Healthcare has seen explosive growth in Data Science, Actuarial Science and AI over the past decade, but may see a point of diminishing returns if these practices were not built on a solid foundation. In recent years, Healthcare companies are finding the need to not only improve their data quality, but to reduce the time it takes to prepare data for meaningful use such as modeling for insights and predictive analytics. 

The Data Quality Challenge in Healthcare

Nearly everyone learned in 8th grade computer lab that “garbage in, equals garbage out” and for some of us that lab was decades ago, but the sentiment still holds true – perhaps now more than ever.  Healthcare, whether payer, provider or pharma, are particularly vulnerable to the pitfalls of poor data quality, given they tend to be highly regulated, have a need to understand margins at a very precise level, need to group, classify, and stratify their membership into risk pools that drive their business decisions, and more. Poor data quality not only undermines their technical pursuits and insights but can make preparing data much more difficult, leading to longer wait times, expensive resources doing manually intensive work, and insights that are less than timely.  The bottom line is that too much is riding on their data to “hope” that the quality is fit for purpose, they need to be certain, and they need to reduce the “time to know”.  

Modern Data Quality Management for Data Preparation

Data Quality has seen an evolution from being a standalone with mostly one-off utilities for things like profiling, to later having more products that provide some level of profiling combined with analysis, but they were never extensive or coordinated toward solving the problem. Today we have Modern Data Quality that can not only profile at a cursory level like the utilities of old, but also at a level of much greater depth and complexity. Add to that the ability to visualize via dashboards, perform discovery, enable remediation, and manage observability in near-real-time, and you have a means to know your data like never before. The DQLabs Platform brings together a comprehensive approach to Modern Data Quality that is highly adoptable, professional grade and enterprise capable.  

Benefits of Data Quality Improvement in Healthcare

Having overcome the lack of a comprehensive platform for Data Quality, data leaders are now faced with compelling their peers, leaders and teams to “buy-in” to the need for improvement. What are the benefits of improving data quality in a way that is tangible and proven?  

How about the following as a few examples:

• Population Health Management – Significantly reducing time for data preparation and ensuring patient data is accurate, complete, and up to date, leads to more precise treatment plans and better patient outcomes. (Improved Decision Making)
• Clinical Research and Trials – DQLabs ensures that clinical trial data is accurate and consistent, leading to more reliable research findings and faster development of new therapies. (Confidence)
• Fraud Detection and Prevention – Advanced analytics and machine learning models are used to detect patterns in billing and claims data that may indicate fraudulent activities. By identifying and preventing fraud, healthcare providers protect their financial health and maintain trust with patients and insurers. DQLabs systematically ensures that the data used for fraud detection is accurate and free from errors, leading to more effective identification of fraudulent activities and reduced financial losses. (Cost avoidance)
• Regulatory Compliance and Reporting – Healthcare organizations must comply with various regulations, such as HIPAA in the U.S. These may require detailed reporting on patient care, data security, and financial health. Accurate data is essential for generating these reports and ensuring compliance, whether internal or regulatory.  (Auditability)

While there are Data Scientists across a broad range of industry verticals, they are heavily relied upon in healthcare, especially among insurance payers as “Actuarial Scientists”. What is actuarial science? Actuarial Science applies mathematical and statistical methods to the observation of natural events, to calculate the risk of events happening (probabilities), so that they can inform policies for risk reduction, ultimately with the goal of saving money and minimizing the impact to members, partners and their organizations. Very important work indeed!  

Unfortunately, if the data is “garbage”, so too will their “actionable insights” be.  Consequently, these Actuarial Scientists may spend an inordinate amount of time preparing data for their models, and accounting for shortcomings. Gartner once reported this time spent at 80% of their total time, which is as expensive as you might imagine. 

Thanks to DQLabs, these highly skilled and well-paid resources can spend their time doing the work they were hired for, and getting further and faster with data at their fingertips that is profiled, scored, tagged and even sorted by domains. Reusable profiling routines, observability and machine learning for things like thresholds and anomaly detection, and detailed scoring make the data quality improvement lifecycle fast and reliable. Data Quality Improvement has become a high-stakes proposition, and with so much riding on the quality of data, it is easy to see why Data Quality is no longer an “IT side dish”, it is a “Business Drivers main course”.

Thinking of what’s most important in this approach to Data Quality, some things to consider:

Are We Ready?  

Everyone can crawl before walking and running, just start. The flexibility and collaborative nature of the DQLabs Platform allows teams to work together across a broad range of sources to gain a deep understanding very quickly – with benefits for all roles and personas. 

What About PII and PHI?

What about PII and PHI, too risky?  No – DQLabs can connect with and do the work directly in source systems – data is not stored anywhere but where it belongs, and metadata is leveraged to tell the story. Additionally, the platform can be leveraged to group and categorize data into Domains with Terms and Tags, making things that much more visible, useful and standardized across the enterprise. 

Can Efficacy Be Proven?

Can efficacy be proven?  Yes, vital metrics for measuring the effectiveness of data quality initiatives are Data Issue Detection Time (DIDT) and Data Issue Resolution Time (DIRT). Observability can reduce detection time to near-real-time, regardless of architecture. DQLabs Observability leverages a client’s data sources and its AI/ML functions to observe, establish and calibrate baselines that allow for quick anomaly detection. Because the DQLabs Platform also tells you precisely where the problem occurs and connects directly to your workflow systems like JIRA, ServiceNow or other tools, a ticket can be created with all the details in the hands of the data practitioners with a mouse click, and the fix can begin. 

Is It Affordable?  

How does one convince the Decision Makers?  The DQLabs Platform will allow you to minimize downtime, and Data Prep time that in most cases will far outweigh the cost.  

As an example, say an average Data Scientist or Actuary makes $150,000 Annually, or $12.5K per month. At this rate, consider 80% of his or her time doing data prep at a cost of roughly 10K per month or 120K per year, per resource. Most Healthcare companies don’t have an Actuary or a couple of Actuaries, they have an Actuarial Department which means tremendous savings just in that discipline alone, with more time spent on actual Data Science.

Start Improving Your Data Quality Now

What is holding you back?  Too busy to start?  Waiting for the next project?  DQLabs can be implemented in a matter of just a few weeks and can begin yielding insights almost immediately on projects new and old.

Experience Augmented Data Quality and Data Observability firsthand. Schedule a Personalized Demo Today.