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Tech Industry Voices—Not all automation is created equally for clinical documentation improvement
SEP 15, 2021
The key to leveraging artificial intelligence in clinical documentation integrity is to utilize technology that can truly emulate the way clinicians think. (Ryzhi/Getty Images)
Healthcare system survival pivots on many metrics, but the ability to generate revenue and to evidence high quality of care are two of the most essential.
At the center of both metrics is the clinical documentation process, where an accurate representation of every patient’s clinical experience while in a provider’s care must be recorded.
As simple as it may sound, achieving that accurate reflection of diagnoses, interventions and the clinical picture is anything but simple. Medicine is as much science as it is art, and complex definitions, levels of specificity and complex medical terminology mean that most hospitals struggle to document everything properly, leading to significant lost revenues and under-reporting on quality metrics.
Health systems have answered this challenge by standing up clinical documentation integrity (CDI) programs, staffed with clinicians. As more healthcare revenue is tied to achieving specific quality metrics, the role of CDI has become even more critical.
However, ensuring integrity and completeness of documentation would require health systems to staff CDI teams with an incredible amount of highly trained clinicians to review and correct documentation on every record, every day. The cost and complexity of such an operation is unimaginable, and no healthcare system has the resources to either employ that many people or even find a supply of that many highly specialized staff.
As a result, many health systems are turning to software to support CDI with technology that scales clinical staff abilities and provides intelligent automation. Unfortunately, the challenge that many have run into is how to identify the right technology for their operation.
All the work CDI specialists perform requires clinical knowledge—the sort of knowledge that is gained only after decades of academic study and real work experience. Automating that work means that the technology must mirror the same level of clinical thinking that any one of these specialists employs every day.
The challenge is immense. Emulating clinical thinking with software is among the loftiest goals of artificial intelligence in healthcare and requires the most sophisticated, cutting-edge technologies available—not to mention years of training. Even with the most advanced technology, AI has sometimes failed to impress the critics, as we’ve seen multiple reports call out the stumbles of larger ambitioned (but similarly conceptualized) efforts like IBM Watson.
But, while there are still areas for improvement, the truth is that AI still is making a significant impact across the healthcare landscape—and especially within CDI, where success is well documented.
While CDI is an excellent and proven use case for AI in healthcare, providers should understand that not all AI is the same. In fact, many legacy systems that deploy “the wrong type” of AI to CDI are unable to see all the gains possible with the correct deployment.
The key to leveraging AI in CDI is to utilize technology that can truly emulate the way clinicians think. It must read, digest, understand and make statistical predictions on the entirety of the clinical record similarly to how physicians look at all the evidence to assess and diagnose to appropriately provide patient care.
That’s where machine learning holds the key. Machine-learning is, at its heart, a pattern-recognition engine that can digest a plethora of individual pieces of data, recognize patterns and then use those patterns to make statistical predictions. If properly applied to clinical information, it is a very powerful technology. Fed over time with millions of patient encounters, machine learning begins to emulate the way clinicians think, automating numerous tasks or challenges that otherwise would only be solvable by a human. While it does not replace clinicians, it does reduce clinical staff burden, providing more time to be spent on patient care.
Additionally, by automatically the review of every patient record in real-time every day, cases can be prioritized so a CDI specialist knows what to look at—versus wasting time on those with no documentation irregularities. This type of machine learning interprets the clinical evidence, compares it to the existing documentation and highlights and prioritizes which cases have discrepancies automatically.
Many legacy applications attempt to use another AI technology, natural language processing (NLP), to automate complex clinical tasks. While NLP has some useful applications for tasks like clinical narration—where the dictionary-like “look up” function of NLP suggests a better or more accurate word—NLP is only a partial solution for CDI.
For example, NLP can translate the narrative documentation from the clinician into text understood by a computer. However, unless it’s paired with a machine learning solution that simultaneously reads and emulates clinical decision-making (thus enabling a comparison between what was written and what the clinical evidence says), it’s an inadequate solution to solving the core challenges in CDI.
Additionally, rules-based technology solutions that utilize “rules” or “markers” to automate clinical tasks fail entirely to emulate the way that clinicians think. As a result, they cannot reflect the many permutations of the way clinical conditions are presented.
Robotic process automation (RPA) is another buzzword in healthcare that has been cited as a tool for handling repeatable basic tasks. However, within the mid-revenue cycle (and thus CDI), nearly all tasks have a clinical element, requiring clinical understanding to complete. That means RPA definitionally is not suited for more complex tasks that require higher-level thinking.
Instead, intelligent process automation (IPA) is the right solution, as IPA applies machine learning to RPA to automate complex tasks that require human judgment (much like the work of CDI). Thus, to apply IPA in the revenue cycle, not only is machine learning critical, it also is the only technology available today that specifically emulates clinical thinking and judgment.
As technology gets better at emulating a clinician’s mind, increasingly powerful AI engines will soon be able to capture documentation and coding instantaneously. By accurately automating clinical condition documentation directly into EMRs and identifying the final code set, the process will become even more efficient and will have fewer translation errors.
Ultimately, that means smaller teams will be able to support the entire documentation process, which reduces costs for providers and stress on clinicians.
There is no doubt that managing a health system has become increasingly complex, and that’s especially true for CDI teams that must capture data accurately and efficiently. However, AI has become a critical tool that is truly making an impact in the mid-revenue cycle, and there is much more innovation to come in the next few years. But, while we wait for that larger revolution, it’s important that health systems implement a stable and efficient CDI program now, powered by the right technology.
William Chan is the co-founder and CEO of Iodine Software.
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