Clinical Decision Support

4 Smart Strategies for EHR-Enabled Innovation

November 1, 2018 2:38 pm

A surprising number of medication errors are entered into the electronic health record (EHR), a recent study shows, with nearly 70 percent of EHR-related medication errors reaching the patient. The study points to the need for EHRs with an intuitive user interface that reduces the potential for user error.

In an era of value, healthcare organizations continually seek ways to incorporate innovative technologies to improve the quality of care, increase efficiency, and reduce costs. But for some organizations, providing greater value at the point of care is less about investing in new technologies than about finding ways to better leverage existing technology, such as the EHR. 

Smart Processes for Smart Technologies

The use of “smart” technologies that interface with the EHR—such as smart forms that provide clinical decision support—is one example of the difference organizations can make by supercharging their existing technology. To be effective, smart technologies must be combined with smart processes that enable physicians and clinicians to easily incorporate actionable intelligence into their workflows. Here are four EHR capabilities that organizations can develop to create EHR smart engines that improve care and efficiency. 

Clinical decision support. EHRs that display alerts in real time at the point of care contribute to improving not only patient outcomes, but also patient satisfaction. For example, patient protocols can vary significantly based on several factors, including patient demographics, previous lab results, and current medications. When physicians are alerted to protocols that best fit their patients’ needs at the point of care, they are better equipped to replace gut intuition with data-driven insights that improve care while minimizing risk, helping patients have a better experience, with more positive outcomes.

The Centers for Medicare & Medicaid Services suggests that, to be effective, clinical decision support tools must support the physician at the point of care without interrupting the physician’s workflows or contributing to “alert fatigue.” Healthcare organizations should consider inviting clinical staff to work with the IT team to develop customized alerts that provide the right information in the right format at the right point in the physician’s workflow. For example, physicians who prescribe opioids could be alerted to Centers for Disease Control and Prevention guidelines around limiting the dosage and length of the prescription to avoid the potential for addiction. 

Clinical documentation. Investing in automated tools that prompt physicians and staff to provide documentation that supports prior authorization requests and medical necessity improves efficiency and speeds authorization approvals. It also helps to prevent denied claims, protecting the financial health of the organization. One key to effectiveness: prompting physicians and clinicians to provide documentation in real time while taking care not to over-query the medical team. Some organizations have found success by supplementing use of these tools with the availability of a clinical documentation improvement specialist who can help clinicians determine the best course of action and who can provide one-on-one education after the encounter that boosts clinician skill levels for future visits. 

Population health analytics. Investment in analytics that provide actionable insight for population health management is the top data-related priority, according to a 2018 survey of healthcare leaders. Most of the detailed clinical information that could be analyzed for population health management comes from the EHR. Clinical data mining tools extract and analyze data from the EHR to provide actionable information that can assist in treating individual patients at the point of care while also informing the organization’s approach to managing specific disease states. But data mining alone doesn’t ensure the insight gained is applied to everyday practice. An organization should have a dedicated team of data analysts and clinicians that meets regularly to explore trends as they are identified and develop targeted interventions for high-risk populations based on evidence and available resources.

At Johns Hopkins University, researchers analyzed six years of intensive care unit data from thousands of EHRs to develop the ability to predict patients’ risk of sepsis with 85 percent accuracy. In two-thirds of sepsis cases, predictions are made before the disease harms the patient, improving outcomes while reducing costs.

Dissemination of clinical knowledge. Imagine physicians being able to gain access to clinically relevant research at the point of care, helping them make decisions regarding care plans, medications, tests, and therapies informed by evidence-based practice. Clinical knowledge engines provide this information to physicians directly within their EHR workflows, with some engines featuring clinical guidance for 98 percent of the clinical scenarios the physicians are likely to encounter. 

Physicians and nurses must commit to using these engines—not their iPhones or iPads—to ensure the information that supports decision making comes from reputable sites and is based on evidence. The information also must be concise and presented in an easy-to-digest format. It’s important that healthcare organizations test clinical knowledge engines with staff of a variety of ages and specialties to determine the types of features needed (e.g., video clips of procedures) and determine the right points in the patient encounter where use of a clinical knowledge engine could prove beneficial.

The need for clinical decision support at the point of care is strong. One study published by the Journal of the Medical Library Association found most clinicians encounter at least a few clinical questions each week. For some, lack of time limits their ability to search for clinically reliable answers to their questions. When clinicians have access to a clinical knowledge engine directly within their workflows, they not only can make more fully informed decisions at the point of care but also can become better able to serve as knowledge resources for patients during the care encounter.

Having an EHR equipped with clinical support tools that improve safety, care, and efficiency is essential in an era of value. Only with such capabilities can healthcare organizations begin to achieve the fundamental objectives of value-based care.

Nicki Anderson is vice president, clinical quality and regulatory compliance for Pulse Systems, Inc., Kansas City, Mo.


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