367 Detectability of medication errors in older people prior to potentially preventable admissions recommendations to change medication regimen could also be deferred to the GP, the decision to accept or ignore STOPP/START signals during an in-hospital medication review is likely to be influenced by a patient’s acute condition. This further highlights the need for regular medication review across healthcare settings. ME detection by STOPP/START CDSS-generated STOPP/START signals detected 60% of present MEs during medication review. STOPP/START version 2 lists 114 explicit criteria and is not definitive in detecting all MEs that may occur in older people [17,50]; many other explicit screening tools have been developed to facilitate the detection of potentially inappropriate drug use in older people with limited overlap between the tools [51,52]. However, the STOPP/START criteria are unique among validated explicit screening tools in targeting underuse, which was the most prevalent ME type in our study. The goal of explicit screening tool development is to achieve a high sensitivity and specificity in detecting MEs associated with negative clinical outcomes in older patients. Refining the STOPP/START criteria may further improve the performance of the tool when applied to clinical practice [48]. One approach to improve detection of MEs by software-based STOPP/START signals could be to clarify textual definitions in the current version of STOPP/ START. Lack of clarity of essential elements has made it challenging to convert these explicit criteria into algorithms suitable for software implementation [35,53]. For example, two MEs not detected by STOPP/START were related to the underuse of analgesics in uncontrolled pain. The START criteria for pain management include ambiguous elements that are difficult to translate into algorithms (e.g. START H1 – ‘high-potency opioids in moderate-severe pain, where paracetamol, NSAIDs or lowpotency opioids are not appropriate to the pain severity or have been ineffective’). Making the essential elements of the criteria as specific as possible (e.g. replacing the term ‘moderate-severe pain’ with ‘a VAS-score ≥5’) could potentially enhance detection of MEs by software-generated STOPP/START signals [53]. Finally, some MEs require an implicit screening approach. For example, MEs related to noncompliance are difficult to identify using explicit screening tools, especially in hospital settings where long-term dispensing data from community pharmacies are not readily available. Although noncompliance was identified by the DRA adjudication teams in only three cases (all related to underuse of diuretics in heart failure exacerbation), the aforementioned study by Uitvlugt et al. reported that one third of all potentially preventable DRAs were related to non-adherence [44] emphasising the relevance of adherence monitoring in older patients to avoid harm. 4
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