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How Clinician Perceptions of EHR Use Can Drive EHR Innovation

Qualitative EHR use research can help researchers gather insight on what to target for quantitative analysis and better inform EHR innovation.

The correlation between EHR use and clinician burnout is well known.

Since the 2009 passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act and the subsequent increase in EHR adoption, clinician burnout has grown rapidly across the healthcare industry.

A 2019 study found that forty percent of physician burnout is attributable to EHRs, up from the previously estimated 13 percent.

However, studies on EHR use and clinician burnout have largely centered on quantitative measures like EHR time metrics. While these studies have laid a foundation for health IT optimization, the qualitative experiences of clinicians are key to EHR innovation, according to Amanda Moy, MPhil, MPH, MA, a biomedical informatics PhD candidate at Columbia University.

"The main issue with research on EHR workflows is that it's so focused on time metrics and not necessarily on the perceptions of clinicians and how it leads to cognitive burden," Moy told EHRIntelligence in an interview.

After all, cognitive burden cannot necessarily be measured by time metrics alone. It may take a clinician a second to copy and paste a note from one place in the EHR to another. However, this duplicate documentation can add cognitive burden for the next clinician reading the patient record.

Alternatively, a clinician could write a note that leaves no burden for the next clinician, but it might take them 10 seconds.

She said that by approaching EHR use research from a qualitative perspective, researchers can gather insight on what to target for quantitative analysis and better inform EHR optimization efforts.

Moy recently co-authored a qualitative, interview-based study that gathered perspectives on EHR use from emergency medicine clinicians. The study focused on EHR use in the ED because of the emergency departments' challenging, fast-paced work environment.

While clinician burnout is widespread across the healthcare industry, the emergency department has been hit the hardest. A 2023 report found that 65 percent of ED physicians experienced burnout, compared to 53 percent of all physicians.

In the study, Moy spoke with 12 ED physicians and 12 ED nurses nationwide about their perceptions of EHR use.

She underscored the importance of including nurses in the study.

"There have been one or two qualitative studies in EHR literature, but none specifically had examined both nurse and the physician experiences," Moy said. "Nurses are understudied in this space, but they are the main conduit to patient care."

The research revealed several high-level areas of EHR-related documentation burden.

Clinicians reported a lack of advanced EHR functionalities, including automated data capture. Moy explained that because documentation workflows are restricted to checkboxes, clinicians must complete duplicate work to explain the context surrounding the patient's presentation.

Providers also said that the fragmented EHR display adds to documentation burden.

"Clinicians have to go into different areas of the EHR like orders, vitals, documentation, and patient history," Moy said. "There's a lot of information foraging that is not associated with patient care, and it just adds a lot of noise."

This is a place where healthcare can learn from other service sectors, even for provider-facing tools. For example, online shopping websites such as Amazon suggest products based on recent activity. Moy suggested that EHR developers use a similar method. She noted that if the EHR system knows a clinician is authoring a note, it could offer lab results or orders to ease information foraging.

Additionally, interviewed clinicians noted that amidst the task switching inherent to the ED, they often lose their place in the system and must log back in and search for the patient.

"If we can find a way to develop more context-aware functionalities or more user-adaptive EHRs, that would solve a lot of the cognitive burden that clinicians face because what they've described is a lot of resistance when they go into the EHR," Moy said.

ED clinicians noted that they often copy and paste data from across the patient record into a note to synthesize information. Copying and pasting within the EHR is extremely popular, with a 2022 study finding that more than half of all text in EHR notes is duplicate content.

Repeated information in EHR notes increases the time for physicians to determine which data is accurate and creates viral copies of errors that can spread through clinical documentation.

Interviewed nurses and physicians also noted that seemingly simple tasks require manual EHR workarounds. Instead of being able to delete text, nurses described having to write another note indicating that they had added the information accidentally.

Nurses also mentioned instances where they had to edit auto-populated data.

For example, if a clinician physically restrains a patient experiencing a mental health crisis, staff check on the patient every 15 minutes. In attempts to streamline documentation, the EHR auto-populates a timestamp 15 minutes later.

However, due to the competing priorities in the ED, providers noted that they might not return to the patient exactly every 15 minutes, so they must update the auto-populated data to the actual time.

"These are simple things that we think the EHR would account for, but add a lot of frustration," Moy said.

Moy's ongoing research is focused on leveraging EHR metadata to better understand clinician workflows to drive EHR innovation. EHR metadata provides a high-level overview of clinician clicks within the EHR.

Moy plans on leveraging a machine learning algorithm to cluster EHR metadata to characterize clinician activities within the EHR and how different tasks co-occur.

"In understanding how these tasks co-occur, we can design an EHR that is more usable and more streamlined," she said. "We know orders and writing a note comprise of specific tasks that cluster together, so why can't we leverage information on the probabilities of those tasks clustering to design a better EHR?"

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