BackgroundThe COVID-19 pandemic strained healthcare systems immensely as of 2020. Switzerland’s hospital pharmacies’ responses during the first wave were surveyed with a view to improving the quality of pharmaceutical management in future health crises.MethodsAn online survey was sent to the heads of all of Switzerland’s hospital pharmacies. The questionnaire was organised into eleven sections of questions covering many topics regarding the management of COVID-19’s first wave. Data collection occurred from May to June 2020.ResultsAnalyses were performed using the 43 questionnaires (66%), with at least one answer per questionnaire, out of 65 distributed. Seventeen of 41 pharmacies responding (41%) had existing standard operating procedures or pandemic plans and 95% of these (39/41) set up crisis management steering committees. Twenty-nine of 43 pharmacies responding (67%) created new activities to respond to the pandemic’s specific needs. Twenty-six of 39 pharmacies responding (67%) created new drug lists for: COVID-19-specific treatments (85%; 22/26), sedatives (81%; 21/26), anaesthetics (77%; 20/26) and antibiotics (73%; 19/26). Drug availability in designated COVID-19 wards was managed by increasing existing stocks (54%; 22/41 pharmacies) and creating extra storage space (51%; 21/41). Two drugs generated the greatest concern about shortages: propofol (49%; 19/39 pharmacies) and midazolam (44%; 17/39). Remdesivir stocks ran out in 26% of pharmacies (10/39). Twelve of 43 pharmacies (28%) drafted specific new documents to respond to medical needs regarding drug administration, 12 (28%) did so for drug preparation and 10 (23%) did so for treatment choices.ConclusionsSwitzerland’s hospital pharmacies encountered many challenges related to the COVID-19 crisis and had to find solutions quickly, effectively and safely. The survey highlighted the key role that hospital pharmacies played in many aspects of the pandemic by providing logistical and clinical support to medical and nursing care teams. The lessons and experiences outlined could be used to improve the quality of hospital pharmacies’ readiness for similar future events.

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Proton pump inhibitors (PPI), these antacid drugs that have revolutionized the treatment of peptic disease, have become, in the daily practice of primary care physicians as well as hospital practitioners, an inescapable treatment since their introduction on the market in 1989, and even the 4th most prescribed drug class in Switzerland. Therefore, multiple studies as well as numerous recommendations and expert opinions on their effectiveness and use have been -published. This article will present their proper use, by reviewing the knowledge available to date on these essential drugs in our -therapeutic arsenal.

BackgroundClinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS.AimDevelop electronic queries that assist pharmacists in conducting medication reviews and an assessment of the performance of this model to detect DRPs.MethodElectronic queries were set up in CDSS using “triggers” from electronic health records: drug prescriptions, laboratory values, medical problems, vital signs, demographics. They were based on a previous study where 315 patients admitted in internal medicine benefited from a multidisciplinary medication review (gold-standard) to highlight potential DRPs. Electronic queries were retrospectively tested to assess performance in detecting DRPs revealed with gold-standard. For each electronic query, sensitivity, specificity, positive and negative predictive value were computed.ResultsOf 909 DRPs, 700 (77.8%) were used to create 366 electronic queries. Electronic queries correctly detected 77.1% of DRPs, median sensitivity and specificity reached 100.0% (IQRs, 100.0%–100.0%) and 99.7% (IQRs, 97.0%–100.0%); median positive predictive value and negative predictive value reached 50.0% (IQRs, 12.5%–100.0%) and 100.0% (IQRs, 100.0%–100.0%). Performances varied according to “triggers” (p < 0.001, best performance in terms of predictive positive value when exclusively involving drug prescriptions).ConclusionElectronic queries based on electronic heath records had high sensitivity and negative predictive value and acceptable specificity and positive predictive value and may contribute to facilitate medication review. Implementing some of these electronic queries (the most effective and clinically relevant) in current practice will allow a better assessment of their impact on the efficiency of the clinical pharmacist.

PurposeAssess whether full-scale simulation exercises improved hospital pharmacies’ disaster preparedness.MethodsSwiss hospital pharmacies performed successive full-scale simulation exercises at least four months apart. An interprofessional team created two scenarios, each representing credible regional-scale disasters involving approximately fifty casualties (a major road accident and a terrorist attack). Four exercise assessors used appraisal forms to evaluate participants’ actions and responses during the simulation (rating them using five-point Likert scales).ResultsFour hospital pharmacies performed two full-scale simulation exercises each. Differences between exercises one and two were observed. On average, the four hospitals accomplished 69% ± 6% of the actions expected of them during exercise one. The mean rate of expected actions accomplished increased to 84% ± 7% (p < 0.005) during exercise two. Moreover, the average quality of actions improved from 3.0/5 to 3.6/5 (p = 0.01), and the time required to gather a crisis management team drastically decreased between simulations (from 23 to 5 min). The main challenges were communication (reformulation) and crisis management. Simulation exercise number one resulted in three hospital pharmacies creating disaster action plans and the fourth improving its already existing plan.ConclusionThis study highlighted the value of carrying out full-scale disaster simulations for hospital pharmacies as they improved overall institutional preparedness and increased staff awareness. The number of expected actions accomplished increased significantly. In the future, large-scale studies and concept dissemination are warranted.

Abstract Background: Clinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS.Aim: Developpment and assessment of electronic queries based on health records to identify DRPs.Method: Electronic queries were set up in CDSS using triggers from electronic health records; drug prescriptions, laboratory values, medical problems, vital signs, demographics). They were based on a previous study where internal medicine patients benefited from a multidisciplinary medication review (gold-standard) to highlight potential DRPs. Electronic queriess were retrospectively tested to assess performance in detecting DRPs revealed with gold-standard. For each EQ, sensitivity, specificity, positive and negative predictive value (PPV, NPV) were computed.Results: Of 909 DRPs found for 315 patients, 700 (77.8%) were used to create 366 EQs. EQs correctly detected 77.0% of DRPs. EQs’ median sensitivity and specificity were 100.0% (100.0%–100.0%) and 99.7% (97.0%–100.0%); median PPV and NPV were 50.0% (12.5%–100.0%) and 100.0% (100.0%–100.0%). Performances varied according to triggers (p&lt;10-5, best performance in terms of PPV when exclusively involving drug prescriptions). Conclusion: EQs based on electronic heath records had high sensitivity and NPV and acceptable specificity and PPV and may contribute to facilitate medication review. Their prospective assessment with pharmacists’ participation in medical rounds will enable improvements to this model.

BackgroundTransition between hospital and ambulatory care is a delicate step involving several healthcare professionals and presenting a considerable risk of drug-related problems.ObjectiveTo investigate pharmaceutical interventions made on hospital discharge prescriptions by community pharmacists.MethodThis observational, prospective study took place in 14 community pharmacies around a Swiss acute care hospital. We recruited patients with discharge prescriptions (minimum three drugs) from the internal medicine ward of the hospital. The main outcome measures were: number and type of pharmaceutical interventions made by community pharmacists, time spent on discharge prescriptions, number of medication changes during the transition of care.ResultsThe study included 64 patients discharged from the hospital. Community pharmacists made a total of 439 interventions; a mean of 6.9 ± 3.5 (range 1–16) interventions per patient. All of the discharge prescriptions required pharmaceutical intervention, and 61 (95%) necessitated a telephone call to the patients’ hospital physician for clarifications. The most frequent interventions were: confirming voluntary omission of a drug (31.7%), treatment substitution (20.5%), dose adjustment (16.9%), and substitution for reimbursement issues (8.8%). Roughly half (52%) of all discharge prescriptions required 10–20 min for pharmaceutical validation. The mean number of medication changes per patient was 16.4: 9.6 changes between hospital admission and discharge, 2.6 between hospital discharge and community pharmacy, and 4.2 between community pharmacy and a general practitioner’s appointment.ConclusionHospital discharge prescriptions are complex and present a significant risk of medication errors. Community pharmacists play a key role in preventing and identifying drug-related problems.

Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort. A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression. The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort. This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.

ObjectivesTo ensure patient safety and the preparedness of medication processes during hospital relocations and evacuations by using Failure Modes, Effects, and Criticality Analysis (FMECA).MethodsThe relocation of six regional hospitals to a single building, resulting in 400 beds being moved, could be compared with an emergency evacuation. An FMECA was performed on the hospital group’s internal medicine and intensive care units (IMU and ICU), examining how medication processes would be affected by a hospital relocation or evacuation.ResultsWe identified 59 hospital relocation and 68 evacuation failure modes. Failure modes were ranked based on their criticality index (CI; range 1–810). The higher the CI, the greater the patient-related risk. Average initial IMU and ICU hospital relocation CI scores were 160 (range 105–294) and 201 (range 125–343), respectively, subsequently reduced to 32 (−80%) and 49 (−76%) after mitigation measures. Average initial IMU and ICU evacuation CI scores were 319 (range 245–504) and 592 (range 441–810), respectively, subsequently reduced to 194 (−39%) and 282 (−52%). Most mitigation measures (17/22), such as for example checklists, could be implemented in both situations. Due to their unpredictable nature, five measures were specific to evacuation situations.ConclusionsThis study highlights the value of using an FMECA on medication processes to anticipate potential negative impacts on patient safety during hospital relocations or evacuations. Preparation for a hospital relocation can provide useful knowledge and an opportunity to test mitigation measures that might prove useful in evacuations.

ObjectivesDocumented experiences of relocating hospital pharmacies are rare, but adequate preparation is vital to ensuring smooth pharmacy operation and patient safety. In the autumn of 2019, the Pharmacy of Eastern Vaud Hospitals, composed of four units (Logistics, Manufacturing, Clinical Pharmacy, and Nursing Home Supply), was relocated to a new hospital in just a few days. In this context, a failure modes, effects and criticality analysis (FMECA) was carried out before the relocation in order to anticipate any failure modes likely to affect the pharmacy’s missions or patient safety during the move.MethodsThe FMECA was performed by a multidisciplinary team (pharmacists and logisticians) which analysed the complete upcoming process of relocating the pharmacy and its implications. Criticality indices (CIs) were defined based on the matrix developed by Williams et al, which sets a maximum score of 810. Every potential failure mode identified was analysed, and mitigation measures were proposed for each one.ResultsThe analysis identified 86 potential failures. The mean initial CI calculated for the entire pharmacy relocation was 177 (min 4–max 567), but this was estimated to be reduced to 39 (−78%) after mitigation measures were identified. Within the whole pharmacy, the failures with the highest CIs were identified in the Logistics unit. Among these, the time necessary to transfer the pharmacy’s drugs from their traditional alphabetical storage location to their new location using robotic, chaotic storage principles was identified as the riskiest potential failure. Indeed, the rapid availability of emergency medicines would have to be guaranteed at all times.ConclusionsThe present study highlighted the relevance of using an FMECA-type evaluation to anticipate the impact of a hospital pharmacy relocation. This tool enabled pharmacy professionals to structure their potential relocation problems and reflect on mitigation measures in order to provide concerted, realistically applicable solutions before the move.