How providers can add patient-generated health data to EHRs without inciting clinician burnout

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Patient-reported results and mobile health data from wearables, smartphone apps, and remote monitoring devices are increasingly used to improve service delivery and outcomes. With such digital technologies, doctors can monitor a patient’s health without the need for an office visit or hospitalization. By integrating patient-generated health data (PGHD) into electronic patient records (EHRs), PGHD data is also available to doctors at the treatment site, so that they can concentrate on the patients in their office or on a telemedicine session.

Unfortunately, as a new study shows (and as many clinicians could have told us), EHR-integrated PGHD can be a burden for clinicians, contributing to burnout. The study, conducted by researchers from Northwestern University’s Feinberg School of Medicine, concluded that “technostress,” time pressures, and inefficiencies related to workflows can be exacerbated by information overload.

The problem for the most part is that PGHD isn’t intelligently built into EHRs. It’s more like directing a data hose at EHRs. As a result, doctors who are already pressed for time during each patient appointment can have difficulty finding and interpreting the data they need quickly. And they may need to learn to navigate new digital interfaces, which adds to their stress levels.

In order for PGHD to work for clinicians, health information technology providers must effectively integrate this patient-generated data into clinical workflows so clinicians can do their jobs more efficiently and effectively. Getting it right is important as the PGHD data explosion will only pick up in the years to come. Here are some tools healthcare organizations should consider in order to gain control over and leverage valuable patient data.

Intelligent filtering

Doctors can see 20 or more patients in a single day, some with appointments and others unexpected. If these clinicians fall behind in searching through multiple documents in a person’s medical record in vain, it may motivate some patients to find another provider.

AI-based clinical filtering software can identify and interpret disorganized, complex, and voluminous medical data – including data from non-interoperable sources and in unstructured formats – and present it to clinicians upon request so they can quickly find relevant details about a particular patient point of care. For example, instead of looking up everything related to a patient’s diabetes or high blood pressure on a chart, intelligent filtering could give the doctor a “diabetes view” or a “hypertension view” of patient data.

Clinically intelligent filtering improves decision-making by providing actionable information and clinical insight into the clinician’s workflow. It also provides a financial benefit by adding value to existing health information systems.

Facilitating interoperability

Make no mistake, great strides have been made in healthcare interoperability. However, problems persist. And while the adoption of EHR is almost ubiquitous today, there is still more work to be done to achieve true interoperability between health organizations.

While vendors and providers use different HL7 exchange standards to deliver clinical care documents that meet regulatory requirements, too often these shared records are in a format that is not easily accessible to the receiving clinician. Instead, important clinical information is stored as plain text in a PDF or similar file, requiring users to search through multiple tabs to find relevant details. This is especially inefficient (and potentially costly) at the point of care when clinicians are trying to focus on their patients.

Vendors need to enable better interoperability so that incoming information is in a format that users can easily access and interpret. Admittedly, this will be a challenge for many health organizations, as not only has the interoperability of the pandemic been distracted, but the depth of the flaws in the sharing of data in health care has been exposed. Fortunately, awareness of interoperability constraints has increased and there is growing pressure on regulators, vendors, and vendors to work seriously to make true interoperability a reality.

However, better interoperability will be of little use if we do not understand and prioritize the needs of clinicians. Healthcare leaders and technology providers must avoid inundating clinicians in such a way that they waste time digging through data to find the patient and condition-specific information they are looking for.

Optimize workflows

Digital technologies are tools designed to help clinicians do their jobs. A confusing and poorly designed user interface that requires searching multiple screens and excessive typing, clicking, and swiping will frustrate doctors and patients as the minutes of appointment go by.

Careful consideration must be given to streamlining EHR workflows to reflect the way clinicians think and work. A smooth and intuitive workflow doesn’t slow doctors down and gives them more time to interact with their patients.

Conclusion

Healthcare organizations can add technology that intelligently sifts through clinical data to make clinical workflows more efficient, improve interoperability, and give users more time to focus on delivering quality care while preserving their EHR investment . This improves patient care and at the same time reduces burnout among doctors.

Jay Anders, MD, is the chief medical officer of Medicomp Systems, which provides medically-controlled point-of-care solutions to enhance EHRs.

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