Towards a Maturity Model for Clinical Decision Support Operations

Abstract

Clinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main “pillars”: “Content Creation,” “Analytics and Reporting,” and “Governance and Management.” Each pillar contains five levels—advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A “roof” represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.

Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review

“…The external interoperability indicator assesses the adoption of standards to integrate systems, services, and data across the entire health care system [ 1 , 11 , 30 , 39 - 41 ]. Conversely, the internal interoperability indicator assesses the integration of systems and data across departments within a single health care organization [ 30 , 40 ].…”

A survey of extant organizational and computational setups for deploying predictive models in health systems

“… 38–40 Traditional non-AI CDS is now primarily developed by EHR vendors, deployed via EHR and managed by IT departments with clinical support. 20 If AI-CDS follows the trajectory of traditional CDS, the role of the EHR and IT department will likely grow while researcher roles decrease. Institutional investment will be key to transforming decentralized translation operations into either subtype of AIHC or IT department led.…”

“… 17–19 Given that the cost of deploying a single model into clinical workflow can be high, 8 setting organizational strategy and competence to do so repeatedly and reliably as healthcare systems transition from pilots to production is likely an important factor for successfully capturing the benefits of AI-enabled healthcare. 20 , 21 …”

“… 23 Orenstein et al lay a roadmap of organizational functions and processes to help healthcare systems more effectively use CDS. 20 However, to our knowledge, no study has systematically identified the different types of configurations regarding organizational and computational setups that healthcare systems may adopt for integrating AI-CDS into clinical workflow nor scrutinized their trade-offs.…”

Incorporating the Voice of Community Based Pediatricians to Improve Discharge Communication

“… 2 , 8 To augment insights from the survey, we also performed semi-structured interviews based on grounded theory 9 in a sample of PCPs who we contacted from a sequential sample of hospital discharges. In the setting of a QI initiative, we used a discount approach 9 , 10 in which the interviewer took notes during phone conversations and member-checked insights with the interviewee but did not transcribe calls verbatim. Out of 422 outpatient pediatricians, 101 (24%) completed the survey, and 12 of the 14 (86%) hospitalists completed the hospitalist survey.…”