Main Results
We considered 18 prescribing and four monitoring medication safety indicators. For prescribing indicators, increasing age and polypharmacy increased the odds of experiencing a prescribing hazard. Similar results were reported by previous studies [3, 5, 6]. Although polypharmacy is the most important risk factor for prescribing hazards, it should be noted that patients’ co-morbidities, previous medication therapies (especially unsuccessful ones), treatment goals, and current health status were not taken into account. These are important contextual factors for analysing prescribing errors [23]; however, with the exception of co-morbidities, these factors are not readily accessible or quantifiable. Some apparent prescribing errors may be the only viable or necessary course of medication therapy in certain cases. This is especially relevant to elderly patients as the clinical trials underpinning many drug therapies exclude frail and elderly patients, thus, creating significant uncertainty around the effects of pharmacotherapy in elderly patients [23, 24].
Monitoring indicators are not usually discussed in their own right in the literature. Increasing age, polypharmacy, and being male decreased the likelihood of missed monitoring. The effect of a practice being a training practice was significant only in the adjusted model; patients in such practices were less likely to miss monitoring. Also, practices in the most deprived areas exhibited much higher levels of missed monitoring. This issue warrants further research to trace the origins of the inequity, through deeper, quantitative and qualitative study of patient- and practice-level determinants.
The observed effects for age and polypharmacy, better monitoring and worse prescribing rates, are not surprising. Given this, we would expect that the increased morbidity burden for the elderly leads to more consultations and higher levels of polypharmacy. Within this context, it may be more relevant to apply prescribing quality standards for the elderly/high polypharmacy groups, as these groups receive numerous interventions and are, on average, exposed to more risks.
The PINCER study [8] reported high prevalence values for several monitoring indicators, namely, M2 (blood/liver function test for patients on methotrexate) 35–36 % and M4 (thyroid function test for patients on amiodarone) 46 %. In our study, M2 was 6.74 % and M4 was 38.01 %. The large difference in the prevalence values of M2 could be explained by additional test data captured by secondary care data in SIR. However, the question that remains unanswered is whether these additional test data were originally requested by practices and simply did not make it into primary care records or whether they were recorded as a result of patients visiting the hospital. The latter seems to be more likely as both the blood and the renal function tests are common tests in the hospital, whereas the thyroid function test is not as common and, hence, the smaller difference in prevalence values of M4. Unless the tests are directly related to the cause of attending the hospital, they are unlikely to be communicated to a practice, although general practitioners in Salford can access the hospital results, if required. The fragmentation of monitoring medication-related biomarkers needs further investigation especially as there might be some missed opportunities when tests done in secondary care are not evaluated against a patient’s long-term medication. The results of this study related to monitoring indicators require further investigation and should be interpreted with caution.
Analysis using only primary care data in SIR showed that the univariate effect of age for monitoring indicators was no longer significant. This might be due to excluding secondary care test data resulting from more frequent hospital visits by older patients. Polypharmacy was still a significant factor for monitoring indicators. As polypharmacy exhibits opposite effects for monitoring and prescribing indicators, it is clear that combining the two would dilute its effect. Also, the univariate effect of software system became significant, where Vision-derived data seemed to indicate higher levels of missed monitoring. This might be explained by the differences in data linkage between practice software systems and the hospital’s information system. However, differences in performance across systems are not unprecedented and can be driven by usability and intuitiveness, use of alerts and notifications, dismissability of reminders, support and training, or adaptability [25].
Adjusting for covariates reduced the variation between practices by 14 and 32 % for prescribing and monitoring indicators, respectively. Clearly, there are other factors that affect the apparent differences in performance between practices. Also, practices with high numbers of prescribing safety issues do not necessarily have high numbers of monitoring issues. This indicates that the procedural and clinical context of medication prescribing and monitoring are different and the two processes should be investigated separately.
Strengths and Limitations of the Study
The main strength of this study is the comprehensive analysis of medication monitoring as well as prescribing safety indicators for a well-researched set of indicators and a defined population with linked primary and secondary care records. We have shown that linked secondary care data are important for medication safety surveillance in primary care. In addition, we have demonstrated the different statistical characteristics of monitoring compared with prescribing indicators, and shown how surveillance systems might deal with this.
One of the main limitations of this study is its cross-sectional nature. Although we have provided some results using alternative reference dates (please see electronic supplementary material), more detailed longitudinal studies could be pursued. The variation in the outcomes within individual patients could change over time as concordance with medication policies change—a highly complex longitudinal picture.
There are also other important covariates that were not considered in this study, such as practitioner-level covariates like clinical experience. The extract of SIR we had access to did not contain practitioner-level data. In addition, a more complete picture of co-morbidity could be studied alongside polypharmacy, attempting to identify more detailed targets for quality improvement and, potentially, clinical decision-support systems.
Polypharmacy as a measure of medication exposure can be difficult to quantify, especially from integrated data such as that in SIR, where dosage and intake instructions need to be inferred through text mining. This requires further work, especially as a more accurate medication exposure measure would need to take into account whether drugs that have been prescribed are actually taken by patients, which could vary significantly depending on the type of medication.
We used a particular set of prescribing indicators which is not exhaustive of the safety concerns in prescribing and monitoring practice. Some of the indicators we investigated related to very small patient groups; hence, it is unlikely these indicators on their own will be adequate in quantifying practice prescribing safety. However, they should be adequate for this task as part of a larger indicator group.
The NHS is by far the largest health provider in the UK, although, alternative private providers do exist. The data we analysed only pertained to the NHS, and our findings are not necessarily generalisable to private health providers.
Although one of the ultimate aims of this study is to improve medication safety through computerised intervention, it should be noted that this is a two-way process. A computerised intervention with a feedback loop should aim at finding the contextual circumstances that lead to medication errors and not aim at providing punitive/summative assessments of practitioners’ medication practice. In the context of manageable two-way interventions, the initial set of indicators is likely to be restricted. Having a large number of indicators can run the risk of over-whelming general practitioners with alerts and lead to “alert fatigue” where practitioners may override the alerts or stop providing feedback [26].
Future Work
As a follow-up to the current study, we are currently planning to roll out and evaluate an electronic audit and feedback intervention to improve medication safety in Salford primary care. The intervention, based on the PINCER trial [8], will interrogate linked electronic health record data to continuously assess the prescribing and monitoring indicators studied here. Each participating general practice will receive access to their own safety scores through a web-based dashboard application and will be visited by a trained pharmacist to review the indicator scores and initiate appropriate action. The results of this intervention study are expected by late 2016.