Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

doi:10.1016/j.annemergmed.2018.10.034

. 2019 Aug;74(2):285-296.

doi: 10.1016/j.annemergmed.2018.10.034. Epub 2019 Jan 3.

Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

Brian W Patterson¹, Michael S Pulia², Shashank Ravi³, Peter L T Hoonakker⁴, Ann Schoofs Hundt⁴, Douglas Wiegmann⁵, Emily J Wirkus⁴, Stephen Johnson⁶, Pascale Carayon⁵

Affiliations

¹ BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI; Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI. Electronic address: bpatter@medicine.wisc.edu.
² BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI.
³ Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT.
⁴ Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI.
⁵ Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI; Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI.
⁶ Ebling Library, University of Wisconsin-Madison, Madison, WI.

PMID: 30611639
PMCID: PMC6610851
DOI: 10.1016/j.annemergmed.2018.10.034

Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

Brian W Patterson et al. Ann Emerg Med. 2019 Aug.

. 2019 Aug;74(2):285-296.

doi: 10.1016/j.annemergmed.2018.10.034. Epub 2019 Jan 3.

Authors

Brian W Patterson¹, Michael S Pulia², Shashank Ravi³, Peter L T Hoonakker⁴, Ann Schoofs Hundt⁴, Douglas Wiegmann⁵, Emily J Wirkus⁴, Stephen Johnson⁶, Pascale Carayon⁵

Affiliations

¹ BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI; Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI. Electronic address: bpatter@medicine.wisc.edu.
² BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI.
³ Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT.
⁴ Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI.
⁵ Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI; Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI.
⁶ Ebling Library, University of Wisconsin-Madison, Madison, WI.

PMID: 30611639
PMCID: PMC6610851
DOI: 10.1016/j.annemergmed.2018.10.034

Abstract

Study objective: As electronic health records evolve, integration of computerized clinical decision support offers the promise of sorting, collecting, and presenting this information to improve patient care. We conducted a systematic review to examine the scope and influence of electronic health record-integrated clinical decision support technologies implemented in the emergency department (ED).

Methods: A literature search was conducted in 4 databases from their inception through January 18, 2018: PubMed, Scopus, the Cumulative Index of Nursing and Allied Health, and Cochrane Central. Studies were included if they examined the effect of a decision support intervention that was implemented in a comprehensive electronic health record in the ED setting. Standardized data collection forms were developed and used to abstract study information and assess risk of bias.

Results: A total of 2,558 potential studies were identified after removal of duplicates. Of these, 42 met inclusion criteria. Common targets for clinical decision support intervention included medication and radiology ordering practices, as well as more comprehensive systems supporting diagnosis and treatment for specific disease entities. The majority of studies (83%) reported positive effects on outcomes studied. Most studies (76%) used a pre-post experimental design, with only 3 (7%) randomized controlled trials.

Conclusion: Numerous studies suggest that clinical decision support interventions are effective in changing physician practice with respect to process outcomes such as guideline adherence; however, many studies are small and poorly controlled. Future studies should consider the inclusion of more specific information in regard to design choices, attempt to improve on uncontrolled before-after designs, and focus on clinically relevant outcomes wherever possible.

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Conflict of interest statement

Conflicts of Interest: None to report

Figures

**Figure 1:**
Study screening and inclusion

**Figure 2:**
Included studies by year of publication. Of note, only 18 days of 2018 were included in the data collection

See this image and copyright information in PMC

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References

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1. Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform. 2008;77:291–304. - PubMed
1. Roque FS, Jensen PB, Schmock H, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7:e1002141. - PMC - PubMed
1. Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM. Bridging the inferential gap: The electronic health record and clinical evidence. Health Aff (Millwood). 2007;26:w181–191. - PMC - PubMed
1. Rosenbaum L Transitional chaos or enduring harm? The EHR and the disruption of medicine. The New England journal of medicine. 2015;373:1585–1588. - PubMed

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[1] Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33:1123–1131. - PubMed

[2] Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33:1123–1131. - PubMed

[3] Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform. 2008;77:291–304. - PubMed

[4] Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform. 2008;77:291–304. - PubMed

[5] Roque FS, Jensen PB, Schmock H, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7:e1002141. - PMC - PubMed

[6] Roque FS, Jensen PB, Schmock H, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7:e1002141. - PMC - PubMed

[7] Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM. Bridging the inferential gap: The electronic health record and clinical evidence. Health Aff (Millwood). 2007;26:w181–191. - PMC - PubMed

[8] Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM. Bridging the inferential gap: The electronic health record and clinical evidence. Health Aff (Millwood). 2007;26:w181–191. - PMC - PubMed

[9] Rosenbaum L Transitional chaos or enduring harm? The EHR and the disruption of medicine. The New England journal of medicine. 2015;373:1585–1588. - PubMed

[10] Rosenbaum L Transitional chaos or enduring harm? The EHR and the disruption of medicine. The New England journal of medicine. 2015;373:1585–1588. - PubMed

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Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

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Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

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Conflict of interest statement

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