SoN LAI 4 Day Intensive May 2022

SOCR News & Events: Leadership, Analytics and Innovation (LAI) Master’s Program 4-Day Intensive (May 17, 2022)

Logistics

• Event Date/Time: May 17, 2022, 1:00-1:30 PM (US Eastern Time)
• Location: LAI Program 4-Day Intensive, SNB Building, University of Michigan.
• Format: Residential/In-person
• RSVP (required): RSVP

• Ivo Dinov's Welcome at the launch of the 2022 4-Day Intensive, LAI in Data-Driven, Evidence-based, and Technology-Enhanced Health Science.

Overview

• Leadership, Analytics and Innovation (LAI) Program aims to prepare scholars for the Next Global Crisis.
• In the 1880’s James Berrill Angell, the 3^rd UM President, declared UM as an institution providing uncommon education for the common folk. Shortly thereafter, the School of Nursling was established.
• At SoN, the UM Leaders & Best Initiative translates into Raising Effective Healthcare leaders and Best trained nursing and health professionals.
• The 4-day intensive will offer unique opportunities for community building, networking, and innovating thinking, e.g., through Nurse Executive Fellow Academy discussions, Implementation Science and Analytics Workshops, Policy Paper sprints, Team-Building exercises, and Hackatons.
• Emphasis on Diversity, Equity and Inclusions - DEI is important for a number of reasons: (1) Social-justice & fairness, exclusivity factors; (2) Economic factors – risk reduction; (3) Evolutionary factors – long-term stability and sustainability.

Challenges

Data, Methods & Implementation Challenges	Effective Nursing, Biomedical, and Health Sciences Approaches
Lack of access to existing, effective, modern, active-learning resources	Embrace Open and FAIR Data Science
Credit, acknowledgement, and recognition	Give credit, entice independent enhancements
Storage, computing, networking limitations	Challenging, but Google, MS, AMZ, NVIDIA, RStudio provide free Ed support
Collaboration	Engage with fellow academics (e.g., MBDH, professional Societies), offer open-enrollment in short courses, MOOCs, other electives, Collaborate with partners on R&D projects
Cross-institutional partnerships (limited time, funding, HR),
Transdisciplinary interactions (non-trivial),	Collaborate with partners on R&D projects
Application domain repurposing (requires team-science support)
Decision science and implementation of ML/AI into clinical practice	Use a team science approach, embedding nurses, clinicians, statisticians and engineers

Core Principles

• Team Science approach to tackling difficult healthcare challenges (science, implementation, translation, costs, outcomes, equity)
• FAIR (Findable, Accessible, Interoperable, and Reusable) resources
• Supporting the common-good, equitable, fair, transparent, trustworthy, rigorous, transdisciplinary, and sustainable Leadership, Analytics & Innovation in Nursing & Healthcare

Demonstrations

• Resource navigators (graphical, key phrase search)
• Electronic books - Data Science and Predictive Analytics (DSPA) active-learning resources, Scientific Methods for Health Sciences, Probability and Statistics EBook
• Case-studies and driving motivational challenges, open data resources
• Webapps - Motion Charts, Pressure Injury Prediction Model, UK Biobank, SOCR Analytical Toolbox
• Collaborative Open-Science (Ongoing Research Project, Consulting, Pubs).

Contact

Questions, comments, collaborations, and suggestions are always welcome.

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