comparing and contrasting the factors associated with successful implementation and sustainability of the EBL initiative. Researchers found three themes associated with success and sustainability of EBL: leadership; culture; and organizational processes. This study offers health system leaders practical guidance on how to effectively implement organizational change initiatives by designing the appropriate system and environment conducive to successful and sustainable implementation of key patient care and management change initiatives.

Schuller, K. A., Kash, B. A., & Gamm, L. D. (2015). Comparing success and sustainability in two health systems. Journal of Health Organization and Management, 29(6), 684-700.

 

Research Contact:
Bita Kash, PhD
bakash@sph.tamhsc.edu

Predicating movement disorders using non-verbals

Design team interactions are one of the least understood aspects of the engineering design process. Given the integral role that designers play in the engineering design process, understanding the emotional states of individual design team members will help us quantify interpersonal interactions and how those interactions affect resulting design solutions. The methodology presented in this paper enables automated detection of individual team member’s emotional states using non-wearable sensors. The methodology uses the link between body language and emotions to detect emotional states with accuracies above 98%. The practical implications include that machine learning to detect emotions using non-invasive sensors in design teams and that there was a high accuracy over 90% achieved for detecting many body language poses.

Behoora, I., & Tucker, C. S. (2015). Machine learning classification of design team members’ body language patterns for real time emotional state detection. Design Studies, 39, 100-127.

 

Research Contact:
Conrad Tucker, PhD
ctucker4@psu.edu