Practical approaches to achieving real-world study data representative of the target population

Abstract

surveillance data collection. Such are defined as an aircraft without a human pilot on board, operated either autonomously by computer or under remote control by a human pilot. Methods: We performed a targeted literature search for medical applications of UAS and rank-ordered strengths and weaknesses according to emerging applications and corresponding difficulty, feasibility and cost. Results: Based on secondary sources, we report conceptual factors that can contribute to the practicality and efficiency of UAS in emergency medical situations. These were 1) frequency of occurrence, 2) time-sensitivity of occurrence, 3) rurality and complex terrain, 4) financial impact and 5) cultural acceptance. The results of our matrix point to a gradation of accepted uses for UAS with the variance in geographical location and urgency directly relating to an increase in operation costs. It is well known that natural disasters are increasing in frequency and intensity. Salient platforms for using UAS in medical delivery would be in the areas of natural and combative disaster relief. During these occurrences the use of UAS to aid in the medical relief could be a great asset. ConClusion: Our model illustrates how Big Data can be leveraged to improve ongoing quality and efficiency of UAS-delivered medical supplies, reduce time for delivery of supplies during times of natural disasters, and thus eschew our reliance on manned aircraft to assist in critical and non-critical medical operations.objeCtive: We propose a paradigm and ranking system for potential medical applications of unmanned aircraft systems (collectively UAS). Over the past three decades, UAS have become a vital component to our armed forces, used notably for combat but also commonly used for work in intelligence, reconnaissance and

Do we need real world data? Polish ISPOR Chapter Therapeutic Programs, Pharmaceutical Care and Pharmaceutical Law Section (TPPCPL) initial discussion

“…E. Gemmen, L. Parmenter and A.B. Mendelsohn look at the most effective approaches to achieve data from real world being representative of the target population 16 .…”