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Appl Clin Inform 2023; 14(05): 981-991
DOI: 10.1055/s-0043-1777107
Research Article

How Safe are Outpatient Electronic Health Records? An Evaluation of Medication-Related Decision Support using the Ambulatory Electronic Health Record Evaluation Tool

Zoe Co
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, United States
,
David C. Classen
3   Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States
,
Jessica M. Cole
3   Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States
,
Diane L. Seger
4   Clinical and Quality Analysis, Mass General Brigham, Somerville, Massachusetts, United States
,
Randy Madsen
5   Biomedical Informatics Core, Clinical and Translational Science Institute, University of Utah, Salt Lake City, Utah, United States
,
Terrance Davis
5   Biomedical Informatics Core, Clinical and Translational Science Institute, University of Utah, Salt Lake City, Utah, United States
,
Patricia McGaffigan
6   Institute of Health Improvement, Boston, Massachusetts, United States
,
David W. Bates
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
5   Biomedical Informatics Core, Clinical and Translational Science Institute, University of Utah, Salt Lake City, Utah, United States
7   Harvard Medical School, Boston, Massachusetts, United States
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Abstract

Background The purpose of the Ambulatory Electronic Health Record (EHR) Evaluation Tool is to provide outpatient clinics with an assessment that they can use to measure the ability of the EHR system to detect and prevent common prescriber errors. The tool consists of a medication safety test and a medication reconciliation module.

Objectives The goal of this study was to perform a broad evaluation of outpatient medication-related decision support using the Ambulatory EHR Evaluation Tool.

Methods We performed a cross-sectional study with 10 outpatient clinics using the Ambulatory EHR Evaluation Tool. For the medication safety test, clinics were provided test patients and associated medication test orders to enter in their EHR, where they recorded any advice or information they received. Once finished, clinics received an overall percentage score of unsafe orders detected and individual order category scores. For the medication reconciliation module, clinics were asked to electronically reconcile two medication lists, where modifications were made by adding and removing medications and changing the dosage of select medications.

Results For the medication safety test, the mean overall score was 57%, with the highest score being 70%, and the lowest score being 40%. Clinics performed well in the drug allergy (100%), drug dose daily (85%), and inappropriate medication combinations (74%) order categories. Order categories with the lowest performance were drug laboratory (10%) and drug monitoring (3%). Most clinics (90%) scored a 0% in at least one order category. For the medication reconciliation module, only one clinic (10%) could reconcile medication lists electronically; however, there was no clinical decision support available that checked for drug interactions.

Conclusion We evaluated a sample of ambulatory practices around their medication-related decision support and found that advanced capabilities within these systems have yet to be widely implemented. The tool was practical to use and identified substantial opportunities for improvement in outpatient medication safety.

Keywords

patient safety - ambulatory care - clinical decision support systems

Protection of Human and Animal Subjects

No real patients were used in the testing scenarios in the Ambulatory EHR Evaluation Tool, only test patients were used.




Publication History

Received: 11 July 2023

Accepted: 24 October 2023

Article published online:
13 December 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Office of the National Coordinator for Health Information Technology. Office-based Physician Electronic Health Record Adoption. Health IT Quick-Stat #50. January 2019. Accessed August 4, 2021 at: https://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php
    PubMed
  • 2 Blumenthal D. Launching HITECH. N Engl J Med 2010; 362 (05) 382-385
    CrossrefPubMedSearch in Google Scholar
  • 3 Bates DW, Teich JM, Lee J. et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321
    CrossrefPubMedSearch in Google Scholar
  • 4 Kuperman GJ, Bobb A, Payne TH. et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007; 14 (01) 29-40
    CrossrefPubMedSearch in Google Scholar
  • 5 Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013; 20 (03) 470-476
    CrossrefPubMedSearch in Google Scholar
  • 6 Tajchman S, Lawler B, Spence N, Haque S, Quintana Y, Ateya M. Implementation and use of risk evaluation and mitigation strategies programs in practice: a scoping review of the literature. Appl Clin Inform 2022; 13 (05) 1151-1160
    Thieme ConnectPubMedSearch in Google Scholar
  • 7 Bates DW, Teich JM, Lee J. et al. The Impact of Computerized Physician Order Entry on Medication Error Prevention. Vol 6. Accessed April 18, 2019 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC61372/pdf/0060313.pdf
    PubMed
  • 8 Austin JA, Barras MA, Woods LS, Sullivan CM. The effect of digitization on the safe management of anticoagulants. Appl Clin Inform 2022; 13 (04) 845-856
    Thieme ConnectPubMedSearch in Google Scholar
  • 9 E Dawson T, Beus J, W Orenstein E, Umontuen U, McNeill D, Kandaswamy S. Reducing therapeutic duplication in inpatient medication orders. Appl Clin Inform 2023; 14 (03) 538-543
    Thieme ConnectPubMedSearch in Google Scholar
  • 10 Chaparro JD, Beus JM, Dziorny AC. et al. Clinical decision support stewardship: best practices and techniques to monitor and improve interruptive alerts. Appl Clin Inform 2022; 13 (03) 560-568
    Thieme ConnectPubMedSearch in Google Scholar
  • 11 Nanji KC, Slight SP, Seger DL. et al. Overrides of medication-related clinical decision support alerts in outpatients. J Am Med Inform Assoc 2014; 21 (03) 487-491
    CrossrefPubMedSearch in Google Scholar
  • 12 Sarkar U, López A, Maselli JH, Gonzales R. Adverse drug events in U.S. adult ambulatory medical care. Health Serv Res 2011; 46 (05) 1517-1533
    CrossrefPubMedSearch in Google Scholar
  • 13 Kaushal R, Kern LM, Barrón Y, Quaresimo J, Abramson EL. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med 2010; 25 (06) 530-536
    CrossrefPubMedSearch in Google Scholar
  • 14 Gandhi TK, Weingart SN, Seger AC. et al. Outpatient prescribing errors and the impact of computerized prescribing. J Gen Intern Med 2005; 20 (09) 837-841
    CrossrefPubMedSearch in Google Scholar
  • 15 Co Z, Holmgren AJ, Classen DC. et al. The development and piloting of the ambulatory electronic health record evaluation tool: lessons learned. Appl Clin Inform 2021; 12 (01) 153-163
    Thieme ConnectPubMedSearch in Google Scholar
  • 16 Holmgren AJ, Co Z, Newmark L, Danforth M, Classen D, Bates D. Assessing the safety of electronic health records: a national longitudinal study of medication-related decision support. BMJ Qual Saf 2020; 29 (01) 52-59
    CrossrefPubMedSearch in Google Scholar
  • 17 Classen DC, Holmgren AJ, Co Z. et al. National trends in the safety performance of electronic health record systems from 2009 to 2018. JAMA Netw Open 2020; 3 (05) e205547
    CrossrefPubMedSearch in Google Scholar
  • 18 Kilbridge PM, Welebob EM, Classen DC. Development of the Leapfrog methodology for evaluating hospital implemented inpatient computerized physician order entry systems. Qual Saf Health Care 2006; 15 (02) 81-84
    CrossrefPubMedSearch in Google Scholar
  • 19 Metzger J, Welebob E, Bates DW, Lipsitz S, Classen DC. Mixed results in the safety performance of computerized physician order entry. Health Aff (Millwood) 2010; 29 (04) 655-663
    CrossrefPubMedSearch in Google Scholar
  • 20 Co Z, Holmgren AJ, Classen DC. et al. The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support. J Am Med Inform Assoc 2020; 27 (08) 1252-1258
    CrossrefPubMedSearch in Google Scholar
  • 21 Leung AA, Keohane C, Lipsitz S. et al. Relationship between medication event rates and the Leapfrog computerized physician order entry evaluation tool. J Am Med Inform Assoc 2013; 20 (e1): e85-e90
    CrossrefPubMedSearch in Google Scholar
  • 22 Phansalkar S, van der Sijs H, Tucker AD. et al. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc 2013; 20 (03) 489-493
    CrossrefPubMedSearch in Google Scholar
  • 23 By the 2019 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2019 Updated AGS Beers Criteria® for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2019; 67 (04) 674-694
    CrossrefPubMedSearch in Google Scholar
  • 24 O'Mahony D. STOPP/START criteria for potentially inappropriate medications/potential prescribing omissions in older people: origin and progress. Expert Rev Clin Pharmacol 2020; 13 (01) 15-22
    CrossrefPubMedSearch in Google Scholar
  • 25 Grion AM, Gallo U, Tinjala DD. et al. A new computer-based tool to reduce potentially inappropriate prescriptions in hospitalized geriatric patients. Drugs Aging 2016; 33 (04) 267-275
    CrossrefPubMedSearch in Google Scholar
  • 26 Tsai CH, Eghdam A, Davoody N, Wright G, Flowerday S, Koch S. Effects of electronic health record implementation and barriers to adoption and use: a scoping review and qualitative analysis of the content. Life (Basel) 2020; 10 (12) 1-27
    PubMedSearch in Google Scholar
  • 27 McCrorie C, Benn J, Johnson OA, Scantlebury A. Staff expectations for the implementation of an electronic health record system: a qualitative study using normalisation process theory. BMC Med Inform Decis Mak 2019; 19 (01) 222
    CrossrefPubMedSearch in Google Scholar
  • 28 Staes CJ, Evans RS, Rocha BHSC. et al. Computerized alerts improve outpatient laboratory monitoring of transplant patients. J Am Med Inform Assoc 2008; 15 (03) 324-332
    CrossrefPubMedSearch in Google Scholar
  • 29 Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA 2007; 297 (08) 831-841
    CrossrefPubMedSearch in Google Scholar
  • 30 Rangachari P, Dellsperger KC, Fallaw D. et al. A mixed-method study of practitioners' perspectives on issues related to EHR medication reconciliation at a health system. Qual Manag Health Care 2019; 28 (02) 84-95
    CrossrefPubMedSearch in Google Scholar
  • 31 Powis M, Dara C, Macedo A. et al. Implementation of medication reconciliation in outpatient cancer care. BMJ Open Qual 2023; 12 (02) e002211
    CrossrefPubMedSearch in Google Scholar
  • 32 Yuan CT, Dy SM, Yuanhong Lai A. et al. Challenges and strategies for patient safety in primary care: a qualitative study. Am J Med Qual 2022; 37 (05) 379-387
    CrossrefPubMedSearch in Google Scholar