Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Abstract

AbstractBackgroundClinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gap-less implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content, but also to recognize every situation in which they are applicable. To not miss situations in which guideline recommendations should be applied, computerized clinical decision support could be given through a system that allows an automated monitoring of adherence to clinical guideline recommendation in individual patients.Objectives(1) To derive the requirements for a system that allows to monitor the adherence to evidence-based clinical guideline recommendations in individual patients, and based on these requirements, (2) to implement a software prototype that integrates clinical guideline recommendations with individual patient data and (3) to demonstrate the prototype’s utility on a COVID-19 intensive care treatment recommendation.MethodsWe performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system for supporting recommendation adherence monitoring in a consensus-based requirements analysis within loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, software developers). Based on these requirements, we implemented a prototype and demonstrated its functionality by integrating clinical data with a treatment recommendation.ResultsBased on our conceptual flow chart model of recommendation adherence monitoring in clinical routine, we identified four main requirements of a software system for automated support of recommendation adherence monitoring of in-hospital patients: (i) Ability to interpret guideline recommendations’ semantics and logics, (ii) integration of clinical routine data from various underlying data structures, (iii) automatic adoption of new or updated guideline recommendations, and (iv) user interfaces optimized for distinct groups of users. Using a prototype implementation that fulfills these requirements, we demonstrate how such a system could be applied to monitor guideline recommendation adherence over time in clinical patients.ConclusionsThe four main requirements identified through our model-based analysis represent the most important aspects that need to be considered when developing a clinical decision support system for monitoring the adherence to evidence-based clinical guideline recommendations in individual patients. As each of the requirements corresponds to a different expertise (guideline development, health data engineering, software development, patient treatment), a modularized software architecture separated by area of required expertise seems favorable. Our prototype successfully demonstrates how such a modular architecture can be implemented to allow real-time monitoring of guideline recommendation adherence. This prototype, which we released as open source to invigorate collaboration, could serve as a basis for further development to integrate guideline recommendations with clinical information systems.