SIMulation of Medication Error induced by Clinical Trial drug labeling: the SIMME-CT study

Impact of extramural DAIT/NIAID pharmacy programs, research pharmacist scientist oversight on study performance, and lessons learned

PURPOSE

Over the past two decades, the involvement of a Pharmacist Scientist in clinical settings has improved patient safety, decreased medication errors, and enabled successful conduct of clinical trials and faster product development [1-5]. The impact of an oversight by a Pharmacist Scientist on clinical trial performance and execution in terms of Pharmacy and Investigational Product (IP)-related deviations has not been evaluated by a sponsor.

METHODS

This was a retrospective observational study conducted by the Division of Allergy, Immunology and Transplantation (DAIT), National Institute of Allergy and Infectious Diseases (NIAID). We assessed the association of the number of Pharmacy and Investigational Product (IP)-related deviations with Pharmacist oversight and use of DAIT Pharmacy/ Pharmaceutical services in two groups: Intervention Group (IG) and the Control Group (CG).

RESULTS

We evaluated monitoring data from 116 studies conducted between 2006 through 2020. Forty-one eligible clinical trials were included and analyzed: 13 trials were in the IG with Pharmacist oversight and use of Pharmacy Services; 28 trials were in the CG with no Pharmacist oversight and zero to partial use DAIT Pharmacy/ Pharmaceutical Services. The evaluation revealed the expected risk of having a pharmacy and IP-related deviations were 2.94 times higher (95% CI 1.28, 6.67, = 0.01) in trials not having Pharmacist oversight and zero to partial use of Pharmaceutical/ Pharmacy Program services. This significant finding was associated with having Pharmacist oversight when adjusting for study size (# of sites and patients needed), anticipated study duration, design complexity, and whether recruitment was completed or not.

CONCLUSION

We found a statistically significant association between Pharmacist Scientist involvement and oversight from protocol development to study execution and a reduction in Pharmacy and IP-related deviations.

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

[Quality Management Of The Experimental Health Products Circuit In Hospital Pharmacies: National Inventory And Proposed Standardised Tools]

OBJECTIVES

Carry out a national inventory of the current situation regarding the quality management of the investigational health products circuit, to develop adapted standardised tools.

METHODS

A survey of 76 questions, developed by a regional working group, was conducted among clinical research pharmacists in French facilities. Tools were developed to meet the identified needs and validated by participating pharmacists, using the Delphi method. The consensus was defined by achieving a score above 80% on relevance, clarity and evaluability.

RESULTS

Among 94 pharmacists participating in the survey, 88 were interested in standardised tools. The score for the implementation of a quality approach depended on the type of health facility (p<0.0005) and increased with the number of active trials (p<0.0005). All nine proposed tools were useful for over two thirds of pharmacists, but the self-assessment and audit grids have been prioritised. Indeed, only 26% of pharmacies carried out a prior risk assessment and 14% carried out internal audits. The review of both grids led to a consensus on 89% and 97% of the criteria respectively. The validated grids include 62 and 72 criteria respectively.

DISCUSSION

The quality approach of the investigational health products circuit is heterogeneous in the participating centres, with a strong need for standardised tools. The two grids are relevant tools developed by and for professionals.

CONCLUSION

The tools developed will enable to optimise the quality approach by identifying the non-conformities of the investigational health products circuit.

[Medication errors reporting in drug clinical trials: Role of the clinical research pharmacist?]

The investigational drugs circuit has specific risks, and medication errors may occur in clinical trials, possibly associated with adverse reactions. These risks must therefore be managed. In fact, there are few reports of medication errors during clinical trials. In a context of regulatory interpretation difficulties on this subject, we conducted a national survey that highlighted the heterogeneity of the methods used by academic sponsors to collect, code and report medication errors and the need to develop a culture of reporting these errors in clinical trials. This is why the REVISE group (safety officers of French institutional sponsors) has issued recommendations to clarify the sponsor and investigator responsibilities and guide them in the management of medication errors. These new guidelines recommend that any serious or potentially serious medication error or other "special situation" (e.g. overdose, misuse, quality defect) should be notified immediately to the sponsor by the investigator. The clinical research pharmacist place is strategic to detect medication errors and other special situations. The integration of the pharmacist into the reporting system, in collaboration with the investigator, could be discussed with clinical research professionals and health authorities.

What do adult outpatients included in clinical trials know about the investigational drugs being assessed: A cross-sectional study in France

This study aimed to assess patient investigational medication knowledge and to identify factors associated with medication understanding by adult outpatients included in clinical trials. A cross-sectional prospectively designed survey was conducted on consecutive volunteers at 21 university teaching hospitals (in France) from February to December 2014. Investigational medication understanding was assessed at the time of the first dispensing using a structured interviewer-administered questionnaire based on information obtained from the literature that provided an 8-point score. Demographic and other baseline data were collected using structured interviews. Of the 236 participants, 139 (58.9%) of the respondents were male, and the median age was 54.9 years (range: 18–83 years). The mean understanding score was 6.24 and 72.5% of the patients had a score of 6 or higher. In univariate analysis, the medication understanding score was negatively correlated with age (r = -0.15, p = 0.0247) and positively correlated with the level of education (r = 0.25, p = 0.0002). In multivariate analysis, prognostic factors of a higher medication understanding score were: graduation from high school or a higher level of education; HIV infection; phase II/III/IV studies; mention of the drug on the prescription form, and the dispensing of a single investigational medication. Only a quarter of the adult outpatients included in clinical trials had a maximum possible investigational medication understanding score. Being old and having a low level of education were found to be important risk factors for inadequate medication understanding. This and other data suggest that sponsors should encourage initiatives aimed at improving investigational medication understanding in adults enrolled in clinical trials.

Investigational drug labeling variability

BACKGROUND/AIMS

In comparison with commercial drugs, there are few regulations concerning the labeling of investigational drugs. This leads to variability in their content and layout. This increases the risk of errors during storage, validation, compounding, dispensing and administration. The aim of this study was to evaluate the conformity and variability of investigational drug labels. Additional exploratory aims were to evaluate the use of an automated script to describe the labels and to identify the factors associated with the ease of finding a kit number.

METHODS

An 87-criterion list was developed to evaluate content, format and readability. It included eight criteria to evaluate the conformity to the Canadian Food and Drugs Regulation. A systematic cross-sectional evaluation of all investigational drug labels in our 500-bed mother-child center was performed. All active protocols during the period of 14-22 February 2018 were included. Labels from drugs that were sourced locally were excluded. Labels affixed to the outside (external) and inside (internal) containers, as well as labels from American and European sponsors, were compared with the chi-square and Student's t tests. A script was developed in Python to automatically determine key information (number of words, main colors and their proportion). A short survey was conducted with a convenience sample of pharmacists to rate the ease of finding the kit number on labels. Correlation was evaluated with different factors.

RESULTS

A total of 27 protocols were included (24 internal and 34 external labels). The majority (33/34) of external labels were compliant with the Regulation. Some internal labels did not state the expiry date (9/13), the sponsor address (2/13) or storing conditions (1/13). A total of 10 criteria were different between internal and external labels, for instance, the number of languages was higher on external labels (median 3 (2-14) vs 10 (2-50); p = 0.013). Five criteria were different depending on the sponsors' location, for instance, European sponsors were more prone to use bold characters (25% vs 61%, p = 0.034). There was a mean of 146 ± 111 words and 78.3% ± 7.3% empty space per label. These were positively correlated (p < 0.001). The proportion of free space on a label was also correlated with the ease of finding the kit number (p = 0.002).

CONCLUSION

We measured a high variability in the labeling of investigational drugs. Key information was missing from labels affixed to internal containers, despite the use of a high number of pages. The automation worked well and further work is needed to identify criteria that may improve readability and reduce error risk. Detailed and harmonized international guidelines are needed.

The proportion of errors in medical prescriptions and their executions among hospitalized children before and during accreditation

Objective

To evaluate the rate of medication related errors in the pediatric ward and pediatric emergency department (PED), before and after implementing intervention strategies according to the Joint Commission International (JCI) accreditation program.

Design

A retrospective cross-sectional study that included chart review.

Setting

A university affiliated pediatric ward and PED.

Participants

Children 0-18 years old admitted on February 2013 (before the JCI program) and February 2014 (during implementation of the JCI program).

Intervention(s)

A training program designed to meet the JCI official standards on medication prescribing.

Main outcome measure(s)

The number of prescribing and medication administration errors in the 2 years.

Results

We collected 937 valid prescription orders and 924 administration orders (1861 medical orders) from February 2013, and 961 valid prescription orders and 958 administration orders (1919 medical orders) from February 2014. There was a significant reduction in prescribing errors from 6.5 to 4.2% between years 2013 and 2014 (P = 0.03). There was no significant difference in administration error rates between the two periods (104 (11.3%) in the first period and 114 (11.9%) in the second; P = 0.61).

Conclusions

The errors rate we found was within the range described in the literature. Quality assurance interventions can significantly reduce medication prescribing errors.