ArticleBMC medical informatics and decision making2025
Accompanying the prostate cancer patient pathway: evaluation of novel clinical decision support software in patients with early diagnosis of prostate cancer.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- Advancing the adoption of oncology decision support tools in Europe: insights from CAN.HEAL.Frontiers in digital health · 2026Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer, the second most prevalent cancer among men with 1.4 million newly diagnosed cases, poses intricate challenges in treatment decision-making due to multifaceted influencing factors. The aim was to assess the efficacy of clinical decision support software (CDSS) in pre-therapeutic prostate cancer management.
methodsThis study evaluated the CDSS "AI Pathway Companion" by comparing traditional manual methods with software-supported processes in patients diagnosed with localized prostate cancer. The assessment included time analysis, user surveys, and data quality evaluations.
resultsThe CDSS notably reduced case preparation time (-41.5% overall time), including accessing laboratory and imaging results, as well as data integration tasks. Users' survey indicated heightened satisfaction and improved information quality using the software. Despite limitations in sample size and single-center focus, the study underscored the CDSS's potential to streamline workflows, enhance data quality, and elevate user experience.
conclusionThe study highlights the CDSS's significant impact on consultation preparation time, decision-making efficiency, and user satisfaction in pre-therapeutic prostate cancer management. While showing promise in this setting, further investigations are needed to gauge its effectiveness in advanced stages and post-therapeutic contexts, aligning with evolving healthcare demands for improved efficiency and patient-centered care.
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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.