Nehal Hassan- Doctor of Philosophy
- Post-Doctoral Research Associate | Clinical Pharmacist at Population Health Sciences Institute Newcastle University
Nehal Hassan
- Doctor of Philosophy
- Post-Doctoral Research Associate | Clinical Pharmacist at Population Health Sciences Institute Newcastle University
Digital health/ AI
About
36
Publications
6,417
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147
Citations
Introduction
I am a clinical pharmacist and currently, I am a Post-Doctoral Research Associate at the Population Health Sciences Institute, School of Pharmacy, Newcastle University. I am interested in the applications of artificial intelligence in medicine, particularly the prediction of the likelihood of infections and subsequent sepsis in high-risk patients, also, how artificial intelligence tools can inform the shared decision-making process, and improve patients engagement in their clinical care.
Current institution
Population Health Sciences Institute Newcastle University
Current position
- Post-Doctoral Research Associate | Clinical Pharmacist
Publications
Publications (36)
Background:
Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive...
Background
Artificial intelligence (AI) decision aids can offer individualised and tailored information to patients and clinicians to inform shared decision making (SDM). We aimed to explore the perspectives of clinicians and patients on the use of AI decision aids to inform SDM in different clinical settings.
Methods
We did a systematic review by...
Background and Objectives
Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learni...
Objective
Artificial intelligence (AI) predictive tools can help inform the clinical decision-making process by, for example, detecting early signs of patient deterioration or predicting the likelihood of a patient developing a particular disease or complications postsurgery. However, it is unclear how acceptable or useful clinicians find these too...
Introduction
Antimicrobial resistance is a global health problem, especially in developing countries. Antimicrobial Stewardship Programmes (AMS) have been shown to be effective at reducing antimicrobial resistance and hospital patient stays. Health information technology (HIT) can support Outpatient Parenteral Antimicrobial Therapy (OPAT) through m...
Background
Outdoor air pollution is a global issue which poses a significant health risk. Modern neuroimaging techniques have revealed the detrimental impact of air pollution on brain health, in particular the development and progression of neurodegenerative diseases such as Alzheimer’s disease (AD).⁽¹⁾ We conducted a systematic review to evaluate...
Artificial intelligence (AI)-enabled decision aids can contribute to the shared decision-making process between patients and clinicians through personalised recommendations. This systematic review aims to understand users’ perceptions on using AI-enabled decision aids to inform shared decision-making. Four databases were searched. The population, i...
Background
Cancer screening programmes have been implemented to facilitate early detection of cancer. However, there are risks associated with cancer screening, such as overdiagnosis. This means identifying problems that were never going to cause harm1. This includes identification of abnormalities that do not progress, or that progress too slowly...
Introduction
Particulate matter (PM) is a mixture of tiny solid materials and liquid particles in the air that can trigger inflammatory reactions in multiple body systems, including the respiratory, cardiovascular and endocrine systems. Currently, there are no licensed pharmacological interventions to prevent or modify the effects of PM on differen...
Digital Health Technologies (DHTs) are revolutionizing healthcare. However, there is a lack of demonstrable health benefits across all populations. To advance digital health equity, we explored the perspectives of underserved groups on strategies to support digital inclusivity. Participants belonged to two or more CLEARS (Culture (ethnicity, langua...
Background
Cancer screening looks for early signs of cancer in people who do not currently exhibit symptoms. However, screening can also identify abnormalities that do not progress to produce symptoms or death. This could include the identification of tumours, which stop growing or grow very slowly; patients usually die with them rather than from t...
Background
Multimorbid patients are at higher risk of hospital readmission due to the complex nature of their conditions. Identifying those who may be at particularly high risk would allow us to intervene early and potentially delay or prevent such readmissions occurring, thus reducing healthcare costs. We conducted a systematic review investigatin...
Air pollution is one of the largest global environmental risks to public health. Over recent years, neuroimaging techniques have started to uncover the detrimental impact air pollution has had on brain health, including the development of Alzheimer’s Diseases (AD). We systematically reviewed the literature to evaluate the effects of long-term expos...
Background
Exposure to poor air quality worsen existing health conditions, particularly amongst vulnerable patients, leading to increased healthcare utilization. We conducted a systematic review to explore the impact of exposure to poor quality on healthcare requirements in multi-morbid patients.
Methods
We searched six major databases (Medline vi...
Air quality is the greatest environmental health threat. Effective communication can support individuals to take actions to protect themselves, however, we know some groups are less likely to engage with exposure reducing behaviours than others. We aimed to explore what information individuals want to receive about raised air pollution levels, when...
The World Health Organisation advocates Digital Health Technologies (DHTs) for advancing population health, yet concerns about inequitable outcomes persist. Differences in access and use of DHTs across different demographic groups can contribute to inequities. Academics and policy makers have acknowledged this issue and called for inclusive digital...
Introduction
Multi-morbid patients have complex care needs and are more likely to utilise healthcare services, which represents a financial burden on healthcare organisations. 30-day readmission of patients is a method of measuring the quality of the services provided within healthcare.[1] Artificial intelligence (AI) models can flag multi-morbidit...
Background
Digital health technologies (DHT) can be used to detect and remotely monitor digital biomarkers associated with prodromal dementia. However, there are growing concerns that DHT are not leading to demonstrable health benefits in all populations, particularly the underserved who are typically neglected in research resulting in worse health...
Background
Mild cognitive impairment (MCI) is the objective decline in neurocognitive functioning, but without significant impairment of the individual’s ability to perform the usual instrumental activities of daily living (1). Diagnosing MCI can be done using a combination of different methods such as cognitive testing, structural neuroimaging, nu...
Background
Digital health technologies (DHT) can be used to detect and remotely monitor digital biomarkers associated with prodromal dementia. However, there are growing concerns that DHT are not leading to demonstrable health benefits in all populations, particularly the underserved who are typically neglected in research resulting in worse health...
Introduction
Antimicrobial resistance was listed among the top ten global health issues by the World Health Organization in 2019.1 By 2050, it is projected to have caused 10 million deaths, surpassing cancer, diabetes, and road traffic accidents.2 The rise in resistant bacterial strains and reduced availability of novel antibiotics makes tackling a...
Introduction
Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management.
Aim
To identify the optimal set of predictors used...
This research evaluates the knowledge and attitudes of Egyptian healthcare professionals towards the use of health information technology (HIT) applications to optimize Outpatient antibiotic therapy (OPAT) strategy. It has been demonstrated that in developing countries, HIT applications in OPAT are still in their infancy with only a few organizatio...
Objective:
Nebulized antibiotics offer high efficacy due to significant local concentrations and safety with minimal blood levels. This study evaluates the efficacy and nephrotoxicity of nebulized versus IV amikacin in postcardiothoracic surgical patients with nosocomial pneumonia caused by multidrug-resistant Gram--negative bacilli.
Design:
Pro...
** Introduction: Infective Endocarditis (IEC) can be a fatal diagnosis, especially if it is complicating the course of RHD. Due to the natural history and pathophysiology of the disease, proper diagnosis and management is crucial to ensure ** Methods and Results: Built-up admissions of IEC cases to our centres necessitates founding a dedicated team...
Introduction: Myocardial infarction (MI) is a rare presentation of infective endocarditis (IEC). Case Report: A 59 year-old male presented to the ER department with acute typical angina of 3 hours duration. He gave a history of smoking, dyslipidaemia and rheumatic heart disease. The patient was distressed, pulse 105 bpm (water-hammer in character),...
Assessment of antibiotic posology modification and its impact on MDR bacterial strains in post-open heart surgery care.
Questions
Questions (2)
Can we use effect size of chi-square for Z test interchangeably??, in other words, if effect size calculated for chi square was 0.3, can I use this 0.3 for the same calculation of sample size with only changing the test family to Z test, or should I calculate a new effect size for the new test family??
Thanks in advance
for G power software users: when to use chi-square and when to use Z test as test family in a priori sample size calculation.
Another question:
If my primary outcome is nominal data (clinical cure), I count the number of patients cured and then I get the % of patients cured in every group, and I am having two groups (test and standard therapy), also previous trials in this discipline used non parametric analysis for their primary endpoint (which is clinical cure also), can I use chi-square in Priori sample size calculation, and use it in post hoc power analysis ?? (the primary outcome was non normally distributed
Thanks in advance
