Applicability of drug-related problem (DRP) classification system for classifying severe medication errors.

BACKGROUND: Several classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses on exploring the applicability of a cause-based DRP classification system for classifying severe MEs and their causes. METHODS: This was a retrospective document analysis study on medication-related complaints and authoritative statements investigated by the Finnish National Supervisory Authority for Welfare and Health (Valvira) in 2013-2017. The data was classified by applying a previously developed aggregated DRP classification system by Basger et al. Error setting and harm to the patient were identified using qualitative content analysis to describe the characteristics of the MEs in the data. The systems approach to human error, error prevention, and risk management was used as a theoretical framework. RESULTS: Fifty-eight of the complaints and authoritative statements concerned MEs, which had occurred in a wide range of social and healthcare settings. More than half of the ME cases (52%, n = 30) had caused the patient's death or severe harm. In total, 100 MEs were identified from the ME case reports. In 53% (n = 31) of the cases, more than one ME was identified, and the mean number of MEs identified was 1.7 per case. It was possible to classify all MEs according to aggregated DRP system, and only a small proportion (8%, n = 8) were classified in the category "Other," indicating that the cause of the ME could not be classified to specific cause-based category. MEs in the "Other" category included dispensing errors, documenting errors, prescribing error, and a near miss. CONCLUSIONS: Our study provides promising preliminary results for using DRP classification system for classifying and analyzing especially severe MEs. With Basger et al.'s aggregated DRP classification system, we were able to categorize both the ME and its cause. More research is encouraged with other ME incident data from different reporting systems to confirm our results.

What Severe Medication Errors Reported to Health Care Supervisory Authority Tell About Medication Safety?

OBJECTIVES: This study investigated severe medication errors (MEs) reported to the National Supervisory Authority for Welfare and Health (Valvira) in Finland and evaluated how the incident documentation applies to learning from errors. METHODS: This study was a retrospective document analysis consisting of medication-related complaints and authoritative statements investigated by Valvira in 2013 to 2017 (n = 58). RESULTS: Medication errors caused death or severe harm in 52% (n = 30) of the cases (n = 58). The majority (83%; n = 48) of the incidents concerned patients older than 60 years. Most likely, the errors occurred in prescribing (n = 38; 47%), followed by administration (n = 15; 19%) and monitoring (n = 14; 17%). The error process often included many failures (n = 24; 41%) or more than one health professional (n = 16; 28%). Antithrombotic agents (n = 17; 13%), opioids (n = 10; 8%), and antipsychotics (n = 10; 8%) were the therapeutic groups most commonly involved in the errors. Almost all error cases (91%; n = 53) were assessed as likely or potentially preventable. In 60% (n = 35) of the cases, the organization reported actions taken to improve medication safety after the occurrence of the investigated incident. CONCLUSIONS: Medication errors reported to the national health care supervisory authority provide a valuable source of risk information and should be used for learning from severe errors at the level of health care systems. High age remains a key risk factor to severe MEs, which may be associated with a wide range of medications including those not typically perceived as high-alert medications or high-risk administration routes. Despite being complex processes, the severe MEs have a great potential to lead to developing systems, processes, resources, and competencies of health care organizations.

Neurosteroid analysis by gas chromatography-atmospheric pressure photoionization-tandem mass spectrometry.

A new and simple APPI interface employing commercially available hardware is used to combine GC to MS. The feasibility of the method is demonstrated in the analysis of urine samples for neurosteroids as their trimethylsilyl (TMS) derivatives. The effect of different dopants (chlorobenzene, toluene, anisole) on the ionization of the TMS derivatives was investigated. With chlorobenzene, the TMS derivatives produced intense molecular ions with minimal fragmentation, and chlorobenzene was selected as best dopant. Protonated molecules in addition to intense molecular ions were produced with toluene and anisole. The performance of the method was verified in the analysis of human urine samples. Chromatographic performance was good with peak half-widths of 3.6-4.3s, linearity (r(2)>0.990) was acceptable, limits of detection (LODs) were in the range of 0.01-10ngmL(-1), and repeatability was good with relative standard deviations (rsd%) below 22%. The results show that the method is well suited for the determination of neurosteroids in biological samples.