Self-Service Data In Healthcare

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What study can better exemplify the importance of self-service data than the ongoing COVID-19 lockdown? Since mid March, almost ⅓ of the world is in a lockdown due to the pandemic, and access to public health data has never been this crucial. The COVID-19 dashboard of Johns Hopkins University is probably the most viewed dashboard in our history. The dashboard is a great example of self-service data with several layers: removing the bottleneck of building and maintaining data pipelines manually, providing high quality data via machine learning and AI, and giving information users the autonomy to access and handle data as they see relevant.


What is self-service data?

“Self-Service analytics is a  form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.” - Source: Gartner

Self-service data is basically accessing and analysing data without IT assistance. Modernizing data architecture via moving into self-service data, on the other hand, can eliminate the data issues and move the organization into the highest standards. First of all, removing the burden from the IT department and utilising automation increases performance, minimizes human error and reduces data access delays from months to weeks/ days. Furthermore, the ability to trace and share all data activities increases data governance transparency, and applies intelligent user anonymisation which in turn reduces cyber risk exposure.

 

Data Self-Service in Healthcare and Why It Matters

According to a 2014 study, the U.S. ranks 50th out of 55  developed countries in terms of healthcare effectiveness. This data shows the need for modernization in the healthcare system in terms of better ways to provide accessible, high quality and affordable healthcare. Data self-service in healthcare is the technology and processes that measure, lead and analyze healthcare using data. Decision makers' capability to achieve these, in turn, is dependent on moving towards a modern data architecture with less IT dependency. In traditional approach, there are several challenges such as data access delays, manual process to increase human error, inconsistent data quality, increased regulatory and cyber risk exposure, lack of data governance and scalability issues are just to name some. The issues posed by the traditional approach can lead to poor performance, poor data quality, user dissatisfaction, delay in reports and eventually lost opportunities.

Via implementing data self-service in healthcare, though,  enables one to make efficient operational and clinical decisions, such as better directing resources to have the most impact on. A recent whitepaper by Deloitte touches on the importance of self-service analytics in healthcare transforming the user experience. Healthcare organizations that are using a traditional approach in analytics face challenges in maintaining an efficient, fast and high-quality data running system. Modern world demands faster and actionable insights in order to speed up the decision-making process, innovate faster and gain competitive advantage. Hence, succeeding in meeting demands of modern world healthcare, requires healthcare organizations to change the way they operate and invest in a modern data architecture to ease the data pipeline preparation & building process.

Moving from a manual process into a modern data architecture with automated solutions can reduce the effort that has been spent on building data pipelines, and shift the efforts towards running and managing data to get more interactive and actionable insights.

 

Real Time Data Processing With Self-Service In Health Care 

With regards to moving into a modern data architecture with real time data processing capabilities, Bluemetrix provides a unique solution to overcome analytics challenges in healthcare. Bluemetrix Data Manager (BDM), a data processing platform which enables data self- service in a controlled manner, empowers the business users to implement the appropriate functions and processing data treatments. 

By design, BDM data self-service platform brings transparency to the data processing and sharing process. In healthcare, data governance transparency acts as a game changer and could lift the barriers in front of innovation. Similarly, intelligent user anonymization (masking and tokenization) features ensures the protection of data throughout the analytics pipeline. Based on organizational needs, either BDM Data Control or BDM Control Plus could be implemented. The solutions that are offered include: automating ingestion, governance, compliance, schema evolution, masking and tokenization, validation, transformations, and scheduling. 

Self-Service Architecture in Health Care

 Diagram: BDM Self-Service Data Architecture in Health Care 

 

Data Self-Service Best Practices in Healthcare

We talked about several benefits of self-service analytics from fast data access without heavy IT intervention to reduced manual action and human error. To exemplify these further, these are the 5 best practices that can help you organise your data self-service initiatives:

  1. Develop a compelling vision focused on the business impact: Healthcare organizations are composed of complex and interconnected processes, data and groups of people. Establishing an effective self-service analytics process within this complexity requires a compelling vision to provide stakeholders with a clear understanding of how it will help them meet their business objectives. 

“Self-service represents an important step in the journey from retrospective reporting to emerging analytics capabilities (e.g., predictive analytics, machine learning, natural language processing, and artificial intelligence).” -  Deloitte

Painting a clear vision would be incremental in implementing a successful self-service analytics process, which in turn would move analytics closer to the point of action and enable health care organizations to be more effective.

  1. Remove barriers to analytics agility and decision making: Given the complexity of the healthcare environment, one can face several challenges like scattered data, poor data quality and repackaging insights to decision makers. Self care analytics can overcome these issues by automating data preparation and moving the analysts’ focus towards discovering and delivering actionable insights.
  2. Establish a modern analytics architecture to foster analytics discovery and data driven decision making: Traditionally, the healthcare ecosystem relies on application reporting features that come with limitations on data breath and access. Modern business analysts and data scientists, however, need flexibility in terms of connecting data from multiple sources or formats. This issue can be eliminated in the self-service architecture via streamlining access to relevant data and applying automation and machine learning capabilities which’d enhance the performance of information consumers.
  3. Provide a meaningful analytics experience to engage users better: It is important to have a customer experience focus while deploying self-service analytics. As mentioned in Deloitte’s report, “self-service analytics can significantly enhance human capabilities, helping enable employees to deliver incremental value to the organization in ways that have been too difficult or costly to do with traditional business intelligence tools.”
  4. Specify strategy for the enterprise via governed analytics and data-driven decisions: Consistency in self-service analytics programs requires implementing controls, standards and decision making rights in line with enterprise data governance. This requires investment in secure workspace and features that’d help users to figure out which data sources are most relevant, e.g. automated data catalogues.

According to a 2016 report by Gartner, self-service analytics will make up 80% of all enterprise reporting by 2020. Within the context of healthcare, it is clear that the organizations who make the transition early will reap the benefits. Utilizing the full potential of healthcare data can lead to shifts in the quality and affordability of care as well as lead to breakthroughs in medical research. 

“Converting this data into actionable insights that are available on-demand to decision-makers across the organization can be the key to unleashing an insight-driven health care organization. By democratizing data across an organization, new insights and opportunities open up that may not be possible with traditional approaches.” - Deloitte

If you’d like to learn more about how this process can be implemented, Bluemetrix’s solution brief is a good resource on data self-service for analytics. You can also request a demo from our experts to get a better understanding of the solutions and products.