5 ways clinical decision support can address current provider challenges
Healthcare consumers demand a lot from care delivery. It’s no wonder why—medical knowledge is increasing at an accelerated rate, and it seems like each day, new standards of evidence-based care are added to our clinical teams are expected to know and have seamlessly available in their workflows.
That’s why the role of clinical decision support (CDS) systems 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 healthcare systems today can have better clinical guidance technology than ever before. Innovation and investments in artificial intelligence will continue to shape healthcare 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)—such as analytics, machine learning, predictive models and rules engines—alongside evidence-based content, organizations can improve their clinical decision support capabilities.
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 CDS can add intelligence at the point of care.
Reducing alert fatigue
Some healthcare organizations don’t have awareness of which alerts are being overridden the most by 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, new capabilities enable alerts show up 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.
Combatting the opioid epidemic
To combat the opioid crisis, providers should have access to evidence-based guidelines, such as specialty-specific academic standards, directly within their workflow when they’re prescribing. 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.
Improving antimicrobial stewardship
CDS 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.
Delivering medication clinical decision support
Medication safety is a top area for preventing patient harm. A CDS can help a healthcare organization configure its EHR rules and alerts to help prevent physicians or pharmacists from creating adverse patient safety events.
Decreasing surgical risk
It should be the goal of any perioperative department to operate at peak efficiency, both clinically and financially. Using CDS tools within the EHR to implement evidence-based protocols can lead to quicker recovery, reduced complications and shorter hospital stays. The use of predictive models—to determine how things like behavior, smoking cessation and body mass index—affect recovery can help deliver improved patient outcomes.
The process for evaluating and incorporating more CDS capabilities takes a methodical approach. Healthcare 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 CDS 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.
After 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 CDS, is an important demonstration of how technology can better serve health and care.
This blog post originally published on Cerner.com. Click here to view the original content.