Reuse of clinical data. Safran, C. Yearbook of Medical Informatics, 9:52–54, August, 2014.
doi  abstract   bibtex   
OBJECTIVES: To provide an overview of the benefits of clinical data collected as a by-product of the care process, the potential problems with large aggregations of these data, the policy frameworks that have been formulated, and the major challenges in the coming years. METHODS: This report summarizes some of the major observations from AMIA and IMIA conferences held on this admittedly broad topic from 2006 through 2013. This report also includes many unsupported opinions of the author. RESULTS: The benefits of aggregating larger and larger sets of routinely collected clinical data are well documented and of great societal benefit. These large data sets will probably never answer all possible clinical questions for methodological reasons. Non-traditional sources of health data that are patient-sources will pose new data science challenges. CONCLUSIONS: If we ever hope to have tools that can rapidly provide evidence for daily practice of medicine we need a science of health data perhaps modeled after the science of astronomy.
@article{safran_reuse_2014,
	title = {Reuse of clinical data},
	volume = {9},
	issn = {2364-0502},
	doi = {10.15265/IY-2014-0013},
	abstract = {OBJECTIVES: To provide an overview of the benefits of clinical data collected as a by-product of the care process, the potential problems with large aggregations of these data, the policy frameworks that have been formulated, and the major challenges in the coming years.
METHODS: This report summarizes some of the major observations from AMIA and IMIA conferences held on this admittedly broad topic from 2006 through 2013. This report also includes many unsupported opinions of the author.
RESULTS: The benefits of aggregating larger and larger sets of routinely collected clinical data are well documented and of great societal benefit. These large data sets will probably never answer all possible clinical questions for methodological reasons. Non-traditional sources of health data that are patient-sources will pose new data science challenges.
CONCLUSIONS: If we ever hope to have tools that can rapidly provide evidence for daily practice of medicine we need a science of health data perhaps modeled after the science of astronomy.},
	language = {eng},
	journal = {Yearbook of Medical Informatics},
	author = {Safran, C.},
	month = aug,
	year = {2014},
	pmid = {25123722},
	pmcid = {PMC4287069},
	keywords = {Data Mining, Electronic Health Records, Evidence-Based Medicine, Humans, Medical Informatics, Societies, Medical, clinical informatics, database, outcomes research},
	pages = {52--54}
}

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