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Appl Clin Inform 2023; 14(03): 521-527
DOI: 10.1055/a-2077-4419
Research Article

Association between Electronic Health Record Implementations and Hospital-Acquired Conditions in Pediatric Hospitals

Naveed Rabbani
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
,
Natalie M. Pageler
1   Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
,
James M. Hoffman
2   Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States
,
Chris Longhurst
3   Department of Biomedical Informatics, University of California San Diego Health, La Jolla, California, United States
,
Paul J. Sharek
4   Center for Quality and Patient Safety, Seattle Children's, Seattle, Washington, United States
5   Department of Pediatrics, University of Washington, Seattle, Washington, United States
› Author Affiliations Funding None.
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Abstract

Background Implementing an electronic health record (EHR) is one of the most disruptive operational tasks a health system can undergo. Despite anecdotal reports of adverse events around the time of EHR implementations, there is limited corroborating research, particularly in pediatrics. We utilized data from Solutions for Patient Safety (SPS), a network of 145+ children's hospitals that share data and protocols to reduce harm in pediatric care delivery, to study the impact of EHR implementations on patient safety.

Objective Determine if there is an association between the time immediately surrounding an EHR implementation and hospital-acquired conditions (HACs) rates in pediatrics.

Methods A survey of information technology leaders at pediatric institutions identified EHR implementations occurring between 2012 and 2022. This list was cross-referenced with the SPS database to create an anonymized dataset of 27 sites comprising monthly HAC and care bundle compliance rates in the 7 months preceding and succeeding the transition. Six HACs were analyzed: central-line associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), adverse drug events, surgical site infections (SSIs), pressure injuries (PIs), and falls, in addition to four associated care bundle compliance rates: CLABSI and CAUTI maintenance bundles, SSI bundle, and PI bundle. To determine if there was a statistically significant association with EHR implementation, the observation period was divided into three eras: “before” (months −7 to −3), “during” (months −2 to +2), and “after” go-live (months +3 to +7). Average monthly HAC and bundle compliance rates were calculated across eras. Paired t-tests were performed to compare rates between the eras.

Results No statistically significant increase in HAC rates or decrease in bundle compliance rates was observed across the EHR implementation eras.

Conclusion This multisite study detected no significant increase in HACs and no decrease in preventive care bundle compliance in the months surrounding an EHR implementation.

Keywords

quality - process improvement - electronic health records - safety - pediatrics - implementations and transitions

Protection of Human and Animal Subjects

The presented work does not qualify as human subjects research, as determined by the Stanford University Institutional Review Board.




Publication History

Received: 17 February 2023

Accepted: 17 April 2023

Accepted Manuscript online:
19 April 2023

Article published online:
12 July 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Nakamura MM, Harper MB, Jha AK. Change in adoption of electronic health records by US children's hospitals. Pediatrics 2013; 131 (05) e1563-e1575
    CrossrefPubMedGoogle Scholar
  • 2 Charles D, Gabriel M, Furukawa M. Adoption of Electronic Health Record Systems among U.S. Non-federal Acute Care Hospitals: 2008–2013. ONC Data Brief [Internet]. 2014 May; Accessed April 27, 2023: https://www.healthit.gov/sites/default/files/oncdatabrief16.pdf
    PubMedGoogle Scholar
  • 3 Kruse CS, Kristof C, Jones B, Mitchell E, Martinez A. Barriers to electronic health record adoption: a systematic literature review. J Med Syst 2016; 40 (12) 252
    CrossrefPubMedGoogle Scholar
  • 4 Adler-Milstein J, Holmgren AJ, Kralovec P, Worzala C, Searcy T, Patel V. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc 2017; 24 (06) 1142-1148
    CrossrefPubMedGoogle Scholar
  • 5 Lammers EJ, Zheng K. Characteristics associated with hospital health IT vendor switching and dropping. AMIA Annu Symp Proc 2011; 2011: 742-749
    PubMedGoogle Scholar
  • 6 Adler KG, Edsall RL. EHR switch survey: responses from 305 family physicians. Fam Pract Manag 2015; 22 (01) 13-18
    PubMedGoogle Scholar
  • 7 Huang C, Koppel R, McGreevey III JD, Craven CK, Schreiber R. Transitions from one electronic health record to another: challenges, pitfalls, and recommendations. Appl Clin Inform 2020; 11 (05) 742-754
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Meeks DW, Takian A, Sittig DF, Singh H, Barber N. Exploring the sociotechnical intersection of patient safety and electronic health record implementation. J Am Med Inform Assoc 2014; 21 (e1): e28-e34
    CrossrefPubMedGoogle Scholar
  • 9 Patterson ES, Anders S, Moffatt-Bruce S. Clustering and prioritizing patient safety issues during EHR implementation and upgrades in hospital settings. Proc Int Symp Hum Factors Ergon Healthc 2017; 6 (01) 125-131
    CrossrefPubMedGoogle Scholar
  • 10 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 (06) 1506-1512
    CrossrefPubMedGoogle Scholar
  • 11 Del Beccaro MA, Jeffries HE, Eisenberg MA, Harry ED. Computerized provider order entry implementation: no association with increased mortality rates in an intensive care unit. Pediatrics 2006; 118 (01) 290-295
    CrossrefPubMedGoogle Scholar
  • 12 Longhurst CA, Parast L, Sandborg CI. et al. Decrease in hospital-wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 2010; 126 (01) 14-21
    CrossrefPubMedGoogle Scholar
  • 13 Whalen K, Lynch E, Moawad I, John T, Lozowski D, Cummings BM. Transition to a new electronic health record and pediatric medication safety: lessons learned in pediatrics within a large academic health system. J Am Med Inform Assoc 2018; 25 (07) 848-854
    CrossrefPubMedGoogle Scholar
  • 14 Saleem JJ, Herout J. Transitioning from one electronic health record (ehr) to another: a narrative literature review. Proc Hum Fact Ergon Soc Annu Meet. 2018; 62 (01) 489-493
    CrossrefPubMedGoogle Scholar
  • 15 Barnett ML, Mehrotra A, Jena AB. Adverse inpatient outcomes during the transition to a new electronic health record system: observational study. BMJ 2016; 354: i3835
    CrossrefPubMedGoogle Scholar
  • 16 Lyren A, Brilli RJ, Zieker K, Marino M, Muething S, Sharek PJ. Children's hospitals' solutions for patient safety collaborative impact on hospital-acquired harm. Pediatrics 2017; 140 (03) e20163494
    CrossrefPubMedGoogle Scholar
  • 17 Lyren A, Coffey M, Shepherd M, Lashutka N, Muething S. SPS Leadership Group. We will not compete on safety: how children's hospitals have come together to hasten harm reduction. Jt Comm J Qual Patient Saf 2018; 44 (07) 377-388
    PubMedGoogle Scholar
  • 18 Keshavjee K, Bosomworth J, Copen J. et al. Best practices in EMR implementation: a systematic review. AMIA Annu Symp Proc 2006; 2006: 982
    PubMedGoogle Scholar
  • 19 Shaffer JP. Multiple hypothesis testing. Annu Rev Psychol 1995; 46 (01) 561-584
    CrossrefPubMedGoogle Scholar
  • 20 Gettinger A, Csatari A. Transitioning from a legacy EHR to a commercial, vendor-supplied, EHR: one academic health system's experience. Appl Clin Inform 2012; 3 (04) 367-376
    Article in Thieme ConnectPubMedGoogle Scholar
  • 21 Wright A, Aaron S, Seger DL, Samal L, Schiff GD, Bates DW. Reduced effectiveness of interruptive drug-drug interaction alerts after conversion to a commercial electronic health record. J Gen Intern Med 2018; 33 (11) 1868-1876
    CrossrefPubMedGoogle Scholar
  • 22 Menon S, Singh H, Giardina TD. et al. Safety huddles to proactively identify and address electronic health record safety. J Am Med Inform Assoc 2017; 24 (02) 261-267
    CrossrefPubMedGoogle Scholar
  • 23 Boonstra A, Versluis A, Vos JFJ. Implementing electronic health records in hospitals: a systematic literature review. BMC Health Serv Res 2014; 14: 370
    CrossrefPubMedGoogle Scholar
  • 24 Stockwell DC, Landrigan CP, Schuster MA. et al. Using a pediatric trigger tool to estimate total harm burden hospital-acquired conditions represent. Pediatr Qual Saf 2018; 3 (03) e081
    CrossrefPubMedGoogle Scholar
  • 25 Scharnweber C, Mollenkopf NL, Fackler J, Dover GJ, Lehmann CU. Utilizing electronic health record data to determine the health of the medication process after the relocation of a children's hospital. Stud Health Technol Inform 2013; 192: 210-214
    PubMedGoogle Scholar
  • 26 Classen DC, Resar R, Griffin F. et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011; 30 (04) 581-589
    CrossrefPubMedGoogle Scholar
  • 27 Stockwell DC, Landrigan CP, Toomey SL. et al; GAPPS Study Group. Adverse events in hospitalized pediatric patients. Pediatrics 2018; 142 (02) e20173360
    CrossrefPubMedGoogle Scholar
  • 28 Singh H, Sittig DF. Measuring and improving patient safety through health information technology: The Health IT Safety Framework. BMJ Qual Saf 2016; 25 (04) 226-232
    CrossrefPubMedGoogle Scholar