Data Science in Healthcare

Data Science in HealthcareThe confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to develop prescriptive analytics, to improve healthcare personalization and precision medicine, and to automate the reporting of health data for clinical decisions. 1Data science in health care has seen recent and rapid progress along 3 paths:(1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices 2 ; ( 2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing 3 ; and(3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data. 4As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation?Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible?Will healthcare systems incorporate and use big data especially from new publically and patient-generated sources?How will physicians and researchers learn from new open-sourced data and big-data analytics?And ultimately, How can they acquire the skills to create a knowledge translation in data sciences? 5 Opportunities and Challenges for the Early Career InvestigatorPracticing in an era of continuous payment reform and decline in research funding, early career investigators are challenged to keep up with the accelerating pace of change in medicine, all while being expected to provide meaningful contributions through productive clinical, educational, and research experiences. 6In this perspective, we aim to highlight how data science can catalyze professional advancement and discuss the implications of big data, open access, and data analytics through 4 main categories for the early career investigator (Figure).These include the following: (1) the evolution and expansion of conventional training programs to incorporate data sciences, (2) changing structure and composition of research teams, (3) new and emerging funding opportunities for data science studies, and (4) academic reward and advancement in the era of open and big data.We aim to provide strategies for how young investigators can maximize benefits and minimize risks through new opportunities afforded by developments in data science.