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Appl Clin Inform 2022; 13(05): 1223-1236
DOI: 10.1055/s-0042-1759513
Review Article

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

Jennifer B. Withall
1   Department of Nursing, Columbia University School of Nursing, New York, New York, United States
,
Jessica M. Schwartz
1   Department of Nursing, Columbia University School of Nursing, New York, New York, United States
,
John Usseglio
2   Augustus C. Long Health Sciences Library, Columbia University Irving Medical Center, New York, New York, United States
,
Kenrick D. Cato
1   Department of Nursing, Columbia University School of Nursing, New York, New York, United States
3   Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, United States
› Author Affiliations Funding J.W. is supported through training grants from the National Institute for Nursing Research (NINR; grant number: 5T32NR007969). J.S. was supported by this training grant (grant number: 5T32NR007969) at the inception of this work and was subsequently supported by a National Library of Medicine training grant (grant number: 5T15LM007079).
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Abstract

Background Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS).

Objective The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care.

Methods This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established.

Results Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term “medication” there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs).

Conclusion The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user–device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

Keywords

integrated devices - clinical decision support - interoperability

Authors' Contributions

J.W. and K.C. conceptualized the review. J.U. advised on the scoping review protocol and search strategy. J.W. and K.C. conducted the title/abstract screening and full-text screening. J.S. resolved screening discrepancies between J.W. and K.C. J.W. conducted data extraction. K.C. verified extracted data. All authors participated in the writing of the manuscript.


Data Availability

All data are incorporated into the article and its online [supplementary material].


Competing Interests

The authors declare no competing interests with respect to this publication.


Human Subjects Research Approval

This work did not involve human subjects and is exempt from requiring Institutional Review Board approval.


Supplementary Material



Publication History

Received: 22 March 2022

Accepted: 17 October 2022

Article published online:
28 December 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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