Clinical Decision Support in Cardiovascular Medicine: Effectiveness, Implementation Barriers, and Regulation

AbstractDespite considerable progress in addressing cardiovascular disease (CVD) over the past 50 years, there remain many gaps in CVD care quality. Multiple missed opportunities have been identified at every step in the prevention and treatment of CVD, such as failure to make risk factor modifications, failure to diagnose CVD, and failure to use proper evidence-based treatments. With the digital transformation of medicine and advances in health information technology, clinical decision support (CDS) tools offer promise to enhance the efficiency and effectiveness of cardiovascular care delivery. Yet, to date, the promise of CDS delivering scalable and sustained value for patient care in clinical practice has not been realized. Here, we review evidence on key emerging questions around the development, implementation, and regulation of CDS with a focus on CVD. We first review evidence on the effectiveness of CDS on patient health and health delivery outcomes related to CVD and features predictive of effectiveness. We then review the barriers encountered during CDS implementation in cardiovascular care with a focus on unintended consequences and strategies to promote successful implementation. Finally, we review the current legal and regulatory environment of CDS with specific examples for CVD.

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A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2018.7104 ◽

2019 ◽

Vol 17 (4) ◽

pp. 331-338 ◽

Cited By ~ 12

Author(s):

Pamala A. Pawloski ◽

Gabriel A. Brooks ◽

Matthew E. Nielsen ◽

Barbara A. Olson-Bullis

Keyword(s):

Systematic Review ◽

Decision Support ◽

Clinical Oncology ◽

Patient Outcomes ◽

Clinical Decision Support ◽

Cancer Care ◽

Care Delivery ◽

Clinical Decision ◽

Care Quality ◽

Patient Reported

Background: Electronic health records are central to cancer care delivery. Electronic clinical decision support (CDS) systems can potentially improve cancer care quality and safety. However, little is known regarding the use of CDS systems in clinical oncology and their impact on patient outcomes. Methods: A systematic review of peer-reviewed studies was performed to evaluate clinically relevant outcomes related to the use of CDS tools for the diagnosis, treatment, and supportive care of patients with cancer. Peer-reviewed studies published from 1995 through 2016 were included if they assessed clinical outcomes, patient-reported outcomes (PROs), costs, or care delivery process measures. Results: Electronic database searches yielded 2,439 potentially eligible papers, with 24 studies included after final review. Most studies used an uncontrolled, pre-post intervention design. A total of 23 studies reported improvement in key study outcomes with use of oncology CDS systems, and 12 studies assessing the systems for computerized chemotherapy order entry demonstrated reductions in prescribing error rates, medication-related safety events, and workflow interruptions. The remaining studies examined oncology clinical pathways, guideline adherence, systems for collection and communication of PROs, and prescriber alerts. Conclusions: There is a paucity of data evaluating clinically relevant outcomes of CDS system implementation in oncology care. Currently available data suggest that these systems can have a positive impact on the quality of cancer care delivery. However, there is a critical need to rigorously evaluate CDS systems in oncology to better understand how they can be implemented to improve patient outcomes.

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Utilization and effects of mobile electronic clinical decision support on pediatric asthma care quality in the emergency department and inpatient setting

JAMIA Open ◽

10.1093/jamiaopen/ooab019 ◽

2021 ◽

Vol 4 (2) ◽

Author(s):

Ellen Kerns ◽

Russell McCulloh ◽

Sarah Fouquet ◽

Corrie McDaniel ◽

Lynda Ken ◽

...

Keyword(s):

Emergency Department ◽

Decision Support ◽

Clinical Decision Support ◽

Pediatric Asthma ◽

Clinical Decision ◽

Care Quality ◽

Asthma Care ◽

Mixed Effect ◽

National Quality ◽

Electronic Clinical Decision Support

Abstract Objective To determine utilization and impacts of a mobile electronic clinical decision support (mECDS) on pediatric asthma care quality in emergency department and inpatient settings. Methods We conducted an observational study of a mECDS tool that was deployed as part of a multi-dimensional, national quality improvement (QI) project focused on pediatric asthma. We quantified mECDS utilization using cumulative screen views over the study period in the city in which each participating site was located. We determined associations between mECDS utilization and pediatric asthma quality metrics using mixed-effect logistic regression models (adjusted for time, site characteristics, site-level QI project engagement, and patient characteristics). Results The tool was offered to clinicians at 75 sites and used on 286 devices; cumulative screen views were 4191. Children’s hospitals and sites with greater QI project engagement had higher cumulative mECDS utilization. Cumulative mECDS utilization was associated with significantly reduced odds of hospital admission (OR: 0.95, 95% CI: 0.92–0.98) and higher odds of caregiver referral to smoking cessation resources (OR: 1.08, 95% CI: 1.01–1.16). Discussion We linked mECDS utilization to clinical outcomes using a national sample and controlling for important confounders (secular trends, patient case mix, and concomitant QI efforts). We found mECDS utilization was associated with improvements in multiple measures of pediatric asthma care quality. Conclusion mECDS has the potential to overcome barriers to dissemination and improve care on a broad scale. Important areas of future work include improving mECDS uptake/utilization, linking clinicians’ mECDS usage to clinical practice, and studying mECDS’s impacts on other common pediatric conditions.

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Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

Applied Clinical Informatics ◽

10.1055/s-0041-1722918 ◽

2021 ◽

Vol 12 (01) ◽

pp. 182-189

Author(s):

Adam Wright ◽

Skye Aaron ◽

Allison B. McCoy ◽

Robert El-Kareh ◽

Daniel Fort ◽

...

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Health Care Organizations ◽

Software Tool ◽

Clinical Decision ◽

Unintended Consequences ◽

Boolean Logic ◽

Safety Issues ◽

Automated Algorithm ◽

Logic Minimization

Abstract Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

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Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes

The Gerontologist ◽

10.1093/geront/gny100 ◽

2018 ◽

Vol 59 (6) ◽

pp. 1024-1033 ◽

Cited By ~ 5

Author(s):

Mustafa Ozkaynak ◽

Blaine Reeder ◽

Cynthia Drake ◽

Peter Ferrarone ◽

Barbara Trautner ◽

...

Keyword(s):

Decision Support ◽

Data Analysis ◽

Nursing Homes ◽

Clinical Decision Support ◽

Care Delivery ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Staff Members ◽

Design And Implementation ◽

Multiple Levels

Abstract Background and Objectives Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. Research Design and Methods We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. Results The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members’ preferences on work activities. We also identified workflow-related differences between the two settings. Discussion and Implications Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

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Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program

Applied Clinical Informatics ◽

10.1055/s-0038-1641594 ◽

2018 ◽

Vol 09 (02) ◽

pp. 248-260 ◽

Cited By ~ 10

Author(s):

Mustafa Ozkaynak ◽

Danny Wu ◽

Katia Hannah ◽

Peter Dayan ◽

Rakesh Mistry

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Antimicrobial Stewardship ◽

Time Allocation ◽

Care Delivery ◽

Tertiary Care ◽

Clinical Decision ◽

Design And Implementation ◽

Decision Points ◽

Stewardship Program

Background Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription. Objective This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR. Methods Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points. Results A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family. Conclusion The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.

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Contributions on Clinical Decision Support from the 2018 Literature

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677929 ◽

2019 ◽

Vol 28 (01) ◽

pp. 135-137 ◽

Cited By ~ 1

Author(s):

Vassilis Koutkias ◽

Jacques Bouaud ◽

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Care Delivery ◽

Metastatic Cancer ◽

Clinical Decision ◽

Data Driven ◽

Editorial Team ◽

Cancer Center ◽

Bibliographic Databases ◽

Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

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A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness

Applied Clinical Informatics ◽

10.1055/s-0039-1693123 ◽

2019 ◽

Vol 10 (03) ◽

pp. 505-512

Author(s):

Julia Whitlow Yarahuan ◽

Amy Billet ◽

Jonathan D. Hron

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Health Care Quality ◽

Platelet Transfusion ◽

Tertiary Care ◽

Clinical Decision ◽

Care Quality ◽

Inpatient Service ◽

The Impact ◽

Order Set

Background and Objectives Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools. Methods Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology. Results We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%. Conclusion We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.

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Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677903 ◽

2019 ◽

Vol 28 (01) ◽

pp. 128-134 ◽

Cited By ~ 16

Author(s):

Farah Magrabi ◽

Elske Ammenwerth ◽

Jytte Brender McNair ◽

Nicolet F. De Keizer ◽

Hannele Hyppönen ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Medical Informatics ◽

Clinical Decision Support ◽

Working Group ◽

Care Delivery ◽

Clinical Decision ◽

Design Development ◽

Ongoing Surveillance ◽

Practical Implications

Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

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Clinical Decision Support Systems for ‘Making It Easy to Do It Right’

Neonatal Monitoring Technologies ◽

10.4018/978-1-4666-0975-4.ch015 ◽

2012 ◽

pp. 324-335

Author(s):

Anne-Marie Scheepers-Hoeks ◽

Floor Klijn ◽

Carolien van der Linden ◽

Rene Grouls ◽

Eric Ackerman ◽

...

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Care Delivery ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Health Care Delivery System ◽

Computer Based ◽

Medical Guidelines

Medical guidelines and best practises are used in medicine to increase the quality of the health-care delivery system. To support implementation and application of these guidelines, clinical decision support systems (CDSS) have been developed. These systems are defined as ‘Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care’ (MeSH) These are integrated with so-called Electronic Health Records (EHR), which have been developed by companies and National Governmental Institutes, and are used to register and present the patient medical data. The integration of an EHR with CDSS modules will revolutionize the way medicine will be practiced. In pediatrics, as well as geriatrics, such systems might prove to be even more needed. The development, use, and maintenance of CDSS in a hospital are complex and far from trivial. This chapter focuses on several aspects and challenges of EHR’s and CDSS-modules in daily clinical practice in the hospital.

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