Almost three-quarters of alerts were overridden, and 40% of the overrides were not appropriate. Future research should optimize alert types and frequencies to increase their clinical relevance, reducing alert fatigue so that important alerts are not inappropriately overridden.

Clinical decision support (CDS) systems link patient data with an electronic knowledge base in order to improve decision-making and computerised physician order entry (CPOE) is a requirement to set up electronic CDS. The medical informatics literature suggests categorising CDS tools into medication dosing support, order facilitators, point-of-care alerts and reminders, relevant information display, expert systems and workflow support. To date, CDS has particularly been recognised for improving processes. CDS successfully fostered prevention of deep-vein thrombosis, improved adherence to guidelines, increased the use of vaccinations, and decreased the rate of serious medication errors. However, CDS may introduce errors, and therefore the term "e-iatrogenesis" has been proposed to address unintended consequences. At least two studies reported severe treatment delays due to CPOE and CDS. In addition, the phenomenon of "alert fatigue" - arising from a high number of CDS alerts of low clinical significance - may facilitate overriding of potentially critical notifications. The implementation of CDS needs to be carefully planned, CDS interventions should be thoroughly examined in pilot wards only, and then stepwise introduced. A crucial feature of CPOE in combination with CDS is speed, since time consumption has been found to be a major factor determining failure. In the near future, the specificity of alerts will be improved, notifications will be prioritised and offer detailed advice, customisation of CDS will play an increasing role, and finally, CDS is heading for patient-centred decision support. The most important research question remains whether CDS is able to improve patient outcomes beyond processes.

Rationale: Glucagon-like peptide-1 receptor (GLP-1R) agonists are approved to treat type 2 diabetes mellitus and obesity. GLP-1R agonists reduce airway inflammation and hyperresponsiveness in preclinical models. Objectives: To compare rates of asthma exacerbations and symptoms between type 2 diabetic adults with asthma prescribed GLP-1R agonists and those prescribed sodium-glucose cotransporter-2 (SGLT-2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, sulfonylureas or basal insulin for diabetes treatment intensification. Methods: Electronic health records-based new-user, active comparator, retrospective cohort study of patients with type 2 diabetes and asthma newly prescribed GLP-1R agonists or comparator drugs, January 2000-March 2018. Primary outcome was asthma exacerbations; secondary outcome was encounters for asthma symptoms. Propensity scores were calculated for GLP-1R agonist and non-GLP-1R agonist use. Zero-inflated Poisson regression models included adjustment for multiple covariates. Measurements and Main Results: Patients initiating GLP-1R agonists (n=448), SGLT-2 inhibitors (n=112), DPP-4 inhibitors (n=435), sulfonylureas (n=2,253) or basal insulin (n=2,692), were identified. At six months, asthma exacerbation counts were lower in persons initiating GLP-1R agonists (reference) compared to SGLT-2 inhibitors (incidence rate ratio [IRR], 2.98 [95% CI, 1.30 to 6.80]), DPP-4 inhibitors (IRR, 2.45 [95% CI, 1.54 to 3.89]), sulfonylureas (IRR, 1.83 [95% CI, 1.20 to 2.77]) and basal insulin (IRR, 2.58 [95% CI, 1.72 to 3.88]). Encounters for asthma symptoms were also lower among GLP-1R agonist users. Conclusions: Adult asthmatics prescribed GLP-1R agonists for type 2 diabetes had lower counts of asthma exacerbations compared to other drugs initiated for treatment intensification. GLP-1R agonists may represent a novel treatment for asthma associated with metabolic dysfunction.

The appropriateness of medication-related clinical decision support overrides in the ICU varied substantially by the type of alert. Inappropriately overridden alerts were associated with an increased risk of ADEs compared to appropriately overridden alerts.

Approximately four of five identified CDS over-rides were appropriately over-ridden, with the rate varying by alert type. However, inappropriate over-rides were six times as likely to be associated with potential and definite ADEs, compared with appropriate over-rides. Further efforts should be targeted at improving the positive predictive value of CDS such as by suppressing alerts that are appropriately over-ridden.

The information contained in patients' drug allergy lists needs to be regularly updated. Most of the drug allergy alerts were overridden, with the majority of alert overrides in the subsample considered appropriate. Some of the rules for these alerts should be carefully reviewed and modified, or removed. Further research is needed to understand providers' overriding of alerts that warned against the risk of 'anaphylaxis', which are more concerning with respect to patient safety.

Objective:To analyze the patterns of potentially avoidable readmissions due to adverse drug events (ADEs) to identify the most appropriate risk reduction interventions. Methods:In this observational study, we analyzed a random sample of 534 potentially avoidable 30-day readmissions from 10,275 consecutive discharges from the medical department of an academic hospital. Readmissions due to ADEs were reviewed to identify the causative drugs and the severity and interventions to prevent them.Results: Seventy cases (13.1%) of readmission were partially or predominantly due to ADEs, of which, 58 (82.9%) were serious ADEs. Overall, 65 (92.9%) of the ADEs have been confirmed to be preventable. Inappropriate prescribing was identified as the cause of ADE in 34 cases (48.6%) mainly involving diuretics, analgesics, or antithrombotics: misprescribing n = 19 (27.1%), underprescribing n = 8 (11.4%), and overprescribing n = 7 (10.0%). The remaining half of preventable ADEs (n = 36; 51.4%) were related to suboptimal patient monitoring/education, such as adherence issues (n = 6; 8.6%) or lack of monitoring (n = 31; 44.3%). In 64 cases (91.4%), the readmission could have been potentially prevented by better monitoring for drug efficacy/disease control, or for predictable side effect. Thirty-three (97.1%) of the 34 ADEs due to inappropriate prescribing could have also been prevented by better monitoring.Conclusions: Adverse drug events accounted for approximately 13% of 30-day preventable readmissions. A half were due to prescription errors involving mainly diuretics, analgesics, or antithrombotics, and the other half were due to suboptimal patient monitoring/education, most frequently with antineoplastics. Both these avoidable causes may represent opportunities to reduce the total drug-related adverse events.

Background Aspirin-exacerbated respiratory disease (AERD) is characterized by three clinical features: asthma, nasal polyposis, and respiratory reactions to cyclooxygenase-1 inhibitors (NSAIDs). Electronic health records (EHRs) contain information on each feature of this triad. Objective To determine if an informatics algorithm applied to the EHR could electronically identify patients with AERD. Methods We developed an informatics algorithm to search the EHRs of patients age 18 and older from the Partners Healthcare system over a 10 year period (2004–2014). Charts with search terms for asthma, nasal polyps and record of respiratory (Cohort A) or unspecified (Cohort B) reactions to NSAIDs were identified as “possible AERD”. Two clinical experts reviewed all charts to confirm a diagnosis of “clinical AERD” and classify cases as “diagnosed AERD” or “undiagnosed AERD” based on physician documented AERD-specific terms in patient notes. Results Our algorithm identified 731 “possible AERD” cases, of which 638 were not in our AERD patient registry. Chart review of cohorts A (n=511) and B (n=127) demonstrated a positive predictive value (PPV) of 78.4% for “clinical AERD”, which rose to 88.7% when unspecified reactions were excluded. Of those with clinical AERD, 12.4% had no mention of AERD by any treating caregiver and were classified as “undiagnosed AERD”. “Undiagnosed AERD” cases were less likely to have been seen by an allergist/immunologist than “diagnosed AERD” cases (38.7% vs. 93.2%, P<.0001). Conclusion An informatics algorithm can successfully identify both known and previously undiagnosed cases of AERD with a high PPV. Involvement of an allergist/immunologist significantly increases the likelihood of an AERD diagnosis.