What Is Data Intelligence? An Insider’s Guide.

Although the healthcare industry has an abundance of data residing in electronic medical records, business and clinical systems, and on apps, phones, and paper, this information is often trapped in narrow siloes and inaccessible due to a lack of data interoperability and stringent security and privacy rules. Data is more beneficial when it is easily reachable and part of a framework with defined rules for collecting, examining, and using it. As large amounts of data are ingested, organized, and sent to the right people and places, it can begin to yield meaningful insights. This is data intelligence. With rising market competition, consumerism, staffing shortages, and healthcare costs, healthcare organizations need robust data intelligence more than ever.

According to global market intelligence firm IDC, data intelligence does the following:

• Identifies who is using the data
• Shows where the information lives and where it came from
• Reveals when data is being accessed and when it was last updated
• Tells why we need specific data and if it should be kept or discarded
• Shows how the data is used and how it should be used
• Exposes relationships within the data

Data intelligence helps highly regulated, complex industries like healthcare, which produces an enormous amount of data, by ingesting and managing vast amounts of information generated across three major areas:

• Healthcare staff and patients
• Healthcare machines
• Healthcare facilities

Hospitals, clinicians, patients, business leaders, vendor companies, and many others rely on data intelligence to make critical operational, financial, and patient care decisions. Data intelligence provides a complete view of everything from a patient’s medical record to how a hospital’s clinical assets are performing. Where traditional methods fail, data intelligence works with structured and unstructured data to offer insights on how to optimize workflows, products, care quality, costs, and more.

Five ways data intelligence is advancing healthcare

1. Analyzes large amounts of information
2. Offers pure visibility into underlying data to create a complete picture
3. Supports clinical, operational, and business objectives
4. Helps organizations become more intelligent over time
5. Ensures sensitive patient data remains secure

Data analytics mines healthcare data, revealing trends and patterns that can’t be detected in the raw form. In other words, data analytics adds color and relevant details to become data intelligence. Combining different data sources and correlating what’s happening can yield powerful results, including deeper insights and better outcomes. Together, data intelligence and analytics provide a meaningful bedrock of truth across the healthcare ecosystem that is key to improving clinical, operational, and financial outcomes.

For Example, data analytics algorithms look at the relationships between different types of data to drive faster, more accurate patient diagnoses; create predictive staffing models; and allow more visibility and predictability into machine performance, equipment utilization, and referral patterns to improve patient care, reduce staff burn out, and drive down costs.

Data intelligence includes four different types of analytics that can build on a maturity model, allowing organizations to start with simple analytics that improve the value they provide to increasingly complex methods that drive more substantial results.

The following are four types of data intelligence in healthcare:

• Descriptive intelligence looks at historical data and patterns, enabling organizations to make decisions more effortlessly using simple reports, charts, and graphs.
• Predictive (proactive) intelligence applies algorithms and machine learning techniques to data to make future predictions in specific areas such as hospital bed capacity, patient adverse events, patient scheduling, and maintenance requirements on clinical assets.
• Prescriptive intelligence captures larger amounts of data, providing actionable insights and informing future strategies. For Example, with the right data intelligence, organizations can provide more meaningful or prescriptive alerts to clinical staff to allow them to act while reducing alert fatigue.
• Learning from user experiences: Healthcare organizations are starting to crowdsource and learn from user interaction data such as patient ratings. Organizations become smarter as they learn more about patient and consumer preferences.

Data in the wild is disorganized and overwhelming, with healthcare organizations constantly needing to decide what is most important to capture, how to keep the information secure, and how to determine the lifecycle of the growing data load. Creating a data strategy is critical—it must be tied to top organizational goals and be able to integrate key information to develop meaningful insights and solutions. At the same time, flexibility is essential in today’s healthcare environment, which continues to experience rapid changes.

A Data strategy should include the following elements:

• Governance
• Data security
• Privacy
• Integrity
• Quality
• Regulatory compliance

Healthcare organizations that rely on solid data intelligence also prioritize data governance, which guides data use. Here, IDC defines the relationship between the two: “Data governance is an organizational discipline with multiple dimensions of people, process, policy and supported by technology. Data intelligence software is part of the technology required to enable and support data governance disciplines…”

Data governance ensures that healthcare organizations use high-quality data, are compliant, and are tapping data for specific organizational goals. A proper governance structure is especially critical in healthcare, where security and privacy are leading concerns. Data governance that gives people the confidence that the data is not misused will provide higher-quality data.

According to McKinsey, a governance structure should include the following:

• A central data management office (DMO) led by a chief data officer (CDO)
• Governance roles organized by data domain
• A data council that includes domain leaders and the DMO

• Supports a data-driven culture
• Allows organizations to turn big data into curated, actionable information to support clinical and business decisions
• Breaks down silo walls, so information is more readily shared
• Fosters smarter, faster decisions and problem solving, optimizing workflows, care quality, and information sharing with patients and healthcare consumers