Integrating AI into the electronic medical record can make patient data more usable and dependable for end-users, according to a review of the relevant scientific literature published this month in the American Journal of Clinical and Medical Research.
What’s more, by applying machine-learning algorithms to EHR data-entry processes, provider organizations can relieve clinicians of manual tasks while maximizing clinical precision and facilitating process standardization.
These are among the conclusions of researchers at the University of Piraeus in Greece. Public health economist Dimitris Karaferis, PhD, and colleagues further note that, when properly applied within the EHR, such algorithms can intelligently autofill patient information, suggest relevant medical codes and even flag potential errors or omissions in real time.
“AI can [also] analyze user interaction patterns to identify pain points within EHR interfaces, enabling developers to create more intuitive designs tailored to user needs,” the authors write. “This proactive approach can significantly mitigate user frustration and increase overall [EHR] adoption rates among healthcare providers.”
The study describes numerous advantages the AI-EHR combo can offer. These include:
1. Upgraded data handling and evaluation.
AI technologies “encourage a more refined method for managing patient care, as they are capable of forecasting patient risks, customizing treatment strategies and observing health status in real time,” Karaferis and co-authors write. Machine learning, natural language processing and predictive analytics “are increasingly being incorporated into EHR systems to address significant challenges in healthcare, including data saturation, the burden of documentation and the necessity for enhanced predictive functionalities.” More:
‘Given the proliferation of healthcare data in recent years, AI provides a solution to utilize [patient] data effectively, enhancing the intelligence and utility of EHRs for healthcare professionals, individuals and officials.’
2. Reduced time for administrative tasks carried out by clinicians.
Recent research showed that AI-driven clinical documentation tools meaningfully cut the time clinicians dedicated to EHR duties, the authors note. “Almost half of healthcare providers said they are dedicating fewer hours to EHRs at home, with a similar percentage experiencing a reduction in EHR-related responsibilities outside of regular work hours.” More:
‘The AI tools helped the intervention group significantly decrease their EHR documentation time in comparison to the control group, indicating that AI has the potential to lessen the administrative tasks that lead to clinician burnout.’
3. Enhanced clinical decision support and improved data accuracy.
EHR systems powered by AI “have the capability to provide immediate clinical decision support (CDS) through the analysis of patient information and the provision of evidence-based suggestions to medical practitioners,” the authors observe. “The extensive adoption of EHR platforms across healthcare institutions enables the collection of comprehensive clinical data from a large patient cohort.” More:
‘These expansive EHR datasets afford researchers the opportunity to: a.) construct more precise predictive models that encompass a wider range of patient attributes; b.) perform more frequent updates to these models with diminished engineering demands; and c.) improve the overall quality of these predictive models.’
4. Optimized usability and user experience.
“Numerous clinicians have voiced their concerns regarding the intricacies and user-unfriendliness of existing EHR systems,” Karaferis and co-researchers point out. “The incorporation of AI could significantly improve the usability of these platforms, consequently alleviating the administrative load faced by medical professionals.” More:
‘Such enhancements are crucial for boosting clinician satisfaction and enabling them to focus more on patient care instead of administrative responsibilities.’
5. Improved patient outcomes.
By processing extensive datasets, such as those from medical histories, laboratory findings and imaging results, AI improves diagnostic precision by “uncovering patterns and relationships that might elude human practitioners,” the authors write. “The ability to identify diseases at an early stage and to formulate personalized treatment plans tailored to the unique needs of each patient is significantly enhanced by this capability.” More:
‘Additionally, AI systems are capable of monitoring patient progress in real time, alerting healthcare providers to any notable changes that might require intervention. This proactive approach not only reduces the risk of complications but also improves the overall quality of patient care.’
The authors also cover a handful of challenges likely to accompany any AI-EHR integration effort. Not least among these are data privacy, security and confidentiality.
The study is available in full for free (PDF).
