Case study · Healthcare

Clinical decision support that improved treatment-plan efficiency by 70%

In intensive care, critically ill patients can deteriorate within hours, and predicting multiple organ failure meant reading many vitals and reports by hand. We built a clinical decision support system that pulls live data from the hospitals' EHRs, scores organ-failure risk as it evolves, and gives clinicians the insight to act sooner.

Industry: Healthcare, US
Client: Emergency & intensive care hospitals
Services: Clinical decision support, EHR integration, data analytics
Engagement: Custom software build

70%

faster daily data retrieval for clinicians

40%

less EHR integration time

Real time

organ-failure risk, tracked over time

Clinical decision supportHL7 & FHIREHR integrationMachine learningPredictive analyticsHuman-centered UX

About the client

Leading hospitals specializing in emergency and intensive care, dedicated to saving lives and improving patient outcomes. Their clinicians work to provide immediate, personalized treatment to critically ill patients, and they were looking for a way to streamline ICU workflows and sharpen how they read a patient's changing condition.

The challenge

Predicting organ failure in time was slow and manual

When a critically ill patient is admitted to the ICU, clinicians have limited time to prepare and execute a treatment plan, and the patient's condition can change quickly.

Assessing the likelihood of multiple organ failure meant correlating many health vitals and reports by hand, an arduous and time-consuming task.

That manual analysis created a bottleneck in delivering timely care, and clinicians could not always intervene early enough to change the outcome.

Patient data was spread across different EHR systems, so building a complete, current picture of a single patient was slow.

The solution

A decision support system that predicts organ failure from live EHR data

We built a clinical decision support system that gathers patient health data from the hospitals' various EHR systems and brings it together through HL7 and FHIR interoperability, so decisions are made on current clinical data rather than a stale snapshot.

By analyzing that data, the system gives physicians clear indicators: it identifies existing organ failure, assesses the likelihood of future organ failure, and recommends suitable treatments or diagnoses based on the patient's health information.

Crucially, it tracks the temporal evolution of organ-failure probability, so clinicians see how risk is trending over time instead of reacting to a single isolated reading.

The approach

How we delivered it

1Aggregate the data. We integrated with the hospitals' EHR systems using HL7 and FHIR to pull vitals, labs, and reports into one place, cutting integration time by up to 40%.

2Model the risk. Machine learning, statistical models, and data-mining detect patterns in the patient's data and score organ-failure probability, tracking how it evolves over time.

3Guide the decision. Risk, trend, and treatment recommendations surface in a human-centered dashboard clinicians can read at a glance.

How it works: EHR data aggregated through HL7 and FHIR, scored for organ-failure risk, and surfaced with treatment recommendations for the clinician.

Showing risk as a trend, not a single reading, is what let clinicians see organ failure coming and intervene while there was still time to act.

The results

70% faster data retrieval and earlier, better-informed care

An intuitive, human-centered dashboard drove an estimated 70% improvement in daily data-retrieval efficiency, so clinicians spend less time hunting for data and more time on care.

Advanced data analysis, using machine learning, statistical models, and data-mining, gave physicians clear insight into each patient's health progression.

HL7 and FHIR interoperability let the platform integrate with diverse EHR systems securely, cutting integration time by up to 40%.

A modular, extensible architecture makes it straightforward to add new research and analysis algorithms as clinical needs evolve.

Tracking organ-failure probability over time helped clinicians spot deterioration earlier and act before it escalated.

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