{'fraction_non_alphanumeric': 0.016361886429258902, 'fraction_numerical': 0.0, 'mean_word_length': 5.011560693641618, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Clinician value from big data: creating a path forwards

Whilst many in healthcare view the arrival of the era of big data as an overwhelmingly positive thing, there are some who refute that claim and increasingly point out the limitations of using big data derived datasets for clinical research in particular. In this paper we examine some of the challenges and constraints regarding access to data for clinicians and researchers, despite the collection and generation of vast amounts of data (big data) in the healthcare industry. We also briefly explore some of the challenges around identifying cohorts from, and performing analysis on, such datasets. As part of this we present on the latest developments with a custom designed search tool (The cohort discovery tool (CDT)) that allows such users flexibility in how they access a vast clinical data repository inside The REASON Discovery Platform®. We also examine some of the strengths and weaknesses of the tool and factors influencing its uptake by clinicians at its primary site.