Evidence map›Paper›PMID 33646459›Full record

ArticleJournal of medical systems2021

Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.

Stefanie Jauk, Diether Kramer, Alexander Avian, Andrea Berghold, Werner Leodolter, Stefan Schulz

Erratum issuedOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 6 pooled it
5.4field-weighted citation impact, top 3% of its field
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

34 citing papers in PubMed, 6 syntheses or guidelines pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Stefanie JaukSteiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Information and Process Management, Graz, Austria. stefanie.jauk@kages.at.ORCID http://orcid.org/0000-0003-1287-594X
Diether KramerSteiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Information and Process Management, Graz, Austria.
Alexander AvianInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria.
Andrea BergholdInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria.
Werner LeodolterSteiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Information and Process Management, Graz, Austria.
Stefan SchulzInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria.
Medical University of Graz · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.

Indexed as

AlgorithmsClinical Decision-MakingAustraliaDeliriumDiagnosis, DifferentialDiagnostic ErrorsElectronic Health RecordsFemaleHumansMachine LearningMaleMiddle AgedPilot ProjectsPsychiatric Status Rating ScalesClinical decision supportDeliriumMachine learningPredictive modellingRisk managementTechnology acceptance model

Identifiers

PMID33646459
PMCPMC7921052
OpenAlexW3135561137

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.