Comparison of Levonorgestrel-releasing Intrauterine System (LNG-IUS) against Laparoscopic Assisted Supracervical Hysterectomy (LASH) for menorrhagia treatment: An economic evaluation.

BACKGROUND: This economic evaluation and literature review was conducted with the primary aim to compare the cost-effectiveness of laparoscopic assisted supracervical hysterectomy (LASH) with NICE's gold-standard treatment of Levonorgestrel-releasing intrauterine system (LNG-IUS) for menorrhagia. MATERIALS AND METHODS: A cost-utility analysis was conducted from an NHS perspective, using data from two European studies to compare the treatments. Individual costs and benefits were assessed within one year of having the intervention. An Incremental Cost-Effectiveness Ratio (ICER) was calculated, followed by sensitivity analysis. Expected Quality Adjusted Life Years (QALYS) and costs to the NHS were calculated alongside health net benefits (HNB) and monetary net benefits (MNB). RESULTS: A QALY gain of 0.069 was seen in use of LNG-IUS compared to LASH. This yielded a MNB between -£44.99 and -£734.99, alongside a HNB between -0.0705 QALYs and -0.106 QALYS. Using a £20,000-£30,000/QALY limit outlined by NICE,this showed the LNG-IUS to be more cost-effective than LASH, with LASH exceeding the upper bound of the £30,000/QALY limit. Sensitivity analysis lowered the ICER below the given threshold. CONCLUSIONS: The ICER demonstrates it would not be cost-effective to replace the current gold-standard LNG-IUS with LASH, when treating menorrhagia in the UK. The ICER's proximity to the threshold and its high sensitivity alludes to the necessity for further research to generate a more reliable cost-effectiveness estimate. However, LASH could be considered as a first line treatment option in women with no desire to have children.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units.

INTRODUCTION: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. METHOD: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. RESULTS: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. CONCLUSIONS: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review.

BACKGROUND: Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. METHODS: The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. RESEARCH: 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. CONCLUSION: Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.

Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.