Health technology assessment of medical devices: a survey of non-European union agencies.

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tThis is the final version of the article. Available from Cambridge University Press via the DOI in this record.OBJECTIVES: The aim of this study was to review and compare current health technology assessment (HTA) activities for medical devices across non-European Union HTA agencies. METHODS: HTA activities for medical devices were evaluated from three perspectives: organizational structure, processes, and methods. Agencies were primarily selected upon membership of existing HTA networks. The data collection was performed in two stages: stage 1-agency Web-site assessment using a standardized questionnaire, followed by review and validation of the collected data by a representative of the agency; and stage 2-semi-structured telephone interviews with key informants of a sub-sample of agencies. RESULTS: In total, thirty-six HTA agencies across twenty non-EU countries assessing medical devices were included. Twenty-seven of thirty-six (75 percent) agencies were judged at stage 1 to have adopted HTA-specific approaches for medical devices (MD-specific agencies) that were largely organizational or procedural. There appeared to be few differences in the organization, process and methods between MD-specific and non-MD-specific agencies. Although the majority (69 percent) of both categories of agency had specific methods guidance or policy for evidence submission, only one MD-specific agency had developed methodological guidelines specific to medical devices. In stage 2, many MD-specific agencies cited insufficient resources (budget, skilled employees), lack of coordination (between regulator and reimbursement bodies), and the inability to generalize findings from evidence synthesis to be key challenges in the HTA of medical devices. CONCLUSIONS: The lack of evidence for differentiation in scientific methods for HTA of devices raises the question of whether HTA needs to develop new methods for medical devices but rather adapt existing methodological approaches. In contrast, organizational and/or procedural adaptation of existing HTA agency frameworks to accommodate medical devices appear relatively commonplace.This study was supported by a research grant from the European Community’s Seventh Framework Program (FP7 - HEALTH Grant Agreement no. 305694). The sponsor had no role in the study design, collection and analysis of data, writing of the report, or submission of the paper for publication. The authors wish to thank all interviewees and agencies’ assessment forms verifiers for their invaluable contribution to the completion of this study

Integrating Mobile Devices with Cohort Analysis into Personalised Weather-Based Healthcare

Mobile healthcare applications can empower users to self-monitor their health conditions without the need to visit any medical centre. However, the lack of attention on engagement aspects of mobile healthcare applications often result in users choosing to uninstall the application after the first usage experience. This results in failure of effective prolonged personalised healthcare, especially for users with chronic disease related to weather conditions such as asthma and eczema which require long-term monitoring and self-care. Therefore, this paper aims to identify the pattern of application user engagement with a weather-based mobile healthcare application through cohort retention analysis. Enhancement features for improving the engagement of personalised healthcare can provide meaningful insight. The proposed application allows the patient to conduct disease control tests to check the severity of their condition on a daily basis. To measure the application engagement, we distribute the mobile application designed for primary testing over a period of ten days. Based on the primary testing, data related to retention rate and the number of control test reported were collected via Firebase Analytic to determine the application engagement. Subsequently, we apply cohort analysis using a machine learning clustering technique implemented in Python to identify the pattern of the engagement by application users. Finally, useful insights were analysed and implemented as enhancement features within the application for improving the personalised weather-based mobile healthcare. The findings in this paper can assist machine learning facilitators design effective use policies for weather-based mobile healthcare with fundamental knowledge enhanced with personalisation and user engagement

Comparing the Efficiency of Hospitals in Italy and Germany: Nonparametric Conditional Approach Based on Partial Frontier

Traditional nonparametric frontier techniques to measure hospital efficiency have been criticized for their deterministic nature and the inability to incorporate external factors into the analysis. Moreover, efficiency estimates represent a relative measure meaning that the implications from a hospital efficiency analysis based on a single-country dataset are limited by the availability of suitable benchmarks. Our first objective is to demonstrate the application of advanced nonparametric methods that overcome the limitations of the traditional nonparametric frontier techniques. Our second objective is to provide guidance on how an international comparison of hospital efficiency can be conducted using the example of two countries: Italy and Germany. We rely on a partial frontier of order-m to obtain efficiency estimates robust to outliers and extreme values. We use the conditional approach to incorporate hospital and regional characteristics into the estimation of efficiency. The obtained conditional efficiency estimates may deviate from the traditional unconditional efficiency estimates, which do not account for the potential influence of operational environment on the production possibilities. We nonparametrically regress the ratios of conditional to unconditional efficiency estimates to examine the relation of hospital and regional characteristics with the efficiency performance. We show that the two countries can be compared against a common frontier when the challenges of international data compatibility are successfully overcome. The results indicate that there are significant differences in the production possibilities of Italian and German hospitals. Moreover, hospital characteristics, particularly bed-size category, ownership status, and specialization, are significantly related to differences in efficiency performance across the analyzed hospitals

Using nonparametric conditional approach to integrate quality into efficiency analysis: empirical evidence from cardiology departments

Health care providers are under pressure to improve both efficiency and quality. The two objectives are not always mutually consistent, because achieving higher levels of quality may require additional resources. The aim of this study is to demonstrate how the nonparametric conditional approach can be used to integrate quality into the analysis of efficiency and to investigate the mechanisms through which quality enters the production process. Additionally, we explain how the conditional approach relates to other nonparametric methods that allow integrating quality into efficiency analysis and provide guidance on the selection of an appropriate methodology. We use data from 178 departments of interventional cardiology and consider three different measures of quality: patient satisfaction, standardized mortality ratio, and patient radiation exposure. Our results refute the existence of a clear trade-off between efficiency and quality. In fact, the impact of quality on the production process differs according to the utilized quality measure. Patient satisfaction does not affect the attainable frontier but does have an inverted U-shaped effect on the distribution of inefficiencies; mortality ratio negatively impacts the attainable frontier when the observed mortality more than doubles the predicted mortality; and patient radiation exposure is not associated with the production process