Medical documentation and EHR (electronic health records) applications are fast-growing, and technology is the prime accelerator behind determined growth. Although the concept of medical documentation is not a 20th-century invention, its consistent and widespread use is; Egyptian artifacts indicate evidence of medical record keeping . Propelled by federal regulations and the concerning healthcare economy, medical facilities aim at improving documentation and billing processes by relying on technology to organize data through increased automation and artificial intelligence . The business implication is that smarter technology decreases backlog and increases accounts receivables–meaning getting paid faster instead of three months and sometimes more from the service date. The research implication is that disease diagnosis and treatment improve considerably  resulting in better population health. Human perception may lead us to interpret that technology acceleration is the ultimate answer to the woes of complex patient flow processing and treatment. If we apply holistic lenses, we may see a different view. Undoubtedly, automation and the future make record collection faster and easier to perform. But, data and systemic quality will increasingly suffer due to a need to design and push technology so that it fulfils the greater perceived need instead of addressing the points in the systems that perpetuate the problem.
Systems thinking is a way of seeing the world as a series of systems and its constituent parts as always being interrelated and never existing in isolation. Human cognition limits how much we see and seek to understand at one time to potentially understand it even greater in that isolated view. Thinking in systems supplements typical human thinking by articulating ideas and concepts for clarity by challenging our assumptions. The first step in this type of thinking is to define what we perceive to be the main moving parts of the system, which will be founded on our mental models and beliefs. The next step is to challenge the perceived system and its components; question what it does and why.
In re-examining the adverse effect of technology adoption in the Healthcare industry, the system can be drawn to identify major components. See figure 1. At a first glance, the diagram invites questions, which it should.
In our systems analyses exercises, we know that new data is generated for every patient encounter (new and ongoing) and that while technology may transcribe the speech data followed by coding automatically, the question around quality stands out. Artificial intelligence depends on a great deal of learning, done quickly, but not without data. If misinformation increases, then the learning done will be based on that misinformation. Similarly, if technologies are designed and developed to improve the speech recognition problem by increasing its accuracy, how is accuracy guaranteed? The assumption is that the data is right from the start, so misinformation will decrease; hospital and practice staff are too busy to review the medical records singularly. We put our trust in the smarter technology, but ongoing quality is not clear. Time will increase the possibility of misinformation growth from what at first is minimal could be significant later on. The more integrated and complex processes are, the more information exchange paths are created, resulting in more risks and error.
Athreon utilizes very smart technology; however, with human and systems analyses, the technology approach is never final and fixed, but always improving. Feedback systems processing, controls, and evaluation are pivotal to our auditing processes and ensuring that quality is very high. Trust is not just left to the technology. With the right approach, acceleration and quality can be met.
To learn more about how we can help you and how this makes a difference in your organization, contact us for a technology demonstration or consultation session.
1 Evans R. S. (2016). Electronic Health Records: Then, Now, and in the Future. Yearbook of medical informatics, Suppl 1(Suppl 1), S48–S61. https://doi.org/10.15265/IYS-2016-s006
2 Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94