ArticleDigital health
Implementing an electronic health record system in a tertiary care surgical oncology setup - A mixed method analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Electronic Health Record (EHR) implementation in low- and middle-income countries (LMICs) remains constrained by infrastructure limitations, dual documentation requirements, and limited local evidence on workflow impact-particularly in time-sensitive oncology settings. We evaluated the implementation of a modular, cloud-based EHR system in a tertiary surgical oncology unit in Sri Lanka operating in parallel with mandatory paper documentation. Methods: A convergent mixed-methods study was conducted during the first six months of implementation. Quantitatively, paired comparisons were performed for 100 consecutive theatre lists and 100 consecutive biopsy-confirmed malignancy cases to assess changes in theatre-list preparation time and biopsy-to-diagnosis interval. Mean differences with 95% confidence intervals (CIs) and effect sizes (Cohen's dz) were calculated. Concurrently, 26 healthcare professionals completed an investigator-designed usability survey; descriptive statistics were reported, and exploratory inferential analyses were performed. Open-text responses underwent reflexive thematic analysis. Results: The EHR significantly reduced theatre-list preparation time from 4.10 minutes to 2.40 minutes (mean reduction 1.70 minutes; 95% (CI 1.44-1.96); Cohen's dz = 1.29; p < 0.0001). The biopsy-to-diagnosis interval decreased from 14.95 days to 8.40 days (mean reduction 6.55 days; 95% (CI 4.52-8.58; Cohen's dz = 0.64; p < 0.0001). Overall user satisfaction was high (mean 4.31/5, SD 0.55). Training adequacy was associated with higher satisfaction (mean difference 0.47; p = 0.035), and satisfaction correlated with ease of data entry (r = 0.60, p = 0.001). Qualitative analysis identified four themes: improved information retrieval, enhanced multidisciplinary coordination, hybrid documentation burden, and infrastructure-related optimisation needs. Survey inferential findings are exploratory due to use of a non-validated instrument. Conclusions: In a resource-constrained oncology setting, implementation of a secure, cloud-based EHR was associated with substantial workflow efficiency gains and strong user acceptability, despite operating within a hybrid paper-electronic environment. Findings support the feasibility and measurable impact of context-sensitive digital transformation in LMIC tertiary services, while underscoring the importance of infrastructure reliability, structured training, and deliberate transition away from dual documentation for sustainable scale-up.
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Registered trials
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.