Healthcare Analytics

The healthcare industry is awash in data, but data silos and regulatory barriers impede access users need to deliver care, manage payments, and bring innovative treatments to market.

This guide will show how healthcare services use large data sets, describe healthcare leaders using analytics, and explain how Starburst streamlines big data management, analysis, and compliance in healthcare settings.

Healthcare analytics use cases

Data analysis has always driven medical care, from doctors diagnosing patients to researchers investigating new treatments, but analytics now empowers more innovative health informatics use cases.

Redefine patient experiences and outcomes

Everything the industry does centers around patient care. Quickly and accurately analyzing clinical data accelerates diagnoses, while the optimization of care delivery improves treatments and patient outcomes. Applying modern analytics to the patient journey redefines what is possible.

A common challenge facing healthcare professionals is simply getting all the relevant patient data in one place so they can make fully informed decisions.  Health information is scattered across often siloed data sources such as electronic health record (EHR) systems. Traditional databases cannot easily handle healthcare’s unstructured data, such as physician notes and medical imagery.

Patient 360 initiatives unify data from these disparate sources to create standardized views of patients. With more complete information, doctors can develop personalized care plans, nurses have near real-time visibility of patient status, and administrators more accurately bill for healthcare services.

Improve operational efficiencies

Healthcare organizations share the operational imperatives of any other industry. Information technology teams must manage data effectively so business leaders can drive efficiency and growth.

For example, hospitals use predictive analytics to optimize inventory management. Their data science teams use historical data to build predictive algorithms that use real-time data streams to balance inventory costs and availability. Analytics tools like data visualization and near real-time dashboards let purchasing departments monitor inventory metrics and automate re-ordering.

Insurers use healthcare data analytics to automate pre-authorization processes. Manually reviewing routine claims is unproductive. Machine learning algorithms process petabytes of claims data to create models for automatically approving claims. Insurers reimburse providers and patients faster without increasing risk.

Reduce financial and regulatory risks

Legislation like the Health Insurance Portability and Accountability Act requires healthcare organizations to secure patients’ medical and financial data while limiting access to this protected health information (PHI). This is a significant challenge when layers of internal and third-party stakeholders must access PHI to deliver appropriate care.

An open data lakehouse balances these competing pressures by federating disparate data sources within a unified interface. Governance teams can develop fine-grained access controls based on user roles, data attributes, regulatory jurisdictions, and other factors. Authorized users get easy access to needed data without compromising PHI or risking a compliance violation.

Organizations also use advanced analytics to manage financial risks. For instance, data scientists use extensive and diverse data sources to build artificial intelligence algorithms for detecting subtle signs of insurance fraud that human auditors would miss.

Accelerate the entire drug lifecycle

Life science and data science combine to generate actionable insights from an ever-increasing diversity of data from technologies like high-throughput laboratory systems and genomic sequencing. Richer datasets open the door to precision medicine and the development of more effective treatments for underserved populations.

Analytics platforms also foster collaboration, letting researchers around the world analyze datasets regardless of institutional or political boundaries. Centrally managed access controls preserve the integrity of clinical trials and protect participants’ privacy while accelerating clinical trials.

Researchers can leverage the scalable performance of modern analytics platforms to analyze large, complex data sets in minutes rather than days, slashing time to insight and identifying promising research paths more reliably.

Analytics solutions based on modern SQL query engines let data analysts use their preferred business intelligence tools to manage supply chains and optimize pricing throughout the product lifecycle.

Healthcare analytics case study

Starburst has helped these and other organizations across the healthcare industry improve data-driven processes by making it easier to generate actionable insights from their complex data architectures.

Novant Health

Novant Health is a not-for-profit system of nineteen medical centers in over 850 locations in North and South Carolina. The organization had run its analytics through an on-premises data warehouse but migrating to a cloud-based data platform promised to make data more accessible and reliable. However, an extended migration project would interfere with the work of the company’s analysts and data scientists.

Starburst created an abstraction layer that virtualized Novant Health’s data sources. As each dataset migrated to the cloud, the change happened transparently. Users no longer need to know where data lives to run their queries since they only see the Starburst catalog.

EMIS Health

EMIS is a healthcare software provider serving over ten thousand organizations across the United Kingdom. Its customers rely on EMIS to store and process data from billions of consultations and trillions of clinical events.

When developing its next-generation analytics platform, EMIS-X, the company chose Starburst’s massively parallel processing query engine to access data in its Delta Lake securely.

Healthcare management can now run complex, multi-source queries and get the operational insights they need from EMIS-X data in near real-time. Furthermore, EMIS-X gives physicians with less technical analytical skills complete pictures of a patient’s medical history throughout the National Health Service.

Optum

Optum is a global healthcare delivery organization with over 300,000 employees worldwide. The company’s primary data repository is a Hadoop data lake, but significant amounts of data reside in siloed sources like Postgres databases and Teradata warehouses. Hive and Spark SQL forced users to develop expensive ETL pipelines that involved significant data movement.

Optum chose an on-premises deployment of Starburst Enterprise to remove these performance and accessibility bottlenecks. Users are getting query results ten times faster than with Hive and up to three times faster than Spark. Since users can build their own SQL queries, they no longer rely on the data team for ETL development, freeing engineers for more productive tasks. Significantly, Starburst has given Optum’s data team a single place for configuring data access, thereby reducing workloads and simplifying regulatory compliance.

SOPHiA GENETICS

SOPHiA GENETICS is a cloud-based bioinformatics company serving customers in over seventy countries. A web of data sovereignty and privacy regulations forces the company to impose geographic and policy barriers restricting user access to data.

The company uses Kubernetes to deploy Starburst as in-country and regional clusters. Starburst Stargate’s cluster-to-cluster connectors let analysts securely query this distributed data without movement. Aggregation operations execute within the regulated jurisdictions where the data lives so only the results move over the network.

Adopting Starburst also made data more accessible, yielding a tenfold increase in available data and up to a thirty-fold increase in the number of users able to query this data. At the same time, SOPHiA GENETICS improved its compliance posture as Starburst’s single point of access simplifies audits and lets the company demonstrate the effectiveness of its controls.

How Starburst helps with healthcare analytics

Starburst’s open data lakehouse analytics platform puts data at the center of healthcare decision-making, from the hospital bed to the research lab, offering benefits including:

Data-rich, holistic insights

Incomplete, stale, and inaccurate data undermines decision-making, especially in healthcare. Unfortunately, technological progress and business mergers result in amalgamations of data sources. Giving healthcare decision-makers the data they need requires significant time and resources.

With over fifty connectors, Starburst unifies on-premises and cloud data sources within a virtualized access layer, letting users query data throughout the organization without knowing anything about the underlying architecture.

Decision-makers gain richer, more holistic insights — often in near real-time — to make better decisions about patient care and operational performance.

Simple, secure, and compliant accessibility

Starburst gives data teams a single interface for managing healthcare data. For example, Gravity, Starburst’s data discovery and cataloging layer, automates the integration of new data sources. Engineers also spend less time developing ETL pipelines since Starburst queries data where it lives.

Starburst further boosts data team productivity by eliminating routine user requests. Any SQL-compatible analytics software can use Starburst to query sources directly, making data more accessible and reducing engineers’ workloads.

Expanding accessibility does not compromise security or compliance. Starburst’s virtual access layer is also a central access control layer. Administrators easily create and enforce granular policies based on user roles and data attributes to ensure PHI remains accessible only to authorized users.

Scalability and optionality

A Starburst open data lakehouse uses open-source technologies like Iceberg’s open table format and Trino’s massively parallel processing query engine to decouple storage from compute. Data teams can scale their storage architecture independently from their compute resources.

Breaking that link between storage and compute also ends vendor lock-in. Organizations can choose cloud service providers that offer the optimal combination of performance, scalability, and pricing without losing the optionality to change providers in the future.