Description

A discussion of the JAMIA paper that makes practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients

Read "Toward a responsible future: recommendations for AI-enabled clinical decision support" in the Journal of the American Medical Informatics Association, 2024, 1–10 https://doi.org/10.1093/jamia/ocae209

See also the DCI Network webinar series on this paper to be held December 6, 2024 hosted by Dr. Yuri Quintana and Dr. Steve Labkoff. Register for the webinar at https://www.dcinetwork.org/events/206

Description
  • Focus: Create a platform for matching patients with clinical trials
  • KPIs:
    • Increased enrollment rate
    • Reduced time-to-enrollment
    • Enhanced diversity in enrollment
    • Improved patient engagement
    • Increased trial awareness
    • Cost reduction
  • Data Models: Patient demographics, medical history, genomic profile, trial eligibility criteria, patient preferences, trial matching algorithm
  • Terminologies: ICD-10, SNOMED CT, LOINC, RxNorm, NCI Thesaurus, CDISC
  • Data Sources: EHRs, genomic databases, clinical trial registries, patient surveys, social media
Description

Use Case 3:

  • Focus: Develop a module for analyzing treatment and outcome data
  • KPIs:
    • Improved treatment selection
    • Reduced adverse events
    • Enhanced patient outcomes
    • Increased efficiency in clinical decision-making
    • Cost-effectiveness
  • Data Models: Patient demographics, medical history, treatment data, outcome data, genomic data, real-world data
  • Terminologies: ICD-10, SNOMED CT, LOINC, RxNorm, MedDRA, NCI Thesaurus, PROMIS, EORTC QLQ-C30
  • Data Sources: EHRs, claims databases, patient registries, genomic databases, PRO surveys

Use Case 4:

Description
  • Focus: Implement standardized data exchange interface for care transitions
  • KPIs:
    • Reduced time to treatment initiation
    • Decreased duplicate testing
    • Enhanced care coordination
    • Improved patient satisfaction
    • Reduced hospital readmissions
    • Increased adherence to treatment plans
  • Data Models: Patient demographics, medical history, diagnostic reports, treatment plans, progress notes, referral information
  • Terminologies: ICD-10, SNOMED CT, LOINC, RxNorm, FHIR, NCI Thesaurus
  • Data Sources: EHRs, laboratory information systems, radiology information systems, pathology information systems, patient portals
Description
  • Focus: Develop unified adverse event reporting system
  • KPIs:
    • Increased AE reporting rate
    • Improved AE detection
    • Enhanced AE classification accuracy
    • Timely identification of safety signals
    • Reduced time and effort for AE reporting
    • Increased patient engagement in AE reporting
  • Data Models: Patient demographics, adverse event details, MedDRA coding, patient-reported outcomes, social media data
  • Terminologies: MedDRA, WHO-ART, SNOMED CT
  • Data Sources: EHRs, PRO measures, social media platforms, spontaneous AE reporting systems
Description
  • Focus: Develop platform for collecting and analyzing virtual tumor board data
  • KPIs:
    • VTB data utilization
    • Novel biomarker discovery
    • Personalized treatment recommendations
    • Patient outcomes
    • Collaboration and knowledge sharing
    • Data quality and completeness
  • Data Models: Patient demographics, clinical characteristics, genomic profile, treatment decisions, outcome data, VTB discussion data
  • Terminologies: Same as Use Case 3, plus HGVS nomenclature
  • Data Sources: VTB platforms, EHRs, genomic databases, imaging repositories, pathology reports, research databases, external data sources
Description
  • Focus: Develop a module for personalized screening recommendations
  • Key Performance Indicators (KPIs):
    • Screening rate increase
    • Patient engagement
    • Disparity reduction
    • Time-to-diagnosis
  • Data Models: Patient demographics, risk factors, communication preferences, screening recommendations, educational materials, outreach
  • Terminologies: ICD-10, LOINC,  CT, NCI Thesaurus, SEER Coding Manual
  • Data Sources: EHRs, cancer registries, patient surveys, SDOH data
Description

Panel Session - Use Cases for Connecting Data Across Silos and Institutions

  • Improving Cancer Screening - Eric Perakslis, PhD, Chief Scientific and Data Officer, Pluto Health; Senior Vice President and Chief Technology Officer, IMIDomics
  • Improving how we analyze and visualize data - Ino de Bruijn, MSc, Manager Bioinformatics Software Engineering, Data Visualization Team Lead, Memorial Sloan Kettering Cancer Center
  • Improving our data vision and data strategies - Xuelu "Jeff" Liu, PMP, CSM, Director, Data Management & Strategy, Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
  • Improving how we learn from virtual tumor boards - Yuri Quintana, PhD, FACMI
Recording: Comparing Outcomes
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Description

Comparing Outcomes

  • Improving how we compare treatment outcomes - Jeremy Warner, MD, Professor of Biostatistics, Professor of Medicine and Professor of Biostatistics at Brown University, the Associate Director of Data Science at the Legorreta Cancer Center at Brown University.
Recording: Observational Data Models
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Description

Observational Data Models

  • Talk Title: OMOP Oncology
  • Speaker: Asieh Golozar, VP, Global Head of Data Science at Odysseus Data Services, Inc. | Professor of the Practice & Director of Clinical Research at the OHDSI Center, Northeastern University
Recording: Minimal Data Standards - mCODE
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Description

Minimal Data Standards - mCODE

  • Talk Title: Cancer on FHIR: Unlocking the Potential of Cancer Data Exchange with mCODE
  • Speakers: May Terry M.Sc., BSEE, BSN, RN; and Su Chen, MD, is a Clinical Science Principal at MITRE
Recording: Emerging Standards - USCDI+
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Description

Emerging Standards - USCDI+

  • Talk Title: Unlocking Cancer Data Insights: Advancing Cancer Data Interoperability with the USCDI+ Cancer
  • Speakers: Umit Topaloglu, PhD,, Chief, Clinical and Translational Research Informatics Branch, and Liz Turi, MEng Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology (hereafter ASTP)
Description

Lessons Learned from Large-Scale Cancer Longitudinal Databases

  • Talk Title: The NCI's Childhood Cancer Data Initiative: Building on the Power of data and Community
  • Speaker: Gregory H. Reaman, M.D., Scientific Director, Childhood Cancer Data Initiative (CCDI), Division of Cancer Treatment and Diagnosis/OD, National Cancer Institute
Description

Patient-Centric Data Science Perspectives

  • Talk Title: Toward health records that serve clinicians and patients
  • Keynote Speaker: Amada Borens, MS Founder Tesselate Data Consulting, LLC
Description

Workshop Introduction and Long-Term Goals

  • Talk Title: Towards better data harmonization and linking across silos with coordinate data architectures, data models and cooperative agreements
  • Speaker: Yuri Quintana, PhD, Chief of Division of Clinical Informatics, Beth Israel Deaconess Medical Center.
  • Bio: https://research.bidmc.org/yuriquintana
Description

Yuri Quintana, Ph.D. Chief of Division of Clinical Informatics, Beth Israel Deaconess Medical Center

This session discussed the objectives of the conference

1. Engage national-level leaders to Identify the top challenges in cancer research and care that require real-evidence data generation and collaboration across institutions.
2. Discuss expectations and requirements for collaborative precision oncology and a national learning health system.
3. Identify data sources, stakeholders, and connections needed to tailor care for each patient’s clinical history, genomics, geography, and social determinants of health such as race, gender, sexual orientation, economic status, education, access to care, neighborhood effects, and social support.

Description

Panel 2: Platform Requirements for Collaboration

Session Chair: Leon Roseblit, MD, Collaborating Scientist, DCI

Terminology Perspectives: Dr. Andrew Kanter (He/Him), Former Chief Medical Officer at IMO / Asst. Prof. in Clinical Biomedical Informatics & Clinical Epidemiology at Columbia University https://www.linkedin.com/in/andrew-kanter-9668851/ 

Data Sharing Perspectives: Dr. Douglas Fridsma, Chief Medical Informatics, Medical Informatics Officer, Former President and CEO, American Medical Informatics Association https://www.linkedin.com/in/dfridsma/

This session discusses the Platform Requirements for Data Harmonization and Governance.

  • What is required for data harmonization? 
  • How do we ensure there is equity and diversity in the data?
  • Where would the data be stored? 
  • What are data governance principles?
Description

DCI Network Keynote – Future directions for real-evidence data generation

FDA Perspectives - Dr. Hilary Marston, U.S. Food & Drug Administration https://www.linkedin.com/in/hilary-marston-ab348/

This presentation provides

1. Overview of the FDA RWE Framework  

2. Notable RWE Approvals & Awards

3. Future Directions

Description

DCI Network Keynote – Lesson Learned from National Initiatives 

Reflections on National Cancer Collaboratives - Greg Simon, Consultant Simonovation, LLC, Former Director, Biden Cancer Initiative https://www.linkedin.com/in/simonovation

This take reflects on lessons learned from the early days of the Biden Cancer Initiative and collaborations between private and non-profit sectors.

Description

Panel 1: What do we want for collaborative precision oncology? Who should be involved?

Session Chair: Steve Labkoff, Quantori and Collaborating Scientist, DCI.

Perspective on Cancer Screening – Dr. Timothy Rebbeck, Vincent L. Gregory, Jr. Professor of Cancer Prevention, Epidemiology, Harvard T.H. Chan School Of Public Health, Professor, Medical Oncology, Dana-Farber Cancer Institute, and Director, Zhu Family Center for Global Cancer Prevention at the Harvard TH Chan School of Public Health https://www.linkedin.com/in/timothy-rebbeck-73560292/

Perspective on Cancer Treatments – Dr. Jeremy Warner, Professor of Medicine at Brown University and Director of the Center for Clinical Cancer Informatics and Data Science (CCIDS), https://www.linkedin.com/in/jeremy-warner-39001512/
 

The goals of this session are to discuss the Requirements for Collaborative Precision Oncology:

Description

Dr. Edward H. (Ted) Shortliffe, Chief Emeritus at the Journal of Biomedical Informatics, Former President and CEO of the American Medical Informatics Association

Goals of Talk:

1. Reflect on Howard Bleich – the man and his professional contributions over a half-century

2. Summarize the waxing/waning roles of data and knowledge in the field of biomedical informatics

3. Summarize where we are today in that evolution, with suggestions for the future

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