Assessment of Automating Safety Surveillance From Electronic Health Records: Analysis for the Quality and Safety Review System. (Record no. 2478)

MARC details
000 -LEADER
fixed length control field 04372nam a22004697a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 170710s20172017 xxu||||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1549-8417
040 ## - CATALOGING SOURCE
Original cataloging agency Ovid MEDLINE(R)
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
PMID 28671914
245 ## - TITLE STATEMENT
Title Assessment of Automating Safety Surveillance From Electronic Health Records: Analysis for the Quality and Safety Review System.
251 ## - Source
Source Journal of patient safety. 17(6):e524-e528, 2021 09 01.
252 ## - Abbreviated Source
Abbreviated source J Patient Saf. 17(6):e524-e528, 2021 09 01.
252 ## - Abbreviated Source
Former abbreviated source J Patient Saf. , 2017 Jun 30
253 ## - Journal Name
Journal name Journal of patient safety
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Year 2021
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Manufacturer FY2022
265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE]
Publication status ppublish
266 ## - Date added to catalog
Date added to catalog 2017-07-10
268 ## - Previous citation
-- Journal of patient safety. , 2017 Jun 30
269 ## - Original dates
Original fiscal year FY2017
501 ## - WITH NOTE
Local holdings Available online through MWHC library: March 2005 - present
520 ## - SUMMARY, ETC.
Abstract BACKGROUND AND OBJECTIVES: In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS. In the absence of real-world testing data and models, which require a substantial investment, we provide a heuristic assessment of the feasibility of automatically populating QSRS questions from EHR data.
520 ## - SUMMARY, ETC.
Abstract CONCLUSIONS: Although EHRs contain a wealth of information, abstracting information from these records is still very challenging, particularly for complex questions, such as those concerning patient adverse events. In this work, we developed a heuristic framework, which can be applied to help guide conversations around the feasibility of automating QSRS data abstraction. This framework does not aim to replace testing with real data but complement the process by providing initial guidance and direction to subject matter experts to help prioritize, which abstraction questions to test for feasibility using real data.
520 ## - SUMMARY, ETC.
Abstract METHODS: To provide an assessment of the automation feasibility for QSRS, we first developed a heuristic framework, the Relative Abstraction Complexity Framework, to assess relative complexity of data abstraction questions. This framework assesses the relative complexity of characteristics or features of abstraction questions that should be considered when determining the feasibility of automating QSRS. Questions are assigned a final relative complexity score (RCS) of low, medium, or high by a team of clinicians, human factors, and natural language processing researchers.
520 ## - SUMMARY, ETC.
Abstract RESULTS: One hundred thirty-four QSRS questions were coded using this framework by a team of natural language processing and clinical experts. Fifty-five questions (41%) had high RCS and would be more difficult to automate, such as "Was use of a device associated with an adverse outcome(s)?" Forty-two questions (31%) had medium RCS, such as "Were there any injuries as a result of the fall(s)?' and 37 questions (28%) had low RCS, such as "Did the patient deliver during this stay?' These results suggest that Blood and Hospital Acquired Infections-Clostridium Difficile Infection (HAI-CDI) modules would be relatively easier to automate, whereas Surgery and HAI-Surgical Site Infection would be more difficult to automate.
546 ## - LANGUAGE NOTE
Language note English
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element *Electronic Health Records
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element *Natural Language Processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Automation
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Delivery of Health Care
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Humans
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element United States
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element United States Agency for Healthcare Research and Quality
651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Institution MedStar Institute for Innovation
657 ## - INDEX TERM--FUNCTION
Medline publication type Journal Article
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Adams, Katharine
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Fong, Allan
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Ratwani, Raj M
790 ## - Authors
All authors Adams K, Chappel T, Fong A, Grace E, McQueen L, Ratwani RM, Samarth A, Terrillion S, Trivedi M
856 ## - ELECTRONIC LOCATION AND ACCESS
DOI <a href="https://dx.doi.org/10.1097/PTS.0000000000000402">https://dx.doi.org/10.1097/PTS.0000000000000402</a>
Public note https://dx.doi.org/10.1097/PTS.0000000000000402
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
Item type description Article
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
          MedStar Authors Catalog MedStar Authors Catalog 07/10/2017   28671914 28671914 07/10/2017 07/10/2017 Journal Article

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