Difference between revisions of "Public health data"

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Public Health data is collected from providers such as hospitals, clinics, private practices, laboratories, School Based Health Centers (SBHCs) Federally Qualified Health Centers (FQHCs, and other sources.  The types of data include disease, environmental, community treatment capacity and utilization, treatment coordination, and licensing and inspection.  A lot of public health work is based on secondary data, usually anonymous or pseudo anonymous, however, there are some core public health functions that require named personal health data. These functions include surveillance against emerging health threats and disease registries. 
  
  Public Health data is collected from providers such as hospitals, clinics, private practices, laboratories, School Based Health Centers (SBHCs) Federally Qualified Health Centers (FQHCs, and other sourcesThe types of data include disease, environmental, community treatment capacity and utilization, treatment coordination, and licensing and inspection.  A lot of public health work is based on secondary data, usually anonymous or pseudo anonymous, however, there are some core public health functions that require named personal health data. These functions include surveillance against emerging health threats and disease registries.   
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A complex web of data sharing rules affect the ability to share data including data sharing agreement contracts, legislative and administrative rules, and private data ownership (often partial).  It takes a lot of time and effort to change rules affecting data sharing, and the current approach is to negotiate individually for each type of usage.   
  
  A complex web of data sharing rules affect the ability to share data including data sharing agreement contracts, legislative and administrative rules, and private data ownership (often partial).  It takes a lot of time and effort to change rules affecting data sharing, and the current approach is to negotiate individually for each type of usage.   
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In addition to complex rules, funding sources also add to the complexity by often dictating data system architecture and functional priorities.  In Oregon for example most funding for Public Health data systems comes from federal sources.  One might think this would help to ensure a uniform architecture, and that federal funding for Public Health would come mostly from the offices of the Centers for Disease Control (CDC), but the sources are much more heterogeneousIn addition to many offices within the CDC, other major federal sources funding Public Health data systems include:  United States Department of Agriculture (USDA), Health Resources and Services Administration (HRSA), the Environmental Protection Agency (EPA), the Centers for Medicare and Medicaid Services (CMS,) the Office for the National Coordinator (ONC), and many others.  Each of these government funding sources typically demands conformance to their own “siloed” architectureThe complete lack of common architecture between the “Public Health Information Network” from the CDC and the “National Health Information Network” from ONC is an example.
  
  In addition to complex rules, funding sources also add to the complexity by often dictating data system architecture and functional priorities.  In Oregon for example most funding for Public Health data systems comes from federal sources.  One might think this would help to ensure a uniform architecture, and that federal funding for Public Health would come mostly from the offices of the Centers for Disease Control (CDC), but the sources are much more heterogeneous.  In addition to many offices within the CDC, other major federal sources funding Public Health data systems include:  United States Department of Agriculture (USDA), Health Resources and Services Administration (HRSA), the Environmental Protection Agency (EPA), the Centers for Medicare and Medicaid Services (CMS,) the Office for the National Coordinator (ONC), and many others.  Each of these government funding sources typically demands conformance to their own “siloed” architecture.  The complete lack of common architecture between the “Public Health Information Network” from the CDC and the “National Health Information Network” from ONC is an example.
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Given the complex government rules and conflicting information architectures, the private ownership and interests and privacy issues, we need federal and state government action to create an environment that adequately balances the health need for data sharing with these competing interests.  The lack of a patient identifier is an example of a major gap affecting linking patient specific data in the US.  By comparison, governments in other advanced countries such as Australia, Canada, New Zealand, Scandinavia, and the European Union have policies in place that deal with authentication of patients for healthcare purposes by using a unique patient identifier.  We need government action to create a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices2 in the US.
 
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  Given the complex government rules and conflicting information architectures, the private ownership and interests and privacy issues, we need federal and state government action to create an environment that adequately balances the health need for data sharing with these competing interests.  The lack of a patient identifier is an example of a major gap affecting linking patient specific data in the US.  By comparison, governments in other advanced countries such as Australia, Canada, New Zealand, Scandinavia, and the European Union have policies in place that deal with authentication of patients for healthcare purposes by using a unique patient identifier.  We need government action to create a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices2 in the US.
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References:    David F. Lobach, MD, PhD, Don E. Detmer, MD, MA, “Research Challenges for Electronic Health Records”, American Journal of Preventive Medicine 2007; California HealthCare Foundation, “Privacy, Security Laws Impede Health Data Sharing”, iHealthbeat, November 09, 2009; Journal of the American Medical Informatics Association, Toward a National Framework for the Secondary Use of Health Data Volume 14, Issue 1, January-February 2007, Pages 1-9
 
References:    David F. Lobach, MD, PhD, Don E. Detmer, MD, MA, “Research Challenges for Electronic Health Records”, American Journal of Preventive Medicine 2007; California HealthCare Foundation, “Privacy, Security Laws Impede Health Data Sharing”, iHealthbeat, November 09, 2009; Journal of the American Medical Informatics Association, Toward a National Framework for the Secondary Use of Health Data Volume 14, Issue 1, January-February 2007, Pages 1-9
  
Submitted by Rus Hargrave, BMI 512 Winter 2010
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Submitted by Rus Hargrave
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[[Category:BMI512-W-10]]

Revision as of 13:01, 22 March 2010

Public Health data is collected from providers such as hospitals, clinics, private practices, laboratories, School Based Health Centers (SBHCs) Federally Qualified Health Centers (FQHCs, and other sources. The types of data include disease, environmental, community treatment capacity and utilization, treatment coordination, and licensing and inspection. A lot of public health work is based on secondary data, usually anonymous or pseudo anonymous, however, there are some core public health functions that require named personal health data. These functions include surveillance against emerging health threats and disease registries.

A complex web of data sharing rules affect the ability to share data including data sharing agreement contracts, legislative and administrative rules, and private data ownership (often partial). It takes a lot of time and effort to change rules affecting data sharing, and the current approach is to negotiate individually for each type of usage.

In addition to complex rules, funding sources also add to the complexity by often dictating data system architecture and functional priorities. In Oregon for example most funding for Public Health data systems comes from federal sources. One might think this would help to ensure a uniform architecture, and that federal funding for Public Health would come mostly from the offices of the Centers for Disease Control (CDC), but the sources are much more heterogeneous. In addition to many offices within the CDC, other major federal sources funding Public Health data systems include: United States Department of Agriculture (USDA), Health Resources and Services Administration (HRSA), the Environmental Protection Agency (EPA), the Centers for Medicare and Medicaid Services (CMS,) the Office for the National Coordinator (ONC), and many others. Each of these government funding sources typically demands conformance to their own “siloed” architecture. The complete lack of common architecture between the “Public Health Information Network” from the CDC and the “National Health Information Network” from ONC is an example.

Given the complex government rules and conflicting information architectures, the private ownership and interests and privacy issues, we need federal and state government action to create an environment that adequately balances the health need for data sharing with these competing interests. The lack of a patient identifier is an example of a major gap affecting linking patient specific data in the US. By comparison, governments in other advanced countries such as Australia, Canada, New Zealand, Scandinavia, and the European Union have policies in place that deal with authentication of patients for healthcare purposes by using a unique patient identifier. We need government action to create a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices2 in the US.

References: David F. Lobach, MD, PhD, Don E. Detmer, MD, MA, “Research Challenges for Electronic Health Records”, American Journal of Preventive Medicine 2007; California HealthCare Foundation, “Privacy, Security Laws Impede Health Data Sharing”, iHealthbeat, November 09, 2009; Journal of the American Medical Informatics Association, Toward a National Framework for the Secondary Use of Health Data Volume 14, Issue 1, January-February 2007, Pages 1-9

Submitted by Rus Hargrave