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Infrastructure and resources that can be difficult to access
Challenges in ensuring data security and privacy
Poorly performing mathematical models
SUPPORT COMPLEX DECISION MAKING
AUTOMATE MUNDANE TASKS
IMPROVE WORKFLOWS, PROCESSES & QUALITY IMPROVEMENT INITIATIVES
BETTER RESOURCE ALLOCATION & GUIDE STAFFING DECISIONS
Considerations
How to...
IMPLEMENT MACHINE LEARNING IN MEDICINE
The process of developing systems that learn from data to recognize patterns and make accurate predictions of future events. ML can also be used for explanation of phenomena.
STEPS TO FOLLOW TO IMPLEMENT ML
There are 3 phases to ML development:
a. Exploration
b. Solution Design
c. Implementation and Evaluation
3. Understand and decompose the problem and how it can be solved
2. Is ML a feasible solution to this problem?
Availability of data
4. Decide on how to measure success, set goals
1. Identify the problem:
Based on impact and priority (how solving it will impact “outcomes”)
Compose team
MACHINE LEARNING DEVELOPMENT
Develop an easy to use tool
Consider RCT for assessing the impact of the ML solution as intervention.
Time series and matched cohort designs are acceptable options.
IMPLEMENTATION CONSIDERATIONS
EVALUATION
Design a ML system with implementation in mind
(easy to implement)
Consider integration with existing work-flow
Consider phased implementation
Engage end-users
Implement iterative evaluation
(training-assessment-retraining-reassessment)
Use a “silent trial”
HOW CAN IT HELP IN HEALTHCARE?
WHAT ARE POSSIBLE BARRIERS TO IMPLEMENTATION?
WHAT IS MACHINE LEARNING (ML)?
5. Consider generalizability:
Your institution vs general
Different aspects and data needs and considerations
Consider sustainability, drift over time, possible retraining
Difficulty integrating tools into existing workflows
Low acceptance of ML solutions by clinician users
Uncertainty about how to evaluate them
(i.e. large, real-time clinical data sets, technical skills in data science, computing power and clinical informatics infrastructure)