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Webinar Recording and Feedback Opportunity Available: Advancing Technology for Quality Reporting at CMS - Burden Reduction and FHIR

July 27, 2020

Webinar Recording and Feedback Opportunity Available: Advancing Technology for Quality Reporting at CMS - Burden Reduction and FHIR

The Centers for Medicare & Medicaid Services (CMS) recorded a presentation on how the Fast Healthcare Interoperability Resources® (FHIR) standard can be used to advance technology to reduce quality reporting burden and increase interoperability for our healthcare community. This presentation discusses the journey to FHIR for quality, the benefits of FHIR, implementation plans for electronic quality reporting using FHIR, roadmap goals, and stakeholder readiness. CMS also recently collaborated with Health IT Vendors to conduct a pilot implementation of FHIR for quality with a vision to streamline the future of quality submissions across our programs and will share pilot results and next steps.

The webinar recording ‘Advancing Technology for Quality Reporting at CMS: Burden Reduction and FHIR’ can be found on YouTube and slides are available on the Electronic Clinical Quality Improvement (eCQI) Resource Center.

The presentation focused on these learning objectives:        

  • Burden reduction for electronic clinical quality measure implementation and reporting
  • Understanding the benefits of FHIR for quality reporting
  • Identifying resources available to find information about FHIR for quality reporting
  • Describing how FHIR can replace current standards for electronic quality data capture and reporting
  • Understanding CMS’s roadmap for transition to FHIR for quality

CMS invites you to provide feedback by responding to a FHIR Readiness Poll by Friday, August 31, 2020. *Please note, the webinar recording states to provide feedback by August 8, but the feedback opportunity has been extended to August 31.

Submit FHIR-related questions to the eCQI Resource Center team at ecqi-resource-center@hhs.gov.

Find information about FHIR and other standards on the eCQI Resource Center.

 

WEBINAR: Complete Patient Data – A Key Element to New York’s Response to COVID-19

In today’s digital age, our connectivity gives us a strong advantage in fighting infectious disease. Advanced technology gives us the ability to analyze data across communities, regions, and state lines to identify outbreaks, and predict future movement to help those most at risk. Collaborating and sharing data to provide real-time guidance and enable research is the key to better understand and control COVID-19.

Data Analytics Workgroup - Social Determinants of Health

June 17, 2020

eHI’s Data Analytics Workgroup met on June 17 to with an overview by two influential HIEs, New York’s Rochester RHIO and Maryland’s CRISP and discussed their initiatives using SDOH to connect people and organizations to community services to improve care.

Topics of discussion included:

  • Creating a data framework to better organize, structure and share data
  • Systems Integration Project to build cross sector data to exchange for social services, education, and health care organizations
  • Developing collaborations with health systems and community-based organizations

Data Analytics Workgroup - Social Determinants of Health

June 26, 2020

eHI’s Data Analytics Workgroup met on June 17 to with an overview by two influential HIEs, New York’s Rochester RHIO and Maryland’s CRISP and discussed their initiatives using SDOH to connect people and organizations to community services to improve care.

 

Topics of discussion included:

  • Creating a data framework to better organize, structure and share data
  • Systems Integration Project to build cross sector data to exchange for social services, education, and health care organizations
  • Developing collaborations with health systems and community-based organizations

Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology

April 23, 2020

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.