Evidence map›Paper›PMID 24491911›Full record

ArticleJournal of medical Internet research2014

Evaluation of the accuracy of smartphone medical calculation apps.

Rachel Bierbrier, Vivian Lo, Robert C Wu

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
25.7field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.

  1. Pooled it
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  13. A validation study of a smartphone application for functional mobility assessment of the elderly.Hong Kong physiotherapy journal : official publication of the Hong Kong Physiotherapy Association Limited = Wu li chih liao · 2016
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  19. Incorporating smartphones into clinical practice.Annals of medicine and surgery (2012) · 2015
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  20. Smartphone Applications for the Clinical Oncologist in UK Practice.Journal of cancer education : the official journal of the American Association for Cancer Education · 2015
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Rachel BierbrierUniversity Health Network, Centre for Innovation in Complex Care, Toronto, ON, Canada.
Vivian Lo
Robert C Wu
University Health Network · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile phones with operating systems and capable of running applications (smartphones) are increasingly being used in clinical settings. Medical calculating applications are popular mhealth apps for smartphones. These include, for example, apps that calculate the severity or likelihood of disease-based clinical scoring systems, such as determining the severity of liver disease, the likelihood of having a pulmonary embolism, and risk stratification in acute coronary syndrome. However, the accuracy of these apps has not been assessed.

objectiveThe objective of this study was to evaluate the accuracy of smartphone-based medical calculation apps.

methodsA broad search on Google Play, BlackBerry World, and the iTunes App Store was conducted to find medical calculation apps for smartphones. The list of apps was narrowed down based on inclusion and exclusion criteria focusing on functions thought to be relevant by a panel of general internists (number of functions =13). Ten case values were inputted for each function and were compared to manual calculations. For each case, the correct answer was assigned a score of 1. A score for the 10 cases was calculated based on the accuracy of the results for each function on each app.

resultsWe tested 14 apps and 13 functions for each app if that function was available. We conducted 10 cases for each function for a total of 1240 tests. Most functions tested on the apps were accurate in their results with an overall accuracy of 98.6% (17 errors in 1240 tests). In all, 6 of 14 (43%) apps had 100% accuracy. Although 11 of 13 (85%) functions had perfect accuracy, there were issues with 2 functions: the Child-Pugh scores and Model for End-Stage Liver Disease (MELD) scores on 8 apps. Approximately half of the errors were clinically significant resulting in a significant change in prognosis (8/17, 47%).

conclusionsThe results suggest that most medical calculating apps provide accurate and reliable results. The free apps that were 100% accurate and contained the most functions desired by internists were CliniCalc, Calculate by QxMD, and Medscape. When using medical calculating apps, the answers will likely be accurate; however, it is important to be careful when calculating MELD scores or Child-Pugh scores on some apps. Despite the few errors found, greater scrutiny is warranted to ensure full accuracy of smartphone medical calculator apps.

Indexed as

Cell PhoneDecision Making, Computer-AssistedMobile ApplicationsRisk AssessmentDiagnosis, DifferentialHumansPhysiciansSeverity of Illness Indexcellular phonecomputers, handheldmedical informatics applicationsmhealthmobile phonesoftware

Identifiers

PMID24491911
PMCPMC3936266
OpenAlexW1993627307

What OpenQuestion 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 OpenQuestion graph.