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Chief Medical Officer

Monitor and respond to emerging public health threats

Enhances◐ 1–3 years

What You Do Today

Track disease outbreaks, drug safety signals, and public health developments that could impact the member population or coverage policies. Coordinate response when something emerges.

AI That Applies

Real-time syndromic surveillance across claims data, detecting unusual patterns in diagnoses, prescriptions, or utilization that might signal an emerging health threat.

Technologies

How It Works

The system monitors network traffic, access logs, and threat intelligence feeds in real time. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The output is a prioritized alert queue, with the highest-confidence findings surfaced first for immediate review.

What Changes

AI can detect a localized outbreak pattern in claims data weeks before it appears in public health reports. Earlier signal, faster response.

What Stays

Deciding what to do about a potential health threat — activate member outreach, engage providers, modify coverage policies — requires medical leadership and judgment.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for monitor and respond to emerging public health threats, understand your current state.

Map your current process: Document how monitor and respond to emerging public health threats works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Deciding what to do about a potential health threat — activate member outreach, engage providers, modify coverage policies — requires medical leadership and judgment. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support IBM Watson Health tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long monitor and respond to emerging public health threats takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

What's our current false positive rate, and how much analyst time does that consume?

They shape expectations for how AI appears in governance

your CTO or CIO

Which risk scenarios do we not monitor today because we don't have the capacity?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.