Your Mission

Each team should turn this broad question into a specific, defensible system proposal. The work should proceed from clinical problem to evidence, data, analytics, and response—not from a preferred model or technology.

1.       Define a meaningful deterioration event or target patient population where earlier detection could plausibly improve care.

2.       Review evidence on relevant monitoring approaches and explain why the selected category or combination is appropriate for the problem.

3.       Identify the signals and data needed for detection and distinguish what is already available from what would need to be newly collected or integrated.

4.       Specify an appropriate AI or analytics approach and the decision it is intended to support.

5.       Design the response workflow that connects detection to clinical action.

6.    Identify the major failure modes, implementation barriers, and evidence gaps that would need to be addressed before deployment.What to Build

The Data Challenge

A central part of the Scope-AI-Thon is determining what data are actually needed. Teams may work with representative healthcare datasets, including MIMIC ICU and emergency-room data, but they should not allow the available dataset to define the clinical problem.

Teams should examine which variables are available, their timing and frequency, missingness, whether they are collected continuously or episodically, and whether they represent the signals needed for the proposed system. If the concept requires bedside monitors, wearable devices, ambient sensors, or other information not present in the provided datasets, teams should explicitly identify those requirements and explain how the sources would be integrated.

From Detection to Response

A strong proposal must go beyond prediction accuracy. Teams should explain how an automated signal becomes a clinical decision and action:

Patient → Data / Sensing → Detection → Decision → Notification → Clinical Response

This means addressing practical questions: Who receives the alert? At what threshold and with what urgency? What information accompanies it? What action is expected? What happens if the alert is ignored or contradicted by clinical judgment? How will the system manage false positives, false negatives, alert fatigue, missing data, sensor failures, and model errors?

Evidence Matters

Teams should use peer-reviewed evidence to understand what is already known and where important gaps remain. The provided appendix offers representative starting examples across the four monitoring categories, including early-warning scores based on manually entered observations, machine-learning systems using EHR data, wearable physiological monitoring, and contactless ambient monitoring. Teams should expand beyond these examples as needed and critically evaluate whether reported improvements concern prediction performance alone or meaningful clinical outcomes.

What Teams Will Deliver

·         Clinical problem and target patient population

·         Comparison of relevant monitoring approaches and supporting evidence

·         Proposed data and sensing architecture, including important gaps

·         AI/analytics approach and the decision it supports

·         End-to-end clinical response workflow

·         Risks, failure modes, implementation barriers, and evidence gaps

·         A concise recommendation explaining why the proposed system is worth testing

How Proposals Will Be Evaluated