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Henderson A, Shakeshaft A, Allan J, Wallace R, Barker D, Farnbach S. A pilot study examining the impact of a pragmatic process for improving the cultural responsiveness of non-Aboriginal alcohol and other drug treatment services using routinely collected data in Australia. J Health Serv Res Policy 2024:13558196241261800. [PMID: 38870027 DOI: 10.1177/13558196241261800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Objective: Routine health data has the potential to identify changes in patient-related outcomes, in close to real time. This pilot project used routine data to explore and compare the impact of changes to cultural responsiveness on service use by Aboriginal and Torres Strait Islander (hereafter, Aboriginal) clients in Australia.Methods: The New South Wales Minimum Data Set (MDS) for alcohol and other drug use treatment services was provided for 11 services for a period of 30 months from March 2019 to September 2021 (four months prior to two years after the intervention; data were analysed between March 2022 to February 2023). Change in cultural responsiveness was assessed via practice audits of services at baseline and two years. The average change in audit rating was analysed using a linear mixed regression model. Generalised Linear Mixed Models were used to identify changes in service use by Aboriginal clients. Results: All 11 services showed increased audit scores at two years, with a statistically significant mean increase of 18.6 (out of 63 points; b = 18.32, 95% CI 12.42-24.22). No statistically significant pre-to post-changes were identified in: (1) the proportion of episodes delivered to Aboriginal versus non-Aboriginal clients (OR = 1.15, 95% CI = 0.94-1.40), (2) the number of episodes of care provided to Aboriginal clients per month (IRR = 1.01, 95% CI = 0.84-1.23), or (3) the proportion of episodes completed by Aboriginal clients (OR = 0.96, 95% CI = 0.82-1.13). Conclusions: The lack of statistically significant impact on service use outcomes using MDS contrasts to the improvements in cultural responsiveness, suggesting further work is needed to identify appropriate outcome measures. This may include patient-reported experience measures. This project showed that routine data has potential as an efficient method for measuring changes in patient-related outcomes in response to health services improvements.
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Affiliation(s)
- Alexandra Henderson
- National Drug and Alcohol Research Centre (NDARC), University of New South Wales, Sydney, AU-NSW, Australia
| | - Anthony Shakeshaft
- National Drug and Alcohol Research Centre (NDARC), University of New South Wales, Sydney, AU-NSW, Australia
| | - Julaine Allan
- Rural Health Research Institute, Charles Sturt University, Orange, AU-NSW, Australia
| | | | - Daniel Barker
- School of Medicine and Public Health, University of Newcastle, University Drive, Callaghan, AU-NSW, Australia
| | - Sara Farnbach
- National Drug and Alcohol Research Centre (NDARC), University of New South Wales, Sydney, AU-NSW, Australia
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Parker KJ, Hickman LD, McDonagh J, Lindley RI, Ferguson C. The prototype of a frailty learning health system: The HARMONY Model. Learn Health Syst 2024; 8:e10401. [PMID: 38633027 PMCID: PMC11019377 DOI: 10.1002/lrh2.10401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 04/19/2024] Open
Abstract
Introduction Rapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real-time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality. Methods This paper aims to describe The HARMONY model (acHieving dAta-dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care. Results The HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real-time. Conclusion This prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.
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Affiliation(s)
- Kirsten J. Parker
- School of Nursing, Faculty of Science, Medicine & HealthUniversity of WollongongWollongongNew South WalesAustralia
- Centre for Chronic and Complex Care ResearchBlacktown HospitalWestern Sydney Local Health DistrictBlacktownNew South WalesAustralia
| | | | - Julee McDonagh
- School of Nursing, Faculty of Science, Medicine & HealthUniversity of WollongongWollongongNew South WalesAustralia
- Centre for Chronic and Complex Care ResearchBlacktown HospitalWestern Sydney Local Health DistrictBlacktownNew South WalesAustralia
| | - Richard I. Lindley
- Centre for Chronic and Complex Care ResearchBlacktown HospitalWestern Sydney Local Health DistrictBlacktownNew South WalesAustralia
- Westmead Applied Research CentreUniversity of SydneyWestmeadNew South WalesAustralia
| | - Caleb Ferguson
- School of Nursing, Faculty of Science, Medicine & HealthUniversity of WollongongWollongongNew South WalesAustralia
- Centre for Chronic and Complex Care ResearchBlacktown HospitalWestern Sydney Local Health DistrictBlacktownNew South WalesAustralia
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Radbron E, McCance T, Middleton R, Wilson V. Using data from mHealth apps to inform person-centred practice: A discussion paper. Contemp Nurse 2024; 60:96-105. [PMID: 38368623 DOI: 10.1080/10376178.2024.2316604] [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: 08/15/2023] [Accepted: 02/04/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area. AIM To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice. METHODS This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study. FINDINGS Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice. CONCLUSIONS This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice.
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Affiliation(s)
- Emma Radbron
- School of Nursing, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia
| | - Tanya McCance
- Institute of Nursing and Health Research, Ulster University, Belfast, UK
| | - Rebekkah Middleton
- School of Nursing, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia
| | - Valerie Wilson
- School of Nursing, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia
- South Western Sydney Local Health District (SWSLHD), Ingham Institute for Applied Medical Research, Liverpool, Australia
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Fasugba O, Sedani R, Mikulik R, Dale S, Vařecha M, Coughlan K, McElduff B, McInnes E, Hladíková S, Cadilhac DA, Middleton S. How registry data are used to inform activities for stroke care quality improvement across 55 countries: A cross-sectional survey of Registry of Stroke Care Quality (RES-Q) hospitals. Eur J Neurol 2024; 31:e16024. [PMID: 37540834 PMCID: PMC10952746 DOI: 10.1111/ene.16024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/23/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND AND PURPOSE The Registry of Stroke Care Quality (RES-Q) is a worldwide quality improvement data platform that captures performance and quality measures, enabling standardized comparisons of hospital care. The aim of this study was to determine if, and how, RES-Q data are used to influence stroke quality improvement and identify the support and educational needs of clinicians using RES-Q data to improve stroke care. METHODS A cross-sectional self-administered online survey was administered (October 2021-February 2022). Participants were RES-Q hospital local coordinators responsible for stroke data collection. Descriptive statistics are presented. RESULTS Surveys were sent to 1463 hospitals in 74 countries; responses were received from 358 hospitals in 55 countries (response rate 25%). RES-Q data were used "always" or "often" to: develop quality improvement initiatives (n = 213, 60%); track stroke care quality over time (n = 207, 58%); improve local practice (n = 191, 53%); and benchmark against evidence-based policies, procedures and/or guidelines to identify practice gaps (n = 179, 50%). Formal training in the use of RES-Q tools and data were the most frequent support needs identified by respondents (n = 165, 46%). Over half "strongly agreed" or "agreed" that to support clinical practice change, education is needed on: (i) using data to identify evidence-practice gaps (n = 259, 72%) and change clinical practice (n = 263, 74%), and (ii) quality improvement science and methods (n = 255, 71%). CONCLUSION RES-Q data are used for monitoring stroke care performance. However, to facilitate their optimal use, effective quality improvement methods are needed. Educating staff in quality improvement science may develop competency and improve use of data in practice.
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Affiliation(s)
- Oyebola Fasugba
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
| | - Rupal Sedani
- International Clinical Research CentreSt. Anne's University HospitalBrnoCzech Republic
| | - Robert Mikulik
- International Clinical Research Centre, Neurology DepartmentSt. Anne's University Hospital and Masaryk UniversityBrnoCzech Republic
| | - Simeon Dale
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
| | - Miroslav Vařecha
- International Clinical Research CentreSt. Anne's University HospitalBrnoCzech Republic
| | - Kelly Coughlan
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
| | - Benjamin McElduff
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
| | - Elizabeth McInnes
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
| | - Sabina Hladíková
- International Clinical Research CentreSt. Anne's University HospitalBrnoCzech Republic
| | - Dominique A. Cadilhac
- Stroke and Ageing Research, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVictoriaAustralia
- Stroke Theme, Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneHeidelbergVictoriaAustralia
| | - Sandy Middleton
- Nursing Research Institute, St Vincent's Health Network SydneySt Vincent's Hospital Melbourne and Australian Catholic UniversitySydneyNew South WalesAustralia
- School of Nursing, Midwifery and ParamedicineAustralian Catholic UniversitySydneyNew South WalesAustralia
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Pizzuti C, Palmieri C, Shaw T. Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts. THE JOURNAL OF CONTINUING EDUCATION IN THE HEALTH PROFESSIONS 2023; 43:S47-S58. [PMID: 38054492 DOI: 10.1097/ceh.0000000000000534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
INTRODUCTION eHealth data analytics is widely used in health care research. However, there is limited knowledge on the role of eHealth data analysis to inform continuing professional development (CPD). The aim of this study was to collate available research evidence on the use of eHealth data for the development of CPD programs and plans for medical practitioners. METHODS A scoping review was conducted using the six-stage Arksey and O'Malley Framework. A consultation exercise (stage 6) was performed with 15 international experts in the fields of learning and practice analytics to deepen the insights. RESULTS Scoping review. The literature searches identified 9876 articles published from January 2010 to May 2022. After screening and full-text review, a total of nine articles were deemed relevant for inclusion. The results provide varied-and at times partial or diverging-answers to the scoping review research questions. Consultation exercise. Research rigor, field of investigation, and developing the field were the three themes emerged from analysis. Participants validated the scoping review methodology and confirmed its results. Moreover, they provided a meta-analysis of the literature, a description of the current CPD ecosystem, and clear indications of what is and should be next for the field. DISCUSSION This study shows that there is no formal or well-established correlation between eHealth data and CPD planning and programming. Overall findings fill a gap in the literature and provide a basis for further investigation. More foundational work, multidisciplinary collaborations, and stakeholders' engagement are necessary to advance the use of eHealth data analysis for CPD purposes.
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Affiliation(s)
- Carol Pizzuti
- Ms. Pizzuti: Industry PhD Candidate, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia; and Senior Research Officer, Professional Practice, The Royal Australasian College of Physicians, Sydney, Australia. Dr. Palmieri: Head of Member Learning and Development, Professional Practice, The Royal Australasian College of Physicians, Sydney, Australia; and Faculty of Arts and Social Sciences, The University of Sydney, Camperdown, Australia. Dr. Shaw: Professor of Digital Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
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Oostendorp RAB, Elvers H, van Trijffel E, Rutten GM, Scholten-Peeters GGM, De Kooning M, Laekeman M, Nijs J, Roussel N, Samwel H. Improved quality of physiotherapy care in patients with Whiplash-Associated Disorders: Results based on 16 years of routinely collected data. FRONTIERS IN PAIN RESEARCH 2022; 3:929385. [PMID: 36110289 PMCID: PMC9468444 DOI: 10.3389/fpain.2022.929385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/05/2022] [Indexed: 12/04/2022] Open
Abstract
Quality improvement is now a central tenet in physiotherapy care, and quality indicators (QIs), as measurable elements of care, have been applied to analyze and evaluate the quality of physiotherapy care over the past two decades. QIs, based on Donabedian's model of quality of care, provide a foundation for measuring (improvements in) quality of physiotherapy care, providing insight into the many remaining evidentiary gaps concerning diagnostics, prognostics and treatment, as well as patient-related outcome measures. In this overview we provide a synthesis of four recently published articles from our project group on the topic of quantitative measures of quality improvement in physiotherapy care, in this context specifically focused on patients with WAD in primary care physiotherapy. A set of process and outcome QIs (n = 28) was developed for patients with WAD and linked to a database consisting of routinely collected data (RCD) on patients with WAD collected over a 16-year period. The QIs were then embedded per step of the clinical reasoning process: (a) administration (n = 2); (b) history taking (n = 7); (c) objectives of examination (n = 1); (d) clinical examination (n = 5); (e) analysis and conclusion (n = 1); (f) treatment plan (n = 3); (g) treatment (n = 2); (h) evaluation (n = 5); and (i) discharge (n = 2). QIs were expressed as percentages, allowing target performance levels to be defined ≥70% or ≤30%, depending on whether the desired performance required an initially high or low QI score. Using RCD data on primary care patients with WAD (N = 810) and a set of QIs, we found that the quality of physiotherapy care has improved substantially over a 16-year period. This conclusion was based on QIs meeting predetermined performance targets of ≥70% or ≤30%. Twenty-three indicators met the target criterium of ≥70% and three indicators ≤30%. Our recommended set of QIs, embedded in a clinical reasoning process for patients with WAD, can now be used as a basis for the development of a validated QI set that effectively measures quality (improvement) of primary care physiotherapy in patients with WAD.
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Affiliation(s)
- Rob A. B. Oostendorp
- Scientific Institute for Quality of Healthcare, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Department of Manual Therapy, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium
- Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Practice Physiotherapy and Manual Therapy, Heeswijk-Dinther, Netherlands
- *Correspondence: Rob A. B. Oostendorp
| | - Hans Elvers
- Department of Public Health and Research, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Methodological Health-Skilled Institute, Beuningen, Netherlands
| | - Emiel van Trijffel
- SOMT University of Physiotherapy, Amersfoort, Netherlands
- Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Ziekenhuisgroep Twente, ZGT Academy, Almelo, Netherlands
| | - Geert M. Rutten
- Research Program of Organization of Healthcare and Social Services, School of Health Studies, HAN University of Applied Science, Nijmegen, Netherlands
| | - Gwendolyne G. M. Scholten-Peeters
- Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Margot De Kooning
- Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Brussels, Belgium
| | - Marjan Laekeman
- Department of Physiological Psychology, Otto-Friedrich University of Bamberg, Bamberg, Germany
| | - Jo Nijs
- Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Brussels, Belgium
| | - Nathalie Roussel
- Department of Physiotherapy and Rehabilitation Sciences (MOVANT), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Han Samwel
- Integrated Health Care Clinics (IHC), 's-Hertogenbosch, Netherlands
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Goodfellow D, Bird J. Using data analytics to enhance quality improvement projects. Nurs Manag (Harrow) 2022; 29:32-40. [PMID: 35578818 DOI: 10.7748/nm.2022.e2042] [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] [Accepted: 03/08/2022] [Indexed: 11/09/2022]
Abstract
With the introduction of electronic health record systems in healthcare organisations, there is a significant opportunity within nursing, and as part of the multidisciplinary team, to access a wealth of data and use this to drive improvements in patient care and outcomes. Quality improvement has often involved the collection of data via manual audit, a time-consuming process with data fed back from small sample sizes over a short time period. In contrast, a data analytics approach enables data collection from larger sample sizes to be automated and for data to be presented in a way that is easy for staff to access and interpret. This article discusses the benefits and challenges of a data analytics approach as well as the resources required, the importance of stakeholder involvement, the setting and review of key performance indicators and how to optimise data presentation to achieve the greatest effect.
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Affiliation(s)
| | - James Bird
- Imperial College Healthcare NHS Trust, London, England
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