Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 163

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Health Professionals’ readiness to implement electronic medical record system at three hospitals in Ethiopia: a cross sectional study

Open access
Biruk, Senafekesh, Yilma, Tesfahun, Andualem, Mulusew, Tilahun, Binyam·2014·BMC Medical Informatics and Decision Making

Abstract

BACKGROUND: Electronic medical record systems are being implemented in many countries to support healthcare services. However, its adoption rate remains low, especially in developing countries due to technological, financial, and organizational factors. There is lack of solid evidence and empirical research regarding the pre implementation readiness of healthcare providers. The aim of this study is to assess health professionals' readiness and to identify factors that affect the acceptance and use of electronic medical recording system in the pre implementation phase at hospitals of North Gondar Zone, Ethiopia. METHODS: An institution based cross-sectional quantitative study was conducted on 606 study participants from January to July 2013 at 3 hospitals in northwest Ethiopia. A pretested self-administered questionnaire was used to collect the required data. The data were entered using the Epi-Info version 3.5.1 software and analyzed using SPSS version 16 software. Descriptive statistics, bi-variate, and multi-variate logistic regression analyses were used to describe the study objectives and assess the determinants of health professionals' readiness for the system. Odds ratio at 95% CI was used to describe the association between the study and the outcome variables. RESULTS: Out of 606 study participants only 328 (54.1%) were found ready to use the electronic medical recording system according to our criteria assessment. The majority of the study participants, 432 (71.3%) and 331(54.6%) had good knowledge and attitude for EMR system, respectively. Gender (AOR = 1.87, 95% CI: [1.26, 2.78]), attitude (AOR = 1.56, 95% CI: [1.03, 2.49]), knowledge (AOR = 2.12, 95% CI: [1.32, 3.56]), and computer literacy (AOR =1.64, 95% CI: [0.99, 2.68]) were significantly associated with the readiness for EMR system. CONCLUSIONS: In this study, the overall health professionals' readiness for electronic medical record system and utilization was 54.1% and 46.5%, respectively. Gender, knowledge, attitude, and computer related skills were the determinants of the presence of a relatively low readiness and utilization of the system. Increasing awareness, knowledge, and skills of healthcare professionals on EMR system before system implementation is necessary to increase its adoption.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12911-014-0115-5

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

See all 163

Metadata

Authors
Biruk, S., Yilma, T., Andualem, M., & Tilahun, B.
Venue
BMC Medical Informatics and Decision Making
Published
2014-12
Volume
14
Issue
1
Pages
115-115
ISSN
1472-6947 (ISSN-L)
OpenAlex
W2157827139
Cited by
163
References
3
FWCI
8.48
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Health Professionals’ readiness to implement electronic medical record system at three hospitals in Ethiopia: a cross sectional study. https://app.cochat.ai/papers/10.1186/s12911-014-0115-5
The paper
Biruk, S., Yilma, T., Andualem, M., & Tilahun, B. (2014). Health Professionals’ readiness to implement electronic medical record system at three hospitals in Ethiopia: a cross sectional study. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-014-0115-5

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.