How does a healthcare organization build a data framework?

Data is the backbone to a growing number of healthcare organizations, with providers gradually recognizing its huge potential when it comes to improving patient care. In this blog post, we’ll talk about the intricacies of building these data frameworks, and how companies can establish best practices to ensure that their data is held safely and securely, whilst still harnessing its huge potential.

In a nutshell, a data framework is a model which organizations within any sector can use to manage their data. This framework essentially sets a series of guidelines concerning management activities – i.e. how this data should be stored and used by the organization and its members. It mainly refers to business activities directly related to the creation or manipulation of data, but can cover a broad spectrum of uses.

Building a data framework – also referred to as a data governance framework – might seem to be a monumental task at first glance, but with the right structure and guidance, it’s a relatively straightforward process.

The first step is to clearly define the purpose of this framework, and to establish concrete goals and reasons behind its creation. What is the organization looking to gain through the creation of a data governance framework? How will having this framework in place make reaching these goals easier? Acknowledging the route the healthcare organization hopes to take and the role of data frameworks on this path will help to create reliable methods that will take the company one step closer towards achieving a realistic set of results.

Once intentions and goals have been defined, the healthcare organization will need to ensure that they build the best possible team before creating this framework. This is commonly known as a data governance office, and involves a variety of roles, such as data analysts and stewards, as well as general management and other relevant company stakeholders. Having a well-oiled team who are easily able to handle working with large amounts of sensitive data – something of particular relevance to the healthcare sector – is key in ensuring the organization can make the most out of this investment. Without informed and experienced individuals making sure the relevant data is stored safely and securely, it’s easy for big mistakes to be made.

As soon as this team has been established, adopting a data governance model is the next step. Who can view and distribute this data? Where will it be shared? How will the organization determine which individuals are authorized to access this data? Breaches in data protection are becoming increasingly messy areas which ought to be avoided at all costs, regardless of how minor they might be perceived to be. Ensuring only those who require access to this material in order to conduct their research is essential in not only keeping the trust of customers, but also that of coworkers and/or employees. During this stage, data collection methods and processes will also need to be determined, making sure they’re in line with the company’s ethical statement – alongside data storage policies and methods of securing it.

Lastly, the organization will need to formalize rules and regulations surrounding the sharing of data – especially when it comes to any relevant third parties. Communication is key here, and reliable processes are needed when it comes to privacy concerns to ensure that there are absolutely no grey areas that might lead to confusion.

Taking all of these stages into consideration, it’s important to reflect on why a healthcare organization would want to build a data framework in the first place. Having a cohesive and comprehensive data governance framework in place allows healthcare organizations to effectively manage huge amounts of data, which might otherwise be overwhelming.

By establishing a solid framework, healthcare companies are also better equipped to safely manage individual data sets and protect them from being lost, stolen, or otherwise compromised. Opening up your business to the world of data is a challenging and complex process, but is hugely worthwhile in the long term, and very much achievable with the right tools and guidance in place.

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