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Roland D, Powell C, Lloyd A, Trubey R, Tume L, Sefton G, Huang C, Taiyari K, Strange H, Jacob N, Thomas-Jones E, Hood K, Allen D. Paediatric early warning systems: not a simple answer to a complex question. Arch Dis Child 2022; 108:archdischild-2022-323951. [PMID: 35868852 PMCID: PMC10176370 DOI: 10.1136/archdischild-2022-323951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/09/2022] [Indexed: 11/04/2022]
Abstract
Paediatric early warning systems (PEWS) to reduce in-hospital mortality have been a laudable endeavour. Evaluation of their impact has rarely examined the internal validity of the components of PEWS in achieving desired outcomes. We highlight the assumptions made regarding the mode of action of PEWS and, as PEWS become more commonplace, this paper asks whether we really understand their function, process and outcome.
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Affiliation(s)
- Damian Roland
- SAPPHIRE Group, Health Sciences, University of Leicester, Leicester, UK
- Paediatric Emergency Medicine Leicester Academic (PEMLA) Group, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Colin Powell
- Department of Emergency Medicine, Sidra Medical and Research Center, Doha, Qatar
- Division of Population Medicine, Cardiff University, Cardiff, UK
| | - Amy Lloyd
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Robert Trubey
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Lyvonne Tume
- School of Health and Society, University of Salford, Salford, Greater Manchester, UK
| | - Gerri Sefton
- Alder Hey Children's NHS Foundation Trust, Liverpool, Merseyside, UK
| | - Chao Huang
- Hull-York Medical School, University of Hull, Hull, UK
| | - Katie Taiyari
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | | | - Nina Jacob
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | | | - Kerenza Hood
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Davina Allen
- Centre for Trials Research, Cardiff University, Cardiff, UK
- School of Healthcare Sciences, Cardiff University Centre for Trials Research, Cardiff, Wales, UK
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Allen D, Lloyd A, Edwards D, Hood K, Huang C, Hughes J, Jacob N, Lacy D, Moriarty Y, Oliver A, Preston J, Sefton G, Sinha I, Skone R, Strange H, Taiyari K, Thomas-Jones E, Trubey R, Tume L, Powell C, Roland D. Development, implementation and evaluation of an evidence-based paediatric early warning system improvement programme: the PUMA mixed methods study. BMC Health Serv Res 2022; 22:9. [PMID: 34974841 PMCID: PMC8722056 DOI: 10.1186/s12913-021-07314-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 11/10/2021] [Indexed: 11/24/2022] Open
Abstract
Background Paediatric mortality rates in the United Kingdom are amongst the highest in Europe. Clinically missed deterioration is a contributory factor. Evidence to support any single intervention to address this problem is limited, but a cumulative body of research highlights the need for a systems approach. Methods An evidence-based, theoretically informed, paediatric early warning system improvement programme (PUMA Programme) was developed and implemented in two general hospitals (no onsite Paediatric Intensive Care Unit) and two tertiary hospitals (with onsite Paediatric Intensive Care Unit) in the United Kingdom. Designed to harness local expertise to implement contextually appropriate improvement initiatives, the PUMA Programme includes a propositional model of a paediatric early warning system, system assessment tools, guidance to support improvement initiatives and structured facilitation and support. Each hospital was evaluated using interrupted time series and qualitative case studies. The primary quantitative outcome was a composite metric (adverse events), representing the number of children monthly that experienced one of the following: mortality, cardiac arrest, respiratory arrest, unplanned admission to Paediatric Intensive Care Unit, or unplanned admission to Higher Dependency Unit. System changes were assessed qualitatively through observations of clinical practice and interviews with staff and parents. A qualitative evaluation of implementation processes was undertaken. Results All sites assessed their paediatric early warning systems and identified areas for improvement. All made contextually appropriate system changes, despite implementation challenges. There was a decline in the adverse event rate trend in three sites; in one site where system wide changes were organisationally supported, the decline was significant (ß = -0.09 (95% CI: − 0.15, − 0.05); p = < 0.001). Changes in trends coincided with implementation of site-specific changes. Conclusions System level change to improve paediatric early warning systems can bring about positive impacts on clinical outcomes, but in paediatric practice, where the patient population is smaller and clinical outcomes event rates are low, alternative outcome measures are required to support research and quality improvement beyond large specialist centres, and methodological work on rare events is indicated. With investment in the development of alternative outcome measures and methodologies, programmes like PUMA could improve mortality and morbidity in paediatrics and other patient populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-021-07314-2.
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Affiliation(s)
- Davina Allen
- School of Healthcare Sciences, Cardiff University, Room 13.08, Eastgate House, Newport Road, Cardiff, CF24 0AB, UK.
| | - Amy Lloyd
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Dawn Edwards
- Children's Services, Swansea Bay University Health Board, Swansea, UK
| | - Kerenza Hood
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Chao Huang
- Hull-York Medical School, University of Hull, Hull, UK
| | - Jacqueline Hughes
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Nina Jacob
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - David Lacy
- Arrow Park Hospital, Wirral University Teaching NHS Foundation Trust, Wirral, UK
| | - Yvonne Moriarty
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Alison Oliver
- Noah's Ark Children's Hospital for Wales, Cardiff and Vale University Health Board, Cardiff, UK
| | - Jennifer Preston
- Alder Hey Clinical Research Facility, Institute in the Park, Alder Hey Children's NHS Foundation Trust, Eaton Rd, Liverpool, UK
| | - Gerri Sefton
- Alder Hey Children's Hospital, Alder Hey Children's NHS Foundation Trust, Eaton Rd, Liverpool, UK
| | | | - Richard Skone
- Noah's Ark Children's Hospital for Wales, Cardiff and Vale University Health Board, Cardiff, UK
| | - Heather Strange
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Khadijeh Taiyari
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Emma Thomas-Jones
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Rob Trubey
- Centre for Trials Research, Cardiff University, Neuadd Meirionnydd, Heath Park, Cardiff, UK
| | - Lyvonne Tume
- School of Health and Society, University of Salford, Manchester, UK
| | - Colin Powell
- Department of Emergency Medicine, Sidra Medicine, Doha, Qatar.,Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK
| | - Damian Roland
- Paediatric Emergency Medicine, Leicester Academic (PEMLA) Group, Emergency Department, University of Leicester, Leicester, UK.,SAPPHIRE Group, Health Sciences, Leicester University, Leicester, UK
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Mann KD, Good NM, Fatehi F, Khanna S, Campbell V, Conway R, Sullivan C, Staib A, Joyce C, Cook D. Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting. J Med Internet Res 2021; 23:e28209. [PMID: 34591017 PMCID: PMC8517822 DOI: 10.2196/28209] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. METHODS An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. RESULTS A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. CONCLUSIONS Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.
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Affiliation(s)
- Kay D Mann
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Norm M Good
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Farhad Fatehi
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Sankalp Khanna
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Victoria Campbell
- Sunshine Coast University Hospital, Sunshine Coast Hospital and Health Service, Birtinya, Australia.,Clinical Excellence Queensland, Queensland Health, Queensland, Australia.,School of Medicine, Griffith University, Nathan Campas, Australia
| | - Roger Conway
- Sunshine Coast University Hospital, Sunshine Coast Hospital and Health Service, Birtinya, Australia
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,Metro North Hospital and Health Service, Brisbane, Australia
| | - Andrew Staib
- Clinical Excellence Queensland, Queensland Health, Queensland, Australia.,Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Christopher Joyce
- Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - David Cook
- Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia.,School of Computer Science, Faculty of Science, Queensland University of Technology, Brisbane, Australia
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Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6128260. [PMID: 34567483 PMCID: PMC8463185 DOI: 10.1155/2021/6128260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/04/2021] [Indexed: 11/18/2022]
Abstract
Norovirus monitoring and early warning can be used for diagnosis without etiological testing, and the treatment of this disease does not require the antibiotics. It often occurs in preschool children and affects their growth and development, so the coping measures for this disease are more prevention than treatment. In this study, the clinical data of 2133 children with diarrhea were collected. Based on the artificial intelligence (AI) algorithm of wavelet transform, a related model for data mining and processing of children's intestinal ultrasound images and stool specimens was constructed. Then, the norovirus infection trend was warned based on the wavelet analysis algorithm model. The results showed that the intestinal ultrasound image processed by the wavelet transform algorithm was clearer. The positive detection rate of norovirus in children with clinical diarrhea was as high as 59%, and the children had different degrees of body damage, of which the probability of compensatory metabolic acidosis was the highest. The epidemiological analysis found that children with norovirus infection were mainly concentrated in the age group under 2 years old and over 5 years old and showed a peak of infection in December. In summary, the intelligent algorithm based on wavelet transform can realize the noise reduction of intestinal ultrasound, and it should protect children with susceptible age and susceptible seasons to reduce the clinical infection rate of norovirus.
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English M, Ogola M, Aluvaala J, Gicheha E, Irimu G, McKnight J, Vincent CA. First do no harm: practitioners' ability to 'diagnose' system weaknesses and improve safety is a critical initial step in improving care quality. Arch Dis Child 2021; 106:326-332. [PMID: 33361068 PMCID: PMC7982941 DOI: 10.1136/archdischild-2020-320630] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Healthcare systems across the world and especially those in low-resource settings (LRS) are under pressure and one of the first priorities must be to prevent any harm done while trying to deliver care. Health care workers, especially department leaders, need the diagnostic abilities to identify local safety concerns and design actions that benefit their patients. We draw on concepts from the safety sciences that are less well-known than mainstream quality improvement techniques in LRS. We use these to illustrate how to analyse the complex interactions between resources and tools, the organisation of tasks and the norms that may govern behaviours, together with the strengths and vulnerabilities of systems. All interact to influence care and outcomes. To employ these techniques leaders will need to focus on the best attainable standards of care, build trust and shift away from the blame culture that undermines improvement. Health worker education should include development of the technical and relational skills needed to perform these system diagnostic roles. Some safety challenges need leadership from professional associations to provide important resources, peer support and mentorship to sustain safety work.
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Affiliation(s)
- Mike English
- Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK .,Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Muthoni Ogola
- Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya,Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
| | - Jalemba Aluvaala
- Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya,Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
| | - Edith Gicheha
- Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya,Kenya Paediatric Research Consortium, Nairobi, Kenya
| | - Grace Irimu
- Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya,Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya,Kenya Paediatric Research Consortium, Nairobi, Kenya
| | - Jacob McKnight
- Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
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