Enrollment records. Diagnostic records. Claims records. Authorization records. Pharmacy records. And so on. Healthcare is extremely good at generating mountains of data. But how do you make sense of all this raw data and convert it into actionable information? How do organizations manage and govern transforming raw data into a reliable long-term asset?

This is where healthcare consulting can help. It begins with data assessments, business-needs analysis, and an understanding of an organization’s current capabilities to start on the road to success. Changing how an organization manages data is not a one-time effort but rather a business transformation — a data journey.

Data analytic capabilities vary from simple to sophisticated

Gartner, an IT advisory firm, outlines four levels of creating and enhancing value from interpreting data and matching this kind of data analytics with specific business goals. The four levels are a series of increasingly sophisticated data-analytics questions and the tools which help answer them:

1. The simplest answers the question: What happened? That’s descriptive analytics.
2. Why did it happen? That’s diagnostic analytics.
3. What will happen? That’s predictive analytics.
4. How can we make it happen for the outcomes we want? That’s prescriptive analytics.

While each level of data-analytic capability is progressively more difficult to execute, each level also becomes progressively more valuable for patients, providers, and payers.

The sheer volume of data in the healthcare universe is large and increasing daily

Earlier this year, Statista, a market research company, estimated that the healthcare industry generated more than 2.3 zettabytes of data worldwide in 2020. How much data is that? If the average low-end smartphone has 128 gigabytes of capacity, then 2.3 zettabytes would fill enough smartphones to circle the Earth more than 70 times. This occurs every year.

Aggregating all this data is the first step. But simply getting data in one place is not enough. To move up Gartner’s data analytics capabilities — to be able to quickly generate predictive or prescriptive data analytics — requires much more. Leveraging data to enable both predictive and prescriptive analytics can impact the health outcomes of millions of members.

In addition to the huge volume of data, why is healthcare data analytics so difficult? Data can be messy.

Data flow starts with the people who input healthcare information

For instance, data generation starts when a person goes to see a doctor. That’s a visit or encounter. Then comes the actual patient-provider interaction, leading to a claims encounter. Then, finally, there’s the digital file for reimbursement or a claim. Each one of these steps has the potential for the source data to change.

How the data enters the system

The data from that visit to the doctor can enter an organization’s data environment in many ways. Data can enter by manual entry on a computer, a mobile app, via application programming interfaces (APIs), or through using electronic data interchange (EDI) file exchange from one location to another. Each of these entry points will have different rules on how to handle and transmit that data.

What happens once data is in the system

Data — once in a data ecosystem — is rarely static. It changes, it’s exchanged, and it moves within and between environments and systems, such as a data warehouse or data lake. And as data moves, it is transformed or adapted for different platforms or systems.

Any one of these steps can introduce error or a new value that may — or may not — work in another program, report, model, and/or context. Ensuring data quality is critical.

Road map to the data destination

Data quality is critical and can be measured on six key areas: 1) timeliness; 2) completeness; 3) uniqueness (the degree to which it cannot be mistaken for other entries); 4) consistency (between data sets); 5) validity (within defined requirements such as formats, type, and range); and finally, 6) accuracy. Some examples of data-quality problems include:

• Uniqueness: a poorly written report introduced duplicates.
• Consistency: system ID used in one report, SSN used in another
• Validity: Member’s ID is a converted member from an old system.
• Accuracy: ID does not belong to that person.

Managing data for accurate and consistent use means a lot of planning and hard work is required on the front end. It’s about moving from basic methods of storing and analyzing data to a more advanced approach in order to transform your organization. Building a checklist with the Change Healthcare Analytics and Insights Consulting team can put you on the road to your data destination.

Organizations should consider people, processes, technology, governance, and great execution. It starts with managing data from a holistic, enterprise perspective rather than trying to analyze information by business function. Immature data-analytic processes rely on segmented, siloed departmental data. Mature data management utilizes a curated, governed data warehouse, with an enterprise-wide business intelligence platform.

A 2020 Harvard Business Review article underscores that companies with strong data-driven cultures have leaders who set expectations that decisions must be anchored in data; that being data-driven is normal, not novel or exceptional. Healthcare organizations need to develop data champions, from business analysts and providers through chief data officers and CEOs.

What is a data-driven culture?

A data-driven culture is anchored in well-defined, socialized security and data-governance frameworks. Frameworks should be set up and agreed upon by knowledgeable users to monitor, manage, and secure data as an asset. Effective data governance considers data quality, data security, master data management, as well as standards and compliance.

For an organization to leverage the benefits of advanced predictive and prescriptive analytics, it should focus on organizational change in people, culture, processes, technology, and governance.

Healthcare consulting can act as a tour guide for your data journey

A proven healthcare consultant can bring experience and expertise to drive organizational change. A data assessment, business-needs analysis, and understanding of current-state capabilities form the starting point on the road to success. But that’s just the beginning. The right team can help guide the trip and execute a planned series of stops along the path to an organization’s data destination. The right consultant can help evaluate progress, set incremental goals, define outcomes, and establish measurable improvements.

Change Healthcare Analytics and Insights Consulting, working with your organization, can help define a data-driven future state with gap analysis and short-term, intermediate, and long-range recommendations. The tools we use focus on project management, staff training and augmentation, new infrastructure build-outs, and change-management processes.