Evidence map›Paper›PMID 30566630›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2019

Impact of document consolidation on healthcare providers' perceived workload and information reconciliation tasks: a mixed methods study.

Masoud Hosseini, Anthony Faiola, Josette Jones, Daniel J Vreeman, Huanmei Wu, Brian E Dixon

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Electronic health records and clinician burnout: A story of three eras.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  5. Article
  6. Can informatics innovation help mitigate clinician burnout?Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
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

6 authors.

Masoud HosseiniDepartment of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA.
Anthony FaiolaDepartment of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, USA.
Josette JonesDepartment of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA.
Daniel J VreemanCenter for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.
Huanmei WuDepartment of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA.
Brian E DixonCenter for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.

Funding

The Indiana Training Program in Public & Population Health InformaticsT15LM012502 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI Brian E. Dixon, William F Fadel · 2017 to 2026
$4.7M
NLM NIH HHS T15 LM012502
6 · The paper itself

Abstract

Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.

Indexed as

Health Information ExchangeHealth PersonnelMedical Records Systems, ComputerizedWorkloadData AggregationElectronic Health RecordsHumansInformation Storage and Retrieval

Identifiers

PMID30566630
PMCPMC6804409

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.