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, 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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Allscripts,care transition,patient transitions,point-of-referral technologies,readmissions,referral management,Referral management software

Handling transitions of care: Strategies for successful referral management

Martha Thorne_1

Martha Thorne, Senior Vice President & General Manager, Population Health, Allscripts

Transitions – when patients move from one care setting to another – represent a state of vulnerability for both the patient and the healthcare organization. Patients need referrals to the most appropriate provider in a timely, seamless fashion so they get the level of care they need. They need to be referred to the right place at the right time, every time.

Providers also need precise transitions of care to avoid negative effects on cost and quality scores. As the industry shifts from fee-for-service models to value-based care, referrals become an increasingly important financial decision.

Visionary healthcare leaders are pursuing strategies to better manage their referral and care transition processes. New point-of-referral technologies can help providers improve transitions of care, for better clinical and financial results.

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accountable care organizations,ACOs,analytics,clinical decision support,predictive modeling,Reed Group

Advancing value-based goals with intelligent clinical decision support


Joe Guerriero, Senior Vice President of MDGuidelines, Reed Group

(Editor’s Note: This article is part one of a three part series. Part two is published here. Part three is published here.)

How do you deliver on the promise of improved outcomes while still tightly managing costs? That’s a question that more than 700 ACOs face every day.[1] It’s also the same dilemma payers and employers have wrestled with for years, which is why it’s critical to learn from their approach. By leveraging both evidence-based guidelines and physiological duration tables, many employers have successfully returned individuals to health quickly and safely, all without wasting resources.

One key challenge, however, is that many physicians don’t have tools at the point of care to help them determine the safest, quickest path or the length of time it may take for a patient to heal and return to their normal lifestyle. As a result, there are tremendous variations in how providers manage specific conditions, injuries and illnesses. And these variations can have a negative impact on both patient outcomes and the bottom line. 

To address this challenge, ACOs can provide intelligent clinical decision support tools to providers at the point of care. With a combination of evidence-based clinical guidelines and physiological duration tables, physicians can better coordinate care around the final goal of returning patients to health, all while taking into account patients’ unique attributes and circumstances. Treatment planning can include personalization through predictive modeling based on factors like age, gender, geographic location, and underlying co-morbid conditions, such as diabetes or heart disease.

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