Data silos in healthcare are a pressing problem for healthcare providers, hospitals, and industry stakeholders due to diverse reasons. Healthcare players now have to contend with big data silos while working out a fine balance between tapping opportunities that arise with more actionable intelligence and insights, while managing increasing technical complexities.
The healthcare sector is known for the sheer depth of these data silos, presenting multifarious challenges or obstacles for providers. It begins with medicine R&D patient records and more. Eliminating data silos will contribute towards a win-win proposition for all stakeholders including patients, healthcare service providers, policymakers, and so on.
There are however concerns relating to data security with a more complicated data landscape and rapidly evolving threats. The need of the hour is proper healthcare analytics with an emphasis on accompanying privacy-by-design framework, security analytics, encryption, multi-factor authentication, and other techniques.
Interoperability is another aspect worth considering. This is the scope of data exchange and interpretation across various IT software applications, systems, and devices. Without proper guiding frameworks for interoperability, data exchange may turn confusing, time-consuming, and complex, hindering information flow and patient care alike. Data complexity has to be reduced by eliminating silos for service providers, doctors, and patients.
There are several hurdles towards interoperability though. While eliminating data silos is possible with big data in healthcare analytics, there are several issues for providers even today. The present interoperability framework is a makeshift system for most healthcare industry players.
93% of hospitals and other healthcare systems make records available online for patients and this has increased from 27% in 2012, as per the Sharing Data, Saving Lives: The Hospital Agenda for Interoperability report in 2019. 88% of hospitals also share their data with ambulatory care as per these reports.
However, the critical challenges include the fact that while 90% of hospitals are deploying certified IT solutions, several out-of-the-box options are muddling data exchange owing to silos. Other issues include concerns relating to privacy and security along with restrictions pertaining to the present HIEs (health information exchanges) and also the absence of any compatible linguistic or technical standards for making sure that shared data stays intact and relevant.
A key hurdle towards extensive interoperability is the absence of suitable technology-driven infrastructure. While most providers use EHR (electronic health records) platforms, many of these were not developed keeping data exchange at the forefront. At the same time, health information exchanges were implemented for electronic leveraging of healthcare data and also in a secure manner.
Yet, many of them cannot finish total data exchange in a reliable manner through varying source technologies or healthcare systems. A few HIEs also do not facilitate access to patient data which is counterproductive to the actual reasons for their implementation.
This report also mentioned how 97% of hospitals were already using certified EHRs, thereby making the case stronger for doing away with data silos. There is a need for proper systemic infrastructure for recording and transferring vital information securely throughout the ecosystem. Other aspects like APIs (application programming interfaces) are also vital for health data sharing.
Accessible, open and FHIR (Fast Healthcare Interoperability Resources) standards-based APIs are seen as some of the best ways to quickly scale up interoperability. More than half of developers of technological solutions will have to ensure access to electronic health data via public and standard APIs in the near future. This should rise further in the current decade.
At the same time, big data in healthcare analytics is steadily attaining higher sophistication and refinement en route towards fusing with better governance and regulatory systems to tap better intelligence and operational efficiencies, along with keeping data silos at bay.
There have to be mechanisms in place for healthcare stakeholders with regard to relying on the accuracy and relevance of the shared healthcare data along with ensuring compliance and security at multiple levels. Privacy issues are still a concern in this space. IT developers and vendors should be able to integrate privacy and security protocols and needs for each infrastructural layer including APIs and third-party applications.
This technological infrastructure should have verification methods for information requests and their authorisation, while each entity which has access to patient information will have responsibility for securing and using data respectively.
With more connected health IT systems, there will be growing cyber-security threats and one system’s vulnerabilities may lead to all connected systems getting exposed as a result. This will be an ongoing resolution for healthcare players, with regard to building data privacy and security standards, while complying with regulatory aspects seamlessly. Third-party security layers may also be possible through testing, identifying threats, and evaluation of technological upgrades.
In the end, eliminating silos is a vital task for the global healthcare industry today. Developing big data analytics techniques for penetrating deeper into available data is a key priority for several healthcare players. They are using these technologies for understanding the connections between applications, SSL certificate installation, server functions, and more.
Machine and wire data is being analysed and gathered for insights while helping organisations zero in on blockage points which lead to these data silos. Integration of disparate systems across the sector is also vital for accomplishing interoperability at a bigger scale.
Data silos naturally form across several data categories and departments have stored information. These make information inaccurate and inaccessible while hindering effective sharing due to blockages.
The challenges include barriers to sharing critical patient and healthcare data across systems, providers, and the entire network. This impedes quicker decisions and end-consumer fulfilment at multiple levels. At the same time, silos prevent a holistic view of the entire framework for providers.
The benefits include more accessible and usable data throughout multiple systems and stakeholders along with better collaboration across departments and improved decision-making.
Healthcare organizations can rely on big data analytics, centralized systems, better APIs and other tools for eventually overcoming data silos.
Big data analytics can be used for overcoming data silos in the healthcare industry, while some other technologies include data democratisation, a cloud-based operational framework, representation learning, and better data management systems.
