ImportancePain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine.ObjectiveThe primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots.Data sourcesWe conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021.Study selectionOur review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review.Data extraction and synthesisThe AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model.Main outcome(s) and measure(s)Before data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue.Results38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: −0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and −0.02 (95% CI: −0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results (I2 = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway).Conclusions and relevanceThe use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.
To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address ‘system gaps’ and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, ‘holistically’ developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
The UK’s National Health Service (NHS) has been dealing with a shortage in the nursing workforce for the past few decades. With the ongoing COVID-19 pandemic and post-Brexit effects, it is important now more than ever to concentrate on recruiting new staff and retaining current staff in the National Health Service. The increasing demand for mental health services stresses the importance of prioritising recruitment of mental health nurses. One of the main strategies being implemented to combat this shortage is the recruitment of internationally trained mental health nurses. Whilst this is a favourable solution, the multiple challenges this proposal brings makes it hard for the National Health Service to practically implement this to increase staff numbers. In this discursive position paper, we consider the difficulties the National Health Service is currently facing in terms of recruiting mental health nurses and then discuss the importance of and need for international recruitment including the strategies that are currently being implemented. The challenges and obstacles associated with this proposed resolution will also be addressed.
UNSTRUCTURED Background: Mental illness has a high disease burden within the UK, attributing to 22.8% in comparison to Cancer (15.9%) and Cardiovascular disease (16.2%). Costs of mental illness in England have been evaluated at £105.2 billion each year. This burden could be reduced by effective use of Electronic Health Records that could provide vital information around diagnosis, prevalence and incidence of mental illnesses to better understand the nuances of clinical and patient reported outcomes. To better evaluate some of the technical methods that could be better used, we explored Natural Language Processing. Objective: Our primary objective was to evaluate the use of Natural Language Processing methods and it’s association with unstructured EHR text data from U.K.-CRIS. Methods: We used a descriptive methodology to demonstrate the use of NLP and validated the method using Southern Health NHS Trust electronic health data. Conclusions: We can conclude that the method used is suitable for the mental health service. However, to generalize our findings, a wider validation study across mental health organisations in the UK would be required.
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