Literature DB >> 35595653

Utility of an Electronic Health Record Report to Identify Patients with Delays in Testing for Poorly Controlled Diabetes.

Jessica L Schwartz, Daisy Duan, Nisa M Maruthur, Samantha I Pitts.   

Abstract

BACKGROUND: Reducing hemoglobin A1c (HbA1c) is essential for patients with poorly controlled diabetes. However, delays in HbA1c testing are common, and incomplete electronic health record (EHR) reports hinder identification of patients who are overdue. This study sought to quantify how often an EHR report correctly identifies patients with HbA1c testing delays and to describe potential contributing factors.
METHODS: Using an EHR report, the researchers identified adult patients who had an HbA1c > 9.0% between October 2017 and March 2018 and a suspected delay (for example, another HbA1c had not resulted within six months). A retrospective chart review of 200 randomly selected records was performed to confirm delays in testing. Secondary measures were collected from 93 charts to evaluate associated factors.
RESULTS: A total of 685 patients with suspected delays were identified. On chart review (N = 200), 82.0% were confirmed. Nine percent of patients had a timely repeat result, but the result was not in a discrete field within the EHR. Another 8.5% were never expected to return. Among a subset of confirmed delays, patients often received lifestyle counseling, but less than half had documented discussions about repeat glycemic testing. Also, 74.2% had a timely follow-up appointment scheduled but the majority (85.5%) were missed.
CONCLUSION: Most suspected delays in HbA1c testing were confirmed; however, a substantial minority were misclassified due to missing data or follow-up care outside the health system. Current solutions to improve data quality for HbA1c are labor intensive and highlight the need for better integration of health care data. Missed appointments were commonly noted among patients with delays in care and are a potential target for improvement.
Copyright © 2022 The Joint Commission. Published by Elsevier Inc. All rights reserved.

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Year:  2022        PMID: 35595653      PMCID: PMC9216238          DOI: 10.1016/j.jcjq.2022.03.002

Source DB:  PubMed          Journal:  Jt Comm J Qual Patient Saf        ISSN: 1553-7250


  27 in total

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Review 2.  Accuracy of data in computer-based patient records.

Authors:  W R Hogan; M M Wagner
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3.  Logical Observation Identifiers Names and Codes for Laboratorians.

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4.  Time Out - Charting a Path for Improving Performance Measurement.

Authors:  Catherine H MacLean; Eve A Kerr; Amir Qaseem
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5.  Using Electronic Health Records to Measure Quality Improvement Efforts: Findings from a Large Practice Facilitation Initiative.

Authors:  David T Liss; Yaw A Peprah; Tiffany Brown; Jody D Ciolino; Kathryn Jackson; Abel N Kho; Linda Murakami; Theresa L Walunas; Stephen D Persell
Journal:  Jt Comm J Qual Patient Saf       Date:  2019-11-05

6.  Moving from Quality Measurement to Quality Improvement: Applying Meaningful Use Lessons to the Quality Payment Program.

Authors:  L Hersey Catherine; Tant Elizabeth; K G Berzin Olivia; G Trisolini Michael; L West Suzanne
Journal:  Perspect Health Inf Manag       Date:  2019-10-01

Review 7.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

Authors:  Nicole Gray Weiskopf; Chunhua Weng
Journal:  J Am Med Inform Assoc       Date:  2012-06-25       Impact factor: 4.497

8.  Understanding the management of electronic test result notifications in the outpatient setting.

Authors:  Sylvia J Hysong; Mona K Sawhney; Lindsey Wilson; Dean F Sittig; Adol Esquivel; Simran Singh; Hardeep Singh
Journal:  BMC Med Inform Decis Mak       Date:  2011-04-12       Impact factor: 2.796

9.  Population-based geographic access to endocrinologists in the United States, 2012.

Authors:  Hua Lu; James B Holt; Yiling J Cheng; Xingyou Zhang; Stephen Onufrak; Janet B Croft
Journal:  BMC Health Serv Res       Date:  2015-12-07       Impact factor: 2.655

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