Features

Features

• Supporting GLP-1 prescribing with digital twin technology

  GLP-1s have exploded in popularity, but are not suitable for every patient. A digital twin could provide decision support for clinicians without access to expert second opinions.  Continue Reading

• Investigating how GenAI can support clinical decision-making

  With the rapid expansion of medical knowledge, clinicians must spend significant time and effort to stay abreast of advances in their field. Can AI help?  Continue Reading

• How AI-driven data curation could advance precision medicine

  The data curation requirements for successful precision medicine initiatives present hurdles for healthcare stakeholders. Can AI help alleviate that burden?  Continue Reading

• Collaboration key to using data standards for value-based care

  Panelists at Xtelligent Healthcare's Payer + Provider Summit discussed the promise and challenges of using data standards for value-based care.  Continue Reading

• Exploring 3 types of healthcare natural language processing

  Natural language processing, understanding and generation might help healthcare stakeholders make better use of their wealth of unstructured, text-based data.  Continue Reading

• How can analytics help triage behavioral health risk?

  Risk stratification is an important aspect of addressing the U.S. behavioral health crisis, and analytics can help providers enhance patient triage and care delivery.  Continue Reading

• Exploring ACR's AI quality assurance program for radiology

  The American College of Radiology's national quality assurance program seeks to help providers safely and effectively implement medical imaging AI.  Continue Reading

• Top data analytics tools for chronic disease management

  Effective chronic disease management requires healthcare stakeholders to effectively utilize data, population health management systems and predictive analytics tools.  Continue Reading

• Can AI-driven care coordination software improve workflows?

  By integrating an AI platform across dozens of hospitals and outpatient settings, University Hospitals aims to streamline care coordination and improve outcomes.  Continue Reading

• Comparing real-world, synthetic and de-identified data

  Real-world, synthetic and de-identified data all play a role in healthcare analytics efforts, but knowing which type of data to use is key for success.  Continue Reading

• Breaking down the 7 types of health informatics

  Health informatics is an ever-growing field aimed at using health data, information technology systems and clinical knowledge to improve patient outcomes.  Continue Reading

• Measuring sickle cell burden with a public health dashboard

  The Indiana Sickle Cell Dashboard gives patients, providers and public health officials interactive visualizations of sickle cell disease prevalence and burden.  Continue Reading

• How healthcare organizations can prioritize AI governance

  As artificial intelligence continues to gain traction in healthcare, health systems and other stakeholders must work on building effective AI governance programs.  Continue Reading

• How can AI help predict, automate hospital discharge?

  When planning discharge, care teams must use their best judgment and available data to inform decision-making. Can artificial intelligence tools streamline this process?  Continue Reading

• Exploring Mayo Clinic's digital health innovation platform

  Mayo Clinic Platform_Solutions Studio uses a federated 'data behind glass' approach to help developers and health systems build robust, secure digital health solutions.  Continue Reading

• Investigating how ambient sensing can improve OR efficiency

  Houston Methodist has successfully piloted ambient clinical intelligence and AI tools to bolster efficiency and cost savings within its operating rooms.  Continue Reading

• 4 high-value use cases for synthetic data in healthcare

  Synthetic data generation and use can bolster clinical research, application development and data privacy protection efforts in the healthcare sector.  Continue Reading

• How AI in nursing could alleviate documentation burden

  As nurses face increasing levels of burnout, researchers are exploring how large language models could streamline clinical documentation and care planning.  Continue Reading

• Will AI hinder digital transformation in healthcare?

  Healthcare digitalization requires integrating new technologies and processes to meet patients' evolving needs, but the process is rife with challenges.  Continue Reading

• Reassessing the use of race in clinical algorithms

  Race is often included as a biological construct in clinical guidance, but experts assert that its use must be reexamined to promote health equity.  Continue Reading

• Fostering health AI development with confidential computing

  Can confidential computing streamline clinical algorithm development by providing a secure collaborative environment for data stewards and AI developers?  Continue Reading

• Advancing transparency, fairness in AI to boost health equity

  Fairness in clinical algorithms is key to mitigating race-based health inequity. Are efforts driven by AI and machine learning up to the task?  Continue Reading

• Investigating electronic phenotyping’s role in clinical analytics

  Electronic phenotyping has significant potential to drive EHR-based data mining and clinical research, but patient privacy remains a major concern.  Continue Reading

• Weighing the pros and cons of synthetic healthcare data use

  Synthetic data is touted as a privacy-preserving alternative to the use of real-world patient information in healthcare analytics.  Continue Reading

• Exploring generative artificial intelligence in healthcare

  As the hype around generative AI continues, healthcare stakeholders must balance the technology’s promise and pitfalls.  Continue Reading

• WEDI Conf. looks at AI for ROI & interoperability

  WEDI’s Spring Conference saw experts discussing best practices and emerging trends in artificial intelligence and health information exchange.  Continue Reading

• Exploring the role of AI in healthcare risk stratification

  Artificial intelligence is taking the healthcare industry by storm, and its applications in risk stratification have significant potential to improve outcomes.  Continue Reading

• Navigating the black box AI debate in healthcare

  How concerned should healthcare stakeholders be about the complexity and lack of transparency in black box artificial intelligence tools?  Continue Reading

• Top 12 ways artificial intelligence will impact healthcare

  Artificial intelligence has become a transformational force in healthcare, bolstering medical research, care delivery and health system operations.  Continue Reading

• 10 high-value use cases for predictive analytics in healthcare

  Predictive analytics can support population health management, financial success, and better outcomes across the value-based care continuum.  Continue Reading

• Artificial intelligence in healthcare: defining the most common terms

  Harnessing artificial intelligence is attractive to healthcare organizations, but understanding the fundamental concepts around these technologies is crucial.  Continue Reading

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

  Population, public, and community health are key in promoting wellness and improving outcomes, but understanding their nuances is critical.  Continue Reading

• Top 10 Challenges of Big Data Analytics in Healthcare

  Big data analytics in healthcare comes with many challenges, including security, visualization, and a number of data integrity concerns.  Continue Reading

• 4 Emerging Strategies to Advance Big Data Analytics in Healthcare

  While deploying big data analytics in healthcare is challenging, implementing a few key strategies can help smooth the transition.  Continue Reading

• What Are the Benefits of Predictive Analytics in Healthcare?

  Predictive analytics can enhance healthcare by supporting clinical decision-making, guiding population health management, and advancing value-based care.  Continue Reading

• What Are the Top Challenges of Clinical Decision Support Tools?

  Clinical decision support tools can provide actionable information, but issues like alarm fatigue can increase clinician burnout.  Continue Reading

• Understanding De-Identified Data, How to Use It in Healthcare

  Healthcare data de-identification provides significant opportunities to bolster medical research and patient care, but the process is not without its pitfalls.  Continue Reading

• Breaking Down the Fast Healthcare Interoperability Resource (FHIR)

  The Fast Healthcare Interoperability Resource (FHIR) standard aims to improve data exchange, but how does it work and how will it impact interoperability?  Continue Reading

• Arguing the Pros and Cons of Artificial Intelligence in Healthcare

  Artificial intelligence in healthcare is a hot topic, sparking ongoing debate about the ethical, clinical, and human impacts the tech can have on patient care.  Continue Reading

• Visualizing, Interpreting, and Disposing of Healthcare Analytics Data

  Following healthcare data analysis, stakeholders must properly visualize, interpret, and dispose of information according to established best practices.  Continue Reading

• Storage, Management, and Analysis in the Health Data Lifecycle

  Data storage, management, and analysis are three of the most crucial steps in a healthcare analytics project, but what does each entail?  Continue Reading

• The Healthcare Data Cycle: Generation, Collection, and Processing

  Understanding data generation, collection, and processing can guide stakeholders looking to tackle various data analytics projects in healthcare.  Continue Reading

• Investigating the Potential of Confidential Computing in Healthcare

  Confidential computing will become key to protecting patient data as healthcare organizations seek to leverage cloud computing environments.  Continue Reading

• Exploring Patient, Provider Perceptions of Healthcare AI

  Advances in healthcare artificial intelligence have received significant hype, but patients and providers are still cautious of these new technologies.  Continue Reading

• Explaining the Basics of Patient Risk Scores in Healthcare

  Patient risk scores and stratification can bolster care management initiatives, but stakeholders must understand the use cases and limitations.  Continue Reading

• Patient Privacy in Healthcare Analytics: The Role of Augmentation PETs

  Balancing data privacy and access is necessary for healthcare analytics stakeholders, and augmentation privacy-enhancing technologies can help.  Continue Reading

• How Architectural Privacy-Enhancing Tools Support Health Analytics

  Architectural privacy-enhancing technologies play a key role in protecting patient data during healthcare analytics projects, but what are they?  Continue Reading

• Using Algorithmic Privacy-Enhancing Technologies in Healthcare Analytics

  Successful healthcare analytics efforts require stakeholders to prioritize data de-identification through the use of algorithmic privacy-enhancing technologies.  Continue Reading

• Data Analytics in Healthcare: Defining the Most Common Terms

  As data analytics becomes essential for healthcare organizations, stakeholders need to understand the basic vocabulary related to the process.  Continue Reading

• Breaking Down the 4 Types of Healthcare Big Data Analytics

  An effective big data strategy requires healthcare stakeholders to define their top priorities in descriptive, diagnostic, predictive, and prescriptive analytics.  Continue Reading

• Explaining the Basics of Blockchain Technology in Healthcare

  In recent years, blockchain technologies have made a splash across industries. But what is blockchain, and how is it relevant to healthcare?  Continue Reading

• Top Data Analytics Tools for Population Health Management

  A comprehensive population health management strategy requires health systems to leverage data integration, risk stratification, and predictive analytics tools.  Continue Reading

• 3 Use Cases for Blockchain in Healthcare

  Blockchain is an innovative data storage and sharing solution, but how can it be leveraged in healthcare?  Continue Reading

• How Big Data Analytics Can Support Preventive Health

  Big data analytics are being incorporated into all aspects of healthcare, but how can they be used to bolster preventive medicine and care?  Continue Reading

• Types of Deep Learning & Their Uses in Healthcare

  Deep learning is a growing trend in healthcare artificial intelligence, but what are the use cases for the various types of deep learning?  Continue Reading

• How to Improve Data Normalization in Healthcare

  Data normalization is critical for healthcare interoperability, but how can organizations address challenges to normalizing their data and improve the process in the future?  Continue Reading

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

  Dr. Nigam Shah, Stanford Health Care's inaugural data chief, discussed in an interview the importance of data access and governance, AI use in healthcare and his top three priorities in his new role.   Continue Reading

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

  Distortion risks associated with AI-based image reconstruction can lead to inaccurate diagnoses, and though the overall risk is low, providers need to be aware of the issue to ensure patient safety.  Continue Reading

• Why UCI Researchers Created a Framework for Analyzing Wearables Data

  The framework provides researchers using wearables data in clinical studies with minimum reporting thresholds to ensure data standardization and validity.  Continue Reading

• How Can Artificial Intelligence Change Medical Imaging?  

  Artificial intelligence can improve medical imaging for screenings, precision medicine, and risk assessment.   Continue Reading

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

  To achieve successful whole-person patient care, providers need access to quality data from a wide variety of sources.   Continue Reading

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

  Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health.  Continue Reading

• How to Use Artificial Intelligence for Chronic Disease Management 

  Artificial intelligence supports efficiency in disease diagnosis, medical decisions, and treatment as part of effective chronic disease management.   Continue Reading

• How Population Health, Risk Stratification Support Value-Based Care 

  Providers must rely on population health management strategies and effective risk stratification to succeed at value-based care.    Continue Reading

• Top Opportunities for Artificial Intelligence to Improve Cancer Care

  By implementing artificial intelligence into cancer care, machine learning tools can detect cancer, assist in decision-making, and recommend treatment approaches.  Continue Reading

• Enhancing Population Health Approaches to Deliver Preventive Care

  In the wake of COVID-19, healthcare organizations are looking to refine their population health strategies and focus on preventive care measures.  Continue Reading

• What Role Could Artificial Intelligence Play in Mental Healthcare?

  Applying artificial intelligence to mental healthcare could expand access and reduce costs, but the field has several challenges to overcome before it can realize these benefits.  Continue Reading

• Exploring the Intersection of Genomic Data and AI in Healthcare

  While artificial intelligence in healthcare has the potential to gain actionable insights from genomic data, longstanding challenges could hamper efforts to achieve precision medicine.  Continue Reading

• Population Health Management Strategies to Reduce Health Disparities

  Health disparities are rampant throughout the healthcare industry. How can population health management strategies reduce their impact?  Continue Reading

• Top Challenges of Applying Artificial Intelligence to Medical Imaging

  Medical imaging is one of the best use cases for artificial intelligence in healthcare, but lack of clinician input and data bottlenecks can make the technology less helpful than promised.  Continue Reading

• Using AI, Data Analytics to Enhance Person-Centered Care for Seniors

  Artificial intelligence and data analytics tools could help one of the nation’s fastest-growing population groups actively participate in their own care.  Continue Reading

• Applying Artificial Intelligence to Chronic Disease Management

  Artificial intelligence tools can help providers navigate the complexities of chronic disease management, leading to more effective, quality treatments.  Continue Reading

• How Healthcare is Leveraging Real-World Data to Improve Outcomes

  To improve outcomes during the COVID-19 pandemic and beyond, organizations are using real-world data for efficient and effective decision-making.  Continue Reading

• Healthcare Data Sharing Connects the Dots for COVID-19 and Beyond

  The COVID-19 pandemic has pushed healthcare to engage in data sharing and research partnerships, facilitating new strategies that many hope will endure after the crisis subsides.  Continue Reading

• Using Social Determinants to Promote Health Equity During a Crisis

  The COVID-19 pandemic has exacerbated existing health equity challenges, leading organizations to leverage social determinants data to get ahead of poor outcomes.  Continue Reading

• Could COVID-19 Help Refine AI, Data Analytics in Healthcare?

  In the rush to contain COVID-19, leaders are rapidly developing artificial intelligence and data analytics in healthcare, paving the way for the future of the technologies.  Continue Reading

• How Machine Learning is Transforming Clinical Decision Support Tools

  With the right data, integration methods, and personnel in place, machine learning has the potential to advance clinical decision support and help providers deliver optimal care.  Continue Reading

• Putting the Pieces Together for a Successful Predictive Analytics Strategy

  Good data, solid use cases, and a culture of transformation form the basis of a successful predictive analytics strategy in healthcare.  Continue Reading

• Providing Holistic Preventive Health with Advanced Primary Care

  With team-based care, data analytics, and an alternative financing model, advanced primary care is creating the path to preventive health that treats the whole patient.  Continue Reading

• How Geographic Data Can Help Address Social Determinants of Health

  An individual’s zip code is more predictive of her health than her genetic code, but it’s not just zip code data that can help tackle social determinants of health.  Continue Reading

• 5 Successful Risk Scoring Tips to Improve Predictive Analytics

  Strategies for successful risk scoring can improve predictive analytics and population health management.  Continue Reading

• What Is Deep Learning and How Will It Change Healthcare?

  What is deep learning, why is it significant, and how will this innovative artificial intelligence strategy change the healthcare industry?  Continue Reading

• Combating Chronic Disease through the Social Determinants of Health

  Reducing the impact of chronic diseases will require payers and providers to get to the root causes of long-term illness, many of which are attributable to the social determinants of health.  Continue Reading

• How to Choose a Population Health Management Company

  Choosing a population health management company requires organizations to clearly define their goals and thoroughly research the health IT market.  Continue Reading

• Using Big Data Analytics for Patient Safety, Hospital Acquired Conditions

  Big data analytics can provide valuable insight into avoiding patient safety events and reducing the incidence of hospital acquired conditions.  Continue Reading

• 5 Steps for Planning a Healthcare Artificial Intelligence Project

  How can organizations planning a healthcare artificial intelligence project set the stage for a successful pilot or program?  Continue Reading

• What Are Precision Medicine and Personalized Medicine?

  Precision medicine, also known as personalized medicine, is a new frontier for healthcare combining genomics, big data analytics, and population health.  Continue Reading

• Understanding the Basics of Clinical Decision Support Systems

  How can clinical decision support systems deliver patient safety and care quality improvements while ensuring health IT usability?  Continue Reading

• What Are the Social Determinants of Population Health?

  What are the social determinants of population health, and how can healthcare providers reduce the socioeconomic impacts of community disparities?  Continue Reading

• Leveraging Business Intelligence for Healthcare Management

  How can organizations leverage business intelligence for healthcare management? What are the first steps for engaging in financial and clinical improvement efforts?  Continue Reading

• Data Integrity Strategies for Patient Matching, Identification

  Patient matching is a critical safety concern. How can providers boost their data integrity to improve their accurate identification rates?  Continue Reading

• Turning Healthcare Big Data into Actionable Clinical Intelligence

  How can healthcare organizations turn their big data assets into actionable clinical intelligence?  Continue Reading

• How Do Artificial Intelligence, Machine Learning Differ in Healthcare?

  Machine learning is a necessary first step towards artificial intelligence in healthcare, but they aren’t the same thing.  Continue Reading

• Judy Faulkner: Epic Is Changing the Big Data, Interoperability Game

  During HIMSS17, Epic Systems founder and CEO Judy Faulkner discussed her philosophy on interoperability and announced some new big data offerings.  Continue Reading

• Health Information Governance Strategies for Unstructured Data

  Information governance becomes particularly important when exploring the use of unstructured data for healthcare analytics.  Continue Reading

• Using Risk Scores, Stratification for Population Health Management

  Risk scores and risk stratification techniques are foundational for any successful population health management program. What are some of the considerations for developing this data?  Continue Reading

• How Healthcare Can Prep for Artificial Intelligence, Machine Learning

  Artificial intelligence and machine learning are on the brink of becoming major forces in the healthcare industry. How can providers start to prepare?  Continue Reading

• How to Get Started with a Population Health Management Program

  Starting with the basics is the key to developing a successful population health management program.  Continue Reading

• What Is the Role of Natural Language Processing in Healthcare?

  Natural language processing may be the key to effective clinical decision support, but there are many problems to solve before the healthcare industry can make good on NLP's promises.  Continue Reading

• The Role of Healthcare Data Governance in Big Data Analytics

  What does data governance mean to healthcare organizations and why is it so crucial to master before engaging in big data analytics?  Continue Reading