Gwen Thompson1, John C O'Horo, Brian W Pickering, Vitaly Herasevich. 1. 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.
Abstract
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; I=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.
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; I=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.
Authors: Mohammed Al-Jaghbeer; Dilhari Dealmeida; Andrew Bilderback; Richard Ambrosino; John A Kellum Journal: J Am Soc Nephrol Date: 2017-11-02 Impact factor: 10.121
Authors: Natalia Olchanski; Mikhail A Dziadzko; Ing C Tiong; Craig E Daniels; Steve G Peters; John C O'Horo; Michelle N Gong Journal: J Med Syst Date: 2017-09-18 Impact factor: 4.460
Authors: Mirela Prgomet; Ling Li; Zahra Niazkhani; Andrew Georgiou; Johanna I Westbrook Journal: J Am Med Inform Assoc Date: 2017-03-01 Impact factor: 4.497
Authors: Daniel Schlieper; Christiane Altreuther; Manuela Schallenburger; Martin Neukirchen; Andrea Schmitz; Christian Schulz-Quach Journal: Int J Integr Care Date: 2017-06-20 Impact factor: 5.120