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Impact of the Electronic Medical Record on Mortality, Length of Stay, and Cost in the Hospital and ICU

A Systematic Review and Metaanalysis*

Thompson, Gwen MD, MPH1; O’Horo, John C. MD, MPH2; Pickering, Brian W. MBBCh, MSc3; Herasevich, Vitaly MD, PhD, MSc3

doi: 10.1097/CCM.0000000000000948
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Objective: To evaluate effects of health information technology in the inpatient and ICU on mortality, length of stay, and cost. Methodical evaluation of the impact of health information technology on outcomes is essential for institutions to make informed decisions regarding implementation.

Data Sources: EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science were searched from database inception through July 2013. Manual review of references of identified articles was also completed.

Study Selection: Selection criteria included a health information technology intervention such as computerized physician order entry, clinical decision support systems, and surveillance systems, an inpatient setting, and endpoints of mortality, length of stay, or cost. Studies were screened by three reviewers. Of the 2,803 studies screened, 45 met selection criteria (1.6%).

Data Extraction: Data were abstracted on the year, design, intervention type, system used, comparator, sample sizes, and effect on outcomes. Studies were abstracted independently by three reviewers.

Data Synthesis: There was a significant effect of surveillance systems on in-hospital mortality (odds ratio, 0.85; 95% CI, 0.76–0.94; I2 = 59%). All other quantitative analyses of health information technology interventions effect on mortality and length of stay were not statistically significant. Cost was unable to be quantitatively evaluated. Qualitative synthesis of studies of each outcome demonstrated significant study heterogeneity and small clinical effects.

Conclusions: Electronic interventions were not shown to have a substantial effect on mortality, length of stay, or cost. This may be due to the small number of studies that were able to be aggregately analyzed due to the heterogeneity of study populations, interventions, and endpoints. Better evidence is needed to identify the most meaningful ways to implement and use health information technology and before a statement of the effect of these systems on patient outcomes can be made.

1Division of General Internal Medicine, Mayo Clinic, Rochester, MN.

2Division of Infectious Diseases, Mayo Clinic, Rochester, MN.

3Multidisciplinary Epidemiology and Translational Research in Intensive Care and Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN.

* See also p. 1342.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

Drs. Pickering and Herasevich and their institutions licensed technology. Drs. Pickering and Herasevich receive royalties and have stock with Ambient Clinical Analytics Inc. Dr Pickering additionally is member on the Board of Directors of Ambient Clinical Analytics Inc. The remaining authors have disclosed that they do not have any potential conflicts of interest.

For information regarding this article, E-mail: herasevich.vitaly@mayo.edu

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