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Hattingh L, Baysari MT, Foot H, Sim TF, Keijzers G, Morgan M, Scott I, Norman R, Yong F, Mullan B, Jackson C, Oldfield LE, Manias E. OPTimising MEDicine information handover after Discharge (OPTMED-D): protocol for development of a multifaceted intervention and stepped wedge cluster randomised controlled trial. Trials 2024; 25:632. [PMID: 39334438 PMCID: PMC11428332 DOI: 10.1186/s13063-024-08496-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Accepted: 09/23/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND General practitioners (GP) and community pharmacists need information about hospital discharge patients' medicines to continue their management in the community. This necessitates effective communication, collaboration, and reliable information-sharing. However, such handover is inconsistent, and whilst digital systems are in place to transfer information at transitions of care, these systems are passive and clinicians are not prompted about patients' transitions. There are also gaps in communication between community pharmacists and GPs. These issues impact patient safety, leading to hospital readmissions and increased healthcare costs. METHODS A three-phased, multi-method study design is planned to trial a multifaceted intervention to reduce 30-day hospital readmissions. Phase 1 is the co-design of the intervention with stakeholders and end-users; phase 2 is the development of the intervention; phase 3 is a stepped wedge cluster randomised controlled trial with 20 clusters (community pharmacies). Expected intervention components will be a hospital pharmacist navigator, primary care medication management review services, and a digital solution for information sharing. Phase 3 will recruit 10 patients per pharmacy cluster/month to achieve a sample size of 2200 patients powered to detect a 5% absolute reduction in unplanned readmissions from 10% in the control group to 5% in the intervention at 30 days. The randomisation and intervention will occur at the level of the patient's nominated community pharmacy. Primary analysis will be a comparison of 30-day medication-related hospital readmissions between intervention and control clusters using a mixed effects Poisson regression model with a random effect for cluster (pharmacy) and a fixed effect for each step to account for secular trends. TRIAL REGISTRATION This trial is registered with the Australian New Zealand Clinical Trials Registry: ACTRN12624000480583p , registered 19 April 2024.
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Affiliation(s)
- Laetitia Hattingh
- Allied Health Research, Gold Coast Health, Southport, QLD, 4215, Australia.
- School of Pharmacy, The University of Queensland, Brisbane, QLD, 4102, Australia.
- School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, QLD, 4222, Australia.
| | - Melissa T Baysari
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2050, Australia
| | - Holly Foot
- School of Pharmacy, The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Tin Fei Sim
- Curtin Medical School, Curtin University, Bentley, WA, 6102, Australia
| | - Gerben Keijzers
- Emergency Department, Gold Coast Health, Southport, QLD, 4215, Australia
| | - Mark Morgan
- Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, 4229, Australia
| | - Ian Scott
- Metro South Digital Health and Informatics, Princess Alexandra Hospital, Woolloongabba, QLD, 4102, Australia
| | - Richard Norman
- School of Population Health, Curtin University, Bentley, WA, 6102, Australia
| | - Faith Yong
- Rural Clinical School, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4350, Australia
- Academy of Medical Education, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia
- The Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, 2145, Australia
| | - Barbara Mullan
- School of Population Health, Curtin University, Bentley, WA, 6102, Australia
| | - Claire Jackson
- General Practice and Primary Care Reform, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Leslie E Oldfield
- School of Pharmacy, The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Elizabeth Manias
- School of Nursing and Midwifery, Monash University, Melbourne, VIC, 3800, Australia
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Carland JE, Elhage T, Baysari MT, Stocker SL, Marriott DJE, Taylor N, Day RO. Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose-recommendation software. Br J Clin Pharmacol 2020; 87:1215-1233. [PMID: 32691902 DOI: 10.1111/bcp.14496] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/14/2020] [Accepted: 07/09/2020] [Indexed: 02/06/2023] Open
Abstract
AIMS Dose-prediction software can optimise vancomycin therapy, improving therapeutic drug monitoring processes and reducing drug toxicity. Success of software in hospitals may be dependent on prescriber uptake of software recommendations. This study aimed to identify the perceived psychosocial and environmental barriers and facilitators to prescriber acceptance of dose-prediction software. METHODS Semi-structured interviews, incorporating prescribing scenarios, were undertaken with 17 prescribers. Participants were asked to prescribe the next maintenance dose of vancomycin for a scenario(s) and then asked if they would accept a recommendation provided by a dose-prediction software. Interviews further explored opinions of dose-prediction software. Interview transcripts were analysed using an inductive approach to identify themes and the Theoretical Domains Framework was used to synthesise barriers and facilitators to software acceptance. RESULTS When presented with software recommendations, half of the participants were comfortable with accepting the recommendation. Key barriers to acceptance of software recommendations aligned with 2 Theoretical Domains Framework domains: Knowledge (uncertainty of software capability) and Beliefs about Consequences (perceived impact of software on clinical outcomes and workload). Key facilitators aligned with 2 domains: Beliefs about Consequences (improved efficiency) and Social Influences (influence of peers). A novel domain, Trust, was identified as influential. CONCLUSION Prescribers reported barriers to acceptance of dose-prediction software aligned with limited understanding of, and scepticism about, software capabilities, as well as concerns about clinical outcomes. Identification of key barriers and facilitators to acceptance provides essential information to design of implementation strategies to support the introduction of this intervention into the workplace.
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Affiliation(s)
- Jane E Carland
- Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, University of NSW, Kensington, NSW, Australia
| | - Tania Elhage
- Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, Darlinghurst, NSW, Australia.,School of Medical Sciences, University of NSW, Kensington, NSW, Australia
| | - Melissa T Baysari
- Sydney School of Health Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
| | - Sophie L Stocker
- Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, University of NSW, Kensington, NSW, Australia
| | - Deborah J E Marriott
- St Vincent's Clinical School, University of NSW, Kensington, NSW, Australia.,Department of Clinical Microbiology and Infectious Diseases, St Vincent's Hospital, Darlinghurst, NSW, Australia
| | - Natalie Taylor
- Sydney School of Health Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, NSW, Australia.,Cancer Council NSW, Woolloomooloo, NSW, Australia
| | - Richard O Day
- Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, University of NSW, Kensington, NSW, Australia.,School of Medical Sciences, University of NSW, Kensington, NSW, Australia
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Duff J, Walker K, Edward KL, Ralph N, Giandinoto JA, Alexander K, Gow J, Stephenson J. Effect of a thermal care bundle on the prevention, detection and treatment of perioperative inadvertent hypothermia. J Clin Nurs 2018; 27:1239-1249. [DOI: 10.1111/jocn.14171] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2017] [Indexed: 11/30/2022]
Affiliation(s)
- Jed Duff
- School of Nursing and Midwifery; University of Newcastle; Newcastle NSW Australia
| | - Kim Walker
- St Vincent's Private Hospital Sydney; Sydney NSW Australia
| | - Karen-Leigh Edward
- School of Health Sciences; Swinburne University; Melbourne Vic. Australia
| | - Nicholas Ralph
- Research Program Leader (Clinical Services); Institute of Resilient Regions; School of Nursing and Midwifery, University of Southern Queensland; Toowoomba Qld Australia
- St Vincent's Private Hospital; Toowoomba Qld Australia
| | | | - Kimberley Alexander
- Holy Spirit Northside Private Hospital; Brisbane Australia
- Queensland University of Technology; Brisbane Qld Australia
| | - Jeff Gow
- School of Commerce; University of Southern Queensland; Toowoomba Qld Australia
- School of Accounting; Economics and Finance; University of KwaZulu-Natal; Durban South Africa
| | - John Stephenson
- Biomedical Statistics; University of Huddersfield; Huddersfield UK
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