Recommendations to advance digital health equity: a systematic review of qualitative studies.

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 health strategies. This systematic review synthesizes literature on these strategies and assesses facilitators and barriers to their implementation. We searched four large databases for qualitative studies using terms relevant to digital technology, health inequities, and socio-demographic factors associated with digital exclusion summarised by the CLEARS framework (Culture, Limiting conditions, Education, Age, Residence, Socioeconomic status). Following the PRISMA guidelines, 10,401 articles were screened independently by two reviewers, with ten articles meeting our inclusion criteria. Strategies were grouped into either outreach programmes or co-design approaches. Narrative synthesis of these strategies highlighted three key themes: firstly, using user-friendly designs, which included software and website interfaces that were easy to navigate and compatible with existing devices, culturally appropriate content, and engaging features. Secondly, providing supportive infrastructure to users, which included devices, free connectivity, and non-digital options to help access healthcare. Thirdly, providing educational support from family, friends, or professionals to help individuals develop their digital literacy skills to support the use of DHTs. Recommendations for advancing digital health equity include adopting a collaborative working approach to meet users' needs, and using effective advertising to raise awareness of the available support. Further research is needed to assess the feasibility and impact of these recommendations in practice.

Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.

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 performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them. FINDINGS: The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action. CONCLUSION: The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.

Treatment Patterns and Unmet Needs in the Management of Alopecia Areata: Results of a Physician's Survey in the Middle East.

INTRODUCTION: Alopecia areata (AA) is an autoimmune disease characterized by nonscarring hair loss involving the scalp, face, and/or body. Literature on the prevalence, patient characteristics, management approaches, and challenges faced by patients with AA across the Middle East is limited. Therefore, a greater understanding of the current AA landscape within the region is needed. This cross-sectional study surveyed dermatologists from four countries to assess dermatologists' perspectives on the prevalence of AA within the Middle East, as well as patient characteristics, unmet needs, and management strategies. METHODS: This blinded, quantitative, observational study surveyed practicing dermatologists in Egypt, Lebanon, Saudi Arabia, and the United Arab Emirates. The survey was conducted between September 2021 and January 2022 and comprised 47 closed-ended, multiple-choice questions as well as Likert scale responses. These questions assessed the characteristics of physicians and the patients in their practices, physicians' familiarity with treatment, and physicians' treatment approaches. RESULTS: The estimated prevalence of AA varied across the region. Across all age groups treated for AA, the majority of patients had AA of mild severity (pediatric: 63%; adolescent: 60%; adult: 54%) and the scalp was reported as the most affected area (65%). Potent topical corticosteroids were the most frequently used treatment for mild to moderate and severe AA (92% and 78%, respectively). There was a lack of awareness of investigative treatments, with only 33% of dermatologists aware of these options. The greatest unmet needs in treating AA included long-term disease control, improved efficacy, faster onset of action, and better safety profiles (62%, 53%, 52%, and 51%, respectively). CONCLUSIONS: This study provided insight into the diagnosis and management of AA in the Middle East. Treatment strategies were similar regardless of the severity of AA. Long-term disease control and improved efficacy and safety profiles were identified as key unmet needs in the treatment of AA.

Patterns of Clinical Management of Atopic Dermatitis: A Survey of Three Physician Specialties in the Middle East.

INTRODUCTION: Atopic dermatitis (AD) is a complex inflammatory disease of the skin that has a significant impact on the well-being of patients and their families. The prevalence of AD has increased in developing countries and regions, including the Middle East. Despite similarities in the presentation of the disease, there is a lack of consistent management and treatment guidelines for AD. The objective of this survey was to develop further insight into the management patterns of AD from dermatologists, pediatricians, and primary care/family medicine physicians in the Middle Eastern nations of Egypt, Lebanon, the United Arab Emirates, and Saudi Arabia. METHODS: The survey was composed of 47 closed-ended, multiple-choice questions. These questions assessed physician and patient characteristics and treatment familiarity and approach. RESULTS: A total of 400 physicians, including 200 dermatologists, 100 pediatricians, and 100 primary care physicians, participated in the survey. The findings provide insight into the management of AD by physician specialty within the region. A diverse array of management approaches was observed for both referral patterns and treatments for AD in the Middle East. CONCLUSION: The diversity of management tactics highlights the lack of a standard approach for the management of AD throughout the Middle East.

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

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 learning algorithms to predict the likelihood of an infection and subsequent sepsis. METHODS: This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion. RESULTS: Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01. CONCLUSION: The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.

Nebulized Versus IV Amikacin as Adjunctive Antibiotic for Hospital and Ventilator-Acquired Pneumonia Postcardiac Surgeries: A Randomized Controlled Trial.

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: Prospective, randomized, controlled study on surgical patients divided into two groups. SETTING: Postcardiac surgery ICU. INTERVENTIONS: The first gtroup was administered IV amikacin 20 mg/kg once daily. The second group was prescribed amikacin nebulizer 400 mg twice daily. Both groups were co-administered IV piperacillin/tazobactam empirically. PATIENTS: Recruited patients were diagnosed by either hospital-acquired pneumonia or ventilator-associated pneumonia where 56 (42.1%) patients were diagnosed with hospital-acquired pneumonia, 51 (38.34%) patients were diagnosed with early ventilator-associated pneumonia, and 26 (19.54%) patients with late ventilator-associated pneumonia. MEASUREMENTS AND MAIN RESULTS: Clinical cure in both groups assessed on day 7 of treatment was the primary outcome. Efficacy was additionally evaluated through assessing the length of hospital stay, ICU stay, days on amikacin, days on mechanical ventilator, mechanical ventilator-free days, days to reach clinical cure, and mortality rate. Lower nephrotoxicity in the nebulized group was observed through significant preservation of kidney function (p < 0.001). Although both groups were comparable regarding length of hospital stay, nebulizer group showed shorter ICU stay (p = 0.010), lower number of days to reach complete clinical cure (p = 0.001), fewer days on mechanical ventilator (p = 0.035), and fewer days on amikacin treatment (p = 0.022). CONCLUSION: Nebulized amikacin showed better clinical cure rates, less ICU stay, and fewer days to reach complete recovery compared to IV amikacin for surgical patients with nosocomial pneumonia. It is also a less nephrotoxic option associated with less deterioration in kidney function.