Currently, the time from evidence to implementation is too long and without standards there are disparities in the same 'guideline’ as implemented at different sites (CDOs)- same expert-vetted recommendations results in disparate patient-specific guidance through various decision support and care delivery process interventions. It is also time consuming and costly to keep guidelines up-to-date and for CDOs and practitioners to implement or even identify the appropriate version. All of this leads to wasted time and effort as well as workflow disruption and lack of trust on the practitioners part.
Further, patient data including the planned care activities aren't readily, easily, or consistently captured as part of routine patient care. This has adverse consequences for reporting and research as well as quality improvement or even effectively managing local practices or large healthcare delivery systems. Evidence, Guidance, Practice, and Reporting (Evidence) need to all share common data, semantics, and knowledge (inferences) where applicable and appropriate.
Faithfully expressing computable representations of Clinical Practice Guidelines (CPGs) as shareable, standards-based formats either concurrent with guideline development of or even after the fact has been shown to address consistency as well as reduce the redundant local re-translations of guidance into actionable practice-oriented interventions. Such computable representations (as models of 'care processes) further afford faithful derivation of: interoperable CDS (alerts, reminders, documentation templates, summary views), digital Quality Measures (as well as real-time, patient-level metrics of compliance and adherence); shareable Apps (providing cognitive support); Comprehensive Shared Care Plans; and patient-level Case Reports (eCaseReports) for registries and cohort studies (including on utilization and outcomes directly related to the CPGs). Such standards-based, computable representations faithful to the intent of the guidelines and recommendations can be invaluable assets for individual participants within as well as across the Learning Health System.
Finally, there are too few individuals with the skills and knowledge needed to author (knowledge engineer) CPGs and related assets(CDS, eCQM). This requires specialized skills, deep understanding of the standards and knowledge representation formats and languages, as well as a cross-functional, integrated team working in a fairly Agile fashion using numerous specialized tools for their given activities that rarely interoperate. Examples of such approaches can be found here and here. However, "Authoring", or rather knowledge engineering and more so the knowledge lifecycle development tooling to support such activities, is not new, they just have not yet been developed for these standards, languages, and/or use case (CPGs and derivatives). Current experts in this field have estimated that such tooling could reduce their work effort by orders of magnitude while enabling numerous much less specialized individuals to perform the knowledge engineering tasks and activities. Such "Authoring Tools" have been shown to vastly improve the quality, quantity, and speed-to-development for similar initiatives (e.g. measure development, business process modeling, software development, data engineering).
As we progress into the future, more and more of this tooling would be oriented to true clinical domain experts and afford them the ability to develop the computable representations of the clinical best practice recommendations they wish to convey to their peers in daily practice- at the bedside, at the point of opportunity for action, and within the context of real-world data and clinical practice (workflow). To support this work there needs to be related developments in strategies and tools to produce and manage living, computable evidence, and to keep guidance living as the evidence base evolves.
Tool Users/Use Cases
Patient Journey illustrates how guidance on specific clinical targets (e.g., Long COVID, COVID Anticoagulation, etc.) is used to support patient and care team decisions and actions; tools described here are how that guidance is expressed in computable/interoperable form so those user-facing tools can be incorporated into workflow as demonstrated in the patient journey.
Produce Guidance, Create Tools