ArticleJMIR medical informatics2017
Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality.
Article in JMIR medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02130895 (A Mixed-method Study to Explore, Assess, and Explain the Translation of a Complex Set of Evidence-based Rules Into Practice Through Electronic Medical Record Based Decision Support), which is not on this map. Cited by 20 papers, 8 of them syntheses that pooled it.
What it found
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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.
A Mixed-method Study to Explore, Assess, and Explain the Translation of a Complex Set of Evidence-based Rules Into Practice Through Electronic Medical Record Based Decision Support: How Can STOPP Effectively be Deployed in Primary Care?
Who cites it
20 citing papers in PubMed, 8 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Evaluating the role of clinical decision support systems in medication safety for older people: a systematic review.Age and ageing · 2025Pooled it
- Deprescribing in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review.BMC medical informatics and decision making · 2024Pooled it
- Detection tools for prediction and identification of adverse drug reactions in older patients: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Deprescribing for Community-Dwelling Older Adults: a Systematic Review and Meta-analysis.Journal of general internal medicine · 2020Pooled it
- Reducing Potentially Inappropriate Prescriptions for Older Patients Using Computerized Decision Support Tools: Systematic Review.Journal of medical Internet research · 2019Pooled it
- The effects of on-screen, point of care computer reminders on processes and outcomes of care.The Cochrane database of systematic reviews · 2009Pooled it
- Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review.Annals of family medicinePooled it
- Comparing Fourteen Behavioral Science Electronic Health Record Deprescribing Tools in Older Adults: NUDGE-EHR Adaptive Trial.Journal of the American Geriatrics Society · 2025Trial
- Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion.BMC proceedings · 2024Article
- Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review.BMC primary care · 2023Article
- Interventions to address potentially inappropriate prescriptions and over-the-counter medication use among adults 65 years and older in primary care settings: protocol for a systematic review.Systematic reviews · 2022Article
- Effectiveness of Interventions to Reduce Potentially Inappropriate Medication in Older Patients: A Systematic Review.Frontiers in pharmacology · 2021Review
- Using Deprescribing Practices and the Screening Tool of Older Persons' Potentially Inappropriate Prescriptions Criteria to Reduce Harm and Preventable Adverse Drug Events in Older Adults.Journal of patient safety · 2020Article
- Embedding "Smart" Disease Coding Within Routine Electronic Medical Record Workflow: Prospective Single-Arm Trial.JMIR medical informatics · 2020Article
- Use of an Electronic Clinical Decision Support System in Primary Care to Assess Inappropriate Polypharmacy in Young Seniors With Multimorbidity: Observational, Descriptive, Cross-sectional Study.JMIR medical informatics · 2020Article
- Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Using electronic health records for clinical trials: Where do we stand and where can we go?CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2019Article
- Using EMR-enabled computerized decision support systems to reduce prescribing of potentially inappropriate medications: a narrative review.Therapeutic advances in drug safety · 2018Review
- Guidelines and mHealth to Improve Quality of Hypertension and Type 2 Diabetes Care for Vulnerable Populations in Lebanon: Longitudinal Cohort Study.JMIR mHealth and uHealth · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPotentially Inappropriate Prescriptions (PIPs) are a common cause of morbidity, particularly in the elderly.
objectiveWe sought to understand how the Screening Tool of Older People's Prescriptions (STOPP) prescribing criteria, implemented in a routinely used primary care Electronic Medical Record (EMR), could impact PIP rates in community (non-academic) primary care practices.
methodsWe conducted a mixed-method, pragmatic, cluster, randomized control trial in research naïve primary care practices. Phase 1: In the randomized controlled trial, 40 fully automated STOPP rules were implemented as EMR alerts during a 16-week intervention period. The control group did not receive the 40 STOPP rules (but received other alerts). Participants were recruited through the OSCAR EMR user group mailing list and in person at user group meetings. Results were assessed by querying EMR data PIPs. EMR data quality probes were included. Phase 2: physicians were invited to participate in 1-hour semi-structured interviews to discuss the results.
resultsIn the EMR, 40 STOPP rules were successfully implemented. Phase 1: A total of 28 physicians from 8 practices were recruited (16 in intervention and 12 in control groups). The calculated PIP rate was 2.6% (138/5308) (control) and 4.11% (768/18,668) (intervention) at baseline. No change in PIPs was observed through the intervention (P=.80). Data quality probes generally showed low use of problem list and medication list. Phase 2: A total of 5 physicians participated. All the participants felt that they were aware of the alerts but commented on workflow and presentation challenges.
conclusionsThe calculated PIP rate was markedly less than the expected rate found in literature (2.6% and 4.0% vs 20% in literature). Data quality probes highlighted issues related to completeness of data in areas of the EMR used for PIP reporting and by the decision support such as problem and medication lists. Users also highlighted areas for better integration of STOPP guidelines with prescribing workflows. Many of the STOPP criteria can be implemented in EMRs using simple logic. However, data quality in EMRs continues to be a challenge and was a limiting step in the effectiveness of the decision support in this study. This is important as decision makers continue to fund implementation and adoption of EMRs with the expectation of the use of advanced tools (such as decision support) without ongoing review of data quality and improvement.
trial registrationClinicaltrials.gov NCT02130895; https://clinicaltrials.gov/ct2/show/NCT02130895 (Archived by WebCite at http://www.webcitation.org/6qyFigSYT).
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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.