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Insight-driven health care through self-service analytics
Harness the full potential of health care analytics and big data
Converting health care data into actionable insights that are available on-demand is key to unleashing an insight-driven health care organization and can have a profound impact on patient outcomes and the quality and affordability of care.
- Modern self-service health care analytics
- Case study
- Implementation guidelines
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Modern self-service health care analytics
The United States ranks 50th out of 55 developed countries in the effectiveness of our health care system, according to a recent study1. Many regulatory agencies and independent health care organizations are recognizing the need for better ways to provide accessible, high quality, and affordable health care. To deliver the information decision makers need, analysts must often overcome a variety of time-consuming challenges such as IT backlogs, rigid data architectures, unnecessary latency, and technological complexity. These challenges can limit the organization's ability to optimize the value of their investments in electronic health records, understand customer needs, and find innovative solutions to health care's toughest problems.
Self-service analytics is an alternative approach to traditional business intelligence, helping enable non-IT professionals to be more independent in accessing and working with data even if they do not have a background in statistical analysis, business intelligence, or data mining. This approach uses agile methods and modern technology to automate data access, preparation, consumption, and analysis. Empowering business users with more timely, meaningful, and flexible analytical capabilities is an essential component of an effective and insight-driven health care organization and can be instrumental in enhancing diagnostic accuracy, improving patient outcomes, developing precision medicine, and supporting value-based care.
After years of relying on busy, understaffed IT departments, many business users are experiencing first-hand access to the insights they need to make a significant contribution to their organizations. Slow turnaround times, rigid templates, and complicated tools no longer need to be barriers to analytics success. Self-service analytics offers an environment in which business users can create and access specific sets of data, queries, and reports on demand, without IT intervention. To enable this environment, a modern self-service health care analytics platform should support:
- Ingesting data across multiple sources, both structured and unstructured, in "right-time"
- Storing, preparing, and provisioning large volumes of disparate data to service analytical requirements
- Serving data to the business in an intuitive consumable format through a flexible and easy-to-use interface
- Managing the quality, integrity, and availability of the data through robust governance
- Empowering the user community through training, adoption, and analytics enablement
Case study: Self-service analytics enabling supply chain analytics
Traditional processes for accessing and analyzing data were ineffective in meeting the business needs and did not provide sufficient visibility for buy-to-pay supply chain analysis. Dependency on IT and manual efforts resulted in delays in addressing business issues and increased risk of missed cost savings opportunities.
Using an agile approach, a collaborative team with representatives from various departments implemented a self-service analytics platform based on a combination of Big Data, automated data preparation, advanced analytics, data visualization, and mobile technologies.
The organization realized significant improvements in analyst productivity such as reducing data preparation time from several days to a few hours, automating data access and profiling activities, and delivering analytics via multiple platforms including laptop, tablet, and mobile devices. As a result, business analysts could access, prepare, and analyze data across multiple sources with minimal IT support.
Implementation guidelines for successful self-service analytics
Self-service analytics represents a fundamental shift in the way IT and business users collaborate to get insights from their information sources. Historical approaches for supporting end users included creating spreadsheet-driven models, shadow systems, and custom SQL queries. These fragmented approaches often fall short in meeting stakeholder expectations. With the introduction of modern technologies, organizations can now implement effective self-service health care analytics programs drastically reducing development time, cost, and data quality issues by considering five important guidelines.
- Guideline 1: Paint the future with a compelling vision focused on the business impact
- Guideline 2: Eliminate unnecessary barriers to analytics agility and decision making
- Guideline 3: Stimulate analytics discovery and data-driven decision making with a modern analytics architecture
- Guideline 4: Engage users with a meaningful analytics experience
- Guideline 5: Define enterprise strategy through governed analytics and data-driven decisions
Harnessing the full potential of health care data can have a profound impact on the quality and affordability of care, can break new grounds in medical research, as well as improve overall member/patient outcomes and population health. 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.
Empower insight-driven health care organizations through self-service analytics. Download the PDF to learn more.
1 Wu, Lisa (2016 September 26). U.S. Health-Care System Ranks as One of the Least-Efficient. Retrieved from: https://www.bloomberg.com/news/articles/2016-09-29/u-s-health-care-system-ranks-as-one-of-the-least-efficient