Evidence map›Paper›PMID 39631859›Full record

ReviewInternal medicine (Tokyo, Japan)2025

Unraveling the Implications of Digit Bias in Digital Health - A Literature Review.

Takahiro Suzuki, Hajime Nagasu, Takeshi Ebara, Nobuyuki Kagiyama, Takuya Kishi, Yuichiro Yano, Kazuomi Kario, Akira Nishiyama, Hisatomi Arima, Fujimi Kawai and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Internal medicine (Tokyo, Japan), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

13 authors.

Takahiro SuzukiDepartment of Cardiovascular Medicine, St. Luke's International Hospital, Japan.
Hajime NagasuDepartment of Nephrology and Hypertension, Kawasaki Medical School, Japan.
Takeshi EbaraDepartment of Ergonomics, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan.
Nobuyuki KagiyamaDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Takuya KishiDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Yuichiro YanoDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Kazuomi KarioDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Akira NishiyamaDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Hisatomi ArimaDigital Health Research Working Group of the Japanese Society of Hypertension, Japan.
Fujimi KawaiLibrary, Center for Academic Resources, St. Luke's International University, Japan.
Shigeru ShibataDivision of Nephrology, Department of Internal Medicine, Teikyo University School of Medicine, Japan.
Koichi NodeDepartment of Cardiovascular Medicine, Saga University, Japan.
Atsushi MizunoDepartment of Cardiovascular Medicine, St. Luke's International Hospital, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health, which encompasses digital medicine and therapy, integrates advanced technologies across healthcare. Central to this transformation is 'digitization,' which converts continuous analog data into a discrete digital form. However, this process is challenging. First, digitization inherently has the potential to introduce information loss, thereby diminishing the richness and complexity of data. Second, "digit bias," a cognitive distortion, emerges in the interpretation phase, where individuals' perceptions of and reactions to digital data are intrinsically skewed. There exist two major cognitive biases during digitization process: "digit preferences," where healthcare providers prioritize specific numbers, and "left digit bias" where continuous variables are disproportionately estimated by focusing on the leftmost digit. Although information loss and cognitive biases can cause significant distortions in healthcare, the effects of this "digitization" process have not been adequately quantified, and the accumulation of further evidence in this field is anticipated.

Indexed as

Digital TechnologyBiasDigital HealthHumansdigitdigital Healthdigitizationdigit preferencesleft digit bias

Identifiers

PMID39631859
PMCPMC12331322

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.