Posts Tagged ‘EMRs’

CDS,clinical decision support,clinical documentation,electronic medical records,Electronic Medical Records and Genomics,EMRs,interoperability,medCPU

How clinical decision support fills gaps in clinical data for higher quality care

liora-guy-david

Liora Guy-David, Ph.D., Vice President of Data, medCPU

As long as the healthcare industry lacks true interoperability among dissimilar systems, clinicians will have incomplete patient information at the point of care. This includes gaps over time, as when a clinician is unaware of imaging tests already completed, and gaps across care team members who record documentation in separate systems. Both types of gaps can compromise patient safety.

While we don’t typically think of gap-closing as being a primary clinical decision support (CDS) function, CDS systems do exactly that. Its success in informing decisions depends largely on the ability to analyze information from multiple systems, closing gaps in real-time. As a result, CDS is emerging as an essential tool for improving quality of care.

Decision-making support built with a more complete view of the patient

CDS systems run on top of EMRs, analyzing documentation as it is being entered and issuing alerts in EMR windows when conditions indicate the possibility of a medical error or compromised patient safety. This is often a matter of giving clinicians information of which they were unaware.

To fully inform alerts, advanced CDS systems supplement the structured data in EMRs and pull information retrieved from other systems such as those in labs and imaging departments. CDS leverages its comprehensive patient view by applying rules-based analysis regarding diagnoses and courses of care. By augmenting a physicians’ expertise with real-time information retrieval and gap-closing, CDS systems play a key role in promoting patient safety.

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clinical decision support,electronic medical records,EMRs,medCPU

Why clinicians ignore clinical decision support systems: How to fix it?

Ami Mayo headshot

Ami Mayo M.D., Chairman of medCPU’s medical department

A major shortcoming of traditional clinical decision support (CDS) systems is that they operate on highly incomplete patient data, which sets the foundation of the usefulness of the tool. With incomplete data – prompts down the line are guaranteed to be inaccurate. Besides having access to all data, precision of this information is imperative for a CDS system to enhance care delivery and patient outcomes.

Traditional CDS systems can read and utilize only structured data entries in electronic medical records (EMRs) and ancillary systems. However, this portion of the patient’s clinical profile, represents somewhere between 30 and 40 percent of all medical information. If the system doesn’t have complete and accurate data, it’s going to error. Data and data comprehension is key.

Capturing all data and precise comprehension of this information requires the CDS system to function as closely as possible to how a physician thinks. Clinicians communicate patient data primarily through narrative reports, follow-up notes, and summaries of CT scans, X-rays and other imaging reports. In general, dictation, turned later in to free text notes, is how a significant data portion is entered into EMRs.

Approximately, 50 to 70 percent of data in healthcare, if not more, resides in non-retrievable, unusable information embedded in free text communication. This is an enormous amount of vital data that traditional systems simply can’t process because they don’t have the “intelligence”.  Seeing only a small portion of the total clinical picture makes traditional CDS systems prone to error.

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electronic medical records,EMRs,graph database,HIMSS,HIMSS'16,NextGate,patient data,social media

Social media technology connects the dots in the healthcare experience

Andy Aroditis, Founder, Chief Executive Officer, NextGate

Andy Aroditis, Founder, Chief Executive Officer, NextGate

Few things are as simultaneously fascinating and disturbing as going onto a social media application such as Facebook or LinkedIn and seeing it present a suggested list of potential connections. It can be disconcerting to think that a bunch of algorithms could so accurately predict that out of billions of users these are actually people you might consider a friend. Or that because you liked “Whiplash” you might be interested in these other videos that other fans of that movie enjoyed.

Yet the same core social media technology that seems to understand the most intimate details about your childhood, current relationships and other interests holds great promise for transforming healthcare by finally showing how all the data are related. 

It’s called the graph database. Designed specifically to interpret relationships between different sets of data, the graph database is the foundational technology that allows social media applications, as well as companies such as Amazon, to build sophisticated social networks around each account owner. For instance, it’s what enables us to look at our friends’ friends and navigate through their interests and connections.

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