First-of-its-Kind Solution Leverages CalmWave’s Longitudinal, High-Frequency Clinical Dataset to Objectively Detect Patient Recovery and Discharge Readiness
SEATTLE–(BUSINESS WIRE)–CalmWave, a leader in the safe reduction of non-actionable alarms through healthcare data science and Transparent AI, today announced the launch of Recovery State, a first-of-its-kind solution that uses the company’s high-fidelity longitudinal data to accurately determine when recovering patients are ready for safe discharge or transfer to lower levels of care. With Recovery State, hospitals can make earlier, data-driven decisions, reducing length of stay, readmissions, and streamlining patient flow.
Hospital Analytics Focus on Predicting Deterioration, Not Recovery
Most healthcare analytics are focused on predicting deterioration. However, hospitals today lack an objective, data-driven way to understand when patients are recovering. As a result, transfer and discharge decisions rely on fragmented, episodic data and subjective judgment.
This problem is structural: hospital data infrastructure was built for documentation and billing, not for recording longitudinal data trends. While electronic medical records (EMRs) capture elements of care recorded in clinical documentation and lab values, there is no consistent way to store and analyze continuous patient physiologic data, such as vitals and alarm patterns, which account for 90% of what actually happens at the bedside. These data are ephemeral: generated, briefly displayed, and discarded just as quickly.
CalmWave’s Data Foundation Delivers A Clear Understanding of Patient Recovery
CalmWave is designed to eliminate these blind spots in healthcare analytics. By ingesting and normalizing billions of high-frequency signals from siloed bedside monitors, medical devices, and EMRs into a unified longitudinal view, the CalmWave Operations Platform creates the data foundation required for analyses that fragmented or low-resolution systems cannot support. Recovery State is the latest application built on this foundation, reframing patient recovery as a detectable, data-driven, dynamic clinical state. The application enables:
- Objective detection of recovery phenotypes: Identify repeatable patterns that indicate readiness for de-escalation or discharge, emerging 24-72 hours prior to safe transitions.
- Longitudinal Patient State (LPS) analysis: Model patient trajectories over time, integrating physiology, alarm burden, and care intensity.
- High-fidelity signal integration: Fuse continuous vital signs, alarms, device data, and EMRs into a unified dataset with robust time alignment.
- Clinical decision support: Enable safer discharge, reduce readmissions, and alleviate cognitive load by turning complex data into clear operational signals.
“At a system level, healthcare has invested heavily in knowing when patients are getting worse, but not when they’re getting better,” said Ophir Ronen, founder and CEO, CalmWave. “The data to answer this question has always existed — it just wasn’t captured in a form that could be analyzed. We built the infrastructure to capture it. Now we’re building the intelligence layer on top.”
Transparent AI: The Trust Architecture
CalmWave has proven its unique approach to clinical data by solving the problem of ICU alarm fatigue, the first and only company to do so at scale. That impact is driven by CalmWave’s Transparent AI, which uses machine learning and data science to deliver clear recommendations worthy of mission-critical domains like healthcare, without risk of black-box predictions or hallucinations.
CalmWave’s Transparent AI is designed with clinician trust and patient safety at its core. For alarm threshold optimization, CalmWave displays safety gates that explain why a recommendation is safe: vital sign stability, medication context, anomaly detection, time since last change, and alignment with unit defaults. In the end, clinicians can evaluate the system’s reasoning and ultimately decide for themselves whether to adopt its recommendations – not because they are mandated, but because every recommendation comes with an explicit rationale.
Recovery State extends this transparency with greater depth. Each discharge-readiness signal is accompanied by detailed physiological evidence: trending stability across multiple vital parameters, medication de-escalation patterns, device liberation events, alarm burden trajectory, and alignment with validated recovery phenotypes. Clinicians see not just that a patient appears ready, but why – all with the supporting data visible and auditable.
By transforming continuous bedside data into an objective signal of recovery, Recovery State advances CalmWave’s mission to bring clarity to hospital operations – starting at the bedside and extending across the entire continuum of care.
About CalmWave
CalmWave’s mission is to objectively improve healthcare, starting with safely eliminating non-actionable alarms. Alarms triggered by bedside monitoring systems that do not require immediate intervention or clinical response comprise 80-99% of all audible alarms in intensive care units (ICUs). They contribute to a well-recognized and widespread issue known as alarm fatigue, endemic to so many hospitals worldwide. Alarm fatigue, rampant in the modern healthcare system, causes great stress for clinicians and patients, ultimately impacting patient outcomes and staff retention. Using Transparent AI, CalmWave outlines the rationale behind each optimized setting, empowering a hospital’s clinical staff to evaluate and implement optimized alarm limit recommendations in the bedside monitor at the patient’s bedside using the CalmWave Operations Platform, which integrates seamlessly with the hospital’s EMR and medical device middleware systems. Doing so remediates alarm fatigue and can improve staff retention and patient outcomes. Visit CalmWave.com for more information and follow us on LinkedIn and X.
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