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Cozeva’s reimagined point-of-care integrations drive seamless iPhone-like usability in EHRs

Cozeva’s  reimagined technology improves quality and risk adjustment performance across the continuum of care. The application of NLP in EHR integration is a pioneering technology with significant impact on population health management, practice management, and digital health innovation.

What is EHR integration?

In 2009, Dr. Mandl and Dr. Kohane, in their publication, laid out their vision for an ‘ iPhone-like platform’. This Substitutable Medical Applications, Reusable Technologies (SMART) platform and its applications would reduce impediments to the transfer of data, in an agreed-upon form, from one system to another. Mandl and Kohane had proposed,
  • Substitutability of applications 
  • Open standards, accommodating both open-source and closed-source software
  • Reducing dependence on individual systems by allowing competition and “natural selection” for high-value, low-cost products.
In a decade since then, such a platform is now  an integral part of the healthcare industry. Electronic Health Records (EHR) integration enables medical records to seamlessly be accessed across electronic software solutions. For practice management, this means medical records such as personal information, medical history, and more can be accessed easily across solutions. Interoperability through EHR integrators increases care coordination and efficiency of clinical decision making. It can provide more accurate diagnosis and treatment. At the same time, it can also flag potential errors, which can range from health conditions to medical interactions. Cozeva’s technology integrates provider EHR systems to,
  • Close care/coding gaps at point-of-care within one system Reduce double documentation
  • Reduce administrative burden
  • Enable Bi-directional data exchange
This technology improves quality and risk adjustment performance across the continuum of care.

NLP in EHR integration

The Cozeva EHR integration technology has the following characteristics which make it unique to the industry.

 

Embedded Cozeva App – Launch Cozeva quality & risk gaps for the patient from within the EHR via single sign-on. It eliminates the need to switch between the EHR & Cozeva.

 

Automated Chart Retrieval- Cozeva will retrieve the patient’s clinical data (CCDA) upon encounter sign-off. Cozeva will ingest and utilize the data to report the most up-to-date, evidence-based clinical guidelines about quality and risk.

 

Clinical Data Writeback- Real-time submission of confirmed HCCs to the EHR’s Problem List & Assessment/Visit Diagnoses. It eliminates the need for double documentation.

 

Browser Extension- Cozeva scans the EHR to detect/match patients and launches a quality-risk dashboard from the extension. It is compatible with any web-based EHR

 

The Director of Product & Partner Strategy at Applied Research Works, Inc. says,

 

“Cozeva-EHR integration is a win-win for Cozeva clients and provider offices. EHR integration results in sustained metric improvement, and reduces an organization’s nonstandard supplemental data burden. Our mission is to launch these integrations so our clients and providers can focus on patient care.”

 

She further says,

 

“Cozeva-EHR integration accelerates decision-making and helps providers minimize administrative burden to identify gaps in their medical charts. Data retrieval integrations have led to a >8% performance increase for HEDIS measures such as HbA1c control, childhood immunizations, and controlling blood pressure.

You can close quality gaps and auto-generate Risk tasks with NLP highlights. Applying NLP in EHR integration is Cozeva’s reimagined technology. and its most important characteristic. Our NLP scans for data in unstructured chart areas and gets it right in one attempt. It works through the following steps,
  • A visit is scheduled to the EHR
  • Cozeva will retrieve the patient’s chart (CCDA clinical summaries and progress notes)
  • It will parse and ingest the data to automatically close quality and risk data gaps
  • The Cozeva NLP will process historical visit diagnoses to identify suspects and missed opportunities
  • During the visit, the provider launches the Cozeva app within the EHR and addresses the patient’s quality and risk gaps
  • After the encounter is signed off, Cozeva will retrieve the patient’s chart once more to close additional gaps.

Conclusion

Integrating with an EHR system allows for the secure transfer of data between clinical continuum of care applications and Cozeva. Connecting external systems to the Cozeva ecosystem negates the need to manually provide quality supplemental data submissions to close gaps for patients. This improves data integrity by removing the need to manually enter data to improve scores.

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EHR integration technology.

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