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

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Abstract

Background

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed.

Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully.

Methods

We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist.

Results

We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy.

Conclusions

The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0772-3) contains supplementary material, which is available to authorized users.

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Most cited references 91

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Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality.

David Bates, Gilad Kuperman, Samuel Wang … (2003)

While evidence-based medicine has increasingly broad-based support in health care, it remains difficult to get physicians to actually practice it. Across most domains in medicine, practice has lagged behind knowledge by at least several years. The authors believe that the key tools for closing this gap will be information systems that provide decision support to users at the time they make decisions, which should result in improved quality of care. Furthermore, providers make many errors, and clinical decision support can be useful for finding and preventing such errors. Over the last eight years the authors have implemented and studied the impact of decision support across a broad array of domains and have found a number of common elements important to success. The goal of this report is to discuss these lessons learned in the interest of informing the efforts of others working to make the practice of evidence-based medicine a reality.

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Overriding of drug safety alerts in computerized physician order entry.

Heleen van der Sijs, Jos Aarts, Arnold Vulto … (2005)

Many computerized physician order entry (CPOE) systems have integrated drug safety alerts. The authors reviewed the literature on physician response to drug safety alerts and interpreted the results using Reason's framework of accident causation. In total, 17 papers met the inclusion criteria. Drug safety alerts are overridden by clinicians in 49% to 96% of cases. Alert overriding may often be justified and adverse drug events due to overridden alerts are not always preventable. A distinction between appropriate and useful alerts should be made. The alerting system may contain error-producing conditions like low specificity, low sensitivity, unclear information content, unnecessary workflow disruptions, and unsafe and inefficient handling. These may result in active failures of the physician, like ignoring alerts, misinterpretation, and incorrect handling. Efforts to improve patient safety by increasing correct handling of drug safety alerts should focus on the error-producing conditions in software and organization. Studies on cognitive processes playing a role in overriding drug safety alerts are lacking.

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A new sociotechnical model for studying health information technology in complex adaptive healthcare systems.

Hardeep Singh, Dean Sittig (2010)

Conceptual models have been developed to address challenges inherent in studying health information technology (HIT). This manuscript introduces an eight-dimensional model specifically designed to address the sociotechnical challenges involved in design, development, implementation, use and evaluation of HIT within complex adaptive healthcare systems. The eight dimensions are not independent, sequential or hierarchical, but rather are interdependent and inter-related concepts similar to compositions of other complex adaptive systems. Hardware and software computing infrastructure refers to equipment and software used to power, support and operate clinical applications and devices. Clinical content refers to textual or numeric data and images that constitute the 'language' of clinical applications. The human--computer interface includes all aspects of the computer that users can see, touch or hear as they interact with it. People refers to everyone who interacts in some way with the system, from developer to end user, including potential patient-users. Workflow and communication are the processes or steps involved in ensuring that patient care tasks are carried out effectively. Two additional dimensions of the model are internal organisational features (eg, policies, procedures and culture) and external rules and regulations, both of which may facilitate or constrain many aspects of the preceding dimensions. The final dimension is measurement and monitoring, which refers to the process of measuring and evaluating both intended and unintended consequences of HIT implementation and use. We illustrate how our model has been successfully applied in real-world complex adaptive settings to understand and improve HIT applications at various stages of development and implementation.

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Author and article information

Contributors

Stijn Van de Velde:

ORCID: http://orcid.org/0000-0001-8908-3823

Stijn.vandevelde@fhi.no

Ilkka Kunnamo: Ilkka.Kunnamo@duodecim.fi

Pavel Roshanov: Pavelroshanov@gmail.com

Tiina Kortteisto: Tiina.Kortteisto@pshp.fi

Bert Aertgeerts: Bert.Aertgeerts@kuleuven.be

Per Olav Vandvik: Per.Vandvik@gmail.com

Signe Flottorp: Signe.Flottorp@fhi.no

Journal

Journal ID (nlm-ta): Implement Sci

Journal ID (iso-abbrev): Implement Sci

Title: Implementation Science : IS

Publisher: BioMed Central (London )

ISSN (Electronic): 1748-5908

Publication date (Electronic): 25 June 2018

Publication date PMC-release: 25 June 2018

Publication date Collection: 2018

Volume: 13

Electronic Location Identifier: 86

Affiliations

[1 ]ISNI 0000 0001 1541 4204, GRID grid.418193.6, Centre for Informed Health Choices, Division for Health Services, , Norwegian Institute of Public Health, ; Oslo, Norway

[2 ]ISNI 0000 0001 0693 4013, GRID grid.483796.7, Duodecim, Scientific Society of Finnish Physicians, ; Helsinki, Finland

[3 ]ISNI 0000 0004 1936 8227, GRID grid.25073.33, Department of Medicine, , McMaster University, ; Hamilton, Canada

[4 ]ISNI 0000 0004 0628 2985, GRID grid.412330.7, Tampere University Hospital, ; Tampere, Finland

[5 ]ISNI 0000 0001 0668 7884, GRID grid.5596.f, Department of Public Health and Primary Care, , KU Leuven, ; Leuven, Belgium

[6 ]MAGIC Non-Profit Research and Innovation Programme, Oslo, Norway

[7 ]ISNI 0000 0004 1936 8921, GRID grid.5510.1, Institute of Health and Society, , University of Oslo, ; Oslo, Norway

Author information

Stijn Van de Velde http://orcid.org/0000-0001-8908-3823

Article

Publisher ID: 772

DOI: 10.1186/s13012-018-0772-3

PMC ID: 6019508

PubMed ID: 29941007

SO-VID: ac572cbb-bfbb-4038-ad61-65d30d2a4870

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 18 January 2018

Date accepted : 30 May 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;

Award ID: 654981

Award Recipient : Stijn Van de Velde

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: clinical computerised decision support systems,practice guidelines,guideline adherence,evidence-based medicine,implementation

Data availability:

ScienceOpen disciplines: Medicine

Keywords: clinical computerised decision support systems, practice guidelines, guideline adherence, evidence-based medicine, implementation

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

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Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

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Sex and gender-sensitive medicine

Most cited references 91

Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality.

Overriding of drug safety alerts in computerized physician order entry.

A new sociotechnical model for studying health information technology in complex adaptive healthcare systems.

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