Evidence map›Paper›PMID 30652611›Full record

SynthesisJCO clinical cancer informatics2018

Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review.

Catherine H Saunders, Curtis L Petersen, Marie-Anne Durand, Pamela J Bagley, Glyn Elwyn

Open access · greenAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in JCO clinical cancer informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 1 institution in 1 country.

Catherine H SaundersCatherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
Curtis L PetersenCatherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
Marie-Anne DurandCatherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
Pamela J BagleyCatherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
Glyn ElwynCatherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
Dartmouth Institute for Health Policy and Clinical Practice · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeClear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular across industries for automated tasks, like analyzing language and spotting readability issues. With the experience of patients with cancer in mind, we reviewed whether anyone has proposed, modeled, or applied machine learning technologies for the assessment of patient education materials and explored the utility of this application.

methodsWe systematically searched the literature to identify English-language articles published in peer-reviewed journals or as conference abstracts that proposed, used, or modeled the use of machine learning technology to assess patient education materials. Specifically, we searched MEDLINE, Web of Science, CINAHL, and Compendex. Two reviewers assessed study eligibility and performed study screening.

resultsWe identified 1,570 publications in our search after duplicate removal. After screening, we included five projects (detailed in nine articles) that proposed, modeled, or used machine learning technology to assess the quality of patient education materials. We evaluated the utility of each application across four domains: multidimensionality (2 of 5 applications), patient centeredness (1 of 5 applications), customizability (0 of 5 applications), and development stage (theoretical, 1 of 5 applications; in development, 3 of 5 applications; complete and available, 1 of 5 applications). Combining points across each domain, the mean utlity score across included projects was 1.8 of 5 possible points.

conclusionGiven its potential, machine learning has not yet been leveraged substantially in the assessment of patient education materials. We propose machine learning systems that can dynamically identify problematic language and content by assessing the quality of patient education materials across a range of flexible, customizable criteria. Assessment may help patients and families decide which materials to use and encourage developers to improve materials overall.

Indexed as

FemaleHumansMachine LearningMalePatient Education as TopicPatientsTeaching Materials

Identifiers

PMID30652611
PMCPMC6874040
OpenAlexW2900176719

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.