14 results found
Gardner C, Halligan J, Fontana G, et al., 2022, Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research, Health Informatics Journal, Vol: 28, ISSN: 1460-4582
There is a growing need for alternative methodologies to evaluate digital health solutions in a short timeframe and at relatively low cost. Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, few studies have described the applicability of SBR methods to evaluate such solutions. This study used SBR to evaluate the feasibility and user experience of a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. Twenty-five clinicians and research staff were recruited to match 10 synthetic patient cases to clinical trials using both the CDS tool and publicly available online trial databases. Participants were significantly more likely to report having sufficient time (p = 0.020) and to require less mental effort (p = 0.001) to complete trial matching with the CDS tool. Participants required less time for trial matching using the CDS tool, but the difference was not significant (p = 0.093). Most participants reported that they had sufficient guidance to participate in the simulations (96%). This study demonstrates the use of SBR methods is a feasible approach to evaluate digital health solutions and to collect valuable user feedback without the need for implementation in clinical practice. Further research is required to demonstrate the feasibility of using SBR to conduct remote evaluations of digital health solutions.
Moshe M, Daunt A, Flower B, et al., 2021, SARS-CoV-2 lateral flow assays for possible use in national covid-19 seroprevalence surveys (REACT2): diagnostic accuracy study, BMJ: British Medical Journal, Vol: 372, Pages: 1-8, ISSN: 0959-535X
Objective: To evaluate the performance of new lateral flow immunoassays (LFIAs) suitable for use in a national COVID-19 seroprevalence programme (REACT2).Design: Laboratory sensitivity and specificity analyses were performed for seven LFIAs on a minimum of 200 sera from individuals with confirmed SARS-CoV-2 infection, and 500 pre-pandemic sera respectively. Three LFIAs were found to have a laboratory sensitivity superior to the finger-prick sensitivity of the LFIA currently used in REACT2 seroprevalence studies (84%). These LFIAs were then further evaluated through finger-prick testing on participants with confirmed previous SARS-CoV-2 infection. Two LFIAs (Surescreen, Panbio) were evaluated in clinics in June-July, 2020, and a third LFIA (AbC-19) in September, 2020. A Spike protein enzyme-linked immunoassay (S-ELISA) and hybrid double antigen binding assay (DABA) were used as laboratory reference standards.Setting: Laboratory analyses were performed at Imperial College, London and University facilities in London, UK. Research clinics for finger-prick sampling were run in two affiliated NHS trusts.Participants: Sensitivity analysis on sera were performed on 320 stored samples from previous participants in the REACT2 programme with confirmed previous SARS-CoV-2 infection. Specificity analysis was performed using 1000 pre-pandemic sera. 100 new participants with confirmed previous SARS-CoV-2 infection attended study clinics for finger-prick testing.Main outcome measures: The accuracy of LFIAs in detecting IgG antibodies to SARS-CoV-2 in comparison to two in-house ELISAs.Results: The sensitivity of seven new LFIAs using sera varied between 69% and 100% (vs S-ELISA/hybrid DABA). Specificity using sera varied between 99.6% and 100%. Sensitivity on finger-prick testing for Panbio, Surescreen and AbC-19 was 77% (CI 61.4 to 88.2), 86% (CI 72.7 to 94.8) and 69% (CI 53.8 to 81.3) respectively vs S-ELISA/hybrid DABA. Sensitivity for sera from matched clinical samples performe
Shaw A, Flott K, Fontana G, et al., 2020, No patient safety without health worker safety Comment, The Lancet, Vol: 396, Pages: 1541-1543, ISSN: 0140-6736
Guo C, Ashrafian H, Ghafur S, et al., 2020, Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches, NPJ DIGITAL MEDICINE, Vol: 3, ISSN: 2398-6352
Flower B, Brown JC, Simmons B, et al., 2020, Clinical and laboratory evaluation of SARS-CoV-2 lateral flow assays for use in a national COVID-19 sero-prevalence survey, Thorax, Vol: 75, Pages: 1082-1088, ISSN: 0040-6376
BackgroundAccurate antibody tests are essential to monitor the SARS-CoV-2 pandemic. Lateral flow immunoassays (LFIAs) can deliver testing at scale. However, reported performance varies, and sensitivity analyses have generally been conducted on serum from hospitalised patients. For use in community testing, evaluation of finger-prick self-tests, in non-hospitalised individuals, is required.MethodsSensitivity analysis was conducted on 276 non-hospitalised participants. All had tested positive for SARS-CoV-2 by RT-PCR and were ≥21d from symptom-onset. In phase I we evaluated five LFIAs in clinic (with finger-prick) and laboratory (with blood and sera) in comparison to a) PCR-confirmed infection and b) presence of SARS-CoV-2 antibodies on two “in-house” ELISAs. Specificity analysis was performed on 500 pre-pandemic sera. In phase II, six additional LFIAs were assessed with serum.Findings95% (95%CI [92.2, 97.3]) of the infected cohort had detectable antibodies on at least one ELISA. LFIA sensitivity was variable, but significantly inferior to ELISA in 8/11 assessed. Of LFIAs assessed in both clinic and laboratory, finger-prick self-test sensitivity varied from 21%-92% vs PCR-confirmed cases and 22%-96% vs composite ELISA positives. Concordance between finger-prick and serum testing was at best moderate (kappa 0.56) and, at worst, slight (kappa 0.13). All LFIAs had high specificity (97.2% - 99.8%).InterpretationLFIA sensitivity and sample concordance is variable, highlighting the importance of evaluations in setting of intended use. This rigorous approach to LFIA evaluation identified a test with high specificity (98.6% (95%CI [97.1, 99.4])), moderate sensitivity (84.4% with fingerprick (95%CI [70.5, 93.5])), and moderate concordance, suitable for seroprevalence surveys.
Gardner C, Ghafur S, Fontana G, et al., 2020, A mixed methods study for the evaluation of a digital health solution for cancer multidisciplinary team meetings using simulation-based research methods., Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0732-183X
Neves AL, Lygydakis H, Fontana G, 2020, The technology legacy of COVID-19 in primary care, BJGP Life
Ghafur S, Fontana G, Halligan J, et al., 2020, NHS data: Maximising its impact on the health and wealth of the United Kingdom
Fontana G, Ghafur S, Torne L, et al., 2020, Ensuring that the NHS realises fair financial value from its data, The Lancet Digital Health, Vol: 2, Pages: e10-e12, ISSN: 2589-7500
Fontana G, Flott K, Dhingra-Kumar N, et al., 2019, Five reasons for optimism on World Patient Safety Day, The Lancet, Vol: 394, Pages: 993-995, ISSN: 0140-6736
Vuik SI, Fontana G, Mayer E, et al., 2017, Do hospitalisations for Ambulatory Care Sensitive Conditions reflect low access to primary care? An observational cohort study of primary care utilisation prior to hospitalisation, BMJ Open, Vol: 7, ISSN: 2044-6055
Objectives To explore whether hospitalisations for ambulatory care sensitive conditions (ACSCs) are associated with low access to primary care.Design Observational cohort study over 2008 to 2012 using the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) databases.Setting English primary and secondary care.Participants A random sample of 300 000 patients.Main outcome measures Emergency hospitalisation for an ACSC.Results Over the long term, patients with ACSC hospitalisations had on average 2.33 (2.17 to 2.49) more general practice contacts per 6 months than patients with similar conditions who did not require hospitalisation. When accounting for the number of diagnosed ACSCs, age, gender and GP practice through a nested case–control method, the difference was smaller (0.64 contacts), but still significant (p<0.001).In the short-term analysis, measured over the 6 months prior to hospitalisation, patients used more GP services than on average over the 5 years. Cases had significantly (p<0.001) more primary care contacts in the 6 months before ACSC hospitalisations (7.12, 95% CI 6.95 to 7.30) than their controls during the same 6 months (5.57, 95% CI 5.43 to 5.72). The use of GP services increased closer to the time of hospitalisation, with a peak of 1.79 (1.74 to 1.83) contacts in the last 30 days before hospitalisation.Conclusions This study found no evidence to support the hypothesis that low access to primary care is the main driver of ACSC hospitalisations. Other causes should also be explored to understand how to use ACSC admission rates as quality metrics, and to develop the appropriate interventions.
Wadge H, Roy R, Sripathy A, et al., 2017, How to harness the private sector for universal health coverage, Lancet, Vol: 390, Pages: E19-E20, ISSN: 0140-6736
Flott KM, Fontana G, Dhingra-Kumar N, et al., 2017, Health care must mean safe care: enshrining patient safety in global health, The Lancet, Vol: 389, Pages: 1279-1281, ISSN: 0140-6736
Parston G, McQueen J, Patel H, et al., 2015, The Science And Art Of Delivery: Accelerating The Diffusion Of Health Care Innovation, Health Affairs, Vol: 34, Pages: 2160-2166, ISSN: 0278-2715
There is a widely acknowledged time lag in health care between an invention or innovation and its widespread use across a health system. Much is known about the factors that can aid the uptake of innovations within discrete organizations. Less is known about what needs to be done to enable innovations to transform large systems of health care. This article describes the results of in-depth case studies aimed at assessing the role of key agents and agencies that facilitate the rapid adoption of innovations. The case studies—from Argentina, England, Nepal, Singapore, Sweden, the United States, and Zambia—represent widely varying health systems and economies. The implications of the findings for policy makers are discussed in terms of key factors within a phased approach for creating a climate for change, engaging and enabling the whole organization, and implementing and sustaining change. Purposeful and directed change management is needed to drive system transformation.
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