ASH Research Collaborative: a real-world data infrastructure to support real-world evidence development and learning healthcare systems in hematology

Blood Adv. 2021 Dec 14;5(23):5429-5438. doi: 10.1182/bloodadvances.2021005902.

Abstract

The ASH Research Collaborative is a nonprofit organization established through the American Society of Hematology's commitment to patients with hematologic conditions and the science that informs clinical care and future therapies. The ASH Research Collaborative houses 2 major initiatives: (1) the Data Hub and (2) the Clinical Trials Network (CTN). The Data Hub is a program for hematologic diseases in which networks of clinical care delivery sites are developed in specific disease areas, with individual patient data contributed through electronic health record (EHR) integration, direct data entry through electronic data capture, and external data sources. Disease-specific data models are constructed so that data can be assembled into analytic datasets and used to enhance clinical care through dashboards and other mechanisms. Initial models have been built in multiple myeloma (MM) and sickle cell disease (SCD) using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and Fast Healthcare Interoperability Resources (FHIR) standards. The Data Hub also provides a framework for development of disease-specific learning communities (LC) and testing of health care delivery strategies. The ASH Research Collaborative SCD CTN is a clinical trials accelerator that creates efficiencies in the execution of multicenter clinical trials and has been initially developed for SCD. Both components are operational, with the Data Hub actively aggregating source data and the SCD CTN reviewing study candidates. This manuscript describes processes involved in developing core features of the ASH Research Collaborative to inform the stakeholder community in preparation for expansion to additional disease areas.

Publication types

  • Multicenter Study

MeSH terms

  • Delivery of Health Care
  • Electronic Health Records
  • Hematology*
  • Humans
  • Learning Health System*