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Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled…

Overview of attention for article published in Implementation Science, November 2016
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  • Good Attention Score compared to outputs of the same age (71st percentile)

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Title
Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial
Published in
Implementation Science, November 2016
DOI 10.1186/s13012-016-0514-3
Pubmed ID
Authors

Lorenzo Moja, Alessandro Passardi, Matteo Capobussi, Rita Banzi, Francesca Ruggiero, Koren Kwag, Elisa Giulia Liberati, Massimo Mangia, Ilkka Kunnamo, Michela Cinquini, Roberto Vespignani, Americo Colamartini, Valentina Di Iorio, Ilaria Massa, Marien González-Lorenzo, Lorenzo Bertizzolo, Peter Nyberg, Jeremy Grimshaw, Stefanos Bonovas, Oriana Nanni

Abstract

Computerized decision support systems (CDSSs) are computer programs that provide doctors with person-specific, actionable recommendations, or management options that are intelligently filtered or presented at appropriate times to enhance health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. The Computerized DEcision Support in ONCOlogy (ONCO-CODES) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio. The trial is designed to evaluate the effectiveness on clinical practice and quality of care of a multi-specialty collection of patient-specific reminders generated by a CDSS in the IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) hospital. We hypothesize that the intervention can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to cancer patients. The primary outcome is the rate at which the issues reported by the reminders are resolved, aggregating specialty and primary care reminders. We will include all the patients admitted to hospital services. All analyses will follow the intention-to-treat principle. The results of our study will contribute to the current understanding of the effectiveness of CDSSs in cancer hospitals, thereby informing healthcare policy about the potential role of CDSS use. Furthermore, the study will inform whether CDSS may facilitate the integration of primary care in cancer settings, known to be usually limited. The increasing use of and familiarity with advanced technology among new generations of physicians may support integrated approaches to be tested in pragmatic studies determining the optimal interface between primary and oncology care. ClinicalTrials.gov, NCT02645357.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 107 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 18%
Student > Bachelor 17 16%
Student > Master 14 13%
Student > Ph. D. Student 8 7%
Student > Doctoral Student 5 5%
Other 15 14%
Unknown 29 27%
Readers by discipline Count As %
Medicine and Dentistry 27 25%
Nursing and Health Professions 11 10%
Computer Science 9 8%
Pharmacology, Toxicology and Pharmaceutical Science 6 6%
Social Sciences 5 5%
Other 17 16%
Unknown 32 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 November 2016.
All research outputs
#6,892,372
of 24,657,405 outputs
Outputs from Implementation Science
#1,123
of 1,774 outputs
Outputs of similar age
#118,444
of 425,725 outputs
Outputs of similar age from Implementation Science
#26
of 37 outputs
Altmetric has tracked 24,657,405 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,774 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 425,725 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.