EHR roll-outs need strategies to mitigate clinician overload
Clinicians are increasingly subject to cognitive overload, and recent studies suggest that without mitigation strategies in place, poor implementation of EHR systems can exacerbate the problem.
In April, a narrative review paper was published in the JMIR Medical Informatics titled Impact of Electronic Health Record Use on Cognitive Loads and Burnout Among Clinicians. My fellow authors and I applied cognitive load theory to explore the impact that routine EHR use has on clinicians and to suggest how the risk of negative effects could be minimised.
It’s important to preface the discussion of our conclusions by acknowledging that EHR systems are essential for the delivery of efficient, joined-up patient care: they allow for improved communication between clinicians, remote access to clinical records and to a high volume of clinician data for research and audit purposes. Rightly, years of effort and significant investment have led to widespread EHR implementation across the NHS: 87 per cent of primary, secondary and community care staff surveyed by the Health Foundation reported using EHRs as part of their work, and in the 2024 Spring Budget, the Chancellor pledged that they would be rolled out across all NHS Trusts by 2026.
However, only 57 per cent of respondents in the same Health Foundation survey chose EHRs as the technology saving them the most time, and their rapid review of 72 studies about EHRs and related tools identified that 44 per cent found no time savings delivered. This indicates that the potential of EHRs is not yet being fully realised.
As our new review concludes, taking a considered, evidence-informed approach to the design and implementation of EHRs makes all the difference when it comes to unlocking their full potential, while mitigating significant potential risk. Importantly, by acknowledging and proactively addressing the relationship between EHRs and cognitive burden, organisations can successfully reduce rates of clinician burnout and minimise risks to patient safety.
EHRs and cognitive overload: examining the evidence
Cognitive load theory explains that human capacity to process information is limited to a few elements in working memory at any given time. When this capacity is overwhelmed by an excessive quantity of information, the resulting cognitive overload can impair decision making, interfere with mental performance and elevate stress levels. Clinicians are typically at high risk of cognitive overload, as they must navigate complex patient data, integrate new information rapidly, and make critical decisions under pressure on a daily basis. The transition to digital records has compounded this challenge by significantly increasing the volume and complexity of data clinicians must handle during patient care.
Recent studies indicate that poorly designed EHR systems can exacerbate cognitive load. The factors contributing to this include inefficient user interfaces, excessive documentation requirements, and the need to navigate through cumbersome electronic systems to access relevant patient information. In addition, dealing with overly-frequent pop-up notifications has been shown to cause distraction and alert fatigue, both of which can lead to clinicians missing important information and result in poor patient outcomes.
Experiencing regular cognitive overload is a major risk factor for burnout. In 2023, 55 per cent of surveyed NHS workers had experienced burnout in recent years, a condition characterised by emotional exhaustion, demoralisation, and a reduced sense of personal accomplishment, which not only affects individual health professionals but also the quality of care they provide. Although burnout has multiple root causes, addressing the design and implementation of EHRs to reduce the cognitive load they place on clinicians is a necessary and important step towards tackling the rise in burnout cases.
Practical recommendations:
- Improving EHR user interfaces: Simplifying the user interface of EHR systems can reduce unnecessary cognitive effort. This involves designing more intuitive menus, reducing the number of steps to complete tasks, and organising patient data more logically.
- Streamlining information presentation: Tailoring the presentation of information to minimise extraneous load is crucial. This could mean displaying critical patient data in a summarised form, with the option to expand details as needed, thus preventing information overload.
- Reducing documentation burdens: Automating routine data entry and employing natural language processing can decrease the time clinicians spend on documentation. This not only frees up cognitive resources but also allows clinicians to devote more attention to patient care.
- Incorporating decision support tools: Advanced decision support tools can aid clinicians by providing contextually relevant information at the point of care, reducing the need for extensive data retrieval and analysis.
- Training and support: Continuous training and real-time support can enhance EHR proficiency among clinicians. Tailored training programs that address the specific needs of users can alleviate stress and improve their interaction with the technology.
Importantly, emerging artificial intelligence and machine learning technologies offer promising avenues to manage cognitive load by automating routine tasks and predicting patient risks through advanced analytics. However, the integration of these technologies must be handled carefully to avoid adding to the cognitive burden – evidencing a need for user-friendly design and time-saving clinical integration.
In summary, clinician burnout is complex and has multiple causes – such as overall workload, inflexibility of rostering and organisational culture – which is why it could never be fully eliminated even by the ‘perfect’ design and implementation of an EHR. However, by scientifically assessing the impact of different EHR technologies and models, it becomes possible to paint a more complete picture of how they alleviate or exacerbate burnout. In turn, this understanding can be used to ensure that clinicians are equipped with the best EHR systems –and the best integrated technologies – that improve their efficiency and improve patient outcomes.
