Global Healthcare Landscape

Prevention

Mental Health

B2B, B2C

21.01 Wellbeing

B2C

21.02 Physical Fitness

B2C

21.03 Nutrition & Supplements

B2C

21.04 Remote Monitoring

B2C

21.05 Diagnosis

Lab Testing

B2C

22.01 POC Testing

B2C

22.02 Diagnostic Tech

B2B

22.03 Decision Support

B2B

22.04 Population Health

B2B

22.05 Treatment

Telehealth

B2C

23.01 Home Health

B2C

23.02 Primary Treatment

B2C

23.03 Specialist Treatment

B2C

23.04 Hospitals

B2C

23.05 Care

Rehabilitation

B2C

24.01 Social Care

B2C

24.02 Chronic Care

B2C

24.03 Aged Care

B2C

24.04 Workforce

Training & Accreditation

B2B, B2C

25.01 Health & Safety

B2B

25.02 Staffing

B2B

25.03 Admin

B2B

25.04 Systems

Health Records

B2B

26.01 Practice Management

B2B

26.02 Bookings & Referrals

B2B

26.03 Health Analytics

B2B

26.04 Hardware

Wearables

B2C

27.01 Medical Devices

B2B

27.02 Medical Equipment

B2B

27.03 Medical Imaging

B2B

27.04 Medical Robotics

B2B

27.05 Finance

Health Benefits

B2C

28.01 Corporate Health

B2B

28.02 Health Insurance

B2B

28.03 Health Asset Finance

B2B

28.04 Health Real Estate

B2B

28.05 Supply Chain

Drug Production

B2B

29.01 Drug Marketing

B2B

29.02 Health Logistics

B2B

29.03 Pharmacies

B2C

29.04 Research

Discovery

B2B

30.01 Clinical Trials

B2B

30.02 Clinical Insights

B2B

30.03 Precision Medicine

B2B

30.04 Genomics

B2B

30.05

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About the Global Healthcare Landscape 1.0

The Global Healthcare Landscape 1.0 is an open-source taxonomy for the future of health and wellness, providing a common structure and language for identifying, tracking and making sense of the breadth and depth of innovation happening in healthtech, medtech and biotech globally. The purpose of the Global Healthcare Landscape is to create a granular and global open-source framework to enable insights such as:

Where are we seeing solutions and innovation? What is the velocity of formation, funding and growth? How is this changing over time?
Where are the gaps? When compared to the challenges we are facing, which areas are under-weight or white-space?
Where are we seeing traction and momentum? Where might new science and technology find a commercial market to apply new novel innovations and achieve self-sustainability?

Licensed under Creative Commons and as an open source project, the taxonomy is available for anyone to support their own work in healthcare innovation, to identify an area of focus, or to locate their organization and their peers on the landscape.

A global community of health, medicine, biotech and pharma innovators can track and contribute to the taxonomy’s ongoing development. Following an agile approach, suggestions, ideas and iterations on enhancements will be open and available, where anyone can contribute, share ideas or transparently trace each step of the landscape's evolution. Join us on Notion to keep updating the Global Healthcare Landscape.

Methodology

The Global Healthcare Landscape embraces two classical approaches to data, analytics and design. ‘Bottom Up’ analysis powered by HolonIQ’s Impact Intelligence Platform leveraging powerful machine learning and artificial intelligence, augmenting ‘Top Down’ analysis driven by a global network of experts across all Health domains.

Bottom Up - Machine Learning

To build the taxonomy, we undertook a ‘bottom-up’ analysis using HolonIQ’s proprietary machine learning and artificial intelligence engines to analyze tens of thousands of Health organizations worldwide, including all Health 1000 applicants and nominees. The self organizing map to the right started the process by looking for patterns in the data to establish natural segments, un-constrained by a traditional or conventional perspective. This step focuses on the boundaries between different segments and defining potential categories and segments that a human expert can investigate and qualify.

Top Down - Human Expertise

HolonIQ’s Health Intelligence Unit and our global network of experts bring deep expertise to our ‘top-down’ approach. The top-down analysis draws on the data-driven foundations of the bottom-up analysis to interpret patterns that the machine learning and artificial intelligence process produced. Considerations include context, history, purpose, business model, technologies and ecosystem relationships to add depth and interpretive understanding to the process. This also enables validation of findings against the models and innovations found in health tech today or expected in the future.