Are you tired of searching for information in your EHR?
Do inefficient workflows diminish your clinicians' productivity and make it difficult to find diagnostically-relevant details within a patient chart?
Medicomp's Clinical AI solutions can help:
- Deliver actionable information at the point of care
- Streamline workflows and increase productivity
- Improve real-time quality compliance
- Integrate care plans for physicians & nurses
- Power existing EHRs with a clinical relevancy engine
News
- July 26, 2021 CPSI and Medicomp Systems Announce Licensing Partnership to Integrate Quippe Clinical Data Engine Into Acute and Post-Acute EHR Platforms
- July 12, 2021 Holy Name Goes Live on Custom-built EHR Powered by Medicomp Systems’ Clinical Data Engine
- Sept 1, 2020 Medicomp Systems Releases Update for Quippe clinical Dcumentation to Satisfy CMS E&M 2021
Event Schedule
HIMSS is abuzz with excitement this year over clinical AI and NLP technologies. Reserve a spot to join colleagues at BOOTH #3421 to see a sneak peak of upcoming technology from Holy Name Medical Center, emtelligent and Medicomp Systems. Refreshments and hors d'oeurves provided by Medicomp Systems.
Reserve a spotSchedule a Meeting!
Quippe Clinical Lens
Today’s healthcare problem isn’t a lack of data; it’s a lack of usable data. Quippe® uses MEDCIN's intelligence links to identify and interpret disorganized, complex arrays of medical data and transforms that data into actionable information and clinical insights – all within the physician’s workflow.
Request a demoRecent Posts
A decade after health care dreaded the complexity of ICD-10, the industry now faces a bigger problem: diagnosis codes still aren’t specific enough to safely power AI-driven clinical decision-making.
CEO David Lareau makes the case for deterministic AI as essential healthcare infrastructure, arguing that without a structured, validated clinical knowledge layer, AI-enabled clinical tools produce outputs too unreliable to …Continue
David Lareau, Medicomp Systems CEO, explains that deterministic, rules-based AI models can validate the probabilistic, often inconsistent outputs that LLMs deliver to avoid errors in clinical documentation.