A study of diverse clinical decision support rule authoring environments and requirements for integration

Zhou, Li; Karipineni, Neelima; Lewis, Janet; Maviglia, Saverio M.; Fairbanks, Amanda; Hongsermeier, Tonya; Middleton, Blackford; Rocha, Roberto A.

doi:10.1186/1472-6947-12-128

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…CDSS rule management and maintenance have been recognised as challenging in large institutions,4 17–19 which usually have in-house technical support and adequate resources. Thus, we anticipate that CDSS rule management and maintenance will be one of the greatest challenges to increased use in office-based settings, especially in solo practices.…”

Section: Discussionmentioning

confidence: 99%

Availability and usage of clinical decision support systems (CDSSs) in office-based primary care settings in the USA

Jing

Himawan

Law

2019

BMJ Health Care Inform

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BackgroundA clinical decision support system (CDSS) covers a broad spectrum of applications, for example, screening reminders, can reduce malpractice, improve preventive services and enable better management of chronic conditions. CDSSs have traditionally been used successfully in large hospitals. The availability (ie, whether the function is provided by the software) and usage (ie, actual use) of a CDSS in office-based primary care settings, however, are less well studied.ObjectiveTo establish a benchmark of CDSS availability and usage in office-based primary care settings, particularly given the large volume of visits in such settings.MethodsWe used the 2015 Centers for Disease Control and Prevention’s National Ambulatory Medical Care Survey to conduct secondary data analysis. We selected preventive services reminders and drug interaction alerts, along with several other variables as examples of a CDSS.ResultsCDSS usage rates ranged from 68.5% to 100% among solo or non-solo primary care practices owned by physicians or physician groups that have electronic medical records (EMRs)/electronic health records (EHRs) and 44.7% to 96.1%, regardless of EMR/EHR status. According to proportion tests, solo practices had significantly lower CDSS usage and availability rates on several measures if the practice is entirely EMR/EHR based and significantly lower (16.3%–28.9%) CDSS usage rates than did non-solo practices on each measure, regardless of EMR/EHR status.ConclusionIn the USA, a CDSS, especially under the categories of basic preventive reminders and drug interaction alerts, is used routinely between 68% and 100% in primary care if a practice is entirely EMR/EHR based. More work is needed, however, to determine the reasons for large usage gaps between solo and non-solo practices and to reduce such gaps.

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Section: Discussionmentioning

confidence: 99%

Availability and usage of clinical decision support systems (CDSSs) in office-based primary care settings in the USA

Jing

Himawan

Law

2019

BMJ Health Care Inform

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“…Referring to the CIG authoring approach proposed by Zhou et al [ 26 ], we designed a rapid CIG development approach that parallelizes data modeling and rule editing. In addition to the original approach, the output of each step was specified to eliminate ambiguities between different participants.…”

Section: Methodsmentioning

confidence: 99%

“…Zhou et al [ 26 ] reported a CIG authoring approach with six steps, including (1) create a knowledge specification, (2) integrate with terminology, (3) author rules, (4) test rules, (5) publish rules, and (6) generate reports. These six steps should be carried out sequentially, and key artifacts are produced in each step.…”

Section: Methodsmentioning

confidence: 99%

“…Developing a CIG is a time-consuming and resource-dependent process that requires a group of informaticists and medical specialists to work together closely for a considerable period of time because they must engage in a significant number of discussions to eliminate ambiguity and misunderstanding of the narrative guideline during development [ 25 , 26 ]. In the conventional approach, CIG development is broken down into several phases, and the input and output artifacts in each phase are defined at a conceptual level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

Nan¹,

Tang²,

Feng³

et al. 2020

JMIR Med Inform

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Background COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

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“…Furthermore, in the particular case of guideline-oriented CDS that is based on rules, a recent study shows the lack of rule languages that allow for shareable and standardised rules in healthcare [ 11 ]. As the computerisation of guidelines often involves their representation as rules, that finding calls for health information systems that are based on standards, both when it comes to their EHRs and CDS components.…”

Section: Introductionmentioning

confidence: 99%

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

Anani

Chen

Moreira

et al. 2014

BMC Med Inform Decis Mak

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BackgroundProviding scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care.MethodsWe extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the cases’ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data.ResultsWe successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results.ConclusionsShareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown.

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A study of diverse clinical decision support rule authoring environments and requirements for integration

Cited by 16 publications

References 29 publications

Availability and usage of clinical decision support systems (CDSSs) in office-based primary care settings in the USA

Availability and usage of clinical decision support systems (CDSSs) in office-based primary care settings in the USA

A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

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