Impact of pharmacist-evaluated clinical decision support system alerts on potentially missing or inappropriately prescribed proton pump inhibitors at hospital discharge: a retrospective cross-sectional study.

BACKGROUND: Proton pump inhibitors (PPIs) are among the most prescribed drugs. A clinical decision support system (CDSS) could improve their rational use. AIM: The impact of an electronic algorithm (e-algorithm) implemented in a CDSS on potentially missing or inappropriately prescribed PPIs at hospital discharge, its specificity and sensitivity, and the outcome of the alerts issued were analysed. METHOD: An e-algorithm continuously monitored patients of a tertiary care hospital for missing or inappropriate PPIs. Following relevance assessment by a pharmacist, the alerts raised were either displayed in the patients' electronic record or dismissed. After a three-month period, all adult patients' records were retrospectively reviewed for missing or inappropriate PPIs at discharge. The results were compared with a corresponding period before CDSS introduction. Sensitivity, specificity and outcome of alerts were quantified. RESULTS: In a 3-month period with 5018 patients, the CDSS created 158 alerts for missing PPIs and 464 alerts for inappropriate PPIs. PPI prescribing was proposed 81 times and PPI termination 122 times, with acceptance rates of 73% and 34%, respectively. A specificity of 99.4% and sensitivity of 92.0% for missing PPIs and a specificity of 97.1% and a sensitivity of 69.7% for inappropriate PPIs were calculated. The algorithm reduced incidents of missing PPIs by 63.4% (p < 0.001) and of inappropriate PPIs by 16.2% (p = 0.022). CONCLUSION: The algorithm identified patients without necessary gastroprotection or inappropriate PPIs with high specificity and acceptable sensitivity. It positively impacted the rational use of PPIs by reducing incidents of missing and inappropriate PPIs.

Development and validation of a clinical decision support system to prevent anticoagulant duplications.

BACKGROUND AND OBJECTIVE: Unintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD). METHODS: The project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts. RESULTS: We identified 91 ADs in 1581 patients receiving two or more anticoagulants during phase I, 70 ADs in 1692 patients in phase II, and 57 ADs in 1575 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 42% (RR: 0.58, CI: 0.42-0.81). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%. CONCLUSION: The stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety.

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm.

BACKGROUND AND OBJECTIVE: The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician. METHODS: We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint. RESULTS: Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases. CONCLUSION: The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.

Tackling alert fatigue with a semi-automated clinical decision support system: quantitative evaluation and end-user survey.

STUDY AIMS: Clinical decision support systems (CDSS) embedded in hospital electronic health records efficiently reduce medication errors, but there is a risk of low physician adherence due to alert fatigue. At the Cantonal Hospital Aarau, a CDSS is being developed that allows the highly accurate detection and correction of medication errors. The semi-automated CDSS sends its alerts either directly to the physician or to a clinical pharmacist for review first. Our aim was to evaluate the performance of the recently implemented CDSS in terms of acceptance rate and alert burden, as well as physicians' satisfaction with the CDSS. METHODS: All alerts generated by the clinical decision support systems between January and December 2021 were included in a retrospective quantitative evaluation. A team of clinical pharmacists performed a follow-up to determine whether the recommendation made by the CDSS was implemented by the physician. The acceptance rate was calculated including all alerts for which it was possible to determine an outcome. A web-based survey was conducted amongst physicians to assess their attitude towards the CDSS. The survey questions included overall satisfaction, helpfulness of individual algorithms, and perceived alert burden. RESULTS: In 2021, a total of 10,556 alerts were generated, of which 619 triggered a direct notification to the physician and 2,231 notifications were send to the physician after evaluation by a clinical pharmacist. The acceptance rates were 89.8% and 68.4%, respectively, which translates as an overall acceptance rate of 72.4%. On average, clinical pharmacists received 17.2 alerts per day, while all of the hospital physicians together received 7.8 notifications per day. In the survey, 94.5% of physicians reported being satisfied or very satisfied with the CDSS. Algorithms addressing potential medication errors concerning anticoagulants received the highest usefulness ratings. CONCLUSION: The development of this semi-automated clinical decision support system with context-based algorithms resulted in alerts with a high acceptance rate. Involving clinical pharmacists proved a promising approach to limit the alert burden of physicians and thus tackle alert fatigue. The CDSS is well accepted by our physicians.

Clinical predictors for Legionella in patients presenting with community-acquired pneumonia to the emergency department.

BACKGROUND: Legionella species cause severe forms of pneumonia with high mortality and complication rates. Accurate clinical predictors to assess the likelihood of Legionella community-acquired pneumonia (CAP) in patients presenting to the emergency department are lacking. METHODS: We retrospectively compared clinical and laboratory data of 82 consecutive patients with Legionella CAP with 368 consecutive patients with non-Legionella CAP included in two studies at the same institution. RESULTS: In multivariate logistic regression analysis we identified six parameters, namely high body temperature (OR 1.67, p < 0.0001), absence of sputum production (OR 3.67, p < 0.0001), low serum sodium concentrations (OR 0.89, p = 0.011), high levels of lactate dehydrogenase (OR 1.003, p = 0.007) and C-reactive protein (OR 1.006, p < 0.0001) and low platelet counts (OR 0.991, p < 0.0001), as independent predictors of Legionella CAP. Using optimal cut off values of these six parameters, we calculated a diagnostic score for Legionella CAP. The median score was significantly higher in Legionella CAP as compared to patients without Legionella (4 (IQR 3-4) vs 2 (IQR 1-2), p < 0.0001) with a respective odds ratio of 3.34 (95%CI 2.57-4.33, p < 0.0001). Receiver operating characteristics showed a high diagnostic accuracy of this diagnostic score (AUC 0.86 (95%CI 0.81-0.90), which was better as compared to each parameter alone. Of the 191 patients (42%) with a score of 0 or 1 point, only 3% had Legionella pneumonia. Conversely, of the 73 patients (16%) with > or =4 points, 66% of patients had Legionella CAP. CONCLUSION: Six clinical and laboratory parameters embedded in a simple diagnostic score accurately identified patients with Legionella CAP. If validated in future studies, this score might aid in the management of suspected Legionella CAP.

Validation of a Prediction Rule for Legionella Pneumonia in Emergency Department Patients.

We validated a clinical prediction rule for Legionella based on clinical parameters (dry cough, fever) and laboratory findings (C-reactive protein, lactate dehydrogenase, sodium, platelet counts) in 713 consecutive patients with community-acquired pneumonia. The Legionella Score performed well in estimating the likelihood for Legionella infection and thus may help to direct diagnostic and therapeutic decisions.