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BACKGROUND AND AIMS: The NICE Highly Specialised Technology (HST) programme evaluates interventions for very rare conditions within the UK. This review aimed to analyse previous NICE HST appraisals and determine commonly used methods to overcome uncertainties relating to health-related quality of life (HRQoL) and disease burden for people with rare diseases and their caregivers. The review also aimed to identify areas where further methodological development is required.
APPROACH AND RESULTS: A targeted review of all previous NICE HST appraisals published by the 28th February 2022, in which at least one committee meeting had taken place, was conducted. A total of 24 appraisals were included (17 fully completed and seven ongoing). Data were extracted by one reviewer. The evidence review group (ERG) and committee comments were compared against the NICE reference case and synthesised to identify the following methodological uncertainties that occurred most commonly: using alternatives to the EuroQol-5 Dimension (EQ-5D), sourcing HRQoL data from single-arm studies, measuring caregiver disutilities and estimating disease burden.
CONCLUSIONS: This review has highlighted the need for new methodology to reflect the impact of the diseases on people with rare diseases and their families. The review identified the following methodological requirements: alternative approaches that should be used when EQ-5D is not appropriate, methods to evaluate paediatric HRQoL and methods to quantify disease burden. This review also highlights the need to establish clear recommendations on the estimation of utilities across different rare diseases.
A vision-based patient monitoring system (VBPMS), Oxevision, has been introduced in approximately half of National Health Service (NHS) mental health trusts in England. A VBPMS is an assistive tool that supports patient safety by enabling non-contact physiological and physical monitoring. The system aims to help staff deliver safer, higher-quality and more efficient care. This paper summarises the potential health economic impact of using a VBPMS to support clinical practice in two inpatient settings: acute mental health and older adult mental health services. The economic model used a cost calculator approach to evaluate the potential impact of introducing a VBPMS into clinical practice, compared with clinical practice without a VBPMS. The analysis captured the cost differences in night-time observations, one-to-one continuous observations, self-harm incidents, and bedroom falls at night, including those resulting in A&E visits and emergency service callouts. The analysis is based on before and after studies conducted at five mental health NHS trusts, including acute mental health and older adult mental health services. Our findings indicate that the use of a VBPMS results in more efficient night-time observations and reductions in one-to-one observations, self-harm incidents, bedroom falls at night, and A&E visits and emergency service callouts from night-time falls. Substantial staff time in acute mental health and older adult mental health services is spent performing night-time observations, one-to-one observations, and managing incidents. The use of a VBPMS could lead to cost savings and a positive return on investment for NHS mental health trusts. The results do not incorporate all of the potential benefits associated with the use of a VBPMS, such as reductions in medication and length of hospital stay, plus the potential to avoid adverse events which would otherwise have a detrimental impact on a patient's quality of life.
INTRODUCTION
Single use medical devices' environmental impact is known to generate sizable carbon emissions over a life cycle compared to a reusable equivalent. The SHTG have recognised the need to evaluate this health technology carbon hotspot as part of the HTA. Specifically, this environmental assessment was appropriate for parallel evaluation alongside a health economic analysis to support the decision-making process.
METHODS
Through the application of HTA and environmental management principles and reporting, the Greenhouse Gas (GHG) Protocols, ISO14040 and Publicly Available Specification 2050, a carbon footprint of single use rhinolaryngoscopes was compared with a reusable equivalent. Process activity data, real world data and secondary data were used to quantify carbon intensity. Data were extrapolated to estimate national GHG emissions and waste volumes. The technologies were categorized using thresholds based on the functional unit, to strengthen support for the HTA decision making process. Sensitivity analyses were performed. Further recommendations were made to promote the use of more sustainable health technologies.
RESULTS
The carbon emissions of a single use flexible rhinolaryngoscope compared to its reusable equivalent was 6.03kgCO2e and 3.26kgCO2e respectively. Data extrapolation (2264 procedures per annum) reveals the annual environmental impact of this technology, throughout NHS Scotland, to be 13,652kgCO2e and 7,381kgCO2e, for the single use and reusable devices, respectively. Raw material acquisition, the use of personal protective equipment and transportation were health technology carbon hotspots. The estimated waste volumes are 12.58 tonnes and negligible volumes of clinical waste per annum for the single use versus reusable equivalent, respectively. This pilot study recommends the use a reusable equivalent in this setting.
CONCLUSIONS
Aligned with NHS net zero targets, these findings confirm the need to perform a parallel environmental assessment of technologies alongside a HTA. LCA is a useful framework to comprehensively assess environmental impact of health technology. Data constraints can be overcome through the use of hybrid methodologies, aligned with HTA principles including transparency and reproducibility to enable sustainable HTA decision making.
Statistical analyses are common practice in the modern world. While statistics is a discipline in its own right, statistical methods are employed across a vast and growing range of fields. It is, therefore, essential that the sharing of statistical information can be adapted to meet the needs of a range of audiences.
A web application (app) is an app that is accessed using a web browser. An advantage of web apps is their accessibility, allowing multiple users to access the same app from different locations, devices and software platforms. Shiny is a free, open-source R package for developing web apps. Shiny provides an excellent opportunity to share results from analyses with others in an interactive, easy-to-build web app.
A Shiny app contains a user interface (ui) and a server function. These two objects interact with each other to create an interactive Shiny app. The ui element controls the layout and appearance of your app whereas the server object performs the necessary computations. While Shiny dashboards can be built using the Shiny package alone, the Shinydashboard package can be used to make it much easier to create a more visually appealing dashboard. This package enables the use of complex dashboards with a variety of complex layouts.
While producing a web app in Shiny requires good knowledge of the R programming language, for those already conducting their analyses in R, Shiny offers the opportunity to create a bespoke, high-quality dashboard that is accessible to the end user.
No abstract available.
