Evidence map›Paper›PMID 34299806›Full record

ArticleInternational journal of environmental research and public health2021

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment.

Martin Michalowski, Szymon Wilk, Wojtek Michalowski, Dympna O'Sullivan, Silvia Bonaccio, Enea Parimbelli, Marc Carrier, Grégoire Le Gal, Stephen Kingwell, Mor Peleg

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Guideline
  2. Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review.International journal of environmental research and public health · 2022
    Article
  3. 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

10 authors.

Martin MichalowskiNursing Informatics, School of Nursing, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0003-2060-5878
Szymon WilkInstitute of Computing Science, Poznan University of Technology, 60-965 Poznań, Poland.
Wojtek MichalowskiTelfer School of Management, University of Ottawa, Ottawa, ON K1N 6N5, Canada.ORCID 0000-0002-9198-6439
Dympna O'SullivanSchool of Computer Science, Technological University Dublin, D02 HW71 Dublin, Ireland.
Silvia BonaccioTelfer School of Management, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Enea ParimbelliDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
Marc CarrierDivision of Hematology, The Ottawa Hospital, Ottawa, ON K1Y 4E9, Canada.ORCID 0000-0001-8296-2972
Grégoire Le GalDepartment of Medicine, The Ottawa Hospital, Ottawa, ON K1Y 4E9, Canada.
Stephen KingwellDepartment of Orthopaedic Surgery, The Ottawa Hospital, Ottawa, ON K1Y 4E9, Canada.
Mor PelegDepartment of Information Systems, University of Haifa, Haifa 3498838, Israel.ORCID 0000-0002-5612-768X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a methodological framework to support the development of personalized courses that improve patients' understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes-condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom's taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients' attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients' understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners.

Indexed as

Computer-Assisted InstructionHealth PersonnelHumansLearningProblem SolvingBloom’s taxonomyeducational learningontologypatient educationpersonalizationprecompiled planningprocedural reasoning systemVARK

Identifiers

PMID34299806
PMCPMC8307382

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.