Deerwalk Blog

An Overview of the Healthcare Analytics Continuum

Posted by Tim Huke on February 15, 2018

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Recent polls by GE and Accenture show that big data analytics is a priority for almost 90% of organizations within healthcare. Data is an essential tool for market participants that want to increase share while improving the quality of customer deliverables. And, in the not too distant future, healthcare providers and stakeholders that implement an analytics strategy will excel in clinical quality and prevention of operational bottlenecks.

Healthcare analytics provides decision-makers with valuable insights and solutions obtained from the analysis of correlations and patterns in existing or incoming information. It is not just about the management of healthcare data; it goes further to find patterns in historical and real-time data, enabling managers to anticipate and plan for imminent or future business developments. This helps healthcare providers cut down on problems while making better decisions about core functions like the delivery of care in a cost-effective fashion.

The healthcare analytics process cannot commence without data, and more importantly, clean, high integrity data. Therefore, health plans and payers must first obtain, capture, cleanse and integrate data from disparate sources into a centralized analytics platform.

Since the healthcare industry continuously generates large amounts of data, the only solution is found in digitization. As technological changes sweep across all industries and sectors, it is imperative for healthcare organizations to make use of an analytics platform to effectively manage and transform the vast amounts of data they generate.

That's where Deerwalk comes in.

Providers and payers who embrace the healthcare analytics continuum find ways of streamlining processes, improving patient care (lower cost, higher quality and better outcomes) and boosting revenue by reducing the cost of delivery. This requires access to accurate data as well as the seamless integration of disparate operational and clinical systems and data sources.

Let's take a brief look at each step of the healthcare analytics continuum.

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Foundational Data Integrity

The accuracy, integrity and comprehensiveness of a healthcare provider's foundational data are vital to reliable and accurate performance reporting. This is the starting point of the analytics continuum and plays a vital role in influencing the subsequent analysis and solutions in all of the other steps along the analytics continuum.

Before moving on to other phases, it is imperative that users check and maintain the accuracy and consistency of the healthcare data they feed into analytics and reporting system. Aside from capturing and recording data correctly, users must ensure that there are no unintentional changes or corruption of the data while it resides in the system. 

Deerwalk takes this data integrity layer very seriously. As a matter of fact, it's quite simply the most important thing we do, and we will describe our process to ensure high quality, high integrity data - The Deerwalk Data Factory - in our next post in this series.

Descriptive Business Intelligence

This makes up a major portion of big data in the healthcare industry. Basic descriptive business intelligence illuminates all activities occurring within the healthcare provider's system, providing a comprehensive view of operations. Deerwalk's Executive Analytics portal addresses a lot of this basic reporting fucntionality for our clients.

It leverages customizable data models populated from various sources (EMRs, external labs, professional billing, claims, etc.) to relay analysis of events that happened in the system. Descriptive business intelligence tools provides users with hindsight into situations and critical events by answering the basic question …"What happened?"

Diagnostic Analytics

This is an extension of descriptive intelligence that yields answers to the key question …"What really happenned and how did it happen?" It is one of the primary stages of analytics discovery and offers useful information by creating a summary of all historical data and facts. This summary allows users to view the series of events, in hindsight, that led to a particular outcome. Our clients utilize our Plan Analytics application for daily diagnostic and discovery analytics.

Discovery Analytics

At its core, discovery analytics gives users an insight into events currently occurring within the system, and is the first step in the continuum from traditional hindsight analysis to current real time, or near real-time insights. It is concerned with the blending, exploration, and analysis of data obtained from the previous steps in the healthcare analytics continuum to discover new opportunities. Such analysis is vital since it may lead to the discovery of present patterns or activities that may enhance delivery of better care and outcomes to patients and impact lower cost.

Predictive Analytics

This part of the healthcare analytics continuum relies on a broad range of statistical techniques including machine learning and predictive modeling to make, anticipate and diagnose future occurrences (foresight). It leverages these techniques by analyzing historical and current trends, and takes the generated data to make predictions. The main focus of predictive analysis is the identification of potentially undesirable events and situations so that decision-makers can take effective steps to avert them. Deerwalk has fully integrated current and prospective risk scores to help identify individuals with the highest risk as well as those with emerging risks that otherwise might go undetected until the full onset of illness. 

Prescriptive Analytics

This is the most complex step within the healthcare analytics continuum. At this stage, it is clear that an issue or event warrants immediate attention. After identifying issues or potential problems at the diagnostic and predictive stages of the healthcare analytics continuum, prescriptive analytics focuses on the identification of possible decision options and implementation of solutions for resolution of issues or to prevent future risk. It also shows the possible implications of all decision options. Deerwalk has incorporated and highlighted several prescriptive savings opportunities within our Plan Analytics application, including prescriptions on drug switch opportunities, place-of-service switch opportunities, and avoidable ER and admissions reporting. This summer we will be incorporating an actuarially sound plan modeling module as well.

To ensure that each phase of the continuum delivers accurate performance reporting, you must ensure that you have a strong foundation of high integrity data. Only then can you begin to cleanse, enrich, translate, and transform the data into actionable insights that will put you ahead of the competition. Our next post in this series will dive into the details of the foundational data integrity layer in the analytics continuum - the Deerwalk Data Factory, which subsequent posts on each layer thereafter.

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To learn more about Deerwalk's Population Health Analytics solutions, please visit us at www.deerwalk.com, or click below to schedule a demo.

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