Human Factors Engineering Paper
Usability of Health Informatics Applications
Electronic Health Records (EHRs) play a crucial role in contemporary healthcare by enhancing patient care, optimizing workflows, and increasing data precision. The effectiveness of these systems is largely influenced by essential design principles that affect the efficiency of healthcare providers and the overall functionality of the system. This paper explores the usability of Epic EHR, focusing on design features that minimize data inaccuracies, the human factors that influence usability, and the heuristic principles that inform interface design.
Example of an EHR Application
Epic EHR is a popular health informatics tool that consolidates multiple healthcare operations, including patient documentation, clinical decision support, order management, and seamless communication between various healthcare systems (Chishtie et al., 2023). This platform enables healthcare providers to retrieve real-time patient information, enhancing their decision-making processes and care coordination. It is suitable for both inpatient and outpatient environments and can be tailored to accommodate diverse clinical workflows.
Key Elements of Design Usability in Epic EHR
Usability in health informatics applications plays a vital role in minimizing data errors and enhancing data integrity. Several design features contribute to the effectiveness of Epic. The user interface and navigation have been improved through the use of structured templates, dropdown menus, and search functions, which streamline user navigation and reduce documentation time, as highlighted by Chishtie et al. (2023). Nonetheless, some users have indicated that intricate workflows may necessitate further training. To ensure error prevention and maintain data integrity, Epic incorporates built-in validation rules, including medication alerts and warnings for duplicate orders, which help uphold data accuracy. According to Ali et al. (2023), these features are essential for preventing transcription errors and enhancing patient safety. Epic also offers clinical decision support (CDS) tools that provide alerts and reminders regarding critical patient information, such as abnormal lab results or potential drug interactions. However, an overabundance of alerts can lead to cognitive overload and alert fatigue, as observed by Kushniruk & Borycki (2023). As noted by Chishtie et al. (2023), Epic enables healthcare organizations to customize workflows to meet specific departmental requirements, ensuring that users engage with pertinent information efficiently.
Comparison of Human Factors and Heuristic Principles
Human factors and heuristic principles both influence the usability of electronic health records (EHR), yet they address different elements of user interaction with the system. Human factors focus on the mental and physical interactions users have with technology. According to Kushniruk & Borycki (2023), Issues such as cognitive overload, alert fatigue, and inefficient workflows can diminish user satisfaction and raise the likelihood of errors. On the other hand, heuristic principles, such as those outlined by Nielsen, provide design guidelines aimed at enhancing interface efficiency through concepts such as error prevention, consistency, and user control, according to Ali et al. (2023).
In the context of Epic EHR, the human factors perspective reveals that the clinical decision support (CDS) system generates numerous alerts, which may cause clinicians to overlook important notifications due to alert fatigue, ultimately reducing adherence to safety protocols. From a heuristic standpoint, while Epic adheres to principles such as system status visibility and consistency, its complexity can sometimes conflict with the simplicity heuristic, necessitating multiple clicks for routine tasks.
Recommended Improvement
A significant usability issue in Epic EHR is alert fatigue, which occurs when clinicians are inundated with excessive and repetitive notifications, resulting in desensitization and a heightened risk of overlooking critical alerts. Although the Clinical Decision Support (CDS) system aims to improve patient safety, research shows that 49% to 96% of alerts are dismissed, underscoring the need for a more sophisticated approach, as highlighted by Chishtie et al. (2023). To tackle this problem, an adaptive alert system that leverages AI and machine learning should be developed to filter and prioritize notifications according to clinical context, user behavior, and patient-specific information. This system would facilitate context-aware prioritization, ensuring that urgent alerts are easily noticeable, while lower-risk notifications are grouped for subsequent review. Customization for individual clinicians would enable the system to modify alert frequency based on the provider’s experience and specialty, minimizing unnecessary disruptions for experienced professionals while still offering crucial support for less experienced clinicians.
An adaptive alert system is in line with research on human factors and usability principles, as it lessens cognitive strain, enhances workflow efficiency, and boosts patient safety (Kushniruk & Borycki, 2023). By cutting down on unnecessary alerts, healthcare professionals can concentrate on vital decision-making instead of dealing with repetitive notifications, which ultimately leads to fewer medical errors and better adherence to evidence-based practices. Furthermore, the addition of a feedback mechanism would enable users to adjust alert thresholds, keeping the system both clinically relevant and user-friendly. As noted by Johnson (2024), the usability of electronic health records (EHR) is crucial for ensuring high reliability in healthcare, where technology plays a key role in minimizing errors and achieving optimal patient outcomes. By adopting adaptive alerting, Epic can evolve its clinical decision support (CDS) system into a more intelligent and intuitive resource, effectively balancing safety, efficiency, and provider satisfaction.
Conclusion
Epic EHR is crucial in the healthcare sector, as it enhances data accuracy, minimizes errors, and facilitates informed decision-making processes. Nonetheless, challenges such as alert fatigue and cognitive overload among users indicate the need for enhancements. Optimizing Epic’s alert management system can significantly enhance usability, leading to increased clinician involvement and improved patient care outcomes.
References
Ali, S. K., Khan, H., Shah, J., & Nadeem Ahmed, K. (2023). An electronic health record system implementation in a resource limited country-lessons learned. Digital Health, 9, 20552076231203660. https://doi.org/10.1177/20552076231203660
Chishtie, J., Sapiro, N., Wiebe, N., Rabatach, L., Lorenzetti, D., Leung, A. A., Rabi, D., Quan, H., & Eastwood, C. A. (2023). Use of Epic electronic health record system for health care research: Scoping review. Journal of Medical Internet Research, 25, e51003. https://doi.org/10.2196/51003
Johnson, R. J. (2024). EPIC® and High Reliability in healthcare: An evidence-based commentary. Journal of Medical Informatics and Decision Making, 1(4), 84–96. https://doi.org/10.14302/issn.2641-5526.jmid-24-4893
Kushniruk, A. W., & Borycki, E. M. (2023). Human factors in healthcare IT: Management considerations and trends. Forum Gestion Des Soins de Sante [Healthcare Management Forum], 36(2), 72–78. https://doi.org/10.1177/08404704221139219
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The purpose of this assignment is to explain the principles of design usability and analyze the impact of human factors on electronic applications for health care systems.
Write a 750-1,000-word paper on the usability of health informatics applications. Include the following:
- Describe an example of an electronic health record (EHR) application used in a health care setting.
- Describe the key elements of design usability for that application by reducing data errors and improving data integrity.
- Compare human factors and heuristic principles and discuss how they affect the chosen EHR application.
- Recommend an improvement to the chosen electronic application.
- Provide a rationale for the suggested improvement based on knowledge of design usability.
Support your findings with a minimum of three scholarly resources.