Feeding the EHR was a daunting task. It consumed time as well as electrons.
Eventually there was HIPAA, and interoperability, as well as blue screens, power failures, malware, viruses and ransom notes.
Software vendors would come and go. Software would become obsolete requiring updates and sometimes replacement Electronic Health Records.
New systems became available. Changing to EHR migration is a daunting task. Muscle memory from an old system would have to be reprogrammed to utilize the new system. Systems were designed to reduce clicks and mouse movements.
There was an epidemic of carpal tunnel syndrome in providers. This created a wave of disability and worker's compensation claims.
Each year, health systems invest millions of dollars in their Electronic Health Records (EHR) and applications to leverage their full potential. In a recent Becker’s Health IT article, it is stated that over the next 10 years, EHRs will become more streamlined, as companies like Epic, Oracle Cerner or MEDITECH continue to refine their current technology to align with provider and patient demands.
As your team prepares your systems and processes for the next generation of digital transformation in healthcare, there’s a good chance that you’re considering (or maybe even decided on) an EHR transition. The process of transitioning from one EHR to another is a complex change for health organizations and is often described by our clients as extremely expensive, labor intensive, and time consuming.
Transitioning to a new EHR system often meets resistance due to fear of the unknown or concerns about disruption to established workflows. Comprehensive training addresses these concerns by familiarizing users with the new system, its benefits, and how it integrates into existing practices. There are organizations that specialize in transitioning to or changing an EHR.
Best Practices for EHR Transition Training
Early Engagement and Communication
Start training initiatives well in advance of the EHR Go-Live date. Communicate the benefits of the new system and the training schedule clearly to build anticipation and encourage buy-in from staff.
Tailored Training Plans
It is important to recognize that different roles within the healthcare organization have varying needs regarding EHR functionality. Developing customized training plans that cater to the specific responsibilities and workflows of clinical staff, administrative personnel, and support teams, is an essential step toward EHR optimization and adoption.
Engage Super Users AND End Users
While it is important to identify and train a group of super users within the organization who possess advanced knowledge of the EHR system, it is just as crucial to engage your end users. Super users can serve as internal champions and mentors, providing support to their peers during and after the transition period. End users are just as instrumental, as they are often the ones who utilize the EHR the most. By engaging and training both types of users, you can ensure a more streamlined approach, resulting in a unified experience for your patients.
Hands-on Learning and Multimodal Approach
Incorporating hands-on training sessions where users can interact directly with the EHR system enables them to apply theoretical knowledge to practical exercises and simulations. Our Epic trainers are classroom certified, provide At-the-Elbow (ATE) support and offer personalization labs to ensure our clients’ IT teams, clinicians, and staff are confident in their ability to utilize the new system.
It is also important to recognize that individuals have different learning preferences. When it comes to selecting a Go-Live training partner, select a team that offers a mix of training modalities such as in-person workshops, webinars, e-learning modules, and printed reference materials. This ensures accessibility and accommodates diverse learning styles.
Feedback Mechanisms
Establish channels for continuous feedback from trainees regarding their learning experience and the usability of the EHR system. Use this feedback to refine training materials, adjust content based on user needs, and address any challenges promptly.
Continuous Learning Opportunities
EHR systems evolve with updates and new features. Be sure to ask your Go-Live partner about ongoing training opportunities to keep users informed about system enhancements, workflow optimizations, and best practices. This can include refresher courses, advanced training modules, or lunch-and-learn sessions.
As your healthcare organization prepares for the future of EHR systems, the investment in comprehensive Go-Live training emerges as a critical strategy for success. Training plays a pivotal role in empowering healthcare providers and staff to maximize the potential of new EHR technologies, enhancing clinical workflows, and ultimately improving patient outcomes. As your team navigates the evolving EHR landscape, consider HCTec as your next Go-Live project partner.
Artificial Intelligence has great potential to reduce workloads, with voice recognition and automated scribe inserting real-time provider-patient conversation in the history of the EHR, automated procedural codes analyzing the length of the examination, the complexity and summarization of the encounter, ICD coding in accordance with the diagnosis. It may provide predictive modelling.
The veracity and trustworthiness of A.I. will evolve over time with vetting, and approval of regulatory agencies, such as the FDA which has the authority to supervise EHR and AI as medical devices.
Studies on patient-provider trust in the context of AI focus on several key themes:
Perceptions of AI: Research indicates that patients often have mixed feelings about AI in healthcare. Trust can be influenced by how well patients understand AI technology and its benefits.
Transparency: Studies show that clear communication about how AI systems work and the rationale behind their recommendations can enhance trust. Patients are more likely to trust AI when they feel informed.
Provider Role: The relationship between healthcare providers and patients remains crucial. Patients tend to trust AI more when their providers endorse and explain the technology.
Outcomes and Accuracy: Trust is also linked to the perceived accuracy and effectiveness of AI tools. Positive outcomes from AI-assisted care can bolster trust.
Ethical Considerations: Ethical concerns regarding data privacy and bias in AI algorithms can hinder trust. Patients want assurances that their data is secure and that AI systems are fair.
Demographic Factors: Trust levels can vary among different demographic groups, influenced by factors like age, education, and prior experiences with technology in healthcare.
Longitudinal Studies: Ongoing research is needed to understand how trust evolves over time as AI technologies become more integrated into healthcare practices.
Overall, fostering trust in AI requires a multifaceted approach, addressing both technological and interpersonal aspects of care.
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