What challenges did Metricwire face before AI implementation?

Scheduling tasks and activities for adaptive, real-time clinical trials that depend on numerous variables is a complex challenge. The system needed to handle dynamic scheduling, increasing data and user requests, and provide an intuitive interface for researchers and participants.

What was the impact of the AI solution?

Average latency dropped to under one second per request. The system processes over 100 scheduling tasks weekly. Overall confidence in the system’s responses exceeds 90%. The solution improved scheduling accuracy and user satisfaction.

Rank#1,077of 3,107

Source

GCP

MetricWire AI-powered scheduling agent using Vertex AI and Gemini

Use case typeWorkflow automationUpdated Jun 13, 2026

Metricwire supports healthcare and life science organizations in improving patient care and achieving better health outcomes. Their digital platform for clinical research integrates real-time cognitive, behavioral, and biometric data, providing a holistic view of patient health. Metricwire needed a scalable way to manage dynamic scheduling with multiple constraints and a seamless user experience. The solution used Google Cloud's Vertex AI and Gemini to develop a scheduling agent, with BigQuery, LangChain, Cloud Functions, and Looker supporting data storage, orchestration, execution, and monitoring.

Organization: Metricwire
Industry: Healthcare
Location: United States
Published: November 2024

Reported outcomes

90%

quantified impactOther quantified impact

Strategic outcomes

New product / capabilityBuilt an AI scheduling agentSpeed & agilityEnabled near-instant scheduling responsesScale & capacityProcesses high scheduling task volumeCustomer experience & trustImproved scheduling accuracy and satisfaction

Primary read

Use case focus

Showing 3 of 3

1Workflow Automation
2Scheduling Optimization
3Customer Support

Scheduling tasks and activities for adaptive, real-time clinical trials that depend on numerous variables is a complex challenge.
The system needed to handle dynamic scheduling, increasing data and user requests, and provide an intuitive interface for researchers and participants.

Google Cloud's Vertex AI and Gemini were used to develop a sophisticated Scheduling Agent tailored to Metricwire’s needs.
Gemini extracted key information from researcher inputs such as survey frequency, preferred times, and trigger events.
BigQuery stored participant data, survey responses, scheduling parameters, and historical interactions.
LangChain managed prompt engineering, response generation, and data retrieval from BigQuery.
Cloud Functions automated actions when scheduling requests or participant conditions triggered.
Looker dashboards monitored scheduling accuracy, processing speed, and user engagement.

Technologies

Vertex AI Gemini BigQuery LangChain Cloud Functions Looker PaLM 2

Average latency dropped to under one second per request.
The system processes over 100 scheduling tasks weekly.
Overall confidence in the system’s responses exceeds 90%.
The solution improved scheduling accuracy and user satisfaction.

Architecture

Vertex AI and Gemini power a scheduling agent; BigQuery stores participant and survey data; LangChain manages prompts and retrieval; Cloud Functions trigger actions; Looker monitors performance.

Implementation partners1

Onix

Sources & evidence1

Groundedness: 5/5Type: Case StudyPublished: Nov 18, 2024Publisher: OnixEvidence: PartnerConfidence: High

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

Explore related AI use cases

Customer evidence