SMART Emergency Medical and First Response Multiteam Systems
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| SMART Emergency Medical and First Response Multiteam Systems | |
|---|---|
| |
 SMART Emergency Medical | |
| Team Organizations | Fairfax Fire & Rescue Inova Fairfax Hospital George Mason University Inflow Interactive Newport News VA ASC Consulting USPHS |
| Team Leaders | Brenda Bannan Hemant Purohit Jeff Segall |
| Participating Municipalities | Fairfax County VA |
| Status | Launched |
| Initiative | {{{initiative}}}Property "Has initiative" (as page type) with input value "{{{initiative}}}" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. |
| Document | None |
Description
SMART Emergency Medical Teams will help inter-disciplinary teams improve transition-of-care quality, promote situational awareness, and enhance the efficacy of simulation debriefing.
- Simulation-based Team Training in medical contexts
- Patient hand-off or transitions between sub-teams
- Interaction among interdisciplinary roles/team
- Wearable sensors
- Enhanced debrief –visualization/display/feedback
- Collaborative reflection, situation awareness and experiential learning
Challenges
- Instrumentation of IoT sensors for data capture, validation
- Synchronous integration of sensors, video, audio and medical simulation device data to usable instructor interface
- Blended analytics/visualizations of proximity and performance data: Data extract-transform-load (ETL) from source systems
Solutions
TBD
Major Requirements
- Build network of design and implementation expertise from diverse fields
- Define use case from different medical teams’ perspectives, surface and facilitate common goals: Successful 2015 GCC project/tools as baseline
- Minimum viable product (MVP) performance support tools to enable provider capabilities - in context
- Data-driven learning designs to support evidence-based medicine
Performance Targets
| Key Performance Indicators (KPIs) | Measurement Methods |
|---|---|
|
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Standards, Replicability, Scalability, and Sustainability
- xAPI (Experience API) experiential learning data standards from international Advanced Distributed Learning (ADL) specifications for REST API and data storage
- FHIR medical data interoperability standards from international HL7 specifications for REST API and data storage
- Standardized emergency medical procedures are not unique to cities or regions. The SMART analytics model can be replicated across localities, and data gathered cost-effectively via a cloud-based SAAS system.
- The system will initially rely on support from major partners and grant funding. As adoption grows, the system will develop its own member subscription model to enable ongoing development and support.
Cybersecurity and Privacy
TBD
Impacts
- Baseline for improving emergency medical system-wide performance
- Directly impacts performance of mobile first responders, disaster recovery teams, hospital based teams
- Long-term benefits to citizens in distress, local and national governments, medical providers and payers
- Advances in simulation debriefing may apply across multiple high-performance fields
Demonstration/Deployment
Phase I Pilot/Demonstration:
- Intra-team focus
- Rapid prototype pilot for select use case(s)
- Refine metrics and data collection
- Final demonstration, with visual analytics
Phase II Deployment:
- Stakeholder approvals
- Select test cohort(s), simulation scenarios
- Intra- and Inter-team focus
- Implement test deployment, measure, report