The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

BACKGROUND  Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. OBJECTIVE  To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. METHODS  The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. RESULTS  For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug-drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. CONCLUSION  Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

[1]  Jonathan M. Teich,et al.  The impact of computerized physician order entry on medication error prevention. , 1999, Journal of the American Medical Informatics Association : JAMIA.

[2]  D. Bates,et al.  Outpatient prescribing errors and the impact of computerized prescribing , 2005, Journal of General Internal Medicine.

[3]  D. Blumenthal Launching HITECH. , 2010, The New England journal of medicine.

[4]  Simran Singh,et al.  Understanding the management of electronic test result notifications in the outpatient setting , 2011, BMC Medical Informatics Decis. Mak..

[5]  D. Bates,et al.  Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. , 1998, JAMA.

[6]  Janey Barnes,et al.  Mind the Gap , 2016, Applied Clinical Informatics.

[7]  Kara Scott,et al.  The Development and Evaluation of an Electronic Health Record Efficiency Workshop for Providers , 2020, Applied Clinical Informatics.

[8]  Lisa P. Newmark,et al.  Assessing the safety of electronic health records: a national longitudinal study of medication-related decision support , 2019, BMJ Quality & Safety.

[9]  David W Bates,et al.  Safe Use of Electronic Health Records and Health Information Technology Systems: Trust But Verify , 2013, Journal of patient safety.

[11]  Roger B. Davis,et al.  Physicians' decisions to override computerized drug alerts in primary care. , 2003, Archives of internal medicine.

[12]  P. Kilbridge,et al.  Development of the Leapfrog methodology for evaluating hospital implemented inpatient computerized physician order entry systems , 2006, Quality and Safety in Health Care.

[13]  Joshua Borus,et al.  Adverse drug events in ambulatory care. , 2003, The New England journal of medicine.

[14]  Steven R Simon,et al.  Relationship between medication event rates and the Leapfrog computerized physician order entry evaluation tool. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[15]  Hardeep Singh,et al.  Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers , 2017, Applied Clinical Informatics.

[16]  Lisa P. Newmark,et al.  National Trends in the Safety Performance of Electronic Health Record Systems From 2009 to 2018 , 2020, JAMA network open.

[17]  Jonathan M. Teich,et al.  Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records , 2012, J. Am. Medical Informatics Assoc..

[18]  Erika L. Abramson,et al.  Electronic Prescribing Improves Medication Safety in Community-Based Office Practices , 2010, Journal of General Internal Medicine.

[19]  Thomas H. Payne,et al.  Review Paper: Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review , 2007, J. Am. Medical Informatics Assoc..

[20]  David W Bates,et al.  Mixed results in the safety performance of computerized physician order entry. , 2010, Health affairs.

[21]  Zoe Co,et al.  The tradeoffs between safety and alert fatigue: Data from a national evaluation of hospital medication-related clinical decision support , 2020, J. Am. Medical Informatics Assoc..

[22]  Daniala L. Weir,et al.  Effect of an Electronic Medication Reconciliation Intervention on Adverse Drug Events , 2019, JAMA network open.

[23]  Dean F Sittig,et al.  Information overload and missed test results in electronic health record-based settings. , 2013, JAMA internal medicine.

[24]  Andrew J. McLachlan,et al.  Impact of electronic medication reconciliation interventions on medication discrepancies at hospital transitions: a systematic review and meta-analysis , 2016, BMC Medical Informatics and Decision Making.

[25]  Michael M. Wagner,et al.  Research Paper: The Accuracy of Medication Data in an Outpatient Electronic Medical Record , 1996, J. Am. Medical Informatics Assoc..

[26]  Jessica S. Ancker,et al.  Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system , 2017, BMC Medical Informatics and Decision Making.

[27]  Mark Spranca,et al.  Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems , 2013, J. Am. Medical Informatics Assoc..

[28]  P. Cram,et al.  The frequency of missed test results and associated treatment delays in a highly computerized health system , 2007, BMC family practice.