Blog Post

07 Jun

Algorithms and Electronic Health Records

A medical algorithm is a formula, computation, look-up table, nomogram, statistical survey, step-by-step procedure, etc. that can be used to develop care pathways, flow chart approaches to healthcare treatment, and other tools to reduce indecision, drive clinical efficiency, and improve patient care. In the past, healthcare providers relied on studies and their own knowledge to diagnose diseases or determine a treatment approach. However, today, the immediate access to medical data we have through electronic health records makes it possible to incorporate medical treatment algorithms, or decision trees, into everyday clinical and operational practice.

Algorithms and Electronic Health Records

A decision support algorithm for the dosing of erythropoiesis (EPO) is an example of how an artificial intelligence (AI) algorithm can improve patient efficiency and clinical care. In fact, an automated EPO medication protocol built within the electronic health record of an ESRD patient can automatically recommend EPO dose adjustments (increase, hold, reduce). This is all done with regards to the patient’s most recent measured hemoglobin lab results (at the recommended intervals). This approach serves to ensure the patient’s care is following the designated medication protocol by automatically providing dosing recommendations, with the added benefit of reducing human error, medication waste, and cost.

Using Algorithms to Diagnose Patients

The human body is very complex and trying to diagnose illness can be a challenging task. In fact, this is true even for the most seasoned care provider. As a result, algorithms in EHRs can assist healthcare providers. This helps providers quickly identify certain disease risk factors, or even diagnose disease conditions. This is all done by using existing healthcare analytics (information), and evidence within the patient’s electronic health record to promptly lead to an appropriate treatment approach.

Algorithms, or clinical decision support tools, would be adopted more widely if they were easy to use, accessible, and easily understood. These algorithms within the patient’s electronic health record are decision-making tools to reduce the overall burden on the healthcare provider.

Additional Reading: Algorithm Scours Electronic Health Records to Reveal Hidden Kidney Disease

Visonex understands the importance of providing our clients with the right algorithms and tools to reduce the burden for the clinician. As a result, this includes assisting with the application of the information, helping streamline workflows, and improving efficiencies. Visonex flagship EHR platform has various decision support tools available. The Visonex analytics data warehouse (ADW), together with our interoperability, provides robust algorithms and tools to assist clients with data curation. Including descriptive, predictive, and prescriptive analytic data solutions.


Visonex is committed to the advancement of the integrated model of care in this value-based healthcare environment and can help your organization successfully manage chronic health conditions such as Chronic Kidney Disease (CKD). Visonex’s proprietary Analytics Data Warehouse (ADW), interoperability with multiple data sources, and chronic care electronic health records EHR platform with AI/decision support tools provide a meaningful, data-driven approach to improved patient health while driving efficiencies reducing costs. To learn more about how your current data could be used to lower risk for your patient population, reach out to [email protected]. Sign up here to receive industry updates. Maximize patient outcomes, improve regulatory compliance, streamline workflows, and incorporate multiple sources into a single landing point for chronic disease management.  Now that is providing “Clarity” to a cloudy situation!

For more information call us at 1-877-285-7944, visit our website at or email us at [email protected]

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