When individuals consider electronic health records (EHRs), they often believe these digital platforms for storing patient data were created for healthcare professionals. However, this is a misconception. EHRs were originally developed for billing and compliance purposes—and this is evident.
Due to the initial intent behind EHRs, it’s understandable that clinical workflows are fragmented. The drawbacks of conventional EHRs have hindered clinicians’ ability to swiftly access pertinent patient information at the point of care. This not only complicates clinician decision-making but also jeopardizes patient safety and outcomes when crucial data is concealed or entirely absent.
As a medical professional, I frequently encounter recurring issues from colleagues, alongside a brief overview of the advancements made to pave a better way forward.
Increasing cognitive overload
A 2023 study published in the National Library of Medicine reveals that EHRs rank lower in usability compared to other technologies when considering efficiency, effectiveness, and user satisfaction. The study’s author states, “the volume and organization of data along with alerts and complex interfaces demand a significant cognitive load and lead to cognitive fatigue.” Escalating documentation demands have overwhelmed doctors with clerical duties that require them to devote excessive time to their computers.
Alert fatigue represents another significant contributor to burnout. When healthcare providers receive 15 or more alerts each hour during their workdays, and many of these are non-essential to their decision-making, they inevitably begin to overlook some. This increases the likelihood of missing crucial information that may signify a potentially serious health issue. Additionally, excessive alerts impede workflow efficiency.
Most providers will assert that EHRs have not fulfilled their promise of facilitating data-sharing and coordination. Historically inadequate interoperability standards have complicated the data-sharing process between systems. This is why hospitals still resort to using fax machines.
To enhance EHR performance, healthcare organizations must tackle technology and process-related hurdles to establish an effective clinical workflow that fully supports the sharing of significant data to complete the patient picture. This is where AI plays a crucial role.
Intelligent automation
Today, cutting-edge AI technologies are being utilized to streamline clinical documentation and boost interoperability and population health management. For instance, ambient voice technology, which we have discussed for years, has finally arrived, assisting clinicians in breaking free from the confines of the EHR. Instead of being engrossed in the computer, they can concentrate on the patient, as ambient listening records all conversation details and generates structured documentation for easy review. This enables physicians to engage fully in the moment with their patients.
AI-driven analytics are also making a substantial impact throughout hospitals and health plans. Predictive analytics are currently employed for bed and block time management, essential for operational efficiency and staffing optimization. Another application of AI-driven predictive analytics is identifying at-risk patients by deciphering subtle trends that a physician might overlook, given the presence of multiple slightly abnormal variables. However, when considered collectively, these variables may indicate an elevated risk for a specific illness or condition. This exemplifies how AI can identify these connections in data and genuinely support clinicians.
AI also enhances interoperability by presenting community data to providers in a meaningful manner. A clinician looking for community data typically faces the challenge of navigating through unstructured data, duplicate entries, and vast amounts of irrelevant information for a specific patient. AI can efficiently scan, normalize, and aggregate the data in a coherent, meaningful format, allowing clinicians to make decisions without unnecessary obstacles.
Moreover, AI can swiftly compile precise, well-organized chart summaries and discharge summaries, both of which consume considerable clinician time. Within these summaries, AI can offer recommendations based on documentation, order sets, and medication history, while linking back to the original sources for verification.
As a physician, I am hopeful that we are finally making strides against provider burnout by incorporating automation into a diverse range of time-intensive tasks including documentation, prior authorizations, referrals, patient follow-ups, and billing. As AI contributes to modernizing EHR workflows, clinicians can practice in a manner that benefits both themselves and their patients.
Genuinely engaging patients
On the patient side, AI tools can be integrated into engagement platforms that provide a significantly more user-friendly and intuitive experience compared to traditional patient portals. AI algorithms embedded in patient engagement systems can adopt a proactive stance on appointment reminders, medication prompts, education, and communication across various channels.
By evaluating social determinants of health (SDOH) data, AI can assist in customizing education for individuals based on aspects such as transportation access, availability of healthy food, and community resources for support. Such tools can empower patients to take an active role in their care, which research correlates with improved outcomes. This type of engagement will be crucial as our healthcare system transitions to value-based care.
As we broaden the use of AI—especially in patient-facing tools—it is essential to prioritize safe and responsible practices. Increased efficiency and personalization are much