The Key to Operating a Data Informed Organization


Healthcare Data Lake: The key to running a data-informed organization
Tom Laughlin, AVP, World Class Customer Service, Inovalon

Two decades ago, business priorities within a healthcare organization were largely determined by a select few leaders. Today, the most successful healthcare organizations use data to validate ideas and further refine them through advanced studies and predictive models.

The data-driven healthcare organization has come of age with recent advances in data technologies, a rise in artificial intelligence and machine learning capabilities, and the availability of compute-intensive, storage-efficient hardware through the commercial cloud (AWS, Azure, Google Cloud). This influx of technology and talent into the market has resulted in a lower barrier to entry for data-driven intelligence. Market competition and large-scale innovation have reduced the learning curve and costs.

The Essential Role of Strong Data Architecture — and How to Achieve It

Becoming a data-centric healthcare organization starts with a strong data architecture. Data needs to be secure, but always available to those who need it. Data must be inexpensive to store at extremely large volumes, but systems must be able to search it in seconds or less. Complex data like JSON or images must be accessible via standard query languages ​​like SQL.

Enter the Healthcare Data Lake – a collection of datasets focused on patient claims medical history, analytical outcomes from quality measurement and risk adaptation programs, clinical data from electronic health record systems, and social determinants of healthcare data. The data lake removes the barriers of siled data sources in disparate formats, creating a comprehensive, consolidated data source that healthcare organizations can access on-demand to support a variety of clinical and business use cases.

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Common data lake misconceptions

When I first heard the term “data lake” and started researching, the overarching promise of an all-encompassing data source sounded a little intimidating; like something that would be very big, messy, and challenging to deal with and gain value from. This is not an uncommon perception – and not entirely unfounded. However, when implemented properly, a data lake provides speed, accuracy, and easy integration into the organization’s current tools and workflows, and avoids these key misconceptions about data lakes:

#1 – “A data lake is complex and with this volume of data it would take weeks to refresh.”

Some data lakes support data refreshes in a few hours. It can take two weeks to populate the same data into a healthcare facility’s own on-prem data warehouse.

#2 – “It will be too difficult to work with and understand this vast amount of data.”

The most effective data lakes are those that provide access to highly structured data—where all sources can be linked by common keys, with data dictionaries describing the data elements.

#3 – “We’ve already invested years and millions of dollars building our own analytics data warehouse, and we don’t want to throw all that work away.”

This is not an either/or proposition. Technologies that power data lakes often leverage data sharing and replication to move data across regions and even across clouds or into private data centers. Data lakes can be an extension and enrichment of existing data warehouses.

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#4“If I use a third-party data lake, my team can’t connect all analytics tools to it.”

Tools like SageMaker, SAS or even business applications can securely connect to the data lake. This means healthcare organizations can view the data lake as an extension of their current data sets and encourage direct connectivity when needed.

Leverage a healthcare data lake for your clinical and business initiatives

Historically, data lakes consist of structured and unstructured raw data; The more structured the data is, the easier it is to understand and use for a variety of use cases. Some data lakes also allow for the integration of additional data sources, meaning healthcare organizations can enrich their data to generate richer, more meaningful insights to drive their clinical and business initiatives.

Let’s look at some data lake use cases for healthcare:

– Use of clinical data to identify populations or diagnoses that may be underreported for risk and quality programs

– Equip care managers with access to real-time clinical data to proactively prevent avoidable ER visits, hospital admissions, and more.

– Integrate meaningful clinical results into vendor report cards

– Monitoring opioid prescribing patterns to identify potential patient safety issues and uncover potential instances of fraud, waste and abuse

– Assess members’ mentoring patterns for use in service design, networking and quality initiatives

Application example: Improving cancer screening rates in older adults

A healthcare plan wants to understand where to focus its patient education campaigns to improve cancer screening rates in older adults, so the data analyst logs into the data lake, captures non-compliant patients for the relevant cancer screening with a simple SQL query, grouped it by zip code and displays the results in table format. The analyst then creates a heat map to visually show where patient-specific measurement gaps are concentrated using a visualization tool. The outreach manager can use this report to quickly identify a few locations to focus on and inform their staffing model for interventions. As a result, a project that would previously have taken months can now be completed in days – bringing rapid time to value for both members and the organization.

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Now is the time to discover the value of a healthcare data lake

If your organization uses data to make clinical and business decisions and you’re not investing in a cloud-based data lake, now is the perfect time to get started. A healthcare data lake can accelerate time-to-value for your business by enabling you to securely bring together and enrich your complex, disparate data to support analytics, business intelligence, and data exploration initiatives that positively impact care delivery and your bottom line .

The data-informed health organization is here.


About Tom Laughlin

Tom Laughlin is a healthcare data management and analytics expert with nearly 20 years of experience developing technology solutions that enable organizations to improve healthcare outcomes and efficiencies. He currently leads solution engineering at Inovalon, where he and his team focus on customizing software solutions to meet the unique needs of health insurance customers.



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