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5 Ways to Leverage a Clinical Decision Support System Today

Published on 5/14/2018
As health care consumers, we demand a lot from our care delivery. It’s no wonder why: medical knowledge is increasing at an accelerated rate in today’s industry, and it seems like each day, new standards of evidence-based care are added to what our clinical teams are expected to know and have seamlessly available in their workflows.   

That’s why the role of clinical decision support systems (CDSS) in care delivery can be so powerful. Decision support can increase safety in helping to avoid errors and adverse events, decrease operational costs, boost clinician and patient satisfaction, and perhaps most importantly, improve the quality of care and enhance health outcomes. When this intelligence is applied effectively, it not only reduces the cognitive burden on what providers are expected to easily recall, but can improve the entire process including experiences and outcomes.

New and more sophisticated capabilities are being added to EHRs, improving data access and usability – which means that health care systems today can have better clinical guidance technology than ever before. Innovation and investments in artificial intelligence will continue to shape health care technology in the years to come, but clinical decision support tools are not limited to a future standard. By leveraging a wide set of tools in the electronic health record (EHR) – like analytics, machine learning, predictive models and rules engines – alongside evidence-based content, organizations can improve their clinical decision support capabilities.

5 challenges CDSS can address

The opportunity to add more seamless clinical guidance exists across a wide variety of every-day health and care experiences. Here are five key ways CDSS can add intelligence at the point of care. 

1. Reducing alert fatigue 

Some health care organizations don’t have awareness of which alerts are being overridden the most by their clinical teams. Measuring this activity with analytics and using EHR capabilities to present information to clinicians in a manner appropriate to level of urgency can combat alert fatigue.  For example, one of the new capabilities available to us presents alerts not as a pop-up on the screen (which can interrupt the clinician’s workflow), but on the side of the screen, where it’s visible and unobtrusive. That's important for non-essential alerts, so that the life-threatening alerts have a meaningful differentiator. 

2. Combatting the opioid epidemic

America is in the grips of an opioid crisis. To combat this, when prescribing opioids, providers should have access to evidence-based guidelines, such as specialty-specific academic standards, directly within their workflow. Every time they work with a patient to manage chronic pain, they can have access to clinical decision support. The EHR can guide the prescribing physician with context on industry best practice. 

3. Improving antimicrobial stewardship

CDSS can help clinicians identify if there is a less expensive or less toxic antibiotic available to a patient. Views can be inserted into the workflows for both pharmacists and prescribers to readily recognize and intervene. 

4. Delivering medication clinical decision support

Medication safety is a top area for preventable patient harm. A CDSS can help a health care organization configure its EHR rules and alerts to help prevent physicians or pharmacists from creating an adverse patient safety events.

5. Decreasing surgical risk 

It should be the goal of any perioperative department to operate at peak efficiency, both clinically and financially. Using CDSS tools within the EHR to implement evidence-based protocols can lead to quicker recovery, reduced complications and shorter hospital stays. Using predictive models to determine how things like behavior, smoking cessation and body mass index (BMI) impact recovery can help deliver improved patient outcomes.

How health care organizations can incorporate CDSS

The process to evaluate and incorporate more CDSS capabilities takes a methodical approach. Health care leadership should consider tools available to add intelligence to workflows at the point of care: analytics, machine learning and predictive models, rules engines and standardization are among the most useful to determine an effective CDSS strategy. 

For example, the first step is for organizational and clinical leadership to analyze the data and look for opportunities to improve. This could include observing and documenting which notifications are being overridden at a high frequency; perhaps those alarms are not necessary and are contributing to alert fatigue. Leadership should consider the departments that treat high-risk patient populations, such as pediatrics or oncology, which may require special safeguards. If there is a new clinical standard available that can more easily be accessed across clinical teams, it can be incorporated into the workflow.

Once leadership has identified the opportunity for improvement and aligned the appropriate areas of the organization and governance process, the next step is to implement the changes and track progress. The baseline should be compared with any new changes, that way the process of advancement may be continued by measuring and adjusting until the right fit for the organization and its clinicians is found. Following this strategy could lead to a safer, more productive experience for the clinician and, most importantly, a healthier outcome for the patient. 

Continuing to add intelligence into clinical workflows, whether to reduce the cognitive burden on clinical teams, improve efficiency or any of the many benefits possible with CDSS, is an important demonstration of how technology can better serve health and care. In the five challenges outlined above, from alert fatigue to opioids to surgical risk, CDS tools aren't the stuff of the future. These capabilities are available to improve care delivery today, right now.

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