Development and dissemination of clinical decision support across institutions: standardization and sharing of refugee health screening modules

Neonatal Intensive Care Workflow Analysis Informing NEC-Zero Clinical Decision Support Design

Decision support in neonatal ICUs is needed, especially for prevention and risk awareness of the devastating complication of necrotizing enterocolitis, a major cause of emergency surgery among fragile infants. The purpose of this study was to describe the current clinical workflow and sociotechnical processes among clinicians for necrotizing enterocolitis risk awareness, timely recognition of symptoms, and treatment to inform decision support design. A qualitative descriptive study was conducted. Focus groups were held in two neonatal ICUs (five groups in Unit A and six in Unit B). Transcripts were analyzed using content analysis and compared with field notes. Clinicians (N = 27) included nurses (37%), physicians (30%), neonatal nurse practitioners (19%), and other staff (16%). Workflow processes differed for nurses (who see necrotizing enterocolitis signs and notify providers to order diagnostic tests and treatments) and providers (who receive notification of necrotizing enterocolitis concern and then decide how to act). Clinicians desired (1) a necrotizing enterocolitis-relevant dashboard to support nutrition tracking and necrotizing enterocolitis recognition; (2) features to support decision-making (eg, necrotizing enterocolitis risk and adherence scoring); (3) breast milk tracking and feeding clinical decision support; (4) tools for necrotizing enterocolitis surveillance and quality reporting; and (5) general EHR optimizations to improve user experience.

Coordinated use of Health Level 7 standards to support clinical decision support: Case study with shared decision making and drug-drug interactions

BACKGROUND

Despite advances in interoperability standards, it remains challenging and often costly to share clinical decision support (CDS) across healthcare organizations. This is due in part to limited coordination among CDS components. To improve coordination of CDS components, Health Level 7 (HL7) has developed a suite of interoperability standards with Fast Health Interoperability Resources (FHIR) specification as a common information model. Evidence is needed to determine the feasibility of implementing these CDS components; therefore, the objective of this study was to investigate the coordination of emerging HL7 standards with modular CDS architecture components.

METHODS

We used a modular, standards-based architecture consisting of four components: data, logic, services, and applications. The implementation use-case was an application to support shared decision making in the context of drug-drug interactions (DDInteract).

RESULTS

DDInteract uses FHIR as the data representation model, Clinical Quality Language for logic representation, CDS Hooks for the services layer, and Substitutable Medical Apps Reusable Technologies for application integration. DDInteract was first implemented in a sandbox environment and then in an electronic health record (Epic®) test environment. DDInteract can be integrated in clinical workflows through on-demand access from a menu or through CDS Hooks upon opening a patient's record or placing a medication order.

CONCLUSION

In the context of drug interactions, DDInteract is the first application to leverage a full stack of emerging interoperability standards for each component of modular CDS architecture. The demonstrated feasibility of interoperable components can be generalized to other modular CDS applications.

OUP accepted manuscript

 

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals.

Objective

The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination.

Materials and Methods

An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer.

Results

The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services.

Discussion

GARDE’s component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

The role of organizational culture in health information technology implementations: A scoping review

Introduction

Methods

Results

Conclusions

Translating an evidence-based clinical pathway into shareable CDS: developing a systematic process using publicly available tools

OBJECTIVE

To develop a process for translating semi-structured clinical decision support (CDS) into shareable, computer-readable CDS.

MATERIALS AND METHODS

We developed a systematic and transparent process using publicly available tools (eGLIA, GEM Cutter, VSAC, and the CDS Authoring Tool) to translate an evidence-based clinical pathway (CP) into a Clinical Quality Language (CQL)-encoded CDS artifact.

RESULTS

We produced a 4-phase process for translating a CP into a CQL-based CDS artifact. CP content was extracted using GEM into discrete clinical concepts, encoded using standard terminologies into value sets on VSAC, evaluated against workflows using a wireframe, and finally structured as a computer readable CDS artifact using CQL. This process included a quality control step and intermediate products to support transparency and reuse by other CDS developers.

DISCUSSION

Translating a CP into a shareable, computer-readable CDS artifact was accomplished through a systematic process. Our process identified areas of ambiguity and gaps in the CP, which generated improvements in the CP. Collaboration with clinical subject experts and the CP development team was essential for translation. Publicly available tools were sufficient to support most translation steps, but expression of certain complex concepts required manual encoding.

CONCLUSION

Standardized development of CDS from a CP is feasible using a systematic 4-phase process. CPs represent a potential reservoir for developers of evidence-based CDS. Aspects of CP development simplified portions of the CDS translation process. Publicly available tools can facilitate CDS development; however, enhanced tool features are needed to model complex CDS statements.

Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network

BACKGROUND AND OBJECTIVE

Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, research on multicenter clinical collaborative networks, which can provide external medical data, has received increasing attention. With the increasing availability of machine learning techniques such as transfer learning, leveraging large-scale patient data from multiple hospitals to build data-driven predictive models with clinical application potential provides an alternative solution to address the problem of limited patient data.

METHODS

A multicenter hybrid semi-supervised transfer learning model (MHSTL) is proposed in this study on the basis of unified common data model to ensure multicenter data standardized representation. Then the hospital-specific features, along with the co-occurrence features across domains, are aligned through a representation learning architecture that is built based on deep neural networks and the newly proposed neural decision forest model. In this process, limited patient data from the target hospital, both labeled and unlabeled, are incorporated during the feature adaptation process, thereby contributing to better model performance. Without patient-level data sharing, the proposed model learning strategy which overcomes feature misalignment and distribution divergence, enables the multi-source transfer learning process in the case of insufficient and unlabeled patient data at target hospital.

RESULTS

The effectiveness of the proposed transfer learning model was evaluated on a collaborative research network of colorectal cancer patients in the US and China. The results demonstrate that the proposed model can achieve much better performance for predicting target risk with limited resources on patient data than baseline models      . Better discrimination and calibration ability are also observed when sufficient labeled data are not available in the target hospital for prognosis prediction tasks      . Further exploratory experiments show that the proposed approach exhibits good model generalizability regardless of the data heterogeneity. With the help of the SHapley Additive exPlanations for model interpretation, the effectiveness of incorporating hospital-specific features in the transfer learning model is shown.

CONCLUSIONS

In this study, the proposed method can develop prediction models from multiple source hospitals and exhibit good performance by leveraging cross-domain hospital-specific feature information, therefore enhancing the model prediction when applied to single medical institution with limited patient data.

To Share is Human! Advancing Evidence into Practice through a National Repository of Interoperable Clinical Decision Support

BACKGROUND

Healthcare systems devote substantial resources to the development of clinical decision support (CDS) largely independently. The process of translating evidence-based practice into useful and effective CDS may be more efficient and less duplicative if healthcare systems shared knowledge about the translation, including workflow considerations, key assumptions made during the translation process, and technical details.

OBJECTIVE

Describe how a national repository of CDS can serve as a public resource for healthcare systems, academic researchers, and informaticists seeking to share and reuse CDS knowledge resources or "artifacts."

METHODS

In 2016, the Agency for Healthcare Research and Quality (AHRQ) launched CDS Connect as a public, web-based platform for authoring and sharing CDS knowledge artifacts. Researchers evaluated early use and impact of the platform by collecting user experiences of AHRQ-sponsored and community-led dissemination efforts and through quantitative/qualitative analysis of site metrics. Efforts are ongoing to quantify efficiencies gained by healthcare systems that leverage shared, interoperable CDS artifacts rather than developing similar CDS de novo and in isolation.

RESULTS

Federal agencies, academic institutions, and others have contributed over 50 entries to CDS Connect for sharing and dissemination. Analysis indicates shareable CDS resources reduce team sizes and the number of tasks and time required to design, develop, and deploy CDS. However, the platform needs further optimization to address sociotechnical challenges. Benefits of sharing include inspiring others to undertake similar CDS projects, identifying external collaborators, and improving CDS artifacts as a result of feedback. Organizations are adapting content available through the platform for continued research, innovation, and local implementations.

CONCLUSION

CDS Connect has provided a functional platform where CDS developers are actively sharing their work. CDS sharing may lead to improved implementation efficiency through numerous pathways, and further research is ongoing to quantify efficiencies gained.