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Similar Case Retrieval

Quick Win

Similar Case Retrieval to improve decision quality and speed.

Operations & Service DeliveryHealthcare

The Pain

Operations teams handle similar case retrieval across care delivery, capacity, and patient flow where timing and care standards matter. Work often depends on bed availability, admissions peaks, and care coordination, but the inputs sit in multiple systems and arrive late. Teams spend time reconciling data instead of making decisions, and gaps show up when conditions shift.

What's Possible

AI can support similar case retrieval by pulling data from EHR systems, staffing tools, and operational dashboards and highlighting patterns. Teams move from manual compilation to review, validation, and scenario testing. Outputs update as operational conditions change, so decisions stay aligned to current reality.

Signals This Is Worth Exploring

Similar case retrieval relies on manual tracking or spreadsheets

Critical inputs arrive late or require manual reconciliation

Exceptions create rework when policies change

Decision makers do not trust the data without extra checks

Impact

30 to 50 percent reduction in time spent on similar case retrieval

Faster decisions when operational conditions change

Fewer errors and rework in similar case retrieval

Clearer visibility into care delivery, capacity, and patient flow priorities

Typical Approach

1

Assess

Map the current similar case retrieval workflow, data sources, and pain points.

2

Pilot

Test with a limited scope and measure accuracy, time saved, and exceptions.

3

Scale

Expand across teams with monitoring, feedback, and integration into existing tools.

What to Watch Out For

Data quality issues can limit accuracy

Process owners need time to trust new outputs

Integrations with existing systems take effort

Rules and thresholds must be maintained as conditions change

Questions to Think About

Before we talk, you might want to consider:

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What volume and cadence does similar case retrieval run on today?

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Which systems hold the source data and approvals?

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Who reviews and signs off on outcomes?

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What exceptions cause the most delays?

Build On This

Once the basics are working, you can expand:

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Exception analytics

Identify the most common drivers and reduce rework

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Scenario testing

Compare options before changing plans

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Workflow integration

Embed outputs into existing tools and approvals

Want to explore if this fits your organization?

Book a 30-minute call to discuss your situation and whether this use case makes sense for you.

Book a 30 min call