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.
The objective of this study was to describe a draft response plan for the tiered triage, treatment, or transportation of 400 adult and pediatric victims (50/million population) of a burn disaster for the first 3 to 5 days after injury using regional resources. Review of meeting minutes and the 11 deliverables of the draft response plan was performed. The draft burn disaster response plan developed for NYC recommended: 1) City hospitals or regional burn centers within a 60-mile distance be designated as tiered Burn Disaster Receiving Hospitals (BDRH); 2) these hospitals be divided into a four-tier system, based on clinical resources; and 3) burn care supplies be provided to Tier 3 nonburn centers. Existing burn center referral guidelines were modified into a hierarchical BDRH matrix, which would vector certain patients to local or regional burn centers for initial care until capacity is reached; the remainder would be cared for in nonburn center facilities for up to 3 to 5 days until a city, regional, or national burn bed becomes available. Interfacility triage would be coordinated by a central team. Although recommendations for patient transportation, educational initiatives for prehospital and hospital providers, city-wide, interfacility or interagency communication strategies and coordination at the State or Federal levels were outlined, future initiatives will expound on these issues. An incident resulting in critically injured burn victims exceeding the capacity of local and regional burn center beds may be a reality within any community and warrants a planned response. To address this possibility within New York City, an initial draft of a burn disaster response has been created. A scaleable plan using local, state, regional, or federal health care and governmental institutions was developed.
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.
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