Data Democratization

Data Democratization?

Why Is It Important And How To Get There?

With the wide-scale adaptation of digital systems like EMRs, and interoperability mandates, healthcare organizations are deluged with exploding data, both structured and unstructured. The organizations also inundated with data from other data sources such as patient- generated, genomics, devices and social determinants of health (SDOH).

There is increasing pressure to use intelligence generated from the data to improve care and business performance. To process this massive amount of data and make it available in a form that is standard and easy to understand to all types of users with varying skills regardless of their technical abilities. There is a pressing demand for data democratization that eliminates the traditional siloed and outdated process we are used to. In a recent HIMSS study, only three out of 10 hospitals noted they were highly effective at democratizing data. Considering many healthcare organizations still struggle to access and integrate multiple data sources, it isn’t surprising.

What Is Data Democratization?

Data democratization means that enterprise-wide users, analysts, and researchers have frictionless access to data for analysis and transformation into actionable information that can be exchanged among internal and external stakeholders, including patients. In contrast with traditional data operations where users’ request for data has to go through IT, lack standardization and data literacy, and most report building and data mining is a technically intensive process.

This is not to say that there will be no gatekeepers or data governance process. It includes purpose-driven curated data marts and data sandboxes with an easy understanding of data so that users can use it to accelerate decision-making by way of augmented low code analytics applications with minimum IT dependency. Data democratization goes well beyond the four walls of the healthcare organization – the ability to securely exchange data with patients and external trading partners like researchers, quality collaboratives and health plans.

Why Is Data Democratization Important?

There are many reasons that data democratization is critical for healthcare organizations. U.S. healthcare organizations, both providers and payers, are under increasing pressure to run their operations efficiently under new risk-based payment models to sustain in the long term.

Keep up with increased demand for analytics

The future holds more data from multiple sources not just limited to claims, clinical and operational, but also patient-generated health data, Social Determinants of Health, device streaming and genomics data. There is significant demand from decision-makers to use the data to generate meaningful insights. The democratization of data facilitates a healthcare organization’s ability to access data easily and make informed decisions in a timely manner.

Expedite enterprise decision making

It allows multichannel data access for decision-makers, analysts, and researchers with varying skills, simplifying report development data analysis, and jump-start the data science journey – from what will happen.

Empower business units

Healthcare organizations will understand their patients better and run their operations efficiently as they face increasing volume and velocity of data from internal and external sources and the need to meet new business demands. It eliminates the barriers to data access and empowers business units to build and manage information.

Improve Patient-centric care

Under new interoperability mandates, health plans and care providers are expected to securely exchange data among themselves as well as with patients. Patients will be able to access their health data and share it with other providers. There is a real opportunity to bring patient-centric care and improve care coordination across the care continuum.

Accelerate analytics Maturity

HIMSS analytics maturity model

Challenges for data democratization

While healthcare organizations acknowledge that there has to be wider access to the data, there is a concern that providing unfettered access to data might lead to misinterpretation of the data, leading to poor business decisions that could have a harmful impact.

Other concerns include security risk, duplication of effort, data misuse and cost to manage.

The aggregation of massive amounts of healthcare data from diverse sources to generate meaningful insights has been a significant challenge in pursuing clinical quality and health system performance management. The majority of the data in healthcare organizations resides in silos and different formats of point solutions tied to a specific vendor. The host-system vendors lack the capabilities to link patient information across the care continuum to drive meaningful actions quickly. The result is data duplication that produces mismatched information even though they consume the same data.

Overall, traditional data warehouse solutions have been limiting factors in meeting changing healthcare business needs, creating unnecessary duplication, fragmented intelligence, and inefficiencies at a high total cost of ownership.

However, technology innovations should be able to read and respond to these challenges from better data governance, data literacy, data quality to security.

How to accelerate your data democratization?

Data democratization is critical for healthcare organizations to enhance capabilities to support rapidly evolving analytics needs. In particular to support care delivery, research, innovation, community outreach, value-based care, patient engagement and business performance.

Additionally, with the volume of data generated from varied sources and new business demands, healthcare organizations started recognizing the power of making data widely available to understand their patients and their business better. The recent technology innovations will only help to accelerate the data democratization journey. Here are a few essential elements for expanding data access for analysis.

Organization Commitment

Strategic shift to be a data-driven organization, leadership support, alignment with priorities of business units, building culture, investment

Domain Centricity

Enterprise-wide data aggregation, including external data sources. Adoptable/expandable or scalable data models to drive unified longitudinal view.

Data Standardization with literacy

Standardized data to drive data literacy for better understanding of organizational data assets. Purpose-driven data marts for non-technical users to interpret data analysis. Education for non-technical users.

Augmented low code applications

Minimizes skills dependency, simplifying report development, data analysis. Reduce IT dependency. Valuable IS resources can be deployed to address other tasks that are business-critical. Expedited or speed to analytics.

Centralized metadata management

A robust centralized metadata management workspace for data exploration, lineage, and governance framework is a key element of managing expanded data access. Organization must ensure the data security, privacy, quality and traceability to minimize data misuse and misinterpretation of data. Security features encrypt or mask data to heighten security.

Multichannel data access

Multichannel data access for decision makers, analysts and researchers of varying skills. No-tech users, SSBI, augmented low-code analytics applications, direct access, AI sandbox, and predictive analytics.

Self-served and ease of access

Self-service capability to empower users and decisions makers.

Scalable and expandable technology

Silos of data, rapid integration of any data in any format, keeping up with the volumes data, simplification of adding new data sources and use cases, low code and low code applications. Able to resizes automatically to meet business needs without compromising performance. Keep up the technology innovations.