Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

Gayle Shier Kricke*, Matthew B. Carson, Young Ji Lee, Corrine Benacka, R Kannan Mutharasan, Faraz Ahmad, Preeti Kansal, Clyde W Yancy, Allen Sawyer Anderson, Nicholas Dean Soulakis

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

Original languageEnglish (US)
Article numberocw083
Pages (from-to)288-294
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume24
Issue number2
DOIs
StatePublished - Mar 1 2017

Fingerprint

Electronic Health Records
Documentation
Quality Improvement
Workflow
Observation
Healthcare Failure Mode and Effect Analysis
Cardiology
Inpatients

Keywords

  • Cardiology hospital service
  • Discharge planning
  • Electronic health record
  • Risk assessment
  • Workflow

ASJC Scopus subject areas

  • Health Informatics

Cite this

@article{1a3814a9f326434eae80600b3efd183d,
title = "Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification",
abstract = "Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35{\%} of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.",
keywords = "Cardiology hospital service, Discharge planning, Electronic health record, Risk assessment, Workflow",
author = "Kricke, {Gayle Shier} and Carson, {Matthew B.} and Lee, {Young Ji} and Corrine Benacka and Mutharasan, {R Kannan} and Faraz Ahmad and Preeti Kansal and Yancy, {Clyde W} and Anderson, {Allen Sawyer} and Soulakis, {Nicholas Dean}",
year = "2017",
month = "3",
day = "1",
doi = "10.1093/jamia/ocw083",
language = "English (US)",
volume = "24",
pages = "288--294",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

AU - Kricke, Gayle Shier

AU - Carson, Matthew B.

AU - Lee, Young Ji

AU - Benacka, Corrine

AU - Mutharasan, R Kannan

AU - Ahmad, Faraz

AU - Kansal, Preeti

AU - Yancy, Clyde W

AU - Anderson, Allen Sawyer

AU - Soulakis, Nicholas Dean

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

AB - Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

KW - Cardiology hospital service

KW - Discharge planning

KW - Electronic health record

KW - Risk assessment

KW - Workflow

UR - http://www.scopus.com/inward/record.url?scp=85016286291&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016286291&partnerID=8YFLogxK

U2 - 10.1093/jamia/ocw083

DO - 10.1093/jamia/ocw083

M3 - Article

VL - 24

SP - 288

EP - 294

JO - Journal of the American Medical Informatics Association : JAMIA

JF - Journal of the American Medical Informatics Association : JAMIA

SN - 1067-5027

IS - 2

M1 - ocw083

ER -