Jay Anders, M.D.

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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 

concludes 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 

, which provides physician-driven, point-of-care solutions that enhance EHRs.

Articles

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.

How Providers Can Add Patient-Generated Health Data to EHRs Without Fueling Clinician Burnout

opinion piece called the dual challenge of fighting COVID-19 while preventing harm to the well-being of clinicians a “parallel pandemic.” The authors noted that an alarmingly high number of health professionals – 45% to 55% – were suffering from burnout prior to the pandemic, and now COVID-19 is putting increased strain on the healthcare workforce.

While pharmaceutical companies worldwide are working around the clock to develop a COVID-19 vaccine, I find myself wondering what it would be like if we could create a vaccine to cure clinician burnout. If we could, our physicians and nurses would have greater satisfaction and lower rates of anxiety, depression, substance abuse and suicidality. 

However, in the absence of a burnout vaccine, it’s more important now than ever for hospital and health system leaders to take tangible steps to reduce clinician stress.

While healthcare leaders are powerless to control many of the factors fueling clinician stress during this time, they can take steps to address one of the biggest ongoing stressors facing clinicians: EHR usability. In 

, researchers found a direct correlation between the usability of EHRs and burnout rates — and unfortunately, the usability of current EHR systems received a grade of F by physician users when evaluated.

As leaders look for solutions to improve EHR usability, an increasingly popular approach is to leverage the power of artificial intelligence (AI)-based technologies. However, when considering AI alternatives, it’s critical to look for solutions that relieve EHR pain points — and don’t add to clinician burden. In particular, clinicians need tools that streamline clinical workflows, provide patient- and problem-specific information at the point of care, enhance clinical decision-making and improve interoperability.

Clinicians often complain of inefficient EHR workflows that force users to click through multiple screens to find the right information. A physician ordering a lab test, for example, might first want to make sure the patient didn’t recently have the same test. This may require searching through multiple previous encounter notes and reports from other providers to confirm the patient’s history. If the doctor can’t easily find the right information, she might give up the search and simply order a new test. That additional test can be inconvenient or even unsafe for the patient, while also driving up healthcare costs.

Clinicians need relief from cumbersome workflows that are often exacerbated by an over-abundance of data that is difficult to access and interpret, especially at the point of care. While some AI solutions are designed to dump even more data into EHRs, what physicians really need are AI tools that streamline workflows by providing easy access to actionable information that enhances clinical decision-making at the point of care.

Delivering the right information at the right time

Rather than scrolling through a patient’s chart to find specific information, clinicians need AI tools that quickly provide access to the data they need, when they need it. For example, when a physician is examining a patient with diabetes, he needs a snapshot “diabetic view” of the patient that includes all of the diabetes-specific history. By minimizing the amount of time required to find information, clinicians are more productive and empowered to assess a patient’s condition.

To produce this patient-and problem-specific snapshot view, the technology must be able to quickly identify and interpret all the disorganized and complex arrays of information from multiple sources, such as previous encounters, lab reports and inpatient records. With AI-powered natural language processing (NLP) tools and AI clinical engines, unstructured clinical data can be converted to a structured format, giving users access to important information embedded within the free text fields. By leveraging the power of AI tools within existing workflows, clinicians have ready access to clinically relevant and high-value information that improves decision-making and patient care.

What clinicians don’t need is a bunch of algorithms to help them diagnose and treat every patient based on the information entered into the computer. Instead, they need solutions that support – rather than replace – clinical decision making.

An experienced physician can quickly diagnose a patient’s condition and recommend an appropriate therapy in less time than it takes to enter all the patient’s symptoms in the computer. With AI tools that enable the delivery of clean, actionable information, a physician is empowered to use the best computer in the exam room: her own brain.

With advancements in interoperability, clinicians are sharing more clinical data than ever before. Too often, though,  the data is disorganized and difficult to interpret, especially at the point of care. To ensure clinicians aren’t drowning in data, we need AI-based filtering solutions to transform data into clean, usable information that can be quickly accessed at the time of need.

Amid this current COVID-19 health crisis, clinicians, as well as researchers and regulatory agencies , have a heightened need to share information about test results, pre-existing conditions, symptoms and therapies. Many hospitals across the country are experiencing surging patient loads, and overburdened clinicians can’t afford to spend hours a day manually searching through various databases to find critical details about underlying health conditions or medication histories.

By adding AI tools to existing clinical systems, healthcare leaders can improve EHR usability, allowing clinicians to remain focused on the delivery of safe and efficient patient care. These solutions can also make it easier for organizations to address regulatory requirements, such as their COVID-related hospitalization and mortality rates.

As the country and the world continues to fight COVID-19, healthcare leaders need to act now to preserve the well-being of health professionals. One way to provide relief is by incorporating AI-based solutions that improve the efficiencies of EHRs and reduce some of the stress and frustrations that fuel clinician burnout.

Jay Anders, M.D., is the chief medical officer of Medicomp Systems.

COVID-19 may have made clinician burnout even more of problem. AI could help by streamlining workflows.

Jay Anders, M.D.

Articles

How Providers Can Add Patient-Generated Health Data to EHRs Without Fueling Clinician Burnout

Clinician Burnout: A Parallel Pandemic