A Multi-step Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs)

Abstract

Introduction:The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data.Model Components:We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels – from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components – focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements.Conclusion:The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) – using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.

Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review

“…The semantic interoperability indicator examines the extent to which information exchanged between digital systems can be accurately interpreted and understood by each system involved [ 34 , 42 ]. As such, the semantic interoperability indicator is dependent on the transparency of the underlying lexicon and data dictionary to ensure the intended meaning of the information exchange is retained [ 42 ].…”

“…The semantic interoperability indicator examines the extent to which information exchanged between digital systems can be accurately interpreted and understood by each system involved [ 34 , 42 ]. As such, the semantic interoperability indicator is dependent on the transparency of the underlying lexicon and data dictionary to ensure the intended meaning of the information exchange is retained [ 42 ]. Alternatively, the syntactic interoperability indicator represents the extent to which technical standards have been defined to enable the consistent, effective, and efficient integration of digital systems and services [ 30 , 34 ].…”

Requirements and validation of a prototype learning health system for clinical diagnosis

“…Other approaches to decision support for interventions and disease management have used rules triggered or combined together in the form of guidelines based on statements using rule engines such as DROOLS and knowledge rule languages like Arden Syntax, GLIF, and GELLO . A more recent approach provides for separation of the actual representation of clinical knowledge using an underlying ontology model and the dissemination of that knowledge using semantic web technologies . These models define the named directional relationships between evidence facts in a unified model structure.…”

Towards a Maturity Model for Clinical Decision Support Operations

“…12 For example, Knosp et al demonstrate the use of a maturity index and a deployment index to assess academic health centers' capacity to provide research IT services and determine organizational investment priorities. 13 Multiple health care IT maturity models exist that concentrate on functional capacities related to a specific technology or a set of tasks [14][15][16][17] or an organization's acceptance of and regular use of a set of methodologies. 18,19 To our knowledge, no standardized maturity model yet exists for health care organizations' CDS capabilities.…”