OPTACIMM Lab
Optimizing with Applied Clinical Informatics Models and Methods
Principal Investigators: Sarah Collins Rossetti, RN, PhD, FACMI, FAMIA, and Kenrick Cato, PhD, RN, CPHIMS, FAAN
Mission & Vision Statements
The mission of the Optimizing with Applied Clinical Informatics Models and Methods (OPTACIMM) Center is to use informatics to advance healthcare delivery and outcomes for individuals and communities.
Our vision is a culture built on teamwork, collaboration, and open sharing of ideas that encourages input from all perspectives – including data scientists, health professionals, trainees, patients, caregivers, and community members – to enable innovative research ideas that can have an impact in the clinical setting.
Goals and Areas of Focus
• Support the conduct of rigorous informatics research
• Enhance and apply analytics to improve outcomes
• Provide clinical informatics training to students, clinicians, and administrators
• Support healthcare organizations to conduct informatics interventions.
• Design and leverage an infrastructure that supports clinical intelligence, research informatics, and education/population health informatics
• Promoting interoperability
• Patient empowerment
• Conducting informatics evaluation
Help shape a better healthcare environment for both patients and healthcare professionals. When you participate in this brief survey on Ambient AI for nurses, you provide insights that will help understand current strategies for use of Ambient AI in the healthcare setting. Our primary goal is to capture the current extent to which Ambient AI is being used by nurses across the U.S.
About the Ambient AI Survey
Aligned with our team’s research focused on reducing documentation burden, we developed this survey for nurse leaders across the U.S. to capture the scope of implementations of Ambient AI for registered nurses. By gathering this information, we aim to gain insights into the extent to which Ambient AI is being used by nurses in the U.S.
Survey insights will not only help us comprehend the current landscape but will also inform and empower stakeholders, policymakers, and healthcare organizations with data about Ambient AI implementations to alleviate burdens associated with documentation.
Participation in this survey can help inform and optimize Ambient AI strategies for nursing which we anticipate to be an important innovation that will shape healthcare environment that supports both excellence in patient care and the holistic well-being of all its health professionals.
Participation is voluntary and open to all nursing leader across various clinical care settings. No identifiable information will be collected. The anonymous dataset of individual level responses will be openly shared for research purposes. There is no compensation for participation in this study. There are no anticipated additional costs to participants.
Domains Covered
• Policy
• Nursing
• Clinical decision support
• Time-motion study methods
• Documentation burden
• Trust in/understandability of machine learning
• Qualitative research
• Aging in place
• Preventive medicine
• Tobacco cessation
Meet Our Lab:
Sarah Collins Rossetti, Principal Investigator
Kenrick Cato, Principal Investigator
Gregory Alexander, Co-Investigator
Suzanne Bakken, Co-Investigator
Victor Castano, Medical Student, Member
Salvatore Crusco, Postdoctoral Fellow, Member
Andrew Geneslaw, MD, Member
Eugene Kim, Co-Investigator
Rachel Lee, Postdoctoral Fellow, Member
Fang Liu, Graduate, Member
Amanda Moy, PhD Candidate, Member
James Pecore, Undergraduate, Member
Jessica Schwartz, Postdoctoral Fellow, Member
Brittany Taylor, PhD Student, Member
Jennifer Withall, Postdoctoral Fellow, Member
