Clinician burnout isn’t just an individual struggle. It’s often a system-wide challenge rooted in (among other things) inefficient workflows and the growing complexity of health IT. According to one US-based study, physicians spend an average of 16 minutes per patient in the EHR. Another study found that 70% of physicians reported stress tied directly to HIT systems. Instead of relieving the burden, many tools have unintentionally added to it.
However, when applied thoughtfully, technology can help in a variety of ways, simplifying clinical documentation, reducing denials (and the subsequent rework), and strengthening payer-provider collaboration for better value-based care.
Learn more about how technology can simplify workflows and lighten the provider load in our ebook, Beyond the Burnout: How smarter health tech is supporting clinicians.
If an excerpt is more your speed, keep scrolling for a quick look at the challenges of cluttered, fragmented medical problem lists and how tools that keep them organized can minimize distractions and manual work.
Organizing patient data to reduce mental fatigue
The challenge
Before seeing a patient, providers need to review key details – diagnoses, symptoms, labs, and medications. However, many EHRs spread this information across the chart, forcing clinicians to click through multiple sections and sift through irrelevant data. This disjointed experience increases the risk of missing important context and drains mental energy before the visit even begins.
Problem lists highlight the issue. Designed to summarize active conditions, they often become cluttered with outdated or duplicate entries. Without clear tools or processes to manage them, they grow unwieldy, forcing clinicians to rely on memory and spend time they don’t have searching through the patient record.
The solution
Reducing mental fatigue starts with smarter data organization. Technology that contextualizes problems and related information makes it easier for clinicians to see what matters – without digging through unrelated labs or medication lists.
A clean, up-to-date medical problem list is central to this effort. Tools that automatically flag outdated, duplicate, or resolved entries help keep lists accurate, allowing clinicians to simply confirm or dismiss suggestions while minimizing manual work.
During the patient encounter, natural language processing (NLP) can also identify documentation gaps in real time. These “discovery nudges” prompt clinicians to capture missing diagnoses or HCC codes without increasing administrative tasks. Ultimately, more structured and accessible data supports better outcomes, more accurate risk adjustment, and cleaner reporting – while reducing the clinician’s cognitive load.