ArticleBMC medical informatics and decision making2012
Development and evaluation of a computerised clinical decision support system for switching drugs at the interface between primary and tertiary care.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Bicentric evaluation of employee satisfaction, patient safety and treatment quality: comparing different Health Information System (HIS) solutions.BMC health services research · 2025Article
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2025Review
- A newly developed algorithm for switching outpatient medications to medications listed in the hospital formulary: a prospective real-word evaluation in patients admitted electively to hospital.European journal of clinical pharmacology · 2024Article
- Harnessing the power of clinical decision support systems: challenges and opportunities.Open heart · 2023Review
- Changing the medication documentation process for discharge: impact on clinical routine and documentation quality-a process analysis.European journal of hospital pharmacy : science and practice · 2022Article
- Clinical Decision Support Systems.Visceral medicine · 2021Review
- Impact of clinical decision support therapeutic interchanges on hospital discharge medication omissions and duplications.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2021Article
- Attitude and Purchase Intention to Generic Drugs.International journal of environmental research and public health · 2021Article
- An overview of clinical decision support systems: benefits, risks, and strategies for success.NPJ digital medicine · 2020Review
- Systematic Review of Medical Informatics-Supported Medication Decision Making.Biomedical informatics insights · 2017Review
- ePrescribing: Reducing Costs through In-Class Therapeutic Interchange.Applied clinical informatics · 2016Article
- Development and evaluation of an algorithm to facilitate drug prescription for inpatients with feeding tubes.European journal of clinical pharmacology · 2015Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUpon admission to a hospital patients' medications are frequently switched to alternative drugs compiled in so called hospital drug formularies. This substitution process is a laborious and error-prone task which should be supported by sophisticated electronic tools. We developed a computerised decision support system and evaluated benefit and potential harm associated with its use.
methodsBased on a multi-step algorithm we identified drug classes suitable for exchange, defined conversion factors for therapeutic interchange, built a web-based decision support system, and implemented it into the computerised physician order entry of a large university hospital. For evaluation we compared medications manually switched by clinical pharmacists with the results of automated switching by the newly developed computer system and optimised the system in an iterative process. Thereafter the final system was tested in an independent set of prescriptions.
resultsAfter iterative optimisation of the logical framework the tool was able to switch drugs to pharmaceutical equivalents and alternatives; in addition, it contained 21 different drug classes for therapeutic substitution. In this final version it switched 91.6% of 202 documented medication consultations (containing 1,333 drugs) automatically, leaving 8.4% for manual processing by clinical professionals. No incorrect drug switches were found.
conclusionA large majority (>90%) of drug switches performed at the interface between primary and tertiary care can be handled automatically using electronic decision support systems, indicating that medication errors and workload of healthcare professionals can be considerably reduced.
Indexed as
Identifiers
What OpenQuestion holds
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.