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How to Create a Healthcare Data Culture

HealthITAnalytics Data analytics and interoperability are necessary for health systems to provide high-quality care services to patients, but transitioning from pen, paper, and fax machines to computers and the cloud for data collection and sharing is only part of the equation. The culture of a healthcare organization must also shift to accommodate the evolving processes surrounding the management and use...


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How Big Data Analytics Can Support Preventive Health

The healthcare sector is an increasingly complex system that incorporates various stakeholders outside of clinicians and patients. This complexity generates significant amounts of data across the continuum of care. To promote health...

Types of Deep Learning & Their Uses in Healthcare

Deep learning (DL), which is also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to one another to process information in animal brains. To imitate...

How to Improve Data Normalization in Healthcare

Data normalization refers to the process of standardizing data to reduce ambiguity and make the data useable across systems. In the context of healthcare, health information is normalized to promote data sharing and analytics across the...

Using Data to Quantify a Key Pandemic SDOH: Social Isolation

Loneliness and social isolation rose alarmingly during the COVID-19 pandemic as lockdowns proliferated and in-person gatherings were discouraged. A critical social determinant of health (SDOH), social isolation is estimated to have...

Assessing AI, Data Use Key Priorities of Stanford’s First Data Chief

As new modes of data analysis evolve and become increasingly integrated into clinical care, healthcare organizations are looking to get ahead of the curve and solidify their approach to data science. Stanford Health Care in California is no...

Value of an Evidence-Based AI Development and Deployment Approach

An evidence-based AI development and deployment approach could address systemic issues and help standardize AI practices in healthcare. "We need a movement for the health AI industry that is analogous to the evidence-based medicine movement...

What Providers Can Do to Minimize AI-Based Image Reconstruction Risks

Artificial intelligence is increasingly being used to reconstruct images from data obtained during magnetic resonance imaging, computerized tomography, or other types of scans. While AI has been shown to improve the quality of scans and...

Why UCI Researchers Created a Framework for Analyzing Wearables Data

Wearables — electronic devices that can be worn as accessories — are gaining steam in healthcare, with Deloitte predicting that 320 million consumer health and wellness wearable devices will ship worldwide in 2022. These devices...

Responsible AI Deployment in Healthcare Requires Collaboration

Responsible, secure, and ethical artificial intelligence (AI) deployment in healthcare requires an informed, multi-disciplinary, and collaborative approach. But a lack of industry standards and consensus on how to responsibly deploy AI...

How Unified Patient Records Support Whole-Person Care

Last year, Franciscan Health, a 14-hospital system with facilities in Indiana, Illinois, and Michigan partnered with Innovaccer to create unified patient records to support whole-person care. Whole-person care is a strategy that allows...

How Machine Learning Can Guide COVID-19 Decision-Making

As the United States continues to see high rates of COVID-19 hospitalizations, providers must make difficult decisions regarding allocating medical resources to patients who may need them most. While physicians can speculate about an...

How Can Artificial Intelligence Change Medical Imaging?  

Increasingly, researchers are looking for ways to implement artificial intelligence into medical imaging. There are several different cases for why a patient might need medical imaging. Whether it’s for a cardiac event, fracture,...

What Are the Benefits of Natural Language Processing Technology?  

To deliver quality care and positive patient outcomes, researchers and clinicians need comprehensive patient data and medical literature.   However, since 80 percent of essential data lies in unstructured...

Prioritizing Data Analytics, Value-Based Care Strategies at Nemours 

In 2021, Nemours Children’s Health, a pediatric health system in Delaware, New Jersey, Pennsylvania, and Florida, took steps to improve its data analytics and value-based...

High-Quality Data Essential to Achieving Whole-Person Patient Care

 During the second annual Payer and Provider Virtual Summit, experts emphasize the role of high-quality data to achieve whole-person care.  Whole person care is the concept that the best way to...

What Is the Role of Data Analytics in Population Health Management? 

Population health management has become an important method for improving community health. As the population health management market continues to develop in the healthcare space, systems must gather data from multiple sources, apply...

How to Use Artificial Intelligence for Chronic Disease Management 

Over the past two decades, the United States has seen a significant increase in chronic disease among the population. According to the Centers for Disease Control and Prevention, six in ten adults in the US have a...

How Population Health, Risk Stratification Support Value-Based Care 

During the COVID-19 pandemic, organizations witnessed the consequences of fee-for-service as providers and patients struggled with communication and revenue.   While value-based care is...

How CVS Health Uses NCCN Guidelines to Advance Precision Medicine

Organizations are constantly looking for ways to enhance treatment and patient experience. By incorporating National Comprehensive Cancer Network (NCCN) guidelines and precision medicine, CVS Health is working to improve cancer patient...