What happened in Hive One?

See also What happened in Hive Two?

Hive One, which took place on May 31, asked the overall questions “How do we shape the future of Laboratory Medicine and Pathobiology? How do we lead and integrate the transformative innovations that are changing the face of LMP?"

In that session, the group discussed the many dimensions of evolution for the fields related to Laboratory Medicine and Pathobiology, including but not limited to:

  • Shifts in the diagnostics that affect clinical training and roles, including less invasive tests, point of care testing, digitization, and more outpatient and chronic needs;
  • Opportunities to integrate AI and machine learning and large data from “omics”, digital pathology to advance clinical diagnostics, research and education;
  • Exponential opportunities in omics, big data and advanced imaging for developing precision medicine;
  • A newfound recognition of the value of microbiology and virology;
  • Increasing focus on translational research and a need to bring together basic scientists and clinical laboratorians;
  • An increasing need for new approaches to education that enable more “precision learning” and individualized programs

Overwhelmingly, the participants underlined the critical nature of collaboration, of trans-disciplinary connections, and to create more flexible structures around research, teaching, and clinical practice. The initial high-level goal articulated by the group around innovation was captured as:

LMP will be recognized as the "go-to" place for bringing together trans-disciplinary collaboration, translational research, and the newest technologies to transform the understanding of disease pathogenesis.

To move in this direction, they identified five high-level zones of action to work toward this goal, with initial possible actions:

1. Collaboration

Create platforms, incentives, and supports for people to create dynamic collaborations for research, teaching, practice, and innovation that match clinical need

Examples of actions include:

  • Create connections between basic scientists and clinicians, across programs and disciplines, to identify and strategize around clinical need
  • Create structures that enable collaborative work
  • Develop joint courses and cross-pollination between departments
  • Strengthen international collaborations/networks

2. Focused Investment

Build critical mass in technology and expertise at the edges of innovation

Examples of actions include:

  • Establish foundational expertise in emerging fields to flex as technology evolves and to allow different areas to naturally emerge
  • Establish hallmark initiatives such as bringing "omics" into a single assay, integration AI into clinical contexts, research and education
  • Invest in virology/microbiology ("get ahead of the next pandemic")
  • Amplify our existing strength in precision medicine

3. Thriving people

Recruit and support faculty and students who have energy and expertise to work in the evolving fields

Examples of actions include:

  • Recruit faculty in key specialist areas (e.g., virology)
  • Recruit faculty who can lead interdisciplinary research/teaching/innovation
  • Outreach to undergraduates
  • Connect to our diaspora of alumni
  • Continue to build strength to translate discoveries into real clinical impact

4. Agile education

Create personalized, flexible learning that reflects the evolution of the field

Examples of actions:

  • Continue to evolve curriculum to incorporate the rapidly changing field, including AI in medicine, less-invasive testing, evolving clinical roles, quantitative biology, bioethics, leadership in translation
  • Develop "precision learning experience"
  • Expand capacity to integrate commercialization / IP into departmental work
  • Evolve hybrid approaches to in-person/virtual learning to expand options

5. Maximize resources

Generate the greatest value from existing resources

Examples of actions:

  • Strengthen shared resources - core facilities, integrative labs
  • Develop frameworks and infrastructure to share data of all kinds (omics, diagnostic, clinical...) e.g., build integrated databases and support of T-CAIREM.