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To make patient-generated health data work for clinicians, health information technology vendors must effectively integrate this patient-generated data into clinical workflows in a way that helps clinicians do their jobs more efficiently and effectively.
Patient-reported outcomes and mobile health data from wearables, smartphone apps and remote monitoring devices are increasingly being used to improve care delivery and outcomes. Such digital technologies enable clinicians to monitor a patient’s health status without requiring an office visit or hospitalization. Integrating patient-generated health data (PGHD) into electronic health records (EHRs) also makes PGHD data available to clinicians at the point of care, thus allowing them to focus on the patients in their office or on a telehealth session.
Unfortunately, as a new study shows (and as many clinicians could have told us), EHR-integrated PGHD can create a burden for clinicians that contributes to burnout. Conducted by researchers at Northwestern University’s Feinberg School of Medicine, the studyconcludes that “technostress,” time pressure, and workflow-related inefficiencies can be exacerbated by information overload.
The problem in most cases is that PGHD is not integrated intelligently into EHRs. Rather, it’s like aiming a firehose of data at EHRs. As a result, clinicians already under intense time pressure during each patient appointment may struggle to quickly find and interpret the data they need. And they might have to learn to navigate new digital interfaces, adding to their stress levels.
To make PGHD work for clinicians, health information technology vendors must effectively integrate this patient-generated data into clinical workflows in a way that helps clinicians do their jobs more efficiently and effectively. Getting it right matters because the PGHD data explosion will only intensify in coming years. Here are some tools that healthcare organizations should consider to gain control of and leverage valuable patient data.
Clinicians may see 20 or more patients, some with appointments and others unexpectedly, in a single day. If these clinicians fall behind schedule as they hunt futilely through multiple documents in a person’s medical chart, 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 on demand so they can quickly find relevant details about a specific patient at the point of care. For example, rather than search through a chart for everything related to a patient’s diabetes or hypertension, intelligent filtering could offer clinician’s a “diabetes view” or “hypertension view” of patient data.
Clinically intelligent filtering enhances decision-making by providing actionable information and clinical insights within the clinician’s workflow. It also provides a financial benefit by extending the value of existing health information systems.
Make no mistake, great gains have been made toward healthcare interoperability. Yet problems persist. And while EHR adoption today is nearly ubiquitous, more work remains to achieve genuine interoperability between healthcare organizations.
Though providers and vendors are using various HL7 exchange standards to transmit clinical care documents that satisfy regulatory requirements, too often these shared records are in a format not easily accessible to the receiving clinician. Instead, important clinical information is stored as unstructured text within a PDF or similar file, forcing 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.
What providers must do is facilitate greater interoperability so that incoming information is in a format that is easy for users to access and interpret. That admittedly will be a challenge for many healthcare organizations, for not only did the pandemic sidetrack interoperability advancements, but it also revealed the depth of healthcare’s data-sharing deficiencies. Fortunately, awareness about interoperability limitations has surged and there is growing pressure on regulators, providers and vendors to get serious about making true interoperability a reality.
Better interoperability will do little good, however, unless we understand and prioritize the needs of clinicians. Healthcare leaders and technology vendors must avoid inundating clinicians to the point that they’re wasting time wading through data to find the patient- and condition-specific information they seek.
Digital technologies are tools that should help clinicians do their jobs. A confusing and poorly designed user interface that requires searches through multiple screens and excessive typing, clicking, and swiping will frustrate clinicians and patients as the appointment minutes tick away.
Careful thought must be given to streamlining EHR workflows to mirror the way clinicians think and work. A smooth and intuitive workflow doesn’t slow down clinicians, freeing up more time for them to interact with their patients.
Healthcare organizations can add technologies that intelligently sift through clinical data to make workflows more efficient for clinicians, improve interoperability, and give users more time to focus on the delivery of quality care while preserving their EHR investment. Doing so will improve patient care while reducing burnout among clinicians.
Jay Anders, M.D. is the chief medical officer of Medicomp Systems, which provides physician-driven, point-of-care solutions that enhance EHRs.