Impact of the Electronic Medical Record on Mortality, Length of Stay, and Cost in the Hospital and ICU: A Systematic Review and Metaanalysis* Gwen Thompson, MD, MPH1; John C. O’Horo, MD, MPH2; Brian W. Pickering, MBBCh, MSc3; Vitaly Herasevich, MD, PhD, MSc3 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 *See also p. 1342. 1 Division of General Internal Medicine, Mayo Clinic, Rochester, MN. 2 Division of Infectious Diseases, Mayo Clinic, Rochester, MN. 3 Multidisciplinary Epidemiology and Translational Research in Intensive Care and Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN. 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: [email protected] Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved. DOI: 10.1097/CCM.0000000000000948
1276
www.ccmjournal.org
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. (Crit Care Med 2015; 43:1276–1282) Key Words: costs and cost analysis; electronic health records; length of stay; medical informatics; mortality
I
n recent years, the U.S. government has invested billions of dollars on the advancing of health information technology (HIT) through the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 (1, 2). This has been done with the hope that HIT will improve the health of Americans by providing better care while simultaneously lowering costs. Proponents of the electronic medical record (EMR) such as politicians, journalists, and the EMR industry claim that EMRs are able to fulfill this expectation. Statements by these groups are often made that EMRs “save lives” (3). However, there has never been a systematic review in inpatient settings supporting this claim. Other systematic reviews of HIT have been conducted. However, they have focused on a particular intervention such as computerized physician order entry (CPOE) (4, 5), different settings such as ambulatory care (6, 7), a particular population (7), different endpoints such as medication prescription errors, medication safety (4, 5), or efficiency (6). No review has been conducted that evaluates all HIT interventions across all inpatient settings. The effect of various HIT interventions such as CPOE, clinical decision support (CDS) systems, and surveillance systems or “sniffers” are heterogeneous, each affecting a June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
different aspect of healthcare delivery, making it important to individually evaluate the impact of each system on patient outcomes. There is also a lack of evidence whether EMRs affect an area where mortality is often an unfortunate reality, ICUs. The aim of this systematic review and metaanalysis is to evaluate the effects of HIT in the inpatient and ICU setting on mortality, length of stay (LOS), and cost.
METHODS Search Strategy The search strategy and literature search was performed by a medical reference librarian with input from the investigators. A search of EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science from database inception through July 2013 was conducted (8). Eligibility criteria included studies that used a form of HIT as the intervention, was conducted in an inpatient setting, and evaluated mortality, LOS, or cost as an endpoint. Exclusion criteria were studies where there was no control group, that were conducted in an acute care or outpatient setting, and that did not evaluate mortality, LOS, or cost as an outcome. Full search strategy is available as supplemental data (Supplemental Digital Content 1, http://links.lww.com/CCM/B218). Abstracts were independently screened, and potentially relevant articles were identified for full-text review. References from included studies were manually inspected to identify all potentially relevant studies for screening and review.
RESULTS Literature Review The initial search yielded 2,736 articles. Sixty-seven additional studies were identified through manual review of references of included studies. Of all studies identified, 45 studies met eligibility criteria and were included in this review. Twenty-six contained quantitative data to be included in the metaanalysis (Fig. 1). Of the studies, five described electronic health record systems (11–15), 11 CPOE (16–26), 17 CDS systems (27–43), six surveillance systems or “sniffers” (44–49), and six electronic medication reconciliation tools not fitting into the prior categories (50–55). Almost all of the studies were peri-implementation designs comparing preintervention with postintervention. Mortality Of the included studies, 33 had mortality as an endpoint (11, 13–15, 17–20, 23, 24, 26, 27, 29–31, 33–37, 39, 40, 42–49, 51–53).
Data Abstraction From each study, data were abstracted on the study year, location, design, setting, inclusion criteria, type of intervention (CPOE, EMR, or CDS), specific system used, implementation timeframe, comparator, population, total study size, size of control group and comparator groups, and effect found on outcomes. Outcomes were categorized as mortality, LOS, and cost. The abstraction template can be found as Supplemental Table 1 (Supplemental Digital Content 2, http://links.lww.com/ CCM/B219). Studies were abstracted and screened by three reviewers, and disagreements were resolved by discussion. Statistical Analyses Dersimonian and Laird random effects models were used for quantitative synthesis of the data where there were at least three articles meeting inclusion criteria for that outcome. This modeling uses standard inverse variance weighting followed by an “unweighting” based on the extent of variability observed, making for larger CIs and less weight to individual studies to make more conservative estimates of pooled effects. Heterogeneity was assessed using an I2 statistic, where 0–30% indicates low heterogeneity, 31–60% moderate heterogeneity, and more than 60% high statistical heterogeneity (9). These analyses were performed with Cochrane Review Manager Software (RevMan, version 5.3; Nordic Cochrane Center, Copenhagen, Denmark) and the Stata Metan package (Stata Statistical Software, Release 13; StataCorp LP, College Station, TX). (10). Critical Care Medicine
Figure 1. Preferred items for reporting in systematic reviews and metaanalyses flow diagram. www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1277
Thompson et al
Of these 33, 24 (11, 17–20, 23, 24, 27, 29–31, 33–36, 39, 40, 44, 46–49) included data that were able to be quantitatively evaluated. Studies looked at in-hospital mortality, in-ICU mortality, or 90-day mortality. Of the studies that could be quantitatively analyzed and examined in-hospital mortality, five evaluated CPOE systems (17, 18, 24, 26, 45), one EHR (11), seven CDS systems (27, 30, 31, 33, 35, 39, 40), and three sniffers (46, 48, 49). Overall, CPOE systems did not show a significant effect on this outcome (odds ratio [OR], 0.91; 95% CI, 0.75–1.10; I2 = 66%), nor did EHRs alone (OR, 0.96; 95% CI, 0.77–1.19). CDS systems had slightly better performance overall (OR, 0.83; 95% CI, 0.69–1.00; I2 = 66%). The best overall performance was seen in “sniffer” systems, which had a pooled OR for in-hospital mortality of 0.85 (95% CI, 0.76–0.94) with moderate heterogeneity (I2 = 59%). This is summarized in Figure 2. Of the studies addressing ICU mortality as an endpoint, there were four using CPOE systems (17, 19, 20, 23), three CDS (30, 34, 40), and two sniffers (46, 47). Heterogeneity was high in all subgroups and none trended toward a significant effect. This is summarized in Figure 3. Three studies reported 90-day mortality, two CDS (29, 36), and one sniffer (44). There was no evidence of effect of any of the interventions on this outcome.
Nine studies (13–15, 37, 42, 43, 51–53) provided information on mortality that could not be quantitatively synthesized via metaanalysis. This included one cross-sectional study (51), one nationwide registry review (52), one clustered randomized trial (37), five pre-post studies (13, 15, 42, 43, 53), and one interrupted time series study (14). Of the nine studies, four demonstrated a decrease in mortality (13, 14, 51, 52), four showed no difference (15, 42, 43, 53), and one (37) had no mortalities in their study population making a difference in mortality difficult to assess. The methods and quality of these studies varied significantly making a conglomerate statement difficult. In addition, the electronic intervention assessed and study population varied between and within studies. For example, one study (51) stated that there was a decrease in mortality; however, this varied significantly by the electronic intervention assessed and the disease process used for the inclusion criteria for the study population even within that individual study. Study interventions ranged from CDS related to antibiotic guidance (37, 42) to full EMRs (14). Another study (52) demonstrated a statistically significant decrease in mortality; however, the clinical applicability of the small decrease in mortality rate needs to be considered as the largest decrease in mortality was from 16.4% to 16.0%. There were also differences in the definition of mortality. For instance, one study only commented on 30-day mortality and not overall mortality (43). Another study (13) demonstrated a decrease in 30-day mortality but no statistically significant difference in inpatient mortality. Hence, which form of mortality is being evaluated between studies could affect the results. Sensitivity analyses excluding older studies failed to decrease heterogeneity or change the overall impact observed for each system.
Figure 2. In-hospital mortality. Overall, there was a trend towards better mortality with sniffer systems and clinical decision support (CDS); computerized physician order entry (CPOE) did not achieve significance. EHR = electronic health record.
LOS Of the included studies, 35 (11–15, 17–19, 21, 22, 24, 25, 27, 29, 30, 32–35, 37–42, 45–54) evaluated LOS as an outcome. Of these 35, nine (17, 19, 21, 22, 24, 27, 33, 34, 46) included data that were able to be quantitatively evaluated. Studies looked at either hospital LOS or ICU LOS. Hospital LOS was evaluated by eight studies (17, 21, 22, 24, 27, 33, 34, 46), including four CPOE studies (17, 21, 22, 24),
1278
June 2015 • Volume 43 • Number 6
www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
cross-sectional studies (51, 52, 54), one cluster (12), one cluster randomized trial (37), two randomized control trials (35, 50), and two interrupted time series (14, 38). Sixteen studies demonstrated no difference in LOS after an electronic intervention (15, 18, 29, 30, 32, 35, 38, 40, 41, 45, 47, 48, 51–54). One study showed a decrease LOS in one of the institutions studied but not in the other (25). Eight studies concluded that there was a significant decreased LOS after intervention (11, 13, 14, 37, 39, 42, 49, 50). One study showed an increase in LOS with EMR (12). Qualitatively, this seems to suggest a no-impact to Figure 3. ICU mortality. Neither sniffers, computerized physician order entry (CPOE), or clinical decision support potential benefit to electronic (CDS) outperformed controls significantly. EHR = electronic medical record. systems, but the clinical sigone sniffer (46), and three CDS systems (27, 33, 34). CPOE nificance and validity of results of the studies that showed a trended toward a reduction in LOS (mean decrease, 0.67 d; decreased LOS should be considered. One study stated that 95% CI, –2.07 to 0.73), though with significant heterogeneity there was a decreased LOS; however, this was not statistically (I2 = 82%). Neither CDS nor sniffers trended toward changes significant within hospitals that implemented the EMR with a in hospital LOS, and the net pooled effect was not significant pre-post study design but was significant with a decreased LOS (Fig. 4). Three studies (17, 19, 46) addressed ICU LOS, none of by 0.11 days when comparing hospitals with EMRs against which found any significant impact. those that did not adopt EMRs (13). Hence, there may be an Twenty-six studies (11–15, 18, 25, 29, 30, 32, 35, 37–42, 45, inherent difference between hospitals that adopt EMRs and 47–54) that evaluated LOS as an outcome were unable to be quan- those that do not that account for this difference. Another of the statistically significant studies showed a decreased LOS titatively synthesized. Of these studies, there were 17 pre-post studies (11, 13, 15, 18, 25, 29, 30, 32, 39–42, 45, 47–49, 53), three with CPOE from 3.91 to 3.71 days in one of the two institutions evaluated (25). One study showed a decreased LOS after CDS implementation for potential adverse drug events that was statistically significant after severity adjustment with a ratio of expected to actual LOS decreasing from 1.02 to 0.996 days (39). The study by Sintchenko et al (42) showed a decrease in bed days in an ICU from 7.15 to 6.22 after implementation of a handheld CDS to assist in antimicrobial choice. One study determined that LOS decreased at a rate of 0.043 bed-days per month after EMR implementation (14). The clinical significance of these statistically significant but small decreases in LOS Figure 4. Hospital length of stay. Neither sniffers, computerized physician order entry (CPOE), or clinical decision support (CDS) outperformed controls significantly. needs to be considered. Critical Care Medicine
www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1279
Thompson et al
One study showed an increased LOS with EMR. This study compared a Veteran Affairs (VA) hospital with an EMR to a non-VA hospital without an EMR (12). Therefore, confounders may be present, and the conclusion that the increased LOS is attributable to EMR cannot be made. Cost There were 14 studies that addressed cost as an outcome. These were unable to be quantitatively synthesized due to heterogeneity of study design and endpoints. Of the 14 studies, there were nine pre-post studies (15, 25–28, 30, 39, 53, 55), one cross-sectional study (51), one randomized control trial (35), one cross-sectional study (54), one cluster randomized trial (37), and one interrupted time series (14). Eight of the studies concluded that there was a decrease in cost after an electronic intervention (15, 27, 28, 30, 35, 37, 39, 51). Four studies showed no significant difference (25, 26, 53, 54) and two stated that there was an increase in costs after intervention (14, 55). Study populations and endpoints varied considerably between studies. The study populations in the studies ranged from adult patients with specific disease processes such as heart failure and pneumonia (51) to infants (53). The endpoints were also heterogeneous. One study addressed the cost per admission (51). Other studies examined a specific CDS and therefore only commented on cost related to that one aspect of care such as blood transfusions, laboratory testing, antibiotic use, or pharmacy costs (27, 28, 30, 35, 37, 39). One study examined decreased costs in specific areas such as radiology utilization or paper use (15) and another only evaluated the number of pathology tests used (26). One study quantified cost in terms of resource intensity weights and not dollars (53). The results also varied within studies. For instance, one study showed a decrease in pharmacy costs after CDS implementation but an increase of total hospitalization costs (39). The two studies that stated that there was an increase in costs associated with EMRs (14, 55) had many confounding variables making this information difficult to interpret. One even demonstrated that after controlling for specific factors that could influence cost, the difference was no longer significant (55).
DISCUSSION Overall, electronic interventions were not shown to have a substantial effect on mortality, LOS, or cost. This may in part 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. Also, most studies were pre-post studies. Many changes occur at the time of implementation and attributing all changes in outcomes to a single intervention may not be appropriate. In addition, failure to observe patient-level outcomes does not necessarily indicate a failure of EMRs. The lack of impact may be attributable to how the EMR is being implemented, the interpretation of correct use of EMRs, or the analysis of the results in individual studies. This review highlights the heterogeneity of the current evidence to support the implementation of EMR to improve patient outcomes. Further research is needed to determine the true impact of HIT on healthcare quality. 1280
www.ccmjournal.org
In regard to mortality, there was no net effect of CPOE- or EHR-based interventions on ICU mortality or 90-day mortality. Sniffers were the only intervention that had a statistically significant effect on hospital mortality. Qualitative synthesis supported these conclusions. Although the majority of studies included in the qualitative synthesis stated that there was a decrease in mortality, this often varied by the electronic intervention studied and had small statistically significant differences that may not be clinically relevant. Similar results were obtained when looking at LOS. No impact of electronic interventions on LOS, either hospital or ICU, was found across all interventions. CPOE did trend toward a decrease in hospital LOS, but this was not statistically significant and clinically may not be applicable as it was a little over half of a day decrease in LOS in our metaanalysis, and overall, most studies reported no effect on LOS. The endpoint of cost was unable to be evaluated quantitatively. Although most studies stated that there was a decrease in cost, the implementation process was not included in the analysis of any study, and therefore, overall cost effectiveness was not evaluated. Also, the interventions evaluated were often very narrow in focus, and secondary endpoints were often used making a final conclusion of effect difficult. The major limitation of this study is the heterogeneity of interventions and outcome assessments of the available literature. This made overall conclusions difficult. Attempts were made to control for this limitation by assessing different interventions separately. However, even within categories not all interventions are equal. For instance, a CDS to assist with antibiotic use is very different than one to assist in blood transfusion practices. In addition, EMR, CPOE, CDS, and sniffer implementation is not an isolated event. All electronic interventions inevitably lead to other practice changes. Endpoints such as mortality, LOS, and cost are affected by a large number of factors, many of which were likely not assessed by the studies. In addition, the majority of the studies were pre-post studies, and due to the nature of the intervention, they could not be blinded. This may have created a Hawthorne effect thereby confounding the effect on the outcomes attributed to the electronic interventions. Publication bias may occur as negative findings are not often published or sought.
CONCLUSIONS The HITECH of 2009 made billions of Medicare and Medicaid dollars available to hospitals who engage in meaningful use of EHRs. In order to fall under meaningful use, five goals have to be met: improving the quality, safety, and efficiency of care while reducing disparities; engaging patients and families in their care; promoting public and population health; improving care coordination; and promoting the privacy and security of health records (1). There is not enough evidence to confidently state that electronic interventions have the ability to achieve the goal of improving quality and safety. There may be other benefits of HIT, such as efficiency, patient engagement, and healthcare information protection that were not evaluated by this review. In addition, failure to observe changes in June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
patient-level outcomes does not necessarily indicate a failure of EHRs in and of themselves, but rather may reflect an upstream problem with how EHRs are being used in specific processes. The quantity and quality of studies related to HIT’s impact on mortality, LOS, and cost are lacking, and there is significant interstudy and intrastudy variability. This study demonstrates that future research is needed with standardized interventions and endpoints that also evaluate processes such as EMR use and implementation before a final statement on the effect of HIT on patient outcomes such as mortality, LOS, and cost can be made.
REFERENCES
1. Blumenthal D: Launching HITECH. N Engl J Med 2010; 362:382–385 2. Lenert L, Sundwall DN: Public health surveillance and meaningful use regulations: A crisis of opportunity. Am J Public Health 2012; 102:e1–e7 3. Still T: Electronic health records can save lives and improve medical care. WTN News, February 8, 2005 4. Kaushal R, Shojania KG, Bates DW: Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review. Arch Intern Med 2003; 163:1409–1416 5. van Rosse F, Maat B, Rademaker CM, et al: The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: A systematic review. Pediatrics 2009; 123:1184–1190 6. Chaudhry B, Wang J, Wu S, et al: Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144:742–752 7. Shekelle PG, Morton SC, Keeler EB: Costs and benefits of health information technology. Evid Rep Technol Assess (Full Rep) 2006; 132:1–71 8. Liberati A, Altman DG, Tetzlaff J, et al: The PRISMA statement for reporting systematic reviews and metaanalyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009; 339:b2700 9. Higgins JP, Thompson SG, Deeks JJ, et al: Measuring inconsistency in metaanalyses. BMJ 2003; 327:557–560 10. Centre TNC: Review Manager (RevMan) Version 5.0. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2008 11. Schenarts PJ, Goettler CE, White MA, et al: An objective study of the impact of the electronic medical record on outcomes in trauma patients. Am Surg 2012; 78:1249–1254 12. Boockvar KS, Livote EE, Goldstein N, et al: Electronic health records and adverse drug events after patient transfer. Qual Saf Health Care 2010; 19:e16 13. Lee J, Kuo YF, Goodwin JS: The effect of electronic medical record adoption on outcomes in US hospitals. BMC Health Serv Res 2013; 13:39 14. Xue Y, Liang H, Wu X, et al: Effects of electronic medical record in a Chinese hospital: A time series study. Int J Med Inform 2012; 81:683–689 15. Zlabek JA, Wickus JW, Mathiason MA: Early cost and safety benefits of an inpatient electronic health record. J Am Med Inform Assoc 2011; 18:169–172 16. Collins S, Currie L, Bakken S, et al: Interruptions during the use of a CPOE system for MICU rounds. AMIA Annu Symp Proc 2006;895 17. Al-Dorzi HM, Tamim HM, Cherfan A, et al: Impact of computerized physician order entry (CPOE) system on the outcome of critically ill adult patients: A before-after study. BMC Med Inform Decis Mak 2011; 11:71 18. Collin S, Reeves BC, Hendy J, et al: Implementation of computerised physician order entry (CPOE) and picture archiving and communication systems (PACS) in the NHS: Quantitative before and after study. BMJ 2008; 337:a939
Critical Care Medicine
19. Del Beccaro MA, Jeffries HE, Eisenberg MA, et al: Computerized provider order entry implementation: No association with increased mortality rates in an intensive care unit. Pediatrics 2006; 118:290–295 20. Han YY, Carcillo JA, Venkataraman ST, et al: Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; 116:1506–1512 21. Holdsworth MT, Fichtl RE, Raisch DW, et al: Impact of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 2007; 120:1058–1066 22. Hwang JI, Park HA, Bakken S: Impact of a physician’s order entry (POE) system on physicians’ ordering patterns and patient length of stay. Int J Med Inform 2002; 65:213–223 23. Keene A, Ashton L, Shure D, et al: Mortality before and after initiation of a computerized physician order entry system in a critically ill pediatric population. Pediatr Crit Care Med 2007; 8:268–271 24. Longhurst CA, Parast L, Sandborg CI, et al: Decrease in hospitalwide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 2010; 126:14–21 25. Mekhjian HS, Kumar RR, Kuehn L, et al: Immediate benefits realized following implementation of physician order entry at an academic medical center. J Am Med Inform Assoc 2002; 9:529–539 26. Westbrook JI, Georgiou A, Rob MI: Test turnaround times and mortality rates 12 and 24 months after the introduction of a computerised provider order entry system. Methods Inf Med 2009; 48:211–215 27. Evans RS, Pestotnik SL, Classen DC, et al: A computer-assisted management program for antibiotics and other antiinfective agents. N Engl J Med 1998; 338:232–238 28. Levick DL, Stern G, Meyerhoefer CD, et al: Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention. BMC Med Inform Decis Mak 2013; 13:43 29. Austrian JS, Adelman JS, Reissman SH, et al: The impact of the heparin-induced thrombocytopenia (HIT) computerized alert on provider behaviors and patient outcomes. J Am Med Inform Assoc 2011; 18:783–788 30. Fernández Pérez ER, Winters JL, Gajic O: The addition of decision support into computerized physician order entry reduces red blood cell transfusion resource utilization in the intensive care unit. Am J Hematol 2007; 82:631–633 31. Filice GA, Drekonja DM, Thurn JR, et al: Use of a computer decision support system and antimicrobial therapy appropriateness. Infect Control Hosp Epidemiol 2013; 34:558–565 32. Holroyd-Leduc JM, Abelseth GA, Khandwala F, et al: A pragmatic study exploring the prevention of delirium among hospitalized older hip fracture patients: Applying evidence to routine clinical practice using clinical decision support. Implement Sci 2010; 5:81 33. Lakshminarayan K, Rostambeigi N, Fuller CC, et al: Impact of an electronic medical record-based clinical decision support tool for dysphagia screening on care quality. Stroke 2012; 43:3399–3401 34. Mazars N, Milési C, Carbajal R, et al: Implementation of a neonatal pain management module in the computerized physician order entry system. Ann Intensive Care 2012; 2:38 35. McGregor JC, Weekes E, Forrest GN, et al: Impact of a computerized clinical decision support system on reducing inappropriate antimicrobial use: A randomized controlled trial. J Am Med Inform Assoc 2006; 13:378–384 36. Mosen D, Elliott CG, Egger MJ, et al: The effect of a computerized reminder system on the prevention of postoperative venous thromboembolism. Chest 2004; 125:1635–1641 37. Paul M, Andreassen S, Tacconelli E, et al; TREAT Study Group: Improving empirical antibiotic treatment using TREAT, a computerized decision support system: Cluster randomized trial. J Antimicrob Chemother 2006; 58:1238–1245 38. Peterson JF, Kuperman GJ, Shek C, et al: Guided prescription of psychotropic medications for geriatric inpatients. Arch Intern Med 2005; 165:802–807 39. Piontek F, Kohli R, Conlon P, et al: Effects of an adverse-drug-event alert system on cost and quality outcomes in community hospitals. Am J Health Syst Pharm 2010; 67:613–620 40. Rana R, Afessa B, Keegan MT, et al; Transfusion in the ICU Interest Group: Evidence-based red cell transfusion in the critically ill: Quality www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1281
Thompson et al improvement using computerized physician order entry. Crit Care Med 2006; 34:1892–1897 41. Roberts GW, Farmer CJ, Cheney PC, et al: Clinical decision support implemented with academic detailing improves prescribing of key renally cleared drugs in the hospital setting. J Am Med Inform Assoc 2010; 17:308–312 42. Sintchenko V, Iredell JR, Gilbert GL, et al: Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J Am Med Inform Assoc 2005; 12:398–402 43. Thursky KA, Buising KL, Bak N, et al: Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. Int J Qual Health Care 2006; 18:224–231 44. Paterno MD, Cina JL, Goldhaber SZ, et al. Preventing DVT and PE in hospitalized patients: Improving a successful electronic alert. AMIA Annu Symp Proc 2006:1058 45. Thompson W, Dodek PM, Norena M, et al: Computerized physician order entry of diagnostic tests in an intensive care unit is associated with improved timeliness of service. Crit Care Med 2004; 32:1306–1309 46. Herasevich V, Tsapenko M, Kojicic M, et al: Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011; 39:34–39 47. Meyfroidt G, Wouters P, De Becker W, et al: Impact of a computergenerated alert system on the quality of tight glycemic control. Intensive Care Med 2011; 37:1151–1157
1282
www.ccmjournal.org
48. Sawyer AM, Deal EN, Labelle AJ, et al: Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Crit Care Med 2011; 39:469–473 49. Jones S, Mullally M, Ingleby S, et al: Bedside electronic capture of clinical observations and automated clinical alerts to improve compliance with an Early Warning Score protocol. Crit Care Resusc 2011; 13:83–88 50. Schnipper JL, Hamann C, Ndumele CD, et al: Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: A cluster-randomized trial. Arch Intern Med 2009; 169:771–780 51. Amarasingham R, Plantinga L, Diener-West M, et al: Clinical information technologies and inpatient outcomes: A multiple hospital study. Arch Intern Med 2009; 169:108–114 52. Jones SS, Heaton P, Friedberg MW, et al: Today’s ‘meaningful use’ standard for medication orders by hospitals may save few lives; later stages may do more. Health Aff (Millwood) 2011; 30:2005–2012 53. King WJ, Le Saux N, Sampson M, et al: Effect of point of care information on inpatient management of bronchiolitis. BMC Pediatr 2007; 7:4 54. Teufel RJ, Kazley AS, Basco WT Jr: Is computerized physician order entry use associated with a decrease in hospital resource utilization in hospitals that care for children? J Med Syst 2012; 36:2411–2420 55. Teufel RJ 2nd, Kazley AS, Ebeling MD, et al: Hospital electronic medical record use and cost of inpatient pediatric care. Acad Pediatr 2012; 12:429–435
June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1276
www.ccmjournal.org
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. (Crit Care Med 2015; 43:1276–1282) Key Words: costs and cost analysis; electronic health records; length of stay; medical informatics; mortality
I
n recent years, the U.S. government has invested billions of dollars on the advancing of health information technology (HIT) through the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 (1, 2). This has been done with the hope that HIT will improve the health of Americans by providing better care while simultaneously lowering costs. Proponents of the electronic medical record (EMR) such as politicians, journalists, and the EMR industry claim that EMRs are able to fulfill this expectation. Statements by these groups are often made that EMRs “save lives” (3). However, there has never been a systematic review in inpatient settings supporting this claim. Other systematic reviews of HIT have been conducted. However, they have focused on a particular intervention such as computerized physician order entry (CPOE) (4, 5), different settings such as ambulatory care (6, 7), a particular population (7), different endpoints such as medication prescription errors, medication safety (4, 5), or efficiency (6). No review has been conducted that evaluates all HIT interventions across all inpatient settings. The effect of various HIT interventions such as CPOE, clinical decision support (CDS) systems, and surveillance systems or “sniffers” are heterogeneous, each affecting a June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
different aspect of healthcare delivery, making it important to individually evaluate the impact of each system on patient outcomes. There is also a lack of evidence whether EMRs affect an area where mortality is often an unfortunate reality, ICUs. The aim of this systematic review and metaanalysis is to evaluate the effects of HIT in the inpatient and ICU setting on mortality, length of stay (LOS), and cost.
METHODS Search Strategy The search strategy and literature search was performed by a medical reference librarian with input from the investigators. A search of EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science from database inception through July 2013 was conducted (8). Eligibility criteria included studies that used a form of HIT as the intervention, was conducted in an inpatient setting, and evaluated mortality, LOS, or cost as an endpoint. Exclusion criteria were studies where there was no control group, that were conducted in an acute care or outpatient setting, and that did not evaluate mortality, LOS, or cost as an outcome. Full search strategy is available as supplemental data (Supplemental Digital Content 1, http://links.lww.com/CCM/B218). Abstracts were independently screened, and potentially relevant articles were identified for full-text review. References from included studies were manually inspected to identify all potentially relevant studies for screening and review.
RESULTS Literature Review The initial search yielded 2,736 articles. Sixty-seven additional studies were identified through manual review of references of included studies. Of all studies identified, 45 studies met eligibility criteria and were included in this review. Twenty-six contained quantitative data to be included in the metaanalysis (Fig. 1). Of the studies, five described electronic health record systems (11–15), 11 CPOE (16–26), 17 CDS systems (27–43), six surveillance systems or “sniffers” (44–49), and six electronic medication reconciliation tools not fitting into the prior categories (50–55). Almost all of the studies were peri-implementation designs comparing preintervention with postintervention. Mortality Of the included studies, 33 had mortality as an endpoint (11, 13–15, 17–20, 23, 24, 26, 27, 29–31, 33–37, 39, 40, 42–49, 51–53).
Data Abstraction From each study, data were abstracted on the study year, location, design, setting, inclusion criteria, type of intervention (CPOE, EMR, or CDS), specific system used, implementation timeframe, comparator, population, total study size, size of control group and comparator groups, and effect found on outcomes. Outcomes were categorized as mortality, LOS, and cost. The abstraction template can be found as Supplemental Table 1 (Supplemental Digital Content 2, http://links.lww.com/ CCM/B219). Studies were abstracted and screened by three reviewers, and disagreements were resolved by discussion. Statistical Analyses Dersimonian and Laird random effects models were used for quantitative synthesis of the data where there were at least three articles meeting inclusion criteria for that outcome. This modeling uses standard inverse variance weighting followed by an “unweighting” based on the extent of variability observed, making for larger CIs and less weight to individual studies to make more conservative estimates of pooled effects. Heterogeneity was assessed using an I2 statistic, where 0–30% indicates low heterogeneity, 31–60% moderate heterogeneity, and more than 60% high statistical heterogeneity (9). These analyses were performed with Cochrane Review Manager Software (RevMan, version 5.3; Nordic Cochrane Center, Copenhagen, Denmark) and the Stata Metan package (Stata Statistical Software, Release 13; StataCorp LP, College Station, TX). (10). Critical Care Medicine
Figure 1. Preferred items for reporting in systematic reviews and metaanalyses flow diagram. www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1277
Thompson et al
Of these 33, 24 (11, 17–20, 23, 24, 27, 29–31, 33–36, 39, 40, 44, 46–49) included data that were able to be quantitatively evaluated. Studies looked at in-hospital mortality, in-ICU mortality, or 90-day mortality. Of the studies that could be quantitatively analyzed and examined in-hospital mortality, five evaluated CPOE systems (17, 18, 24, 26, 45), one EHR (11), seven CDS systems (27, 30, 31, 33, 35, 39, 40), and three sniffers (46, 48, 49). Overall, CPOE systems did not show a significant effect on this outcome (odds ratio [OR], 0.91; 95% CI, 0.75–1.10; I2 = 66%), nor did EHRs alone (OR, 0.96; 95% CI, 0.77–1.19). CDS systems had slightly better performance overall (OR, 0.83; 95% CI, 0.69–1.00; I2 = 66%). The best overall performance was seen in “sniffer” systems, which had a pooled OR for in-hospital mortality of 0.85 (95% CI, 0.76–0.94) with moderate heterogeneity (I2 = 59%). This is summarized in Figure 2. Of the studies addressing ICU mortality as an endpoint, there were four using CPOE systems (17, 19, 20, 23), three CDS (30, 34, 40), and two sniffers (46, 47). Heterogeneity was high in all subgroups and none trended toward a significant effect. This is summarized in Figure 3. Three studies reported 90-day mortality, two CDS (29, 36), and one sniffer (44). There was no evidence of effect of any of the interventions on this outcome.
Nine studies (13–15, 37, 42, 43, 51–53) provided information on mortality that could not be quantitatively synthesized via metaanalysis. This included one cross-sectional study (51), one nationwide registry review (52), one clustered randomized trial (37), five pre-post studies (13, 15, 42, 43, 53), and one interrupted time series study (14). Of the nine studies, four demonstrated a decrease in mortality (13, 14, 51, 52), four showed no difference (15, 42, 43, 53), and one (37) had no mortalities in their study population making a difference in mortality difficult to assess. The methods and quality of these studies varied significantly making a conglomerate statement difficult. In addition, the electronic intervention assessed and study population varied between and within studies. For example, one study (51) stated that there was a decrease in mortality; however, this varied significantly by the electronic intervention assessed and the disease process used for the inclusion criteria for the study population even within that individual study. Study interventions ranged from CDS related to antibiotic guidance (37, 42) to full EMRs (14). Another study (52) demonstrated a statistically significant decrease in mortality; however, the clinical applicability of the small decrease in mortality rate needs to be considered as the largest decrease in mortality was from 16.4% to 16.0%. There were also differences in the definition of mortality. For instance, one study only commented on 30-day mortality and not overall mortality (43). Another study (13) demonstrated a decrease in 30-day mortality but no statistically significant difference in inpatient mortality. Hence, which form of mortality is being evaluated between studies could affect the results. Sensitivity analyses excluding older studies failed to decrease heterogeneity or change the overall impact observed for each system.
Figure 2. In-hospital mortality. Overall, there was a trend towards better mortality with sniffer systems and clinical decision support (CDS); computerized physician order entry (CPOE) did not achieve significance. EHR = electronic health record.
LOS Of the included studies, 35 (11–15, 17–19, 21, 22, 24, 25, 27, 29, 30, 32–35, 37–42, 45–54) evaluated LOS as an outcome. Of these 35, nine (17, 19, 21, 22, 24, 27, 33, 34, 46) included data that were able to be quantitatively evaluated. Studies looked at either hospital LOS or ICU LOS. Hospital LOS was evaluated by eight studies (17, 21, 22, 24, 27, 33, 34, 46), including four CPOE studies (17, 21, 22, 24),
1278
June 2015 • Volume 43 • Number 6
www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
cross-sectional studies (51, 52, 54), one cluster (12), one cluster randomized trial (37), two randomized control trials (35, 50), and two interrupted time series (14, 38). Sixteen studies demonstrated no difference in LOS after an electronic intervention (15, 18, 29, 30, 32, 35, 38, 40, 41, 45, 47, 48, 51–54). One study showed a decrease LOS in one of the institutions studied but not in the other (25). Eight studies concluded that there was a significant decreased LOS after intervention (11, 13, 14, 37, 39, 42, 49, 50). One study showed an increase in LOS with EMR (12). Qualitatively, this seems to suggest a no-impact to Figure 3. ICU mortality. Neither sniffers, computerized physician order entry (CPOE), or clinical decision support potential benefit to electronic (CDS) outperformed controls significantly. EHR = electronic medical record. systems, but the clinical sigone sniffer (46), and three CDS systems (27, 33, 34). CPOE nificance and validity of results of the studies that showed a trended toward a reduction in LOS (mean decrease, 0.67 d; decreased LOS should be considered. One study stated that 95% CI, –2.07 to 0.73), though with significant heterogeneity there was a decreased LOS; however, this was not statistically (I2 = 82%). Neither CDS nor sniffers trended toward changes significant within hospitals that implemented the EMR with a in hospital LOS, and the net pooled effect was not significant pre-post study design but was significant with a decreased LOS (Fig. 4). Three studies (17, 19, 46) addressed ICU LOS, none of by 0.11 days when comparing hospitals with EMRs against which found any significant impact. those that did not adopt EMRs (13). Hence, there may be an Twenty-six studies (11–15, 18, 25, 29, 30, 32, 35, 37–42, 45, inherent difference between hospitals that adopt EMRs and 47–54) that evaluated LOS as an outcome were unable to be quan- those that do not that account for this difference. Another of the statistically significant studies showed a decreased LOS titatively synthesized. Of these studies, there were 17 pre-post studies (11, 13, 15, 18, 25, 29, 30, 32, 39–42, 45, 47–49, 53), three with CPOE from 3.91 to 3.71 days in one of the two institutions evaluated (25). One study showed a decreased LOS after CDS implementation for potential adverse drug events that was statistically significant after severity adjustment with a ratio of expected to actual LOS decreasing from 1.02 to 0.996 days (39). The study by Sintchenko et al (42) showed a decrease in bed days in an ICU from 7.15 to 6.22 after implementation of a handheld CDS to assist in antimicrobial choice. One study determined that LOS decreased at a rate of 0.043 bed-days per month after EMR implementation (14). The clinical significance of these statistically significant but small decreases in LOS Figure 4. Hospital length of stay. Neither sniffers, computerized physician order entry (CPOE), or clinical decision support (CDS) outperformed controls significantly. needs to be considered. Critical Care Medicine
www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1279
Thompson et al
One study showed an increased LOS with EMR. This study compared a Veteran Affairs (VA) hospital with an EMR to a non-VA hospital without an EMR (12). Therefore, confounders may be present, and the conclusion that the increased LOS is attributable to EMR cannot be made. Cost There were 14 studies that addressed cost as an outcome. These were unable to be quantitatively synthesized due to heterogeneity of study design and endpoints. Of the 14 studies, there were nine pre-post studies (15, 25–28, 30, 39, 53, 55), one cross-sectional study (51), one randomized control trial (35), one cross-sectional study (54), one cluster randomized trial (37), and one interrupted time series (14). Eight of the studies concluded that there was a decrease in cost after an electronic intervention (15, 27, 28, 30, 35, 37, 39, 51). Four studies showed no significant difference (25, 26, 53, 54) and two stated that there was an increase in costs after intervention (14, 55). Study populations and endpoints varied considerably between studies. The study populations in the studies ranged from adult patients with specific disease processes such as heart failure and pneumonia (51) to infants (53). The endpoints were also heterogeneous. One study addressed the cost per admission (51). Other studies examined a specific CDS and therefore only commented on cost related to that one aspect of care such as blood transfusions, laboratory testing, antibiotic use, or pharmacy costs (27, 28, 30, 35, 37, 39). One study examined decreased costs in specific areas such as radiology utilization or paper use (15) and another only evaluated the number of pathology tests used (26). One study quantified cost in terms of resource intensity weights and not dollars (53). The results also varied within studies. For instance, one study showed a decrease in pharmacy costs after CDS implementation but an increase of total hospitalization costs (39). The two studies that stated that there was an increase in costs associated with EMRs (14, 55) had many confounding variables making this information difficult to interpret. One even demonstrated that after controlling for specific factors that could influence cost, the difference was no longer significant (55).
DISCUSSION Overall, electronic interventions were not shown to have a substantial effect on mortality, LOS, or cost. This may in part 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. Also, most studies were pre-post studies. Many changes occur at the time of implementation and attributing all changes in outcomes to a single intervention may not be appropriate. In addition, failure to observe patient-level outcomes does not necessarily indicate a failure of EMRs. The lack of impact may be attributable to how the EMR is being implemented, the interpretation of correct use of EMRs, or the analysis of the results in individual studies. This review highlights the heterogeneity of the current evidence to support the implementation of EMR to improve patient outcomes. Further research is needed to determine the true impact of HIT on healthcare quality. 1280
www.ccmjournal.org
In regard to mortality, there was no net effect of CPOE- or EHR-based interventions on ICU mortality or 90-day mortality. Sniffers were the only intervention that had a statistically significant effect on hospital mortality. Qualitative synthesis supported these conclusions. Although the majority of studies included in the qualitative synthesis stated that there was a decrease in mortality, this often varied by the electronic intervention studied and had small statistically significant differences that may not be clinically relevant. Similar results were obtained when looking at LOS. No impact of electronic interventions on LOS, either hospital or ICU, was found across all interventions. CPOE did trend toward a decrease in hospital LOS, but this was not statistically significant and clinically may not be applicable as it was a little over half of a day decrease in LOS in our metaanalysis, and overall, most studies reported no effect on LOS. The endpoint of cost was unable to be evaluated quantitatively. Although most studies stated that there was a decrease in cost, the implementation process was not included in the analysis of any study, and therefore, overall cost effectiveness was not evaluated. Also, the interventions evaluated were often very narrow in focus, and secondary endpoints were often used making a final conclusion of effect difficult. The major limitation of this study is the heterogeneity of interventions and outcome assessments of the available literature. This made overall conclusions difficult. Attempts were made to control for this limitation by assessing different interventions separately. However, even within categories not all interventions are equal. For instance, a CDS to assist with antibiotic use is very different than one to assist in blood transfusion practices. In addition, EMR, CPOE, CDS, and sniffer implementation is not an isolated event. All electronic interventions inevitably lead to other practice changes. Endpoints such as mortality, LOS, and cost are affected by a large number of factors, many of which were likely not assessed by the studies. In addition, the majority of the studies were pre-post studies, and due to the nature of the intervention, they could not be blinded. This may have created a Hawthorne effect thereby confounding the effect on the outcomes attributed to the electronic interventions. Publication bias may occur as negative findings are not often published or sought.
CONCLUSIONS The HITECH of 2009 made billions of Medicare and Medicaid dollars available to hospitals who engage in meaningful use of EHRs. In order to fall under meaningful use, five goals have to be met: improving the quality, safety, and efficiency of care while reducing disparities; engaging patients and families in their care; promoting public and population health; improving care coordination; and promoting the privacy and security of health records (1). There is not enough evidence to confidently state that electronic interventions have the ability to achieve the goal of improving quality and safety. There may be other benefits of HIT, such as efficiency, patient engagement, and healthcare information protection that were not evaluated by this review. In addition, failure to observe changes in June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Review Articles
patient-level outcomes does not necessarily indicate a failure of EHRs in and of themselves, but rather may reflect an upstream problem with how EHRs are being used in specific processes. The quantity and quality of studies related to HIT’s impact on mortality, LOS, and cost are lacking, and there is significant interstudy and intrastudy variability. This study demonstrates that future research is needed with standardized interventions and endpoints that also evaluate processes such as EMR use and implementation before a final statement on the effect of HIT on patient outcomes such as mortality, LOS, and cost can be made.
REFERENCES
1. Blumenthal D: Launching HITECH. N Engl J Med 2010; 362:382–385 2. Lenert L, Sundwall DN: Public health surveillance and meaningful use regulations: A crisis of opportunity. Am J Public Health 2012; 102:e1–e7 3. Still T: Electronic health records can save lives and improve medical care. WTN News, February 8, 2005 4. Kaushal R, Shojania KG, Bates DW: Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review. Arch Intern Med 2003; 163:1409–1416 5. van Rosse F, Maat B, Rademaker CM, et al: The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: A systematic review. Pediatrics 2009; 123:1184–1190 6. Chaudhry B, Wang J, Wu S, et al: Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144:742–752 7. Shekelle PG, Morton SC, Keeler EB: Costs and benefits of health information technology. Evid Rep Technol Assess (Full Rep) 2006; 132:1–71 8. Liberati A, Altman DG, Tetzlaff J, et al: The PRISMA statement for reporting systematic reviews and metaanalyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009; 339:b2700 9. Higgins JP, Thompson SG, Deeks JJ, et al: Measuring inconsistency in metaanalyses. BMJ 2003; 327:557–560 10. Centre TNC: Review Manager (RevMan) Version 5.0. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2008 11. Schenarts PJ, Goettler CE, White MA, et al: An objective study of the impact of the electronic medical record on outcomes in trauma patients. Am Surg 2012; 78:1249–1254 12. Boockvar KS, Livote EE, Goldstein N, et al: Electronic health records and adverse drug events after patient transfer. Qual Saf Health Care 2010; 19:e16 13. Lee J, Kuo YF, Goodwin JS: The effect of electronic medical record adoption on outcomes in US hospitals. BMC Health Serv Res 2013; 13:39 14. Xue Y, Liang H, Wu X, et al: Effects of electronic medical record in a Chinese hospital: A time series study. Int J Med Inform 2012; 81:683–689 15. Zlabek JA, Wickus JW, Mathiason MA: Early cost and safety benefits of an inpatient electronic health record. J Am Med Inform Assoc 2011; 18:169–172 16. Collins S, Currie L, Bakken S, et al: Interruptions during the use of a CPOE system for MICU rounds. AMIA Annu Symp Proc 2006;895 17. Al-Dorzi HM, Tamim HM, Cherfan A, et al: Impact of computerized physician order entry (CPOE) system on the outcome of critically ill adult patients: A before-after study. BMC Med Inform Decis Mak 2011; 11:71 18. Collin S, Reeves BC, Hendy J, et al: Implementation of computerised physician order entry (CPOE) and picture archiving and communication systems (PACS) in the NHS: Quantitative before and after study. BMJ 2008; 337:a939
Critical Care Medicine
19. Del Beccaro MA, Jeffries HE, Eisenberg MA, et al: Computerized provider order entry implementation: No association with increased mortality rates in an intensive care unit. Pediatrics 2006; 118:290–295 20. Han YY, Carcillo JA, Venkataraman ST, et al: Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; 116:1506–1512 21. Holdsworth MT, Fichtl RE, Raisch DW, et al: Impact of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 2007; 120:1058–1066 22. Hwang JI, Park HA, Bakken S: Impact of a physician’s order entry (POE) system on physicians’ ordering patterns and patient length of stay. Int J Med Inform 2002; 65:213–223 23. Keene A, Ashton L, Shure D, et al: Mortality before and after initiation of a computerized physician order entry system in a critically ill pediatric population. Pediatr Crit Care Med 2007; 8:268–271 24. Longhurst CA, Parast L, Sandborg CI, et al: Decrease in hospitalwide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 2010; 126:14–21 25. Mekhjian HS, Kumar RR, Kuehn L, et al: Immediate benefits realized following implementation of physician order entry at an academic medical center. J Am Med Inform Assoc 2002; 9:529–539 26. Westbrook JI, Georgiou A, Rob MI: Test turnaround times and mortality rates 12 and 24 months after the introduction of a computerised provider order entry system. Methods Inf Med 2009; 48:211–215 27. Evans RS, Pestotnik SL, Classen DC, et al: A computer-assisted management program for antibiotics and other antiinfective agents. N Engl J Med 1998; 338:232–238 28. Levick DL, Stern G, Meyerhoefer CD, et al: Reducing unnecessary testing in a CPOE system through implementation of a targeted CDS intervention. BMC Med Inform Decis Mak 2013; 13:43 29. Austrian JS, Adelman JS, Reissman SH, et al: The impact of the heparin-induced thrombocytopenia (HIT) computerized alert on provider behaviors and patient outcomes. J Am Med Inform Assoc 2011; 18:783–788 30. Fernández Pérez ER, Winters JL, Gajic O: The addition of decision support into computerized physician order entry reduces red blood cell transfusion resource utilization in the intensive care unit. Am J Hematol 2007; 82:631–633 31. Filice GA, Drekonja DM, Thurn JR, et al: Use of a computer decision support system and antimicrobial therapy appropriateness. Infect Control Hosp Epidemiol 2013; 34:558–565 32. Holroyd-Leduc JM, Abelseth GA, Khandwala F, et al: A pragmatic study exploring the prevention of delirium among hospitalized older hip fracture patients: Applying evidence to routine clinical practice using clinical decision support. Implement Sci 2010; 5:81 33. Lakshminarayan K, Rostambeigi N, Fuller CC, et al: Impact of an electronic medical record-based clinical decision support tool for dysphagia screening on care quality. Stroke 2012; 43:3399–3401 34. Mazars N, Milési C, Carbajal R, et al: Implementation of a neonatal pain management module in the computerized physician order entry system. Ann Intensive Care 2012; 2:38 35. McGregor JC, Weekes E, Forrest GN, et al: Impact of a computerized clinical decision support system on reducing inappropriate antimicrobial use: A randomized controlled trial. J Am Med Inform Assoc 2006; 13:378–384 36. Mosen D, Elliott CG, Egger MJ, et al: The effect of a computerized reminder system on the prevention of postoperative venous thromboembolism. Chest 2004; 125:1635–1641 37. Paul M, Andreassen S, Tacconelli E, et al; TREAT Study Group: Improving empirical antibiotic treatment using TREAT, a computerized decision support system: Cluster randomized trial. J Antimicrob Chemother 2006; 58:1238–1245 38. Peterson JF, Kuperman GJ, Shek C, et al: Guided prescription of psychotropic medications for geriatric inpatients. Arch Intern Med 2005; 165:802–807 39. Piontek F, Kohli R, Conlon P, et al: Effects of an adverse-drug-event alert system on cost and quality outcomes in community hospitals. Am J Health Syst Pharm 2010; 67:613–620 40. Rana R, Afessa B, Keegan MT, et al; Transfusion in the ICU Interest Group: Evidence-based red cell transfusion in the critically ill: Quality www.ccmjournal.org
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
1281
Thompson et al improvement using computerized physician order entry. Crit Care Med 2006; 34:1892–1897 41. Roberts GW, Farmer CJ, Cheney PC, et al: Clinical decision support implemented with academic detailing improves prescribing of key renally cleared drugs in the hospital setting. J Am Med Inform Assoc 2010; 17:308–312 42. Sintchenko V, Iredell JR, Gilbert GL, et al: Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J Am Med Inform Assoc 2005; 12:398–402 43. Thursky KA, Buising KL, Bak N, et al: Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. Int J Qual Health Care 2006; 18:224–231 44. Paterno MD, Cina JL, Goldhaber SZ, et al. Preventing DVT and PE in hospitalized patients: Improving a successful electronic alert. AMIA Annu Symp Proc 2006:1058 45. Thompson W, Dodek PM, Norena M, et al: Computerized physician order entry of diagnostic tests in an intensive care unit is associated with improved timeliness of service. Crit Care Med 2004; 32:1306–1309 46. Herasevich V, Tsapenko M, Kojicic M, et al: Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011; 39:34–39 47. Meyfroidt G, Wouters P, De Becker W, et al: Impact of a computergenerated alert system on the quality of tight glycemic control. Intensive Care Med 2011; 37:1151–1157
1282
www.ccmjournal.org
48. Sawyer AM, Deal EN, Labelle AJ, et al: Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Crit Care Med 2011; 39:469–473 49. Jones S, Mullally M, Ingleby S, et al: Bedside electronic capture of clinical observations and automated clinical alerts to improve compliance with an Early Warning Score protocol. Crit Care Resusc 2011; 13:83–88 50. Schnipper JL, Hamann C, Ndumele CD, et al: Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: A cluster-randomized trial. Arch Intern Med 2009; 169:771–780 51. Amarasingham R, Plantinga L, Diener-West M, et al: Clinical information technologies and inpatient outcomes: A multiple hospital study. Arch Intern Med 2009; 169:108–114 52. Jones SS, Heaton P, Friedberg MW, et al: Today’s ‘meaningful use’ standard for medication orders by hospitals may save few lives; later stages may do more. Health Aff (Millwood) 2011; 30:2005–2012 53. King WJ, Le Saux N, Sampson M, et al: Effect of point of care information on inpatient management of bronchiolitis. BMC Pediatr 2007; 7:4 54. Teufel RJ, Kazley AS, Basco WT Jr: Is computerized physician order entry use associated with a decrease in hospital resource utilization in hospitals that care for children? J Med Syst 2012; 36:2411–2420 55. Teufel RJ 2nd, Kazley AS, Ebeling MD, et al: Hospital electronic medical record use and cost of inpatient pediatric care. Acad Pediatr 2012; 12:429–435
June 2015 • Volume 43 • Number 6
Copyright © 2015 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
