Data-sharing and interoperability obstacles
Issues of Interoperability and Data Sharing
The CDC has developed myriad informatics applications in support of disease surveillance at the federal
level. Information sharing among these applications enables public health personnel to better understand
disease trends that may be difficult to interpret from the examination of a single system. However, a
variety of legal, ethical, and practical issues arise as a result of this sharing of information. The potential
for problems is magnified when information is available to and shared among multiple systems.
post an analysis of the obstacles that impact interoperability of the disease surveillance systems. Propose
ways to improve/address the legal, ethical, and practical data-sharing obstacles. Evaluate how this
impacts the future of local, state, national, and international systems/agencies sharing and use of
information.
Department of Health and Human Services (HHS) estimates that 20% of medical errors
are preventable and occur due to lack of immediate access to health education. The
interoperability obstacles are bothersome especially in this technology era. In fact, banking
industries and telecommunication industry interoperability have excelled substantially. One can
transact from ant ATM with minimal interruption. Why is this not replicable in the health care
sector? Despite the fact that 10 billion dollars have been allocated to facilitate the interoperability
in health care industry, technical challenges on the same are insurmountable (King Et al, 2012).
First, the data collected is inadequate due to the significant gaps in the disease surveillance
systems, particularly in the developing countries. Additionally, most public health information is
stored hard copy, and where electronic formats are use; they are often incompatible with the
other advanced disease surveillance systems. Data format standards and metadata for health
care including Data Documentation Initiative (DDI), the established International Classification
of Diseases (ICD) and Metadata eXchange are insufficiently functional. This limits
interoperability and secondary data usage. In fact, about 20% of deaths that occurred in certain
countries between 1950 and 2010 were due to ill-fated use of the established ICD due to limited
data sharing (CDC, 2012).
Another issue that holds back data sharing is the ownership and copyright issues.
Institutions and agencies that collect data are liable for the person and the community privacy.
This makes most of these agencies reluctant to carry out their tasks sufficiently as they feel like
guardianship role has been bestowed on them. Additionally, government agencies such as Health
Insurance Portability and Accountability Act (HIPAA) in US regulations on data acquisition
using identifiers and the anonymous data makes it hard to use data collected in certain contexts.
Some data are not released to anyone because they are perceived as sensitive and are only used
for security purposes. Additionally, some stewards may be reluctant to share disease
surveillance data because they fear that inexperienced individuals may abuse the information. In
some cases, they may be reluctant because of socioeconomic impacts. For instance, the fears
interoperability will reduce their relevancy or feasibility of the study. Other barriers include
insufficient incentives and resources for data sharing and limited guidelines (Peter et al, 2012).
In cases of remaining relevant, the new approaches that protect the person or agency such
as data perturbation should be established. This is a strategy which allows the researcher to
provide key but limited information of their work while ensuring that their security is not
breached. For instance, researchers can access restricted information through Research Data
Center (RDC). For effective interoperability and data sharing, legal barriers that prevent data
sharing must be annulled. The involved stakeholders should collaborate and debunk the existing
miscommunications and misconceptions regarding the legal barriers (CDC, 2012). A
standardized format should be established. This entails establishing a standard language, coding
system, formatting and process involved in data sharing. This will ensure that disease
surveillance systems are user friendly and data disseminated is adequate. Moreover, an efficient,
secure disease surveillance system and the software program should be established for effective
communication and exchange of information. If the obstacles are not eliminated, scientific
discoveries will be reduced. The reduced understanding of individual study and pooling of data
will negatively impact innovations. The accuracy and quality of research will reduce due to the
limited sharing of data on critical issues such as effective treatment options and evidence based
strategies (NCBI, 2013).
References
CDC (2012) Public health surveillance data: legal, policy, ethical, regulatory and practical issues.
Supplements 61; 3, p30-34.
King, G. Et al.(2012) Boundaries and e-health implementation in health and social care. BMC
Medical informatics and decision making 12; 100, p1-12
NCBI (2013) Sharing Clinical research data: Workshop summary.
Peter, B. Et al. (2012). Mining electronic health records: towards better research applications and
clinical care. Nature reviews 13; p395 -407
