Real-Time Care Coordination and Clinical Intelligence with Condense

Business Context 

The modern healthcare ecosystem demands immediacy. Whether it’s critical lab result alerts, bed availability routing, patient triage prioritization, or predictive readmission scoring, care delivery is becoming increasingly dependent on real-time data interpretation and workflow orchestration. 

Hospitals, digital health providers, and clinical SaaS vendors are under pressure to integrate fragmented systems, eliminate delays in decision loops, and adapt to patient-specific conditions in real time — all while maintaining strict data protection standards and regulatory compliance. 

While electronic health records (EHRs), clinical data warehouses, and patient monitoring systems have matured, the infrastructure needed to react to events as they happen, across systems and contexts, remains highly fragmented and backend-intensive. 

Technical Challenges in Building Responsive Health Applications 

Across hospitals, telehealth platforms, and care networks, common challenges include: 

• Integrating and correlating patient events from diverse systems (EHR, LIS, RIS, wearable devices, nurse call systems) 

• Manually coding backend services to detect thresholds, join streams, apply clinical rules, and coordinate responses 

• Managing infrastructure for event ingestion, stream enrichment, alerting, retries, and delivery coordination 

• Ensuring compliance with data privacy regulations while supporting cross-platform deployment (cloud, edge, in-hospital) 

• Maintaining observability across patient-level logic flows and triggering paths — especially under load 

As a result, even common workflows — such as notifying a care team when a monitored patient exceeds a vitals threshold — require orchestrating a full backend stack of message brokers, databases, schedulers, API endpoints, and audit systems. 

Condense: Unified Real-Time Execution for Healthcare Workflows 

Condense provides a fully managed, real-time application platform where healthcare developers define logic once, and the platform executes, observes, and scales that logic as live pipelines. It unifies data ingestion, condition evaluation, rule-based decisioning, and action triggering — across patient events, clinical data, and system interfaces. 

Unlike general-purpose stream processors or workflow engines, Condense is vertically optimized for real-time, privacy-sensitive environments. Its declarative application model allows developers and clinical data engineers to build and deploy logic as stream-native transforms — backed by code, state tracking, version control, and operational observability. 

Developers implement clinical workflows using Condense built-in IDE, prebuilt transforms, or low-code logic utilities — supporting real-world languages (Python, Go, JS, Java). These workflows are deployed to Condense runtime in the cloud, at the edge, or within hospital infrastructure. 

Use Case: Real-Time Patient Deterioration Detection 

Let’s walk through a real-world clinical scenario: automating early intervention alerts for inpatient deterioration using real-time vitals. 

A patient is admitted to a monitored bed. Vitals like heart rate, blood pressure, oxygen saturation, respiratory rate are collected continuously from bedside monitors or connected wearables. Normally, this data is logged to the EHR for retrospective review. But early signs of deterioration are time-sensitive. 

In Condense, these streams are ingested natively using schema-bound connectors. No external integration layer is needed, the system directly receives vitals.event messages from devices or aggregators via MQTT, HL7-over-HTTP, or Kafka. These records include patient ID, timestamp, metric type, and value. 

Once inside the stream, Condense executes logic in real time. Transforms continuously evaluate conditions like: 

• Consecutive drops in oxygen saturation below threshold within a rolling 15-minute window 

• High respiratory rate sustained for more than 3 minutes 

• Sudden blood pressure change combined with tachycardia in the same time window 

These patterns are expressed either as conditional expressions inside transforms or prebuilt stateful blocks. Developers don’t need to write complex stream joins or maintain window logic. Condense provides native support for time-based pattern detection, correlation, and threshold logic. 

When deterioration is detected, Condense emits structured alerts: 

• To the assigned nurse station dashboard 

• To mobile devices of the care team (via REST/notification broker) 

• To the EHR system via HL7 or FHIR webhook 

• To the central event log for compliance and follow-up analysis 

The system supports output routing by patient group, ward, or severity. All message delivery includes retry, escalation logic, and full audit logs. Failed dispatches are placed into a dead-letter queue with root-cause visibility. 

Managed State, Observability, and Deployment 

Condense handles patient-specific state internally — such as current vitals trend, alert count, or escalation level — as part of the pipeline. There is no need to maintain an external key-value store. State is available in-stream and observable through Condense execution view. 

Operators can trace any alert back to its originating vitals stream, view the rule path taken, and observe delivery outcomes. Developers can test changes against historical streams via replay mode, version their logic with Git, and safely promote updates to production workflows. 

For hospital or country-specific deployment requirements, Condense supports Bring Your Own Cloud (BYOC). Healthcare providers can deploy Condense pipelines in their own AWS, Azure, or GCP environments— ensuring full data sovereignty and regulatory alignment. All computation happens within their trust boundary, with no external data sharing. 

Benefits for Healthcare Teams and Platform Providers 

Condense transforms how healthcare services are automated: 

• From microservices and cron jobs to in-stream clinical logic 

• From data lakes and dashboards to live operational triggers 

• From backend maintenance to managed, declarative workflows 

Developers no longer need to build backend engines to detect patterns, maintain state, coordinate escalation, or deliver multi-channel alerts. Instead, they define the workflow logic and Condense runs, scales, and observes it across hospital systems, cloud platforms, and device networks. 

Use cases that benefit from this model include: 

• Continuous vitals monitoring and rapid response team notification 

• Time-based medication scheduling and missed-dose alerts 

• Patient-specific triage routing in emergency settings 

• Multi-system orchestration for discharge, bed turnover, or handoff coordination 

• Automated compliance logging for high-risk workflows 

Healthcare delivery is increasingly data-driven — but timeliness, accuracy, and response coordination remain major challenges. Systems must go beyond recording events to acting on them intelligently in real time. 

Condense provides a unified, fully managed platform for ingesting health data, evaluating clinical logic, maintaining patient state, and triggering operational outcomes — all inside a single, observable pipeline. 

Its fully managed BYOC model ensures that providers retain full data control while gaining the operational benefits of a real-time decisioning engine. From continuous monitoring to escalated care coordination, Condense becomes the execution layer for a smarter, safer, and faster healthcare ecosystem.