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Swift J, O'Kelly N, Barker C, Woodward A, Ghosh S. A Digital Respiratory Ward in Leicester, Leicestershire, and Rutland, England, for Patients With COVID-19: Economic Evaluation of the Impact on Acute Capacity and Wider National Health Service Resource Use. JMIR Form Res 2024; 8:e47441. [PMID: 38349716 PMCID: PMC10866202 DOI: 10.2196/47441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/04/2023] [Accepted: 12/22/2023] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic stressed global health care systems' acute capacity and caused a diversion of resources from elective care to the treatment of acute respiratory disease. In preparing for a second wave of COVID-19 infections, England's National Health Service (NHS) in Leicester, Leicestershire, and Rutland sought to protect acute capacity in the winter of 2020-2021. Their plans included the introduction of a digital ward where patients were discharged home early and supported remotely by community-based respiratory specialists, who were informed about patient health status by a digital patient monitoring system. OBJECTIVE The objective of the digital ward was to maintain acute capacity through safe, early discharge of patients with COVID-19 respiratory disease. The study objective was to establish what impact this digital ward had on overall NHS resource use. METHODS There were no expected differences in patient outcomes. A cost minimization was performed to demonstrate the impact on the NHS resource use from discharging patients into a digital COVID-19 respiratory ward, compared to acute care length of stay (LOS). This evaluation included all 310 patients enrolled in the service from November 2020 (service commencement) to November 2021. Two primary methods, along with sensitivity analyses, were used to help overcome the uncertainty associated with the estimated comparators for the observational data on COVID-19 respiratory acute LOS, compared with the actual LOS of the 279 (90%) patients who were not discharged on oxygen nor were in critical care. Historic comparative LOS and an ordinary least squares model based on local monthly COVID-19 respiratory median LOS were used as comparators. Actual comparator data were sourced for the 31 (10%) patients who were discharged home and into the digital ward for oxygen weaning. Resource use associated with delivering care in the digital ward was sourced from the digital system and respiratory specialists. RESULTS In the base case, the digital ward delivered estimated health care system savings of 846.5 bed-days and US $504,197 in net financial savings across the 2 key groups of patients-those on oxygen and those not on oxygen at acute discharge (both P<.001). The mean gross and net savings per patient were US $1850 and US $1626 in the base case, respectively, without including any savings associated with a potential reduction in readmissions. The 30-day readmission rate was 2.9%, which was below comparative data. The mean cost of the intervention was US $223.53 per patient, 12.1% of the estimated gross savings. It was not until the costs were increased and the effect reduced simultaneously by 78.4% in the sensitivity analysis that the intervention was no longer cost saving. CONCLUSIONS The digital ward delivered increased capacity and substantial financial savings and did so with a high degree of confidence, at a very low absolute and relative cost.
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Affiliation(s)
- Jim Swift
- Spirit Health, Leicester, United Kingdom
| | | | | | - Alex Woodward
- Leicestershire Partnership NHS Trust, Leicester, United Kingdom
| | - Sudip Ghosh
- Leicestershire Partnership NHS Trust, Leicester, United Kingdom
- Department of Allied Health Sciences, De Montford University, Leicester, United Kingdom
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Loo WK, Hasikin K, Suhaimi A, Yee PL, Teo K, Xia K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S, Azizan MM, Lai KW. Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies. Front Public Health 2022; 10:898254. [PMID: 35677770 PMCID: PMC9168237 DOI: 10.3389/fpubh.2022.898254] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/20/2022] [Indexed: 01/19/2023] Open
Abstract
In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.
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Affiliation(s)
- Wei Kit Loo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Anwar Suhaimi
- Department of Rehabilitation Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Por Lip Yee
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kareen Teo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kaijian Xia
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Samiappan Dhanalakshmi
- Department of ECE, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
- Samiappan Dhanalakshmi
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
- Muhammad Mokhzaini Azizan
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Khin Wee Lai
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