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.

DeepScribe ambient AI understands and adjusts to different patient visit types, placing data into the right context. For instance, when a physician and patient discuss a condition during a follow-up visit, information from previous visits is automatically pulled forward into the current chart - but modified as needed within the context of the current conversation.

DeepScribe offers bi-directional integration to pull and push discrete field information with leading EHR systems including Epic, athenahealth, NextGen Healthcare, Veradigm, Ontada and more. This enables easier access to information that drives continuity of care.

DeepScribe’s ambient AI platform is HIPAA-compliant and SOC-2-certified. All data captured and processed by DeepScribe is encrypted using end-to-end AES-256 technology. To ensure data security and privacy, encrypted data is stripped of all personally identifiable information, ensuring patient information cannot be associated with an individual. DeepScribe also includes an additional layer of security to ensure any user accessing the platform is authorized to do so.

DeepScribe AI has been trained on the learnings from more than 5 million patient conversations. Clinicians simply need to have a conversation with the patient naturally during the encounter, moving through their workflow as they normally would. The ambient AI understands the details and nuances – including essential patient anecdotes – and generates a complete clinical note. The interface is highly regarded for its simplicity. Clinicians begin the encounter with just two taps, then hit one button to record, and another to “scribe” the visit.

DeepScribe was developed with scalability in mind, and supports rapidly increasing user bases at large health systems such as Ochsner Health, Texas Oncology, Covenant HealthCare. Regardless of the practice or organization size, the DeepScribe platform can onboard and accommodate a high volume of clinician users at one time.

The DeepScribe ambient AI platform is continuously learning via patient conversations and built upon a foundation of the largest clinical dataset in the category. In addition to refinements made within the AI model, DeepScribe clinical experts also collaborate with specialty care physicians to ensure each medical group’s workflow and needs are satisfied and reflected in the notes that are generated.