“The computer says no” Are there tools and algorithms that will help us stop potentially inappropriate medications?

Emergency admissions due to adverse drug reactions (ADRs) have increased markedly in England from 60 000 hospitalizations per year in 2008 to 92 000 hospitalizations in 2015. These ADRs are estimated to cost £83.7 million per year, with a projected loss of 627 lives. More broadly, a meta-analysis of 42 studies from across the world found that 8.7% of hospital admissions of older people were due to ADRs. In order to stem this tide, experts have emphasized the importance of de-prescribing high-risk drugs in primary care to “reduce appreciably the number of preventable drug-related admissions to hospital. ” However, Elliott et al.'s evidence synthesis has reported that some ADR admissions cannot be avoided. Resourceintensive, mass pre-emptive screening may only be worthwhile if a substantial proportion of hospitalized patients actually have preventable drug-related admissions. A wide range of tools or algorithms have therefore been developed to enable more precise prediction of ADR risk, and targeted identification of susceptible patients for medication review. Typically, the tools are in the form of different sets of predictor variables or markers (derived through expert consensus) for screening and surveillance of patients with potentially elevated risk of ADRs. Some ADR prediction tools can be used to summarize an overall risk score for an individual based on patient characteristics, laboratory findings and the entire drug history. Alternatively, many tools take the approach of assessing ADR risk related to specific drug classes in a clinical condition that renders the patient particularly susceptible. Examples of specific markers are aspirin use in patients with history of peptic ulcer (where the patient is at risk of ADR of recurrent ulcer and bleeding), or beta-blockers in patient with asthma (where patient is at risk of ADR of worsening airway obstruction). These surveillance sets of ADR predictor markers can be deployed in different ways through General Practitioner's (GP) computer systems. Real-time alerts to warn of potential ADRs can be programmed (e.g. through clinical decision support systems) to pop up on the screen when GPs are seeing an individual patient who happens to trigger certain risk criteria. Conversely, a more formal approach, akin to surveillance or screening programmes, involves regular interrogation of the GP computer system for whole cohorts of patients who all share specific markers of ADR risk. Clinicians can run an electronic search of GP records, and then call back all those judged “at risk of ADR” for targeted medication review and de-prescribing to reduce subsequent harm. Although both the above approaches seem eminently plausible, there are major issues with delivery and implementation into clinical practice. First is the (yet unproven) assumption that the chosen predictor variables are able to correctly and reliably distinguish cases who subsequently suffer ADRs from those who are unlikely to be harmed by ADRs. Here, we emphasize that the concept of “potentially inappropriate or hazardous medication” does not mean definitely inappropriate, nor that a harmful error has clearly occurred and must be rectified. Elliott et al.'s review estimated that only 1.7% of the prescribing errors in primary care would lead to serious harm, whereas the vast majority (98.3%) would have mild to moderate consequences for the health service. We acknowledge that this classification is from a healthcare provider perspective and a patient may interpret the consequences to a different degree. The researchers concluded that “Very few, or no data were found that indicated direct links between errors and harm, . and what proportion of those errors reaching patients caused actual harm. ” Similarly, a meta-analysis of 67 studies found only a modest association (relative risk 1.25, 95% confidence interval 1.09–1.44) between “potentially inappropriate medications” (as measured by tools such as Beers or STOPP) and subsequent hospitalization with ADR. Moreover, current pop-up alerts and predictive ADR markers are often blunt tools that simply flag up crude binary categories of “at-risk” or “not at-risk”. The absence of any actual numerical estimate of harm is unhelpful. Both clinicians and patients need data regarding the specific ADR risk (whether it be say, 4%, or 10%, or 50%) to make informed treatment choices, especially when trying to balance benefit of the drug against the harm. Clinicians may find it difficult to prioritize the highest risk individuals for urgent medication review because Received: 30 May 2020 Revised: 23 July 2020 Accepted: 28 July 2020