The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Velde, Stijn Van de; Kunnamo, Ilkka; Roshanov, Pavel S; Kortteisto, Tiina; Aertgeerts, Bert; Vandvik, Per Olav; Flottorp, Signe

doi:10.1186/s13012-018-0772-3

Cited by 89 publications

(77 citation statements)

References 85 publications

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“…To date, what is known is that CDS tools can play an integral role in assisting healthcare providers in providing patients optimal care by providing patient-specific recommendations at the point of need [11,35]. Using the current body of literature on CDS challenges [36] and guidelines including best practices and principle guidelines outlined by Wright et al [33] and the GUIDES checklist [37], we anticipated and developed strategies to overcome these challenges. Leveraging key stakeholders, aligning with organizational priorities and goals, and employing an iterative process for continual improvements and monitoring the usability led to our success [33].…”

Section: Discussionmentioning

confidence: 99%

Design and implementation of a clinical decision support tool for primary palliative Care for Emergency Medicine (PRIM-ER)

Tan

Durbin

Chung

et al. 2020

BMC Med Inform Decis Mak

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Background: The emergency department is a critical juncture in the trajectory of care of patients with serious, lifelimiting illness. Implementation of a clinical decision support (CDS) tool automates identification of older adults who may benefit from palliative care instead of relying upon providers to identify such patients, thus improving quality of care by assisting providers with adhering to guidelines. The Primary Palliative Care for Emergency Medicine (PRIM-ER) study aims to optimize the use of the electronic health record by creating a CDS tool to identify high risk patients most likely to benefit from primary palliative care and provide point-of-care clinical recommendations. Methods: A clinical decision support tool entitled Emergency Department Supportive Care Clinical Decision Support (Support-ED) was developed as part of an institutionally-sponsored value based medicine initiative at the Ronald O. Perelman Department of Emergency Medicine at NYU Langone Health. A multidisciplinary approach was used to develop SupportED including: a scoping review of ED palliative care screening tools; launch of a workgroup to identify patient screening criteria and appropriate referral services; initial design and usability testing via the standard System Usability Scale questionnaire, education of the ED workforce on the SupportED background, purpose and use, and; creation of a dashboard for monitoring and feedback. Results: The scoping review identified the Palliative Care and Rapid Emergency Screening (P-CaRES) survey as a validated instrument in which to adapt and apply for the creation of the CDS tool. The multidisciplinary workshops identified two primary objectives of the CDS: to identify patients with indicators of serious life limiting illness, and to assist with referrals to services such as palliative care or social work. Additionally, the iterative design process yielded three specific patient scenarios that trigger a clinical alert to fire, including: 1) when an advance care planning document was present, 2) when a patient had a previous disposition to hospice, and 3) when historical and/or current clinical data points identify a serious life-limiting illness without an advance care planning document present. Monitoring and feedback indicated a need for several modifications to improve CDS functionality. Conclusions: CDS can be an effective tool in the implementation of primary palliative care quality improvement best practices. Health systems should thoughtfully consider tailoring their CDSs in order to adapt to their unique workflows and environments. The findings of this research can assist health systems in effectively integrating a primary palliative care CDS system seamlessly into their processes of care.

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Section: Discussionmentioning

confidence: 99%

Design and implementation of a clinical decision support tool for primary palliative Care for Emergency Medicine (PRIM-ER)

Tan

Durbin

Chung

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…Based on the systematic review of Van de Velde et al, a 16-factor checklist was developed consisting of four domains which may affect the use of the system and the success of the effect of CCDS systems (context, content, system, and implementation) [41]. When evaluating our intervention with the EBMeDS system against the GUIDES checklist, we discovered that improvements could be made in several areas.…”

Section: Interpretation Of Main Resultsmentioning

confidence: 99%

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

et al. 2020

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Background: The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. Methods: A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre-to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.Results: Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. Conclusions: The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.

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“…Nonetheless, CDS systems have variable effectiveness at improving adherence to evidence-based practices that lead to better outcomes. [1][2][3][4][5][6][7] The "five rights" CDS framework, 8 GUIDES checklist, 9 and similar tools 10 can help identify success factors of CDS systems that are associated with effective interventions. To incorporate these success factors reliably into the creation and maintenance of CDS systems, health care systems must coordinate CDS design, measurement, and governance.…”

Section: Background and Significancementioning

confidence: 99%

“…Similarly, an organization may develop a new capacity such as usercentered design but not yet apply it systematically or comprehensively across all CDS artifacts or processes. Advancing in tandem along the columns can confer additional advantages; for example, a robust analytics platform can yield greater benefits when coupled with interdisciplinary Chief medical information officer (CMIO) 49 (39) Physician informaticist (not CMIO) 47 (37) Nonphysician informaticist 11 (9) Knowledge manager/engineer 7 (5) Other 13 (10) Abbreviation: CDS, clinical decision support. a Each individual categorized into a single role based on online profiles.…”

Section: Proposed Maturity Modelmentioning

confidence: 99%

Towards a Maturity Model for Clinical Decision Support Operations

et al. 2019

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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.

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The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Cited by 89 publications

References 85 publications

Design and implementation of a clinical decision support tool for primary palliative Care for Emergency Medicine (PRIM-ER)

Design and implementation of a clinical decision support tool for primary palliative Care for Emergency Medicine (PRIM-ER)

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

Towards a Maturity Model for Clinical Decision Support Operations

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