Publications by authors named "Anmol Arora"

15 Publications

  • Page 1 of 1

Innovation Pathways in the NHS: An Introductory Review.

Ther Innov Regul Sci 2021 May 19. Epub 2021 May 19.

Department of Surgery, University of Cambridge, Cambridge, UK.

Healthcare as an industry is recognised as one of the most innovative. Despite heavy regulation, there is substantial scope for new technologies and care models to not only boost patient outcomes but to do so at reduced cost to healthcare systems and consumers. Promoting innovation within national health systems such as the National Health Service (NHS) in the United Kingdom (UK) has been set as a key target for health care professionals and policy makers. However, while the UK has a world-class biomedical research industry, several reports in the last twenty years have highlighted the difficulties faced by the NHS in encouraging and adopting innovations, with the journey from idea to implementation of health technology often taking years and being very expensive, with a high failure rate. This has led to the establishment of several innovation pathways within and around the NHS, to encourage the invention, development and implementation of cost-effective technologies that improve health care delivery. These pathways span local, regional and national health infrastructure. They operate at different stages of the innovation pipeline, with their scope and work defined by location, technology area or industry sector, based on the specific problem identified when they were set up. In this introductory review, we outline each of the major innovation pathways operating at local, regional and national levels across the NHS, including their history, governance, operating procedures and areas of expertise. The extent to which innovation pathways address current challenges faced by innovators is discussed, as well as areas for improvement and future study.
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http://dx.doi.org/10.1007/s43441-021-00304-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132486PMC
May 2021

COVIDReady2 study protocol: cross-sectional survey of medical student volunteering and education during the COVID-19 pandemic in the United Kingdom.

BMC Med Educ 2021 Apr 14;21(1):211. Epub 2021 Apr 14.

Nuffield Department of Primary Care Health Sciences, Medical Sciences Division, University of Oxford, Oxford, UK.

Background: The coronavirus disease 2019 pandemic has led to global disruption of healthcare. Many students volunteered to provide clinical support. Volunteering to work in a clinical capacity was a unique medical education opportunity; however, it is unknown whether this was a positive learning experience or which volunteering roles were of most benefit to students.

Methods: The COVIDReady2 study is a national cross-sectional study of all medical students at medical schools in the United Kingdom. The primary outcome is to explore the experiences of medical students who volunteered during the pandemic in comparison to those who did not. We will compare responses to determine the educational benefit and issues they faced. In addition to quantitative analysis, thematic analysis will be used to identify themes in qualitative responses.

Discussion: There is a growing body of evidence to suggest that service roles have potential to enhance medical education; yet, there is a shortage of studies able to offer practical advice for how these roles may be incorporated in future medical education. We anticipate that this study will help to identify volunteer structures that have been beneficial for students, so that similar infrastructures can be used in the future, and help inform medical education in a non-pandemic setting.

Trial Registration: Not Applicable.
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http://dx.doi.org/10.1186/s12909-021-02629-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045566PMC
April 2021

Comparing the fit of N95, KN95, surgical, and cloth face masks and assessing the accuracy of fit checking.

PLoS One 2021 22;16(1):e0245688. Epub 2021 Jan 22.

Cambridge University, San Francisco, CA, United States of America.

Introduction: The COVID-19 pandemic has made well-fitting face masks a critical piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks has been acknowledged, there remains a lack of understanding about the role of good fit in rendering protective equipment useful. In addition, supply chain constraints have caused some organizations to abandon traditional quantitative or/and qualitative fit testing, and instead, have implemented subjective fit checking. Our study seeks to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit checks by comparing fit check results to quantitative fit testing results.

Methods: Seven participants first evaluated N95 and KN95 respirators by performing a fit check. Participants then underwent quantitative fit testing wearing five N95 respirators, a KN95 respirator, a surgical mask, and fabric masks.

Results: N95 respirators offered higher degrees of protection than the other categories of masks tested; however, it should be noted that most N95 respirators failed to fit the participants adequately. Fit check responses had poor correlation with quantitative fit factor scores. KN95, surgical, and fabric masks achieved low fit factor scores, with little protective difference recorded between respiratory protection options. In addition, small facial differences were observed to have a significant impact on quantitative fit.

Conclusion: Fit is critical to the level of protection offered by respirators. For an N95 respirator to provide the promised protection, it must fit the participant. Performing a fit check via NHS self-assessment guidelines was an unreliable way of determining fit.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245688PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822328PMC
February 2021

How do associations between sleep duration and metabolic health differ with age in the UK general population?

PLoS One 2020 23;15(11):e0242852. Epub 2020 Nov 23.

Centre for Diet and Activity Research (CEDAR) & MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom.

Background: Despite a growing body of evidence suggesting that short sleep duration may be linked to adverse metabolic outcomes, how these associations differ between age groups remains unclear. We use eight years of data from the UK National Diet and Nutritional Survey (NDNS) (2008-2016) to analyse cross-sectional relationships between sleep duration and metabolic risk in participants aged 11-70 years.

Methods: Participants (n = 2008) who provided both metabolic risk and sleep duration data were included. Self-reported sleep duration was standardised by age, to account for differences in age-related sleep requirements. A standardised metabolic risk score was constructed, comprising: waist circumference, blood pressure, serum triglycerides, serum high-density lipoprotein cholesterol, and fasting plasma glucose. Regression models were constructed across four age groups from adolescents to older adults.

Results: Overall, decreased sleep duration (hrs) was associated with an increased metabolic risk (standard deviations) with significant quadratic (B:0.028 [95%CI: 0.007, 0.050]) and linear (B:-0.061 [95%CI: -0.111, -0.011]) sleep duration coefficients. When separated by age group, stronger associations were seen among mid-aged adults (36-50y) (quadratic coefficient: 0.038 [95%CI: 0.002, 0.074]) compared to other age groups (e.g. adolescents (11-18y), quadratic coefficient: -0.009 [95%CI: -0.042, 0.025]). An increased difference between weekend and weekday sleep was only associated with increased metabolic risk in adults aged 51-70 years (B:0.18 [95%CI: 0.005, 0.348]).

Conclusions: Our results indicate that sleep duration is linked to adverse metabolic risk and suggest heterogeneity between age groups. Longitudinal studies with larger sample sizes are required to explore long-term effects of abnormal sleep and potential remedial benefits.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242852PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682906PMC
January 2021

Dried blood spots for the identification of bio-accumulating organic compounds: current challenges and future perspectives.

Curr Opin Environ Sci Health 2020 Jun 14;15:66-73. Epub 2020 Jul 14.

Mouse Imaging Centre, Hospital for Sick Children, 25 Orde Street, Toronto M5T 3H7, Canada.

The is a concept that underlines the critical relationship between health and environmental exposures, including environmental toxicants. Currently, most environmental exposures that contribute to the exposome have not been characterized. Dried-blood spots (DBS) offer a cost-effective, reliable approach to characterize the blood exposome, which consists of diverse endogenous and exogenous chemicals, including persistent and bioaccumulating organic compounds. Current challenges involve prioritizing the identification by state-of-the-art mass spectrometry of likely up to tens of thousands of compounds present in blood; characterizing substances that represent a mixture of myriad constituent compounds; and detecting trace level contaminants, especially in quantity-limited matrices like DBS. This contribution reviews recent trends in DBS analysis of chemical pollutants and highlights the need for continued research in analytical chemistry to advance the field of exposomics.
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http://dx.doi.org/10.1016/j.coesh.2020.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560987PMC
June 2020

Pathology training in the age of artificial intelligence.

J Clin Pathol 2021 Feb 5;74(2):73-75. Epub 2020 Oct 5.

School of Clinical Medicine, University of Cambridge, Cambridge, UK

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http://dx.doi.org/10.1136/jclinpath-2020-207110DOI Listing
February 2021

Performing Qualitative Mask Fit Testing Without a Commercial Kit: Fit Testing Which Can Be Performed at Home and at Work.

Disaster Med Public Health Prep 2020 Sep 10:1-5. Epub 2020 Sep 10.

Department of Engineering, Engineering Design Centre, Cambridge University, United Kingdom.

Objective: Qualitative fit testing is a popular method of ensuring the fit of sealing face masks such as N95 and FFP3 masks. Increased demand due to the coronavirus disease 2019 (COVID-19) pandemic has led to shortages in testing equipment and has forced many institutions to abandon fit testing. Three key materials are required for qualitative fit testing: the test solution, nebulizer, and testing hood. Accessible alternatives to the testing solution have been studied. This exploratory qualitative study evaluates alternatives to the nebulizer and hoods for performing qualitative fit testing.

Methods: Four devices were trialed to replace the test kit nebulizer. Two enclosures were tested for their ability to replace the test hood. Three researchers evaluated promising replacements under multiple mask fit conditions to assess functionality and accuracy.

Results: The aroma diffuser and smaller enclosures allowed participants to perform qualitative fit tests quickly and with high accuracy.

Conclusions: Aroma diffusers show significant promise in their ability to allow individuals to quickly, easily, and inexpensively perform qualitative fit testing. Our findings indicate that aroma diffusers and homemade testing hoods may allow for qualitative fit testing when conventional apparatus is unavailable. Additional research is needed to evaluate the safety and reliability of these devices.
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http://dx.doi.org/10.1017/dmp.2020.352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642972PMC
September 2020

Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review.

Authors:
Anmol Arora

Med Devices (Auckl) 2020 20;13:223-230. Epub 2020 Aug 20.

School of Clinical Medicine, University of Cambridge, Cambridge, UK.

Artificial intelligence (AI) is widely recognised as a transformative innovation and is already proving capable of outperforming human clinicians in the diagnosis of specific medical conditions, especially in image analysis within dermatology and radiology. These abilities are enhanced by the capacity of AI systems to learn from patient records, genomic information and real-time patient data. Uses of AI range from integrating with robotics to creating training material for clinicians. Whilst AI research is mounting, less attention has been paid to the practical implications on healthcare services and potential barriers to implementation. AI is recognised as a "Software as a Medical Device (SaMD)" and is increasingly becoming a topic of interest for regulators. Unless the introduction of AI is carefully considered and gradual, there are risks of automation bias, overdependence and long-term staffing problems. This is in addition to already well-documented generic risks associated with AI, such as data privacy, algorithmic biases and corrigibility. AI is able to potentiate innovations which preceded it, using Internet of Things, digitisation of patient records and genetic data as data sources. These synergies are important in both realising the potential of AI and utilising the potential of the data. As machine learning systems begin to cross-examine an array of databases, we must ensure that clinicians retain autonomy over the diagnostic process and understand the algorithmic processes generating diagnoses. This review uses established management literature to explore artificial intelligence as a digital healthcare innovation and highlight potential risks and opportunities.
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http://dx.doi.org/10.2147/MDER.S262590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455610PMC
August 2020

Artificial intelligence: a new frontier for anaesthesiology training.

Authors:
Anmol Arora

Br J Anaesth 2020 11 15;125(5):e407-e408. Epub 2020 Jul 15.

School of Clinical Medicine, University of Cambridge, Cambridge, UK. Electronic address:

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http://dx.doi.org/10.1016/j.bja.2020.06.049DOI Listing
November 2020

Robots Will Perform Anesthesia in the Near Future: Comment.

Authors:
Anmol Arora

Anesthesiology 2020 09;133(3):670

School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.

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http://dx.doi.org/10.1097/ALN.0000000000003434DOI Listing
September 2020

Moving towards sustainable dermatological practice.

Authors:
Anmol Arora

Int J Dermatol 2020 Sep 23;59(9):e343-e345. Epub 2020 Jun 23.

School of Clinical Medicine, University of Cambridge, Cambridge, UK.

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http://dx.doi.org/10.1111/ijd.15017DOI Listing
September 2020

Disrupting clinical education: Using artificial intelligence to create training material.

Authors:
Anmol Arora

Clin Teach 2020 08 22;17(4):357-359. Epub 2020 Jun 22.

School of Clinical Medicine, University of Cambridge, Cambridge, UK.

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http://dx.doi.org/10.1111/tct.13177DOI Listing
August 2020

Ethics in the age of COVID-19.

Intern Emerg Med 2020 08 21;15(5):889-890. Epub 2020 May 21.

School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0SP, UK.

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http://dx.doi.org/10.1007/s11739-020-02368-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240805PMC
August 2020

Intranasal delivery of tetrabenazine nanoemulsion via olfactory region for better treatment of hyperkinetic movement associated with Huntington's disease: Pharmacokinetic and brain delivery study.

Chem Phys Lipids 2020 08 19;230:104917. Epub 2020 May 19.

Research Scholar, Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, Hamdard Nagar, New Delhi 110062, India. Electronic address:

Tetrabenazine reduces chorea symptoms associated with Huntington's disease by depleting monoamines in pre-synaptic vesicles. It exhibits low aqueous solubility and undergoes first pass metabolism due to which it has low oral bioavailability. The aim of present work was to formulate intranasal tetrabenazine loaded nanoemulsion for better management and treatment of hyperkinesia related with Huntington's disease. A quality by design (QbD) technique was employed as statistical multivariate approach for formulation and optimization of nanoemulsion. Optimized formulation showed droplet size of 106.80 ± 1.96 nm with polydispersity index (PDI) value of 0.198 ± 0.005 and -9.63 ± 0.63 mV zeta potential. Ex-vivo drug permeation studies were carried out and found that the formulation has an augmented permeation by 1.68 times as compared to tetrabenazine suspension. MTT assay on neuro-2a cell lines showed that tetrabenazine loaded nanoemulsion displayed better cell viability than placebo and aqueous drug solution at ½ × C, C and 2 × C. Pharmacokinetic parameters in brain after intranasal administration of tetrabenazine nanoemulsion were found to be C = 3.497 ± 0.275 μg/mL, AUC = 29.196 ± 0.870 μg h/mL and elimination rate constant (k) = 0.097 ± 0.012 h where as in plasma the pharmacokinetic parameters were C = 1.400 ± 0.084 μg/mL, AUC = 12.925 ± 0.340 μg h/mL and k = 0.061 ± 0.010 h. Histopathological studies of porcine nasal mucosa showed that nasal mucosa remains intact when treated with tetrabenazine loaded nanoemulsion. Thus it can be concluded from study that optimized nanoemulsion formulation of a tetrabenazine was robust and its delivery through nasal route is a viable alternative to other routes of administration for treatment of hyperkinesia associated with Huntington's disease.
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http://dx.doi.org/10.1016/j.chemphyslip.2020.104917DOI Listing
August 2020