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In recognition of the 10 year anniversary of the incorporation of the internet search firm Google, the journal ''Nature'' issued a special supplement on big data and what the availability of large datasets meant and will mean for scientists and researchers.<ref name="MillerComm08">{{cite journal |title=Community cleverness required |journal=Nature |author=Miller, E. |volume=455 |issue=1 |year=2008 |doi=10.1038/455001a}}</ref> In particular, the supplement focused on the opportunities that will be possible when issues such as interoperable [[Information management|data infrastructures]], [[Information security|security]], data standardization, storage and transfer requirements, and data governance are resolved. Now, nearly 10 years later, users of big data—characterized by the 5 Vs (huge volume, high velocity, high variety, low veracity, and high value)—still encounter the issues presented in the ''Nature'' special supplement.<ref name="KruseChallenges16">{{cite journal |title=Challenges and Opportunities of Big Data in Health Care: A Systematic Review |journal=JMIR Medical Informatics |author=Kruse, C.S.; Goswamy, R.; Raval, Y.; Marawi, S. |volume=4 |issue=4 |page=e38 |year=2016 |doi=10.2196/medinform.5359 |pmid=27872036 |pmc=PMC5138448}}</ref> In particular, the primary challenges to utilizing big data center around the diversity of data types (variety), the resources required to handle data collection, storage and processing (velocity), and uncertainties inherent in mixing and cleaning data from varied data streams that generates unpredictability in the data (veracity).<ref name="JinSignif15">{{cite journal |title=Significance and Challenges of Big Data Research |journal=Big Data Research |author=Jin, X.; Wah, B.W.; Cheng, X.; Wang, Y. |volume=2 |issue=2 |year=2015 |doi=10.1016/j.bdr.2015.01.006}}</ref>
In recognition of the 10 year anniversary of the incorporation of the internet search firm Google, the journal ''Nature'' issued a special supplement on big data and what the availability of large datasets meant and will mean for scientists and researchers.<ref name="MillerComm08">{{cite journal |title=Community cleverness required |journal=Nature |author=Miller, E. |volume=455 |issue=1 |year=2008 |doi=10.1038/455001a}}</ref> In particular, the supplement focused on the opportunities that will be possible when issues such as interoperable [[Information management|data infrastructures]], [[Information security|security]], data standardization, storage and transfer requirements, and data governance are resolved. Now, nearly 10 years later, users of big data—characterized by the 5 Vs (huge volume, high velocity, high variety, low veracity, and high value)—still encounter the issues presented in the ''Nature'' special supplement.<ref name="KruseChallenges16">{{cite journal |title=Challenges and Opportunities of Big Data in Health Care: A Systematic Review |journal=JMIR Medical Informatics |author=Kruse, C.S.; Goswamy, R.; Raval, Y.; Marawi, S. |volume=4 |issue=4 |page=e38 |year=2016 |doi=10.2196/medinform.5359 |pmid=27872036 |pmc=PMC5138448}}</ref> In particular, the primary challenges to utilizing big data center around the diversity of data types (variety), the resources required to handle data collection, storage and processing (velocity), and uncertainties inherent in mixing and cleaning data from varied data streams that generates unpredictability in the data (veracity).<ref name="JinSignif15">{{cite journal |title=Significance and Challenges of Big Data Research |journal=Big Data Research |author=Jin, X.; Wah, B.W.; Cheng, X.; Wang, Y. |volume=2 |issue=2 |year=2015 |doi=10.1016/j.bdr.2015.01.006}}</ref>
Nevertheless, within the health care sector, despite these challenges, big data also promises great opportunities to improve quality of health care delivery, population management, early detection of disease, decision-making, and cost reduction.<ref name="NambiarALook13">{{cite journal |title=A look at challenges and opportunities of big data analytics in healthcare |journal=Proceedings from the 2013 IEEE International Conference on Big Data |author=Nambiar, R.; Bhardwaj, R.; Sethi, A.; Vargheese, R. |pages=17–22 |year=2013 |doi=10.1109/BigData.2013.6691753}}</ref> Major contributors to the explosion of big data are investments in information technology (IT), such as increased adoption of [[electronic medical record]] systems<ref name="JosephHITECH14">{{cite journal |title=HITECH spurs EHR vendor competition and innovation, resulting in increased adoption |journal=American Journal of Managed Care |author=Joseph, S.; Sow, M.; Furukawa, M.F. et al. |volume=20 |issue=9 |page=734-40 |year=2014 |pmid=25365748}}</ref>, and the creation of health information exchanges (HIEs)<ref name="RoskiCreating14">{{cite journal |title=Creating value in health care through big data: Opportunities and policy implications |journal=Health Affairs |author=Roski, J.; Bo-Linn, G.W.; Andrews, T.A. |volume=33 |issue=7 |page=1115-22 |year=2014 |doi=10.1377/hlthaff.2014.0147 |pmid=25006136}}</ref> which facilitate sharing of electronic data and information between health care organizations.<ref name="GrovesTheBig13">{{cite web |url=https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/the%20big%20data%20revolution%20in%20us%20health%20care/the_big_data_revolution_in_healthcare.ashx |title=The 'big data' revolution in healthcare: Accelerating value and innovation |author=Groves, P.; Kayyali, B.; Knott, D.; Van Kuiken, S. |publisher=McKinsey & Company |date=January 2013}}</ref> While the focus of HIEs has been on sharing patient information between clinics, [[hospital]]s, pharmacies, [[Laboratory|laboratories]], and payers, public health agencies (PHAs) are increasingly included in HIEs.<ref name="ShahInter16">{{cite journal |title=Interoperability of Information Systems Managed and Used by the Local Health Departments |journal=Journal of Public Health Management and Practice |author=Shah, G.H.; Leider, J.P.; Luo, H.; Kaur, R. |volume=22 |issue=Suppl. 6 |page=S34-S43 |year=2016 |doi=10.1097/PHH.0000000000000436 |pmid=27684616 |pmc=PMC5049946}}</ref> PHA participation in a HIE provides a single stream of data collated across disparate systems and sources for public health.


==References==
==References==

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Full article title Big data in the era of health information exchanges: Challenges and opportunities for public health
Journal Informatics
Author(s) Baseman, Janet G.; Revere, Debra; Painter, Ian
Author affiliation(s) University of Washington
Primary contact Email: jbaseman at uw dot edu
Editors Ge, Mouzhi; Dohnal, Vlastislav
Year published 2017
Volume and issue 4(4)
Page(s) 39
DOI 10.3390/informatics4040039
ISSN 2227-9709
Distribution license Creative Commons Attribution 4.0 International
Website http://www.mdpi.com/2227-9709/4/4/39/htm
Download http://www.mdpi.com/2227-9709/4/4/39/pdf (PDF)
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Public health surveillance of communicable diseases depends on timely, complete, accurate, and useful data that are collected across a number of health care and public health systems. Health information exchanges (HIEs) which support electronic sharing of data and information between health care organizations are recognized as a source of "big data" in health care and have the potential to provide public health with a single stream of data collated across disparate systems and sources. However, given these data are not collected specifically to meet public health objectives, it is unknown whether a public health agency’s (PHA’s) secondary use of the data is supportive of or presents additional barriers to meeting disease reporting and surveillance needs. To explore this issue, we conducted an assessment of big data that is available to a PHA—laboratory test results and clinician-generated notifiable condition report data—through its participation in an HIE.

Keywords: big data, communicable diseases, data mining, data quality, epidemiology, health information exchange, infectious diseases, population surveillance, public health

Introduction

We evaluated two datasets—for sexually-transmitted infections (STIs) and non-STIs—for the time period of January 1, 2012 to September 15, 2013 used by a PHA that is part of one of the largest and oldest HIE infrastructures in the U.S. The two datasets were independently analyzed for their data quality, utility, and appropriateness for meeting public health surveillance objectives: (1) timeliness, defined as the difference between earliest date of a disease report and date the report is received at the PHA; (2) volume, defined as the number of disease report cases received by the PHA; and (3) completion, defined as the number of days to close a disease case report.

Our assessment uncovered the following challenges for effective utilization of big data by public health:

  1. While PHAs almost exclusively rely on secondary use data for surveillance, big data that has been collected for clinical purposes omits data fields of high value for public health.
  2. Big data is not always smart data, especially when the context within which the data is collected is absent.
  3. Data collected by disparate, varying systems and sources can introduce uncertainties and limit trustworthiness in the data, which may diminish its value for public health purposes.
  4. The process by which data is obtained needs to be evident in order for big data to be useful to public health.
  5. Big data for public health purposes needs to answer both "what" and "why" questions.

Despite these and other issues—such as measurement error and confounding, well-known challenges to both big and small data—strategies traditionally employed by public health epidemiologists and other public health professionals can uncover limitations and contribute to the design of solutions in collection, integration, warehousing, and analysis of big data so its value and utility to public health can be optimized.

In recognition of the 10 year anniversary of the incorporation of the internet search firm Google, the journal Nature issued a special supplement on big data and what the availability of large datasets meant and will mean for scientists and researchers.[1] In particular, the supplement focused on the opportunities that will be possible when issues such as interoperable data infrastructures, security, data standardization, storage and transfer requirements, and data governance are resolved. Now, nearly 10 years later, users of big data—characterized by the 5 Vs (huge volume, high velocity, high variety, low veracity, and high value)—still encounter the issues presented in the Nature special supplement.[2] In particular, the primary challenges to utilizing big data center around the diversity of data types (variety), the resources required to handle data collection, storage and processing (velocity), and uncertainties inherent in mixing and cleaning data from varied data streams that generates unpredictability in the data (veracity).[3]

Nevertheless, within the health care sector, despite these challenges, big data also promises great opportunities to improve quality of health care delivery, population management, early detection of disease, decision-making, and cost reduction.[4] Major contributors to the explosion of big data are investments in information technology (IT), such as increased adoption of electronic medical record systems[5], and the creation of health information exchanges (HIEs)[6] which facilitate sharing of electronic data and information between health care organizations.[7] While the focus of HIEs has been on sharing patient information between clinics, hospitals, pharmacies, laboratories, and payers, public health agencies (PHAs) are increasingly included in HIEs.[8] PHA participation in a HIE provides a single stream of data collated across disparate systems and sources for public health.


References

  1. ↑ Miller, E. (2008). "Community cleverness required". Nature 455 (1). doi:10.1038/455001a. 
  2. ↑ Kruse, C.S.; Goswamy, R.; Raval, Y.; Marawi, S. (2016). "Challenges and Opportunities of Big Data in Health Care: A Systematic Review". JMIR Medical Informatics 4 (4): e38. doi:10.2196/medinform.5359. PMC PMC5138448. PMID 27872036. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5138448. 
  3. ↑ Jin, X.; Wah, B.W.; Cheng, X.; Wang, Y. (2015). "Significance and Challenges of Big Data Research". Big Data Research 2 (2). doi:10.1016/j.bdr.2015.01.006. 
  4. ↑ Nambiar, R.; Bhardwaj, R.; Sethi, A.; Vargheese, R. (2013). "A look at challenges and opportunities of big data analytics in healthcare". Proceedings from the 2013 IEEE International Conference on Big Data: 17–22. doi:10.1109/BigData.2013.6691753. 
  5. ↑ Joseph, S.; Sow, M.; Furukawa, M.F. et al. (2014). "HITECH spurs EHR vendor competition and innovation, resulting in increased adoption". American Journal of Managed Care 20 (9): 734-40. PMID 25365748. 
  6. ↑ Roski, J.; Bo-Linn, G.W.; Andrews, T.A. (2014). "Creating value in health care through big data: Opportunities and policy implications". Health Affairs 33 (7): 1115-22. doi:10.1377/hlthaff.2014.0147. PMID 25006136. 
  7. ↑ Groves, P.; Kayyali, B.; Knott, D.; Van Kuiken, S. (January 2013). "The 'big data' revolution in healthcare: Accelerating value and innovation". McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/the%20big%20data%20revolution%20in%20us%20health%20care/the_big_data_revolution_in_healthcare.ashx. 
  8. ↑ Shah, G.H.; Leider, J.P.; Luo, H.; Kaur, R. (2016). "Interoperability of Information Systems Managed and Used by the Local Health Departments". Journal of Public Health Management and Practice 22 (Suppl. 6): S34-S43. doi:10.1097/PHH.0000000000000436. PMC PMC5049946. PMID 27684616. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5049946. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. Several URL from the original were dead, and more current URLs were substituted.

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