Example Use Case

How early risk identification enables proactive care and reduces avoidable hospitalizations.

Oncology Care

In oncology, avoidable hospitalizations often occur between treatment cycles.

Patients may begin to deteriorate days or weeks before a hospitalization, but early warning signs are not always identified in time.

SmithAI’s P2A framework helps surface those signals earlier and connects them to a structured outreach workflow.

The Challenge

Cancer patients frequently experience:

  • Subtle symptom progression
  • Lab value changes
  • Complications related to treatment

These signals often exist in routine clinical data, but without a structured system, they may not trigger timely intervention.

As a result:

  • Patients deteriorate at home
  • Emergency department visits increase
  • Avoidable hospitalizations occur

How P2A Applies

Using routine clinical and laboratory data, P2A identifies patients at elevated risk of hospitalization in the near term.

Each day, high-risk patients are surfaced in a nurse outreach queue.

Care teams can then:

Contact Patients Earlier

Assess Symptoms Before Escalation

Adjust Medications if Needed

Coordinate Care With Oncology Providers

The workflow fits within existing care management processes.

What Changes

Without P2A, care is often reactive.

With P2A, care becomes proactive.

Instead of responding after deterioration, teams can intervene earlier when patients are still stable enough to manage outside the hospital.

Example Workflow

Day
0

Patient risk score increases based on lab and clinical data

Day
1

Patient appears in nurse outreach queue

Day
1-2

Nurse contacts patient and identifies early symptoms

Day
2-3

Care plan adjusted or follow-up scheduled

Outcome

Potential hospitalization avoided

Measurable Impact

Early intervention can lead to:

  • Fewer avoidable hospitalizations
  • Reduced emergency department utilization
  • Improved patient experience
  • Better alignment with quality metrics

This includes improvements in measures such as oncology-related hospitalization rates and overall care quality performance.

Designed for Real Clinical Use

This is not a theoretical model.

P2A is designed to operate within real-world clinical environments:

  • Uses existing data
  • Fits current workflows
  • Supports, not replaces, clinical judgment
  • Includes governance and monitoring

The result is a practical, scalable approach to improving patient outcomes.

Explore What This Could Look Like in Your System

We work with health systems to define use cases, validate data, and implement P2A in a controlled, measurable way.

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