1
|
Meeraus W, Joy M, Ouwens M, Taylor KS, Venkatesan S, Dennis J, Tran TN, Dashtban A, Fan X, Williams R, Morris T, Carty L, Kar D, Hoang U, Feher M, Forbes A, Jamie G, Hinton W, Sanecka K, Byford R, Anand SN, Hobbs FDR, Clifton DA, Pollard AJ, Taylor S, de Lusignan S. AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: The RAVEN cohort study. J Infect 2024; 88:106129. [PMID: 38431156 DOI: 10.1016/j.jinf.2024.106129] [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: 09/20/2023] [Revised: 11/27/2023] [Accepted: 02/22/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Despite being prioritized during initial COVID-19 vaccine rollout, vulnerable individuals at high risk of severe COVID-19 (hospitalization, intensive care unit admission, or death) remain underrepresented in vaccine effectiveness (VE) studies. The RAVEN cohort study (NCT05047822) assessed AZD1222 (ChAdOx1 nCov-19) two-dose primary series VE in vulnerable populations. METHODS Using the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub, linked to secondary care, death registration, and COVID-19 datasets in England, COVID-19 outcomes in 2021 were compared in vaccinated and unvaccinated individuals matched on age, sex, region, and multimorbidity. RESULTS Over 4.5 million AZD1222 recipients were matched (mean follow-up ∼5 months); 68% were ≥50 years, 57% had high multimorbidity. Overall, high VE against severe COVID-19 was demonstrated, with lower VE observed in vulnerable populations. VE against hospitalization was higher in the lowest multimorbidity quartile (91.1%; 95% CI: 90.1, 92.0) than the highest quartile (80.4%; 79.7, 81.1), and among individuals ≥65 years, higher in the 'fit' (86.2%; 84.5, 87.6) than the frailest (71.8%; 69.3, 74.2). VE against hospitalization was lowest in immunosuppressed individuals (64.6%; 60.7, 68.1). CONCLUSIONS Based on integrated and comprehensive UK health data, overall population-level VE with AZD1222 was high. VEs were notably lower in vulnerable groups, particularly the immunosuppressed.
Collapse
Affiliation(s)
- Wilhelmine Meeraus
- Medical Evidence, Vaccines & Immune Therapies, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Mario Ouwens
- Medical & Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Mölndal, Sweden
| | - Kathryn S Taylor
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sudhir Venkatesan
- Medical & Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | | | - Trung N Tran
- Biopharmaceutical Medicine Respiratory and Immunology, AstraZeneca, Gaithersburg, MD, USA
| | - Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Xuejuan Fan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Robert Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - Lucy Carty
- Medical & Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Debasish Kar
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Michael Feher
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Anna Forbes
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Gavin Jamie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William Hinton
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Kornelia Sanecka
- Medical Evidence, Vaccines & Immune Therapies, BioPharmaceuticals Medical, AstraZeneca, Warsaw, Poland
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sneha N Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Andrew J Pollard
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Sylvia Taylor
- Medical Evidence, Vaccines & Immune Therapies, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Royal College of General Practitioners Research and Surveillance Centre, London, UK.
| |
Collapse
|
2
|
Yusof MM, Takeda T, Shimai Y, Mihara N, Matsumura Y. Evaluating health information systems-related errors using the human, organization, process, technology-fit (HOPT-fit) framework. Health Informatics J 2024; 30:14604582241252763. [PMID: 38805345 DOI: 10.1177/14604582241252763] [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] [Indexed: 05/30/2024]
Abstract
Complex socio-technical health information systems (HIS) issues can create new error risks. Therefore, we evaluated the management of HIS-related errors using the proposed human, organization, process, and technology-fit framework to identify the lessons learned. Qualitative case study methodology through observation, interview, and document analysis was conducted at a 1000-bed Japanese specialist teaching hospital. Effective management of HIS-related errors was attributable to many socio-technical factors including continuous improvement, safety culture, strong management and leadership, effective communication, preventive and corrective mechanisms, an incident reporting system, and closed feedback loops. Enablers of medication errors include system sophistication and process factors like workarounds, variance, clinical workload, slips and mistakes, and miscommunication. The case management effectiveness in handling the HIS-related errors can guide other clinical settings. The potential of HIS to minimize errors can be achieved through continual, systematic, and structured evaluation. The case study validated the applicability of the proposed evaluation framework that can be applied flexibly according to study contexts to inform HIS stakeholders in decision-making. The comprehensive and specific measures of the proposed framework and approach can be a useful guide for evaluating complex HIS-related errors. Leaner and fitter socio-technical components of HIS can yield safer system use.
Collapse
Affiliation(s)
- Maryati Mohd Yusof
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia(UKM), Bangi, Malaysia
| | - Toshihiro Takeda
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yoshie Shimai
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Naoki Mihara
- Medical Informatics & Systems Management, Hiroshima UniversityHospital, Hiroshima, Japan
| | - Yasuhsi Matsumura
- National Hospital Organization, Osaka National Hospital, Osaka, Japan
| |
Collapse
|
3
|
Weik L, Fehring L, Mortsiefer A, Meister S. Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study. J Med Internet Res 2024; 26:e52085. [PMID: 38252468 PMCID: PMC10845021 DOI: 10.2196/52085] [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: 08/22/2023] [Revised: 11/18/2023] [Accepted: 12/16/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Various studies propose the significance of digital maturity in ensuring effective patient care and enabling improved health outcomes, a successful digital transformation, and optimized service delivery. Although previous research has centered around inpatient health care settings, research on digital maturity in general practices is still in its infancy. OBJECTIVE As general practitioners (GPs) are the first point of contact for most patients, we aimed to shed light on the pivotal role of GPs' inherent characteristics, especially their personality, in the digital maturity of general practices. METHODS In the first step, we applied a sequential mixed methods approach involving a literature review and expert interviews with GPs to construct the digital maturity scale used in this study. Next, we designed a web-based survey to assess digital maturity on a 5-point Likert-type scale and analyze the relationship with relevant inherent characteristics using ANOVAs and regression analysis. RESULTS Our web-based survey with 219 GPs revealed that digital maturity was overall moderate (mean 3.31, SD 0.64) and substantially associated with several characteristics inherent to the GP. We found differences in overall digital maturity based on GPs' gender, the expected future use of digital health solutions, the perceived digital affinity of medical assistants, GPs' level of digital affinity, and GPs' level of extraversion and neuroticism. In a regression model, a higher expected future use, a higher perceived digital affinity of medical assistants, a higher digital affinity of GPs, and lower neuroticism were substantial predictors of overall digital maturity. CONCLUSIONS Our study highlights the impact of GPs' inherent characteristics, especially their personality, on the digital maturity of general practices. By identifying these inherent influencing factors, our findings support targeted approaches to drive digital maturity in general practice settings.
Collapse
Affiliation(s)
- Lisa Weik
- Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Leonard Fehring
- Helios University Hospital Wuppertal, Department of Gastroenterology, Witten/Herdecke University, Wuppertal, Germany
- Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Achim Mortsiefer
- General Practice II and Patient-Centredness in Primary Care, Institute of General Practice and Primary Care, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
| | - Sven Meister
- Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Witten, Germany
- Department Healthcare, Fraunhofer Institute for Software and Systems Engineering ISST, Dortmund, Germany
| |
Collapse
|
4
|
Tam W, Alajlani M, Abd-Alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. J Med Internet Res 2023; 25:e42950. [PMID: 37594791 PMCID: PMC10474516 DOI: 10.2196/42950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
Collapse
Affiliation(s)
- William Tam
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | - Mohannad Alajlani
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | | |
Collapse
|
5
|
Woods L, Eden R, Duncan R, Kodiyattu Z, Macklin S, Sullivan C. Which one? A suggested approach for evaluating digital health maturity models. Front Digit Health 2022; 4:1045685. [PMID: 36506845 PMCID: PMC9731136 DOI: 10.3389/fdgth.2022.1045685] [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: 09/15/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
Background Digital health maturity models allow healthcare organizations to evaluate digital health capability and to develop roadmaps for improving patient care through technology. There are many models available commercially for healthcare providers to use to assess their digital health maturity. Currently, there are limited evidence-based methods to assess the quality, utility, and efficacy of maturity models to select the most appropriate model for the given context. Objective To develop a framework to assess digital maturity models and facilitate recommendations for digital maturity model selection. Methods A systematic, consultative, and iterative process was used. Literature analyses and a stakeholder needs analysis (n = 23) was conducted to develop content and design considerations. These considerations were incorporated into the initial version of the framework developed by researchers in a design workshop. External stakeholder review (n = 20) and improvements strengthened and finalized the framework. Results The criteria of the framework include assessment of healthcare context, feasibility, integrity, completeness and actionability. Users can compare model performance in order to select the most appropriate model for their context. Conclusion The framework provides healthcare stakeholders with a consistent and objective methodology to compare digital health maturity models, informing approaches to choosing a suitable model. This is a critical step as healthcare evolves towards a digital health system focused on improving the quality of care, reducing costs and improving the provider and consumer experience.
Collapse
Affiliation(s)
- Leanna Woods
- Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia,Queensland Digital Health Centre, The University of Queensland, Herston, QLD, Australia,Correspondence: Leanna Woods
| | - Rebekah Eden
- School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rhona Duncan
- School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia
| | - Zack Kodiyattu
- Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia
| | - Sophie Macklin
- Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia
| | - Clair Sullivan
- Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia,Queensland Digital Health Centre, The University of Queensland, Herston, QLD, Australia,Digital Metro North, Metro North Hospital and Health Service, Herston, QLD, Australia
| |
Collapse
|
6
|
Duncan R, Eden R, Woods L, Wong I, Sullivan C. Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review. J Med Internet Res 2022; 24:e32994. [PMID: 35353050 PMCID: PMC9008527 DOI: 10.2196/32994] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/02/2021] [Accepted: 12/28/2021] [Indexed: 01/05/2023] Open
Abstract
Background Digital health in hospital settings is viewed as a panacea for achieving the “quadruple aim” of health care, yet the outcomes have been largely inconclusive. To optimize digital health outcomes, a strategic approach is necessary, requiring digital maturity assessments. However, current approaches to assessing digital maturity have been largely insufficient, with uncertainty surrounding the dimensions to assess. Objective The aim of this study was to identify the current dimensions used to assess the digital maturity of hospitals. Methods A systematic literature review was conducted of peer-reviewed literature (published before December 2020) investigating maturity models used to assess the digital maturity of hospitals. A total of 29 relevant articles were retrieved, representing 27 distinct maturity models. The articles were inductively analyzed, and the maturity model dimensions were extracted and consolidated into a maturity model framework. Results The consolidated maturity model framework consisted of 7 dimensions: strategy; information technology capability; interoperability; governance and management; patient-centered care; people, skills, and behavior; and data analytics. These 7 dimensions can be evaluated based on 24 respective indicators. Conclusions The maturity model framework developed for this study can be used to assess digital maturity and identify areas for improvement.
Collapse
Affiliation(s)
- Rhona Duncan
- School of Information Systems, Queensland University of Technology, Brisbane, Australia
| | - Rebekah Eden
- School of Information Systems, Queensland University of Technology, Brisbane, Australia
| | - Leanna Woods
- Centre for Health Services Research, The University of Queensland, Herston, Australia.,Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia.,Digital Health Research Network, The University of Queensland, Brisbane, Australia
| | - Ides Wong
- Clinical Excellence Queensland, Queensland Health, Brisbane, Australia
| | - Clair Sullivan
- Centre for Health Services Research, The University of Queensland, Herston, Australia.,Digital Health Research Network, The University of Queensland, Brisbane, Australia.,Metro North Hospital and Health Service, Brisbane, Australia
| |
Collapse
|