With access to more data than ever before, IT professionals in healthcare are faced with sorting and connecting disparate data. And the automation of data, so that it’s clean and ready for machine learning and AI usage, is leading to better patient outcomes.
“Accessibility to clean and ready-to-use data is a huge barrier for today’s healthcare organizations, and this not only affects hospitals and their bottom lines but, most importantly, the patients they serve,” explained Heather Gittings, Principal Strategic Advisor, Global Public Sector and Healthcare, Qlik. “ However, in order to remove this barrier, it involves transforming data efficiently and effectively and moving away from manual processes that have previously hindered healthcare organizations.”
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Traditional methods of data transformation can transform large quantities of data. Despite this, “such heavy tools are no longer suited to the agile approach to data that modern business demands,” explained Qlik’s Adam Mayer. “Batch processes for moving transactional data into data warehouses where it can be governed, cleansed, and queried, for example, can take between six to nine months. This means highly skilled individuals end up spending a huge amount of time transforming data when their resource could be better invested in the higher-value activity.”
Another problem is that, for most healthcare organizations only employing one or two highly skilled individuals, these traditional methods leave room for risk and human error. Because of the shortage of skilled data scientists, when skilled individual leaves there are often delays in finding the right candidate with a similar skillset to transform the data; this doesn’t account for the onboarding process or teaching the new hire the organization’s data handling or management process.
Future-focused and forward-thinking healthcare organizations are looking at how automation can help alleviate the burden placed on IT staff. These organizations must commit to, “move away from batch uploads of data and toward a continuous model leveraging technology like Change Data Capture (CDC). This enables data from any source to be replicated and streamed in near real-time for analysis,” explained Mayer.
“Some more advanced solutions also eliminate the manual-coding process, automating and accelerating data ingestion, data replication, and the loading of data into new locations. This significantly increases the speed at which data is transformed and simplifies scripting to lower the technical barrier, relieving programmers of the burden of writing thousands of lines of code when integrating new sources into data warehouses. Furthermore, moving away from manual integration processes both reduces the risk of human error in the scripting process and of this specialized expertise leaving the business.” To reap these benefits, healthcare organizations must use the right technologies that help them automate the data integration process and alleviate pain points.
Today’s healthcare organizations are sitting on a goldmine of valuable insights courtesy of the data that they collect with every patient interaction. To be able to put this data to work and uncover these valuable insights, manual processes are no longer sufficient. However, with automation, today’s healthcare organizations can expect to positively impact patient experiences and outcomes.
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