In Part 1 of this 2-part blog series, we take a look at the development and importance of data analytics, its significance within the digital healthcare software industry, and the technologies that are driving the changes.
Data Analytics and Its Evolution
Let’s start by defining what we mean by Data Analytics. In simple terms, it is the process of taking raw data, transforming it into meaningful insights from which conclusions can be drawn and trends identified. For any organization, vast amounts of raw data are captured and retained on either local servers or on the cloud. Although raw data holds some potential, implementing process automation techniques will unlock the potential to make informed decisions, to help better understand customer behavior, drive specific initiatives, and boost organizational performance with proper business analytics.
Businesses have collected data and statistics since time immemorial and this was becoming an increasingly important role in any organization. With the digital revolution came the means for organizing and managing data more effectively, but the unprecedented expansion of data in the 21st century drove the need for data warehousing and tighter data security. The availability of a variety of advanced technology emerged, along with migration into cloud, that significantly advanced data management, retrieval, and access capabilities and revolutionized statistical data analytics. Where previously data analytics could answer the “what happened?” question, with the development of ‘big data’ and cloud modernization, organizations could start getting answers to the question “why did it happen?”.
It All Begins with Data Management
Before organizations can even begin to analyze their data, there need to be robust procedures in place for managing and storing large volumes of data. Managing the data effectively requires some key considerations: building ‘fit-for purpose’ database(s), efficient uploading of the data, processes to validate and, where necessary, clean the data, ensuring data is regularly and routinely backed-up, and implementing the highest data security measures for access. Organizations create data at exceptional rates, therefore employing good data management practices are key in being able to make sense of it all.
Today, to avoid the associated costs and maintenance of data storage on-premise, many organizations have already achieved migration into cloud, providing the benefits of flexibility, scalability, quick access to real-time data, but above all improved data security (Read our recent Blog: Cloud Modernization: Direct your Migration Goals to a Digital Ready Future).
From that foundation, analyzing data on the cloud and embedding a cloud analytics solution, such as AWS SageMaker, will then open the gates for any type of business analytics required on-demand.
Why Data Analytics matters in Healthcare
When it comes to healthcare, managing and storing this type of data involves some of the greatest challenges of any industry, such as multiple input sources, access to data through different platforms, the need for real-time monitoring, and HIPAA data security regulatory compliances necessary for sensitive and personally identifiable health records. All this before even starting to think about statistical data analytics, and yet an industry where healthcare analytics are crucial, for example, monitoring real-time cases and the spread of COVID-19.
Healthcare providers are required to report on many key performance indicators. And, for revolutionizing digital healthcare software, real-time data analytics is now a core function. This can improve efficiencies, manage day-to-day operations, and plan for the future through trend analysis. Most important is how healthcare analytics can benefit different stakeholders of the healthcare world.
Benefits of Data Analytics for the Healthcare Industry:
- Reducing patient waiting times in Emergency Rooms
- Allocating & scheduling adequate staffing levels
- Reducing readmission rates
- Monitoring clinical trial data & patient outcomes
- Identifying & targeting prospective members
- Creating targeted campaigns for specific health conditions
- Review pricing data to recommend the best healthcare provider
- Quickly identify potential fraud or at-risk claims
- Early detection & diagnosis for better patient outcomes
- Real-time information of disease cures
- Tracking chronic health conditions
- Improving quality of life & patient satisfaction
The Bottom Line on Data Analytics
Establishing good practices around data management is the first step towards being able to achieve comprehensive data analytics. Business organizations can better understand their customers by reviewing behavioral trends, personalizing relevant content and enabling process automation techniques. This would successfully lead to enhanced customer engagement, adding value to customer interactions, improving organizational workflows, identifying root-causes to any business-related issues, and generating new revenue streams.
Many of us have heard the expression “prevention is better than cure”, and considering the average human lifespan is increasing throughout the world’s population, healthcare providers, just like business organizations, are making their data work for them: to enhance patient care, reduce costs, limit errors, improve processes, and predict future health crises.
Part 2 in this blog series examines the technologies that are driving the changes in data analytics and what the future holds for data analytics.
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