Evidence map›Paper›PMID 32819359›Full record

ArticleBMC medical informatics and decision making2020

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system.

Zahra Niazkhani, Mahsa Fereidoni, Parviz Rashidi Khazaee, Afshin Shiva, Khadijeh Makhdoomi, Andrew Georgiou, Habibollah Pirnejad

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 11% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

7 authors at 3 institutions in 3 countries.

Zahra NiazkhaniNephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran.
Mahsa FereidoniDepartment of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran.
Parviz Rashidi KhazaeeFaculty of IT and Computer Engineering, Urmia University of Technology, Urmia, Iran.
Afshin ShivaDepartment of Clinical Pharmacy, Urmia University of Medical Sciences, Urmia, Iran.
Khadijeh MakhdoomiNephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran.
Andrew GeorgiouCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Habibollah PirnejadPatient Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran. pirnejad.h@umsu.ac.ir.ORCID 0000-0001-9284-5702
Urmia University · IRMacquarie University · AUUrmia University of Technology · IR

Funding

Urmia University of Medical Sciences registered student thesis 2245
6 · The paper itself

Abstract

backgroundDrug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients.

methodsFor the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The "Questionnaire for User Interface Satisfaction" was used to measure user satisfaction of the human-computer interface.

resultsAmong 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system.

conclusionsTo our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

Indexed as

Decision Support Systems, ClinicalKidney TransplantationMedical Order Entry SystemsCross-Sectional StudiesDrug InteractionsFemaleHumansMaleProspective StudiesClinical decision support systemsDrug-lab interactionsKidney transplantMedication errorsPatient safety

Identifiers

PMID32819359
PMCPMC7439664
OpenAlexW3056991213

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.