Primers

10 high-value use cases for predictive analytics in healthcare

April 10, 2024 - As healthcare organizations pursue improved care delivery and increased operational efficiency, digital transformation remains a key strategy to help achieve these goals. Many health systems’ digital transformation journey involves identifying the value of their data and capitalizing on that value through big data analytics. Of the four types...

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Artificial intelligence in healthcare: defining the most common terms

by Editorial Staff

As healthcare organizations collect more and more digital health data, transforming that information to generate actionable insights has become crucial. Artificial intelligence (AI) has the potential to significantly bolster these...

How do population health, public health, community health differ?

by Editorial Staff

The rapid proliferation of value-based care arrangements, driven by electronic health record (EHR) adoption and the subsequent explosion of big data, has given providers the opportunity and the...

Top 10 Challenges of Big Data Analytics in Healthcare

by Editorial Staff

Big data analytics is a major undertaking for the healthcare industry.  Providers who have barely come to grips with putting data into their electronic health records (EHRs) are now tasked with...

4 Emerging Strategies to Advance Big Data Analytics in Healthcare

by Editorial Staff

While the potential for big data analytics in healthcare has been a hot topic in recent years, the possible risks of using these tools have received just as much attention. Big data analytics...

Exploring the National Academy of Medicine’s AI Code of Conduct

by Shania Kennedy

The question of how the healthcare industry can leverage artificial intelligence (AI) ethically and responsibly has become a hot topic in recent years. Throughout 2022 and 2023, industry professionals, policymakers, and regulatory...

Breaking Down the Fast Healthcare Interoperability Resource (FHIR)

by Editorial Staff

As health data interoperability becomes an increasingly pressing concern for providers, developers and vendors are paying a great deal more attention to the data standards that will enable seamless,...

Arguing the Pros and Cons of Artificial Intelligence in Healthcare

by Editorial Staff

In what seems like the blink of an eye, mentions of artificial intelligence (AI) have become ubiquitous in the healthcare industry.  From deep learning algorithms that can read computed...

How Will Biden’s Executive Order on Trustworthy AI Impact Healthcare?

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In October, President Joe Biden signed the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (AI). The order establishes guardrails focused on promoting safety and security, protecting...

The Clinical Promise and Ethical Pitfalls of Electronic Phenotyping

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As information technology (IT) methods for disease surveillance, predictive analytics, and clinical decision support become more advanced, big data mining will be crucial to ensure tools use large, high-quality datasets. Data mining can...

Visualizing, Interpreting, and Disposing of Healthcare Analytics Data

by Shania Kennedy

The success of a healthcare analytics project is predicated on how well project stakeholders navigate the data lifecycle, which consists of data generation, collection, processing, storage, management, analysis, visualization,...

Storage, Management, and Analysis in the Health Data Lifecycle

by Shania Kennedy

The data lifecycle drives data analytics projects across industries, and healthcare is no exception. Healthcare stakeholders need to have a firm grasp on each of the steps in the cycle — data generation, collection, processing,...

The Healthcare Data Cycle: Generation, Collection, and Processing

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As data analytics become more necessary to advance population and public health, healthcare stakeholders may find themselves increasingly working on analytics projects. The outcomes of these projects depend on many factors, but healthcare...

Breaking Down 3 Types of Healthcare Natural Language Processing

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Healthcare generates massive amounts of data as patients move along their care journeys, often in the form of notes written by clinicians and stored in EHRs. These data are valuable to improve health outcomes but are often difficult to...

Investigating the Potential of Confidential Computing in Healthcare

by Shania Kennedy

Healthcare data analytics requires some of the highest data privacy and protection measures across the industry, making implementing privacy-preserving technologies a top priority. Doing so requires health systems to investigate, assess,...

Exploring Patient, Provider Perceptions of Healthcare AI

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Explaining the Basics of Patient Risk Scores in Healthcare

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As medicine advances and healthcare organizations move toward value-based care, providers and health systems are prioritizing population health and preventive care. But to prevent disease and adverse outcomes for patients, health systems...

Patient Privacy in Healthcare Analytics: The Role of Augmentation PETs

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How Architectural Privacy-Enhancing Tools Support Health Analytics

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Data analytics is critical to advancing healthcare quality and medical breakthroughs, but protecting patient data must be a priority throughout the process. Privacy-enhancing technologies (PETs) are critical tools healthcare organizations...

Using Algorithmic Privacy-Enhancing Technologies in Healthcare Analytics

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Big data analytics in healthcare have the potential to advance medical research and lead to important breakthroughs, but ensuring both security and access to protected health information (PHI) can be a challenge. Healthcare data access is...

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