UNASSIGNED: When a patient is discharged from hospital it is essential that their general practitioner (GPs) and community pharmacist are informed of changes to their medicines. This necessitates effective communication and information-sharing between hospitals and primary care clinicians.
UNASSIGNED: To identify priority medicine handover issues and solutions to inform the co-design and development of a multifaceted intervention.
UNASSIGNED: A modified nominal group technique was used to reach consensus on medicine handover priority areas. The first hour of an interactive 2-hr workshop focused on ranking pre-identified issues drawn from literature. In the second hour, participants identified solutions that they then ranked from highest to lowest priority through an online platform. Descriptive statistics were used to analyse workshop data.
UNASSIGNED: In total 32 participants attended the workshop including hospital doctors (n = 8, 25.0%), GPs and hospital pharmacists (n = 6 each, 18.8%), consumers and community pharmacists (n = 4 each, 12.5%), and both hospital and aged care facility nurses (n = 2 each 6.3%). From the list of 23 issues, the highest ranked issue was high workload and time pressures impacting the discharge process (22/32). From the list of 36 solutions, the participants identified two solutions that were equally ranked highest (12/27 each). They were mandating that patients leave hospital with a discharge summary, including medication reconciliation information and, developing an integrated information technology system where medication summary and notes are accessible for primary, secondary and tertiary health provider.
UNASSIGNED: The consensus process highlighted challenges in hospital procedures where potential solutions may be implemented through co-design of a multifaceted intervention to improve medicine handover quality.

BACKGROUND: Despite the publication of a European wide competency framework for hospital pharmacy by the European Association of Hospital Pharmacist (EAHP) in 2017, not all countries have adopted and implemented such a framework.
OBJECTIVE: This study aimed to develop and validate a bespoke national hospital pharmacy competency framework for Austria that supports the hospital pharmacy workforce development.
METHODS: A multi-method study was carried out in three phases. (I) A systematic literature review across 48 websites of healthcare-related associations and six scientific databases was conducted, identifying competency frameworks, guidelines and related documents. (II) Extracted behaviour competencies were reviewed for contextual national appropriateness by three researchers prior to mapping against the \"Patient Care and Clinical Pharmacy Skills\" domain of European Common Training Framework (CTF). (III) Validation of the resultant draft clinical skills competency framework took place by an expert panel (n = 4; Austrian Association of Hospital Pharmacists (AAHP) board members) discussion. Reporting of findings is aligned with the recommendations for reporting Competency Framework Development in health professions (CONFERD-HP guidelines) and the PRISMA 2020 checklist.
RESULTS: The systematic review (SR) resulted in 28 frameworks, guidelines and related documents and the identification of 379 behaviour competencies, with nineteen mapped to the \"Patient Care and Clinical Pharmacy Skills\" domain of the CTF (after removal of duplicates). Expert panel discussion resulted in suggested changes to ensure contextual national appropriateness.
CONCLUSIONS: This study resulted in the development and validation of the first clinical national pharmacy competency framework for Austria. Future studies should focus on political and practical structures necessary for its successful implementation.

Background and objective Drugs that act on the central nervous system have a high potential to cause drug-related problems (DRPs). A clinical pharmacist aided by collaborative efforts within an interdisciplinary healthcare team can prevent, detect, and resolve DRPs, thereby contributing to the promotion of medication safety and improving the quality of life of individuals under care. This study aimed to assess DRPs identified in the neurology ward of a tertiary hospital from February 2016 to November 2019. Methods This was a descriptive study with a cross-sectional and retrospective design involving secondary data collected from pharmaceutical care (PC) records. Student\'s t-tests, Pearson correlation coefficients, Poisson models, and logistic regression models were used to analyze the associations between age, number and type of medications, duration of hospitalization, and the occurrence of DRPs. Results A total of 130 patients were included in the study, and a total of 266 DRPs were detected, with 93 patients experiencing more than one DRP and 37 not presenting any DRPs. Necessity-related DRPs were the most prevalent (46.6%) type, followed by safety-related DRPs (28.6%). The prevalence of safety-related DRPs was higher in individuals older than 60 years (p<0.001). Conclusions Of note, 84.6% of the interventions suggested by pharmacists to resolve DRPs were accepted by the healthcare team. The high number of DRPs found underscores the importance of the clinical role of the pharmacist and interprofessional collaboration in the care of neurological patients, especially in the pharmaceutical follow-up of elderly individuals.

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

Drug information (DI) provided by hospital pharmacies aims to promote rational and safe drug therapy. While quality assessment for this task is recommended, more knowledge on the factors determining the quality is needed. We aimed to evaluate the impacts of different factors on the quality of DI provided by hospital pharmacies to healthcare professionals. Retrospectively, answers on fictitious enquiries about annual DI tests for German hospital pharmacies over five years were evaluated for content-related and structural requirements. Multivariate analysis was performed for the impact of the enquiry complexity, DI organization (specialized DI center; pharmacist responsible per day; DI on top of other routine tasks), and quality measures (second look; experience of answering pharmacist in DI/on ward; use of documentation database). In 2017-2021, 45, 71, 79, 118, and 122 hospital pharmacies participated. The enquiry complexity had a statistically significant impact on the content-related quality, with poor results for a higher complexity (years 2018/2021, OR 0.25/0.04, p < 0.01). The DI centers achieved better results regarding content-related quality than for a pharmacist responsible per day (OR 0.76/p = 0.65) or DI on top of routine tasks (OR 0.35/p = 0.02). The DI centers scored better in structural quality. The second look showed an overall trend of a better content-related and structural quality. In conclusion, specialized DI centers and second looks are recommended as quality-improving measures. Training for answering complex enquiries should be intensified.

OBJECTIVE: Adverse drug reactions (ADRs) are a major drug safety concern and a frequent topic of enquiries to hospital drug information services. Our goal was to analyse these enquiries regarding background, complexity, nature of ADR, and involved drug classes to improve in-hospital drug safety.
METHODS: Retrospectively, ADR enquiries to a German university hospital pharmacy drug information 2018-2022 were analysed regarding enquirer (profession, medical specialty) and enquiry details (drugs, suspected ADR/enquiry prior to drug initiation, ADR system organ class, probable cause identified, and enquiry complexity).
RESULTS: Of 543 enquiries, 516 (95%) were asked by physicians, 493 (91%) patient-specific, 390 (71%) on suspected ADRs, and 153 (28%) prior to drug initiation. Enquiries originated frequently from internal medicine (74/13.6%), paediatrics (71/13.1%), neurology (70/12.9%), and haemato-oncology (62/11.4%). Most frequent ADRs were haematologic (94/17%) and hepatic (72/13%). The median number of drugs per enquiry was three (range 0-37), 209 (38%) enquiries referred to one specific drug, 165 (30%) concerned ≥11 drugs. A probable cause for suspected ADRs was identified in 75 (36%) enquiries concerning one drug and 155 (94%) with ≥11 drugs. Most frequent drugs were antineoplastic (54/25.8%), nervous-system-drugs (42/20.1%), and anti-infective (40/19.1%). Most enquiries (342/63%) were complex (multiple/specialist resources).
CONCLUSIONS: Enquiries were usually asked by physicians referring to suspected ADRs in specific clinical situations. A probable cause was identified in many cases pointing to a direct positive impact on patient care. Enquiries prior to drug initiation should be encouraged to increase drug safety. Information on main ADR effects and drug classes helps with targeted counselling.

The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyzes artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer\'s block. Python libraries such as LangChain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasized. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimize information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.

OBJECTIVE: Results of the 2023 ASHP National Survey of Pharmacy Practice in Hospital Settings are presented.
METHODS: Pharmacy directors at 1,497 general and children\'s medical-surgical hospitals in the United States were surveyed using a mixed-mode method of contact by email and mail. Survey completion was online using Qualtrics. IQVIA supplied data on hospital characteristics; the survey sample was drawn from IQVIA\'s hospital database.
RESULTS: The response rate was 21.6%. Inpatient pharmacists independently prescribe medications in 26.7% of hospitals. Advanced analytics are used in 5.7% of hospitals. Basic analytics are used in 87.3% of hospitals. Pharmacists work in ambulatory or primary care clinics in 54.2% of hospitals operating outpatient clinics. Most hospitals (86.1%) use automated dispensing cabinets as the primary method of maintenance dose distribution. Machine-readable coding is used in 73.6% of hospitals to verify doses during dispensing in the pharmacy. Autoverification functionality in the electronic health record system is used in 73.4% of hospitals. Most hospitals report some integration of pharmacy services to optimize patient care transitions (60.0%), while 24.9% report no integration. Traditional technician activities still predominate, but more advanced roles are emerging. Technologies to assist sterile product preparation are used in 62.8% of hospitals.
CONCLUSIONS: Drug distribution continues to trend toward decentralized models with medications available closer to patients. Technologies are enabling this transition to occur without a significant negative impact on patient safety. The pharmacy workforce is stable, and more advanced responsibilities are being assigned to pharmacy technicians, enabling pharmacists to increase their clinical role.

UNASSIGNED: To describe the experience of a dispensing model of outpatient hospital medicines (OHM) via collaboration of hospital and community pharmacies, and to explore patient satisfaction with the strategy as compared with the hospital pharmacy only service.
UNASSIGNED: Patient satisfaction is an important component of the quality of health care.
UNASSIGNED: A new model of dispensing OHM was conducted in the Outpatients Unit of the Service of Hospital Pharmacy of Hospital del Mar, in Barcelona, Spain. Participants were patients on stable chronic treatment with clinical or social fragility, immunocompromised patients, and those whose residence was located at a distance from the hospital that justified drug delivery through the community pharmacy. A cross sectional study was done using an ad hoc 14-item questionnaire collecting demographic data, duration of treatment, usual mode of collecting medication, and the degree of satisfaction regarding waiting time for the collection of medication, attention received by professionals, information received on treatment, and confidentiality.
UNASSIGNED: The study population included a total of 4,057 patients (66.8% men) with a mean age of 53 (15.5) years, of whom 1,286 responded, with a response rate of 31.7%. Variables significantly associated with response to the survey were age over 44 years, particularly the age segment of 55-64 years (odds ratio [OR] 2.51) and receiving OHM via the community pharmacy (OR 12.76). Patients in the community pharmacy group (n = 927) as compared with those in the hospital pharmacy group (n = 359) showed significantly higher percentages of \'satisfied\' and \'very satisfied\' (p < 0.001) in the waiting time for the collection of OHM (88.1% vs. 66%), attention received by professionals (92.5% vs. 86.1%), and information received on treatment (79.4% vs. 77.4%). In relation to confidentiality, results obtained were similar in both pharmacy settings.
UNASSIGNED: Dispensing OHM through the community pharmacy was a strategy associated with greater patient satisfaction as compared with OHM collection at the hospital pharmacy service, with greater accessibility, mainly due to close distance to the patient\'s home. The participation of community pharmacists could further optimize the care received by patients undergoing OHM treatment.