Quantifying the amount of information available in order to prescribe, dispense and administer drugs
Bibliographic record
Abstract
A myriad of papers have been published on prescribing errors 1 and a similar observation can be made regarding the role of the pharmacist in reviewing prescriptions 2. This impressive amount of data contrasts strikingly with the fact that nobody has ever sought to quantify the amount of drug information that is currently available when prescribing and dispensing drugs. We aimed at quantifying the amount of available information gathered in the whole Summary of Product Characteristic (SPC) dataset by using the content of the fully structured drug database THERIAQUE (http://www.theriaque.org). This database currently used in France was accredited by the French agency for quality health care in February 2009. One of the main characteristics of this database is that all the information has been structured in both coded form and in textual form. This feature allows computerized queries. For the purpose of our work, queries were made on the number of information fields related to therapeutic indications, posology and method of administration, contraindications, drug–drug interactions, special warnings and precautions for use (including pregnancy and lactation, effects on ability to drive and use machines, use in specific populations), undesirable effects and physicochemical incompatibilities. As drug–drug interaction and drug–drug physicochemical incompatibility generally involve two drugs, the result concerning the number of information fields was divided by two for these groups of information. On May 6 2012, 12 471 brand names were marketed in France and 279 investigational drugs were available for compassionate use. The number of information fields is given in Table 1. Regarding SPC contents, the drug database THERIAQUE is made up of more than 1.5 million fields. Given the method of calculation, one might consider this number to be an overestimation of the reality. Indeed, there is a redundancy of information with regard to generic drugs. One could also consider it to be underestimated due to the fact that SPCs have a delay in updating, especially regarding drug interactions 3. Our result was obtained for medicines marketed in France. The current number of presentations available in France is a little higher than in Great-Britain, and is well below that of Germany, Canada, Japan or the United States 4-6. We could consider this result as an order of magnitude that applies to countries of the European Union and North America. Our result is important for several reasons. From an academic point of view the sum of information contained in SPCs is regarded as the basic drug knowledge to be assimilated by the professional before registration. Given the sheer volume of information contained in the SPCs, it appears obvious that a knowledge deficit must therefore be considered as an intrinsic feature of the activity of health care professionals. Prescribing the right drug for the right disease is at the heart of a physician's competence and one of the priorities of schools of medicine towards their future doctors, and this represents 3.4% of the total amount of information. This percentage rises to 6.3% and 24.8% if we add Posology and method of administration and Contraindications and Special warnings, respectively. Our result strengthens the following statement: ‘The solo doctor who embodies every process needed to ensure highest quality care is now nearly a myth’ 7. This statement established from the results of extensive research has not yet permeated the minds of all health professionals. Quality problems remain pervasive and prevention of medication errors requires collaborative work. However developing collaborative care is difficult and takes time 8. Patients taking multiple medications are at increased risk for adverse drug events. These risks can be reduced if physicians and pharmacists regularly review the adverse reaction profiles of their patients' medications. However, practitioners report difficulties in differentiating between medical conditions and symptoms due to side effects of medication 9. Evaluating medications for potential adverse events is a time consuming process, typically involving manual searching for information. As the number of drugs increases, so too does the time required to review all of the potential side effects. This and the fact that ‘undesirable effects’ represent 70% of the whole amount of available information is the second key finding of our work. It is a pressing reason for active research into how the review of potential adverse drug events can be speeded up and the search for needed information facilitated 10. Disseminating the message that 1.5 million pieces of information are gathered in the SPCs could be a helpful argument to convince medical and pharmacy schools to focus on teaching how to develop collaborative care and to insist on the need for professionals to have skills in managing information. XD is president of Centre National Hospitalier d'Information sur le Médicament (CNHIM http://www.cnhim.org/). SK is an employee of Centre National Hospitalier d'Information sur le Médicament (CNHIM). All authors have completed the Unified Competing Interest form and declare no support from any organization for the submitted work. There are no other competing interests to declare. We thank Dr Alison Foote of the Grenoble Clinical Research Centre for critically editing the manuscript including for English usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".