Evidence map›Paper›PMID 33657634›Full record

ArticleApplied clinical informatics2021

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

Zoe Co, A Jay Holmgren, David C Classen, Lisa P Newmark, Diane L Seger, Jessica M Cole, Barbara Pon, Karen P Zimmer, David W Bates

Abstract read
In one paragraph

Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zoe CoDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
A Jay HolmgrenHarvard Business School, Boston, Massachusetts, United States.
David C ClassenDepartment of Clinical Epidemiology, University of Utah, Salt Lake City, Utah, United States.
Lisa P NewmarkClinical and Quality Analysis, Mass General Brigham, Somerville, Massachusetts, United States.
Diane L SegerClinical and Quality Analysis, Mass General Brigham, Somerville, Massachusetts, United States.
Jessica M ColeDepartment of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States.
Barbara PonCollaborative Healthcare Patient Safety Organization, Sacramento, California, United States.
Karen P ZimmerDepartment of Pediatrics, Thomas Jefferson University, Philadelphia, Pennsylvania, United States.
David W BatesDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSubstantial 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.

objectiveTo 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.

methodsThe 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.

resultsFor 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.

conclusionClinics 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.

Indexed as

Electronic Health RecordsMedical Order Entry SystemsAmbulatory CareAmbulatory Care FacilitiesHumansMedication ReconciliationUnited States

Identifiers

PMID33657634
PMCPMC7929716

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