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In the present age of information, the usage of data has become a necessity for making decisions in the public health sector at the local level as well as the national and global level. Although there is a commitment to the usage and the sharing of the health data of the public, yet the task is challenging in the real world. There are no guidelines for global operation or systematic framework for the sharing of data pertaining to the public health (Teale, Young & Sleigh, 2013). There are barriers present at various levels and thus have resulted in limiting the sharing of data about public health. Incomplete evidence on the variety and scope of the barriers has led to the limiting of the opportunities for maximising of the values and the usage of the health data of the public for policy formulation and science.
It has been seen that a wide range of health care entities and other health institutions are involved in the process of collecting data. The collected data does not flow between the various entities in a standardised or cohesive manner (Pattanasri, Pradhan & Tanaka, 2013). Addressing the disparities in the health care and health institutions requires the complete participation of the different organisations, which have an existing infrastructure for the measurement of quality and improvement.
We address the issue pertaining to the formulation of strategies that will help the healthcare sector to overcome the barriers that are limiting the abilities for these organisations to share data among themselves. It is necessary to understand the different types of barriers that exist in the sharing of the data. The following table shall provide a brief view of the barriers, which shall be explained after the table.
Technical Barriers are the most common barriers that are present in the sharing of information in the healthcare system. These barriers are well understood and considered to be strong challenges for the health information systems.
Data Collection not done: Data sharing shall not be considered to be a primary objective for any healthcare institution as long as the process of data collection is not made efficient and effectively collects data.
Data Preservation not done: Data pertaining to public health is not collected for long term effects (Teale, Young & Sleigh, 2013). The data is normally collected for handling short-term problems. After the problem has been mitigated the data is not recorded or preserved and as such the data is often lost with time.
The barrier in Language: The data that are often collected by the public health centres are seen to be recorded in the local languages. This limits the integration of the data in a standard format for usage on a national or global scale.
Data in the restrictive format: There has been a development in the technology that is being used for the collection of data. But there are places where the health data are still being recorded using the old conventional methods and outdated hardware and software, which cannot be integrated with the present technologies.
Unavailability of technical solutions: There are software and hardware that have the ability to record complex health data and to share them with a particular network. These technological developments are only available in the private sector only, the solutions have not yet been made available to the public health sector.
Lack of Standards and Metadata: It has often been observed that standards and Metadata are not present, which helps to define the format of the data, the variables for observation, etc. Although there are international standards for the collection of data, yet these standards are not being properly used in the public health department (Nancarrow, 2013).
Motivational Barriers are the barriers which are reliant on the institutional and personal motivations and their beliefs, which act to limit the sharing of the data.
Lack of incentives: The process of data sharing is a time-consuming process and also requires a lot of resources. Incentives are often needed to prioritise the sharing of the data over any other duties in the health care organisations. As there is not much of a benefit provided for the sharing the data thus it is often neglected by the individuals working in the healthcare department.
Disagreement on the usage of data: There might be disagreement on the part of the data handlers and operators whether to use the intended data for any other purpose or not. This leads to decisions of not sharing the recorded data with anybody else.
The possibility of criticism: If the data that is provided by the various data operators are found to be erroneous in nature or are fabricated then the data operators shall have to face criticism for their mishandling of the public health data.
Economic Barriers are the concerns regarding the costs related to the process of sharing of data.
Lack of proper resources: The sharing of data is a process that needs both technological resources as well as human resources (Nancarrow, 2013). These are the resources that are often not found in the agencies in the public sector due to the low income and weak economic conditions.
The possibility of economic damages: The sharing of the data in the area of public health is being challenged by the damages to the economy to the various data providers. Such economic downfalls create reluctance among the healthcare organisations to share data with each other.
Political Barriers are considered to be the barriers due to the governance of the public healthcare system.
Policies that are restrictive in nature: There are various organisations in the public health care department that have formulated policies that restrict sharing of data. These policies can be due to any prior negative experience or distrust or any other factor.
Lack of proper guidelines: There are no official guidelines that will help the organisations in sharing of data. Even if guidelines are present, they are inconsistent in nature and unclear.
Lack of trust: The trust that exists between a data provider and the user is one of the major factors that enable the process of data sharing (Nancarrow, 2013). If trust is not present then, the data sharing process could be hampered as the data provider may think that the data provided by them shall either be misused of misinterpreted.
Ethical Barriers are considered to be the normative restrictions that involve the conflicting nature of the values and moral principles.
Lack of any reciprocity: The practices pertaining to data sharing are not completely fair. The data handlers and producers often feel cheated out for not being credited for their work.
Lack of any proportionality: This is the problem associated with the secondary usage of the provided data by the data providers, which can potentially affect the process of sharing of data.
Legal Barriers are the tools that are used for the purpose of restricting the process of data sharing, which is a result of the willingness to either share or not shares the data with anyone else.
Privacy Protection: The public health organisations are known to collect personal data from the public. Private data is different from anonymous data. Thus to protect the privacy of the individuals from whom the data has been collected, the legal barriers are placed for the protection of the privacy of an individual.
The complexity of the interaction between the various barriers at different levels has the potential to affect the effectiveness and efficiency of the solutions severely (Lamont, 2011). Any strategy for resolving the different barriers may not help in the process of sharing of data if the barriers that are associated with it are not determined at the same time also. Specific strategies should be formulated for the sharing of data based on the type of the data.
Most of the barriers falling under the motivational, economic and technical categories are found to be embedded in the health information systems of the middle and low-income nations. Solutions for these problems are being developed on a global scale. There are projects for the development of infrastructure and use of standard technologies and data collection methods are being formulated for integrating all data collection systems under one roof.
The legal, political and ethical barriers to the sharing of data require a different method of approach. These barriers are different and transparent in nature as compared to the technical barriers present in the sharing of data (Toussaint, Shortell & Mannon, 2014). There is a need for proper guidelines and framework for setting up an operational guideline for the sharing of the data. A central system should be formed for the act of monitoring and mediating and facilitating the sharing of data between the various nodes in the sharing network. The central system shall also be responsible for ensuring an efficient and fair usage of the public health data for development of the health of the global population.
There are a numerous number of opportunities for the creation of cooperation in the global health conditions, programs for the effective control of diseases and other scientific discoveries due to the advancements in the sharing of health data, which have been collected from the public. In contrast to the advancements in the domain, the potential and real barriers to these advancements have limited the efficient usage of the collected and shared data. A process on a global scale is necessary for the efficient use of the known solutions in building up of a consensus for the use of the new solutions in harnessing the potential of the data in helping advance the health of the population in the 21st Century.
References
Lamont, S. (2001). Data collection barriers can be overcome by schemes such as MEDICS. BMJ,322(7287), 674-674. http://dx.doi.org/10.1136/bmj.322.7287.674
Nancarrow, S. (2013). Barriers to the routine collection of health outcome data in an Australian community care organization. Journal Of Multidisciplinary Healthcare, 1. http://dx.doi.org/10.2147/jmdh.s37727
Pattanasri, N., Pradhan, S., & Tanaka, K. (2013). Extending information unit across media streams for improving retrieval effectiveness. Data & Knowledge Engineering, 83, 70-92. http://dx.doi.org/10.1016/j.datak.2012.10.003
Teale, E., Young, J., & Sleigh, I. (2013). A point of care electronic stroke data collection system. Br J Healthcare Management, 19(1), 10-15. http://dx.doi.org/10.12968/bjhc.2013.19.1.10
Toussaint, J., Shortell, S., & Mannon, M. (2014). Improving the value of healthcare delivery using publicly available performance data in Wisconsin and California. Healthcare, 2(2), 85-89. http://dx.doi.org/10.1016/j.hjdsi.2014.01.002
