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Williams A, Lennox L, Harris M, Antonacci G. Supporting translation of research evidence into practice-the use of Normalisation Process Theory to assess and inform implementation within randomised controlled trials: a systematic review. Implement Sci 2023; 18:55. [PMID: 37891671 PMCID: PMC10612208 DOI: 10.1186/s13012-023-01311-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The status of randomised controlled trials (RCTs) as the 'gold standard' for evaluating efficacy in healthcare interventions is increasingly debated among the research community, due to often insufficient consideration for implementation. Normalisation Process Theory (NPT), which focuses on the work required to embed processes into practice, offers a potentially useful framework for addressing these concerns. While the theory has been deployed in numerous RCTs to date, more work is needed to consolidate understanding of if, and how, NPT may aid implementation planning and processes within RCTs. Therefore, this review seeks to understand how NPT contributes to understanding the dynamics of implementation processes within RCTs. Specifically, this review will identify and characterise NPT operationalisation, benefits and reported challenges and limitations in RCTs. METHODS A qualitative systematic review with narrative synthesis of peer-reviewed journal articles from eight databases was conducted. Studies were eligible for inclusion if they reported sufficient detail on the use of NPT within RCTs in a healthcare domain. A pre-specified data extraction template was developed based on the research questions of this review. A narrative synthesis was performed to identify recurrent findings. RESULTS Searches identified 48 articles reporting 42 studies eligible for inclusion. Findings suggest that NPT is primarily operationalised prospectively during the data collection stage, with limited sub-construct utilisation overall. NPT is beneficial in understanding implementation processes by aiding the identification and analysis of key factors, such as understanding intervention fidelity in real-world settings. Nearly three-quarters of studies failed to report the challenges and limitations of utilising NPT, though coding difficulties and data falling outside the NPT framework are most common. CONCLUSIONS NPT appears to be a consistent and generalisable framework for explaining the dynamics of implementation processes within RCTs. However, operationalisation of the theory to its full extent is necessary to improve its use in practice, as it is currently deployed in varying capacities. Recommendations for future research include investigation of NPT alongside other frameworks, as well as earlier operationalisation and greater use of NPT sub-constructs. TRIAL REGISTRATION The protocol for this systematic review was accepted for public registration on PROSPERO (registration number: CRD42022345427) on 26 July 2022.
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Affiliation(s)
- Allison Williams
- Department of Primary Care and Public Health, Imperial College London, National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northwest London, Charing Cross Campus, London, W6 8RP, UK.
| | - Laura Lennox
- Department of Primary Care and Public Health, Imperial College London, National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northwest London, Charing Cross Campus, London, W6 8RP, UK
- Business School, Centre for Health Economics and Policy Innovation (CHEPI), Imperial College London, Chelsea and Westminster Campus, London, SW10 9N, UK
| | - Matthew Harris
- Department of Primary Care and Public Health, Imperial College London, National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northwest London, Charing Cross Campus, London, W6 8RP, UK
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Grazia Antonacci
- Department of Primary Care and Public Health, Imperial College London, National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) Northwest London, Charing Cross Campus, London, W6 8RP, UK
- Business School, Centre for Health Economics and Policy Innovation (CHEPI), Imperial College London, Chelsea and Westminster Campus, London, SW10 9N, UK
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Klappe ES, Cornet R, Dongelmans DA, de Keizer NF. Inaccurate recording of routinely collected data items influences identification of COVID-19 patients. Int J Med Inform 2022; 165:104808. [PMID: 35767912 PMCID: PMC9186787 DOI: 10.1016/j.ijmedinf.2022.104808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/11/2022] [Accepted: 06/03/2022] [Indexed: 11/20/2022]
Abstract
Background During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients. Methods The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled ‘COVID-19′ if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled ‘non-COVID-19′ if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences. Results The total dataset consisted of 402 patients: 196 ‘COVID-19′ and 206 ‘non-COVID-19′ patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods. Conclusions Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.
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Affiliation(s)
- Eva S Klappe
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands.
| | - Ronald Cornet
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Dave A Dongelmans
- Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, Netherlands
| | - Nicolette F de Keizer
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
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Vasileiou E, Shi T, Kerr S, Robertson C, Joy M, Tsang R, McGagh D, Williams J, Hobbs R, de Lusignan S, Bradley D, OReilly D, Murphy S, Chuter A, Beggs J, Ford D, Orton C, Akbari A, Bedston S, Davies G, Griffiths LJ, Griffiths R, Lowthian E, Lyons J, Lyons RA, North L, Perry M, Torabi F, Pickett J, McMenamin J, McCowan C, Agrawal U, Wood R, Stock SJ, Moore E, Henery P, Simpson CR, Sheikh A. Investigating the uptake, effectiveness and safety of COVID-19 vaccines: protocol for an observational study using linked UK national data. BMJ Open 2022; 12:e050062. [PMID: 35165107 PMCID: PMC8844955 DOI: 10.1136/bmjopen-2021-050062] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 01/19/2022] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION The novel coronavirus SARS-CoV-2, which emerged in December 2019, has caused millions of deaths and severe illness worldwide. Numerous vaccines are currently under development of which a few have now been authorised for population-level administration by several countries. As of 20 September 2021, over 48 million people have received their first vaccine dose and over 44 million people have received their second vaccine dose across the UK. We aim to assess the uptake rates, effectiveness, and safety of all currently approved COVID-19 vaccines in the UK. METHODS AND ANALYSIS We will use prospective cohort study designs to assess vaccine uptake, effectiveness and safety against clinical outcomes and deaths. Test-negative case-control study design will be used to assess vaccine effectiveness (VE) against laboratory confirmed SARS-CoV-2 infection. Self-controlled case series and retrospective cohort study designs will be carried out to assess vaccine safety against mild-to-moderate and severe adverse events, respectively. Individual-level pseudonymised data from primary care, secondary care, laboratory test and death records will be linked and analysed in secure research environments in each UK nation. Univariate and multivariate logistic regression models will be carried out to estimate vaccine uptake levels in relation to various population characteristics. VE estimates against laboratory confirmed SARS-CoV-2 infection will be generated using a generalised additive logistic model. Time-dependent Cox models will be used to estimate the VE against clinical outcomes and deaths. The safety of the vaccines will be assessed using logistic regression models with an offset for the length of the risk period. Where possible, data will be meta-analysed across the UK nations. ETHICS AND DISSEMINATION We obtained approvals from the National Research Ethics Service Committee, Southeast Scotland 02 (12/SS/0201), the Secure Anonymised Information Linkage independent Information Governance Review Panel project number 0911. Concerning English data, University of Oxford is compliant with the General Data Protection Regulation and the National Health Service (NHS) Digital Data Security and Protection Policy. This is an approved study (Integrated Research Application ID 301740, Health Research Authority (HRA) Research Ethics Committee 21/HRA/2786). The Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub meets NHS Digital's Data Security and Protection Toolkit requirements. In Northern Ireland, the project was approved by the Honest Broker Governance Board, project number 0064. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journals.
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Affiliation(s)
| | - Ting Shi
- The University of Edinburgh, Usher Institute, Edinburgh, UK
| | - Steven Kerr
- The University of Edinburgh, Usher Institute, Edinburgh, UK
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Mark Joy
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ruby Tsang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Dylan McGagh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Declan Bradley
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Dermot OReilly
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Siobhan Murphy
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Antony Chuter
- BREATHE - The Health Data Research Hub for Respiratory Health, London, UK
| | - Jillian Beggs
- BREATHE - The Health Data Research Hub for Respiratory Health, London, UK
| | - David Ford
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Chris Orton
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Gareth Davies
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Lucy J Griffiths
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Rowena Griffiths
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Emily Lowthian
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Laura North
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Malorie Perry
- Vaccine Preventable Disease Programme, Public Health Wales, Cardiff, UK
| | - Fatemeh Torabi
- Population Data Science, Swansea University Medical School, Swansea, UK
| | | | | | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Utkarsh Agrawal
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Rachael Wood
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Public Health Scotland, Edinburgh, UK
| | - Sarah Jane Stock
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Public Health Scotland, Edinburgh, UK
| | | | - Paul Henery
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Colin R Simpson
- The University of Edinburgh, Usher Institute, Edinburgh, UK
- Wellington School of Health, Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Aziz Sheikh
- The University of Edinburgh, Usher Institute, Edinburgh, UK
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Chang E, Mostafa J. The use of SNOMED CT, 2013-2020: a literature review. J Am Med Inform Assoc 2021; 28:2017-2026. [PMID: 34151978 DOI: 10.1093/jamia/ocab084] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/30/2021] [Accepted: 04/26/2021] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020. MATERIALS AND METHODS In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020. The retrieved articles were categorized and analyzed according to SNOMED CT focus categories (ie, indeterminate, theoretical, pre-development, implementation, and evaluation/commodity), usage categories (eg, illustrate terminology systems theory, prospective content coverage, used to classify or code in a study, retrieve or analyze patient data, etc.), medical domains, and countries. RESULTS After applying inclusion and exclusion criteria, 622 articles were selected for final review. Compared to the papers published between 2001 and 2012, papers published between 2013 and 2020 revealed an increase in more mature usage of SNOMED CT, and the number of papers classified in the "implementation" and "evaluation/commodity" focus categories expanded. When analyzed by decade, papers in the "pre-development," "implementation," and "evaluation/commodity" categories were much more numerous in 2011-2020 than in 2001-2010, increasing from 169 to 293, 30 to 138, and 3 to 65, respectively. CONCLUSION Published papers in more mature usage categories have substantially increased since 2012. From 2013 to present, SNOMED CT has been increasingly implemented in more practical settings. Future research should concentrate on addressing whether SNOMED CT influences improvement in patient care.
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Affiliation(s)
- Eunsuk Chang
- Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Javed Mostafa
- Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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