Dear Editors,
We recognize that inefficient and idiosyncratic workflows in health care contribute to a myriad of obstacles for all healthcare stakeholders, including misuse of resources, provider burnout, and increased burden on patients and their caregivers.1 Therefore, we were pleased to read Zayas-Cabán et al’s2 article, “Priorities to accelerate workflow automation in healthcare.” We applaud them for their work illuminating determinants, priorities, and associated strategies for workflow automation. We augment their findings by proposing a seventh priority to stand alongside their original 6—develop and promote infrastructure to facilitate findability, accessibility, interoperability, and reusability of workflows (Table 1).
Table 1.
An addendum to Table 1 from Zayas-Cabán et al’s article entitled “Priorities to accelerate workflow automation in health care”
| Priorities | Desired end state | Goals |
|---|---|---|
| … | … | … |
| 7. Develop and promote infrastructure to facilitate findability, accessibility, interoperability, and reusability of workflows. | Computable artifacts necessary for workflow automation can readily be shared, modified, and scaled across health systems. |
|
Note: Priorities 1–6 are unchanged.
In our view, the automation of healthcare workflows depends on the deployment of reliable, valid, robust, and proven computable biomedical knowledge (CBK) artifacts. We define CBK artifacts as separately packaged software implementations of evidence-based procedural, logical, mathematical, and statistical algorithms.3 We hold this view at an equal level of importance as the need for high-quality, interoperable data and a deep understanding of workflows, as emphasized by Zayas-Cabán et al. Our perspective is that automation of workflows will depend on formalizing and explicitly representing current and desired (optimal) workflows so that they can be tracked and processed by computers in valuable ways. Moreover, to achieve powerful automation, infrastructure must be developed to coordinate and combine computable workflows or process models with AI models, computable guidelines, and other CBK artifacts.
Of course, we recognize the enormous work ahead to realize our vision of a CBK-based ecosystem from which application and automation developers can draw on many disparate CBK artifacts. Consider the large-scale effort needed to develop further and adopt standards for workflow models, such as those for process, case, and decision modeling, promoted by the Business Process Modeling Plus Health (BPM+) community of practice.4 Building appropriate computer actionable workflow models is labor intensive, requiring workflow observation, iterative expert review, pilot testing, and so on. Once workflow models are built, they evolve and are subject to change, bringing versioning to the fore. To keep up with rapidly growing knowledge, we see a significant need for better CBK artifact packaging so that all CBK carries rich metadata, making them easier to manage and combine in large numbers to form new knowledge bases. Suppose we can get the packaging of individual CBK artifacts right. In that case, we anticipate new knowledge bases will be created and deployed on a massive scale to support healthcare apps and automation. All of this may accelerate the adoption of workflow automation by reducing the burden of redundant knowledge development, increasing stakeholder access to models, and allowing procedural knowledge to be scaled and integrated with other types of knowledge. Hence, promoting findability, accessibility, interoperability, and reusability (FAIR).5
To achieve the goals of FAIR digital knowledge objects for healthcare workflow automation, we suggest extending the supporting strategies of demonstrate, incent, and scale, proposed by Zayas-Cabán et al, to account for CBK infrastructure. Work needs to be done to test and evaluate automation approaches and demonstrate the packaging and appropriate use cases for packaged workflow artifacts. Policy needs to be drafted to incentivize knowledge developers and users to uptake standards for packaging and sharing CBK artifacts. Finally, packaging should be approached with relevant scale stories to ensure the reusability and adaptability of knowledge artifacts. Along with our colleagues in the Mobilizing Computable Biomedical Knowledge movement and many others, we are actively working with these strategies to promote FAIR principles for healthcare workflow automation.
In a world where medical knowledge is ever-expanding while inefficiencies and inequities persist,1 the need for workflow automation will continue to grow. We believe stakeholders interested in researching, building, and implementing workflow automation should prioritize building and supporting the infrastructure to package and share CBK artifacts and the knowledgebases formed by combining them.
Contributor Information
Philip D Barrison, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Allen Flynn, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA; School of Information, University of Michigan, Ann Arbor, Michigan, USA.
Rachel Richesson, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Marisa Conte, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Zach Landis-Lewis, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Peter Boisvert, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Charles P Friedman, Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA.
REFERENCES
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