Automation of the medication process in Swiss hospitals: results of a survey.

OBJECTIVES: Medication management is a core process in hospital administration. The safety, timeliness and efficiency of medication distribution may be improved by automating logistical and administrative aspects of the process. Forming an accurate high-level picture of current practices may help decision-makers to better advance the state of automation. This study aims to identify which systems for automating the medication process are currently in use in Swiss hospitals, and to what extent each system is used. METHODS: A 27-question survey was developed and distributed to Swiss Association of Public Health Administration and Hospital Pharmacists (GSASA) members. The survey focused on enterprise resource planning (ERP) systems, automation of in-hospital distribution and dispensing of pharmaceutical goods, bedside scanning, and the management of drug master data. RESULTS: The response rate was 98% (58/59 hospital pharmacies). All institutions had an ERP system in use, most frequently SAP (n=23, 39%). Electronic invoices from suppliers were fully processed by 37% and partially processed by 17% of respondents. Twenty-five percent of respondents reported performing bedside scanning for the purpose of medication administration. Automated medication distribution systems were available in 20 hospitals (34%), of which 13 were central robots and seven were decentralised systems. CONCLUSION: A considerable gap remains to achieve closed loop processes between multiple systems. The present results provide an inventory of existing systems and current trends for use by decision-makers in hospitals and hospital pharmacies.

Digitalisation of the drug prescribing process in Swiss hospitals - results of a survey.

BACKGROUND: The state of digitalisation in the healthcare sector in Switzerland is lagging, even as the national electronic health record (EHR) is being gradually implemented. Little is known about the implementation of electronic prescribing systems, their auxiliary features or drug datasets in Swiss hospitals.The aim of this study was to understand which electronic systems are implemented to support doctors in Swiss hospitals during the medication prescribing process. METHODS: The survey was sent in spring 2021 to the chief pharmacists of the main Swiss hospitals. The survey focused on the introduction of the EHR, the clinical information system (CIS) and its prescribing module, as well as drug information data and clinical decision support systems (CDSS). RESULTS: The response rate was 98% (58/59 hospitals). Almost half of the hospitals (47%) were connected to the national EHR, almost all hospitals (86%) used a CIS and a vast majority of the hospitals (84%) had implemented electronic prescribing systems in their CIS. 10 years ago, around 63% of hospitals used a CIS and 40% were equipped with an electronic prescribing system. Today, CDSS of any kind were implemented in 50% of the hospitals, predominantly for drug-drug interactions. Drug master data were maintained in most hospitals (76%) via an automated interface, but mostly supplemented manually. Clinical drug information data were maintained in 74% of hospitals. In 67% of hospitals, datasets were imported via an automated interface. CONCLUSIONS: The digitalisation of the medical prescribing process in Swiss hospitals has progressed over the last decade. Drug prescriptions via electronic prescribing systems were introduced in most hospitals. However, this survey suggests that the current use of CDSS is far from exhausted, and that clinical drug information data could be maintained more efficiently. Optimising electronic support for healthcare professionals during the prescribing process still has considerable potential.

How to Develop and Implement a Computerized Decision Support System Integrated for Antimicrobial Stewardship? Experiences From Two Swiss Hospital Systems.

Background: Computerized decision support systems (CDSS) provide new opportunities for automating antimicrobial stewardship (AMS) interventions and integrating them in routine healthcare. CDSS are recommended as part of AMS programs by international guidelines but few have been implemented so far. In the context of the publicly funded COMPuterized Antibiotic Stewardship Study (COMPASS), we developed and implemented two CDSSs for antimicrobial prescriptions integrated into the in-house electronic health records of two public hospitals in Switzerland. Developing and implementing such systems was a unique opportunity for learning during which we faced several challenges. In this narrative review we describe key lessons learned. Recommendations: (1) During the initial planning and development stage, start by drafting the CDSS as an algorithm and use a standardized format to communicate clearly the desired functionalities of the tool to all stakeholders. (2) Set up a multidisciplinary team bringing together Information Technologies (IT) specialists with development expertise, clinicians familiar with "real-life" processes in the wards and if possible, involve collaborators having knowledge in both areas. (3) When designing the CDSS, make the underlying decision-making process transparent for physicians and start simple and make sure to find the right balance between force and persuasion to ensure adoption by end-users. (4) Correctly assess the clinical and economic impact of your tool, therefore try to use standardized terminologies and limit the use of free text for analysis purpose. (5) At the implementation stage, plan usability testing early, develop an appropriate training plan suitable to end users' skills and time-constraints and think ahead of additional challenges related to the study design that may occur (such as a cluster randomized trial). Stay also tuned to react quickly during the intervention phase. (6) Finally, during the assessment stage plan ahead maintenance, adaptation and related financial challenges and stay connected with institutional partners to leverage potential synergies with other informatics projects.

Prevalence, clinical relevance and predictive factors of medication discrepancies revealed by medication reconciliation at hospital admission: prospective study in a Swiss internal medicine ward.

OBJECTIVE: Medication reconciliation (MedRec) is a relevant safety procedure in medication management at transitions of care. The aim of this study was to evaluate the impact of MedRec, including a best possible medication history (BPMH) compared with a standard medication history in patients admitted to an internal medicine ward. DESIGN: Prospective interventional study. Data were analysed using descriptive statistics followed by univariate and multivariate Poisson regression models and a zero-inflated Poisson regression model. SETTING: Internal medicine ward in a secondary care hospital in Southern Switzerland. PARTICIPANTS: The first 100 consecutive patients admitted in an internal medicine ward. PRIMARY AND SECONDARY OUTCOME MEASURES: Medication discrepancies between the medication list obtained by the physician and that obtained by a pharmacist according to a systematic approach (BPMH) were collected, quantified and assessed by an expert panel that assigned a severity score. The same procedure was applied to discrepancies regarding allergies. Predicting factors for medication discrepancies were identified. RESULTS: The median of medications per patient was 8 after standard medication history and 11 after BPMH. Total admission discrepancies were 524 (5.24 discrepancies per patient) with at least 1 discrepancy per patient. For 47 patients, at least one discrepancy was classified as clinically relevant. Discrepancies were classified as significant and serious in 19% and 2% of cases, respectively. Furthermore, 67% of the discrepancies were detected during the interview conducted by the pharmacist with the patients and/or their caregivers. The number of drugs used and the autonomous management of home therapy were associated with an increased number of clinically relevant discrepancies in a multivariable Poisson regression model. CONCLUSION: Even in an advanced healthcare system, a standardised MedRec process including a BPMH represents an important strategy that may contribute to avoid a notable number of clinically relevant discrepancies and potential adverse drug events.

Study protocol for a multicentre, cluster randomised, superiority trial evaluating the impact of computerised decision support, audit and feedback on antibiotic use: the COMPuterized Antibiotic Stewardship Study (COMPASS).

INTRODUCTION: Inappropriate use of antimicrobials in hospitals contributes to antimicrobial resistance. Antimicrobial stewardship (AMS) interventions aim to improve antimicrobial prescribing, but they are often resource and personnel intensive. Computerised decision supportsystems (CDSSs) seem a promising tool to improve antimicrobial prescribing but have been insufficiently studied in clinical trials. METHODS AND ANALYSIS: The COMPuterized Antibiotic Stewardship Study trial, is a publicly funded, open-label, cluster randomised, controlled superiority trial which aims to determine whether a multimodal CDSS intervention integrated in the electronic health record (EHR) reduces overall antibiotic exposure in adult patients hospitalised in wards of two secondary and one tertiary care centre in Switzerland compared with 'standard-of-care' AMS. Twenty-four hospital wards will be randomised 1:1 to either intervention or control, using a 'pair-matching' approach based on baseline antibiotic use, specialty and centre. The intervention will consist of (1) decision support for the choice of antimicrobial treatment and duration of treatment for selected indications (based on indication entry), (2) accountable justification for deviation from the local guidelines (with regard to the choice of molecules and duration), (3) alerts for self-guided re-evaluation of treatment on calendar day 4 of antimicrobial therapy and (4) monthly ward-level feedback of antimicrobial prescribing indicators. The primary outcome will be the difference in overall systemic antibiotic use measured in days of therapy per admission based on administration data recorded in the EHR over the whole intervention period (12 months), taking into account clustering. Secondary outcomes include qualitative and quantitative antimicrobial use indicators, economic outcomes and clinical, microbiological and patient safety indicators. ETHICS AND DISSEMINATION: Ethics approval was obtained for all participating sites (Comission Cantonale d'Éthique de la Recherche (CCER)2017-00454). The results of the trial will be submitted for publication in a peer-reviewed journal. Further dissemination activities will be presentations/posters at national and international conferences. TRIAL REGISTRATION NUMBER: NCT03120975; Pre-results.