The Pieces Ambulatory Platform leverages context-awareness by integrating and interpreting diverse patient data from ambulatory EHRs. It dynamically adjusts patient risk scores, personalizes insights, and modulates alert thresholds based on the patient’s current condition, medical history. This ensures proactive care interventions, minimizes unnecessary interruptions, and provides a holistic view of the patient, enabling care teams to deliver more efficient, tailored care.

It integrates natively with ambulatory EHR systems, ensuring a consistent user experience across devices and platforms, from desktops to mobile applications. By aligning with care team workflows and providing real-time insights directly within the EHR, the platform minimizes disruption and enhances efficiency, enabling care teams to focus on delivering high-quality, patient-centered care.

The Pieces Ambulatory Platform supports scalability and interoperability, making it suitable for deployment across individual clinics, ambulatory networks, and large healthcare systems. Its architecture allows seamless integration with multiple EHR platforms through open standards and APIs, ensuring compatibility with existing healthcare IT systems and devices. The platform's modular design enables efficient scaling, from small clinics to expansive healthcare systems, while maintaining consistent functionality and performance. This capability facilitates future expansion and allows organizations to adapt to evolving needs without disrupting workflows, making it a robust solution for diverse care settings.

With low/negligible hallucination rates in its AI models, the Pieces ambulatory platform consistently generates accurate and trustworthy patient summaries. This reliability is critical in tailoring clear, concise, and actionable insights for care teams, ensuring that critical information is always available, even during connectivity challenges, to support informed decision-making and efficient patient care.

The Pieces Ambulatory Platform delivers measurable impact by providing clear metrics and analytics that demonstrate its value in improving patient care and operational efficiency. It aligns with key performance indicators such as reducing length of stay, enhancing documentation quality, and decreasing clinician burnout. By offering tools for continuous quality improvement, such as real-time performance dashboards and detailed impact reports, the platform empowers organizations to track progress, optimize workflows, and achieve their clinical and financial goals effectively.

The Pieces Ambulatory Platform ensures robust data protection and compliance with healthcare regulations by adhering to HIPAA standards, safeguarding patient privacy, and enabling secure data sharing among relevant care team members. The platform's TX-RAMP certification further demonstrates its commitment to maintaining rigorous security protocols for cloud-based services, ensuring compliance with Texas state requirements. Additionally, Pieces is actively upgrading to FEDRAMP certification, which will enhance its ability to meet federal security and privacy standards. These measures include granular access controls, audit trails, and advanced encryption, ensuring that patient information is shared securely and only with authorized individuals, supporting both privacy and care coordination.

Sickbay collects, integrates, & displays time-aligned medical device data for over 30k signals. Information is displayed to end users in near real-time (~25ms) on local and mobile devices.

Sickbay's Patient Monitor (PatMon) displays per patient waveform and EHR data alerting clinical end users based on parameters defined by the hospital. Sickbay's Multiple Patient Monitor (MultiMon) displays that same information for up to 100 patients on a single screen to support virtual monitoring at scale. Sickbay's Risk Stratification module displays risk score data customized per hospital to improve awareness of patient risk in higher acuity beds. Sickbay's Alert Queue module displays alerts for telemetry patients that have been newly admitted, shift changes, dietary requirements, and rhythm validation.

Sickbay integrates EHRs using interfaces for ADT information, staffing assignments, meds, & labs. Sickbay integrates directly into the EHR to automate the capture and storage of ECG strips on the patient's record without additional, time-consuming, error-prone manual steps.

Sickbay simplifies the clinical theater by centralizing patient monitoring data from devices, EHRs, camera-enabled technology, and 3rd party analytics into a single screen. This enhances the readability and interpretation required to reduce the time-to-treat a deteriorating patient. Sickbay's interface is shared between bedside and remote care teams to drive scale and efficiency. This approach reduces the risk associated with new nursing team members on staff and improves the training / onboarding by using a single platform for patient monitoring. Sickbay's analytics workbench directly connects clinical researchers with bedside teams to develop and implement custom algorithms for specific patient cohorts in near real-time.

Sickbay integrates camera-enabled technology into a single interface alongside near real-time physiological data to provide remote care teams with video support to reduce patient risk and improve the time-to-treat for patients that are deteriorating.