Siemens Healthineers as REDI's Primary Strategic Partner: The Case and the Playbook

strategic April 2026

The argument for pursuing Siemens Healthineers as REDI’s main distribution and co-development partner is based on six interlocking rationales. Each addresses a specific gap in what REDI can build independently. Together, they position Siemens not just as one option among many, but as the most suitable first choice.

1. Siemens Produces the Data REDI Needs, at Scale, Globally

Siemens Healthineers is one of the five major players commanding approximately 65 percent of the global hematology analyzer market. Their ADVIA analyzer line operates in hospitals across more than 70 countries. Every one of those analyzers generates the complete blood count (CBC) data (hemoglobin, lymphocyte count, platelet indices, neutrophil ratios, red cell parameters) that REDI algorithms consume. Siemens does not merely sell one-off tests; their analyzers are integrated into laboratory workflows under long-term service contracts. Their instruments are often the machine that produces the annual blood draw results for tens of millions of patients. REDI does not need to find new data—Siemens’s existing customers already have it, generated daily.

2. They Have the Distribution Channel REDI Cannot Build Independently

In 2023, Siemens Healthineers held the highest market share in the critical North American diagnostics segment. Their services and software revenue reached €5.3 billion in fiscal 2024, accounting for 24.4 percent of the company’s total revenue, and the company is actively transitioning from hardware sales to recurring digital services. A REDI algorithm running on Siemens instruments, distributed through Siemens’s enterprise relationships with hospital systems, reaches a global installed base without REDI needing to build a single independent sales relationship.

Roche understood exactly this logic when they brought ColonFlag into navify. Siemens has no equivalent platform for lab-data-based cancer risk algorithms. Their competitors at Roche have one, and it is growing. That slot at Siemens is currently empty.

3. Their Stated Corporate Strategy Creates a Specific Gap That REDI Fills

Siemens Healthineers announced a new strategic phase, “Elevating Health Globally,” explicitly targeting AI and custom-built partnerships focused on the most prevalent diseases, with its Diagnostics segment given its own strategic mandate. Siemens has guided mid-single-digit growth for Diagnostics through 2030, shifting toward long-term value-based partnerships of 10 to 15 years. To hit those recurring digital revenue targets, they need software and algorithm products to layer on top of their hardware. They are building toward that model. REDI is an algorithmic company with validated intellectual property for cancer detection. The fit is structural, not incidental.

4. They Already Proved They Can Build Lab-Data AI—Applied to Disease Severity, Not Yet to Cancer Detection

Siemens developed a COVID-19 Severity Algorithm that uses routine lab values and age to generate a clinical severity score predicting progression to ventilator use and 30-day in-hospital mortality, built in partnership with Hospital Universitario La Paz, Spain. That is precisely the architecture REDI employs: standard blood parameters in, risk score out. Siemens knows how to build this class of product. They have not applied it to early cancer detection because they lack a cancer detection algorithm asset. REDI provides that asset.

5. Their SHIFT Innovation Ecosystem Is Designed for Exactly This Kind of Partnership

Siemens runs the SHIFT Innovation Ecosystem, which orchestrates scientific partnerships and collaboration consortium projects on topics with high social relevance. Their active projects already include PANCAIM—a European consortium applying AI to pancreatic cancer treatment—and a formal collaboration agreement with Charité Berlin to develop innovative concepts in preventive medicine. Pancreatic cancer AI is one of REDI’s highest-priority algorithm targets. The Charité preventive medicine collaboration is conceptually identical to what REDI is building. REDI does not enter this conversation as an external vendor, but as a partner to complete the work Siemens has already started.

6. The Roche/navify Precedent Makes the Ask Immediately Legible to Siemens Leadership

Roche structured the EarlySign ColonFlag relationship by integrating a validated third-party CBC-AI cancer detection algorithm into its navify Algorithm Suite, creating a distribution channel that reaches physicians globally. Siemens product leadership is monitoring this development. Approaching Siemens with a proposal to co-develop or distribute a suite of CBC-AI cancer detection algorithms is not a novel concept—it is the clear competitive response to what Roche already built. The question for Siemens is not whether this product category is strategically interesting. It is whether they will build it themselves or partner with someone who already has the validated algorithm assets.

The Approach Playbook

Step 1: Do Not Approach Through Standard Partnership or Sales Channels

Siemens’s Value Partnerships program is structured around enterprise services, operational efficiency, and technology management for existing hospital customers. Entering that space puts REDI in contact with procurement-oriented account managers whose job is to handle equipment contracts, not to co-develop algorithms. That is the wrong conversation at the wrong level.

Step 2: Target Three Specific Entry Points Simultaneously

Entry Point A — The SHIFT Innovation Ecosystem. This is the correct door for a research and algorithm co-development proposal. REDI fits the program’s published criteria precisely: early cancer detection, preventive medicine, and AI on clinical data. Identify the current head of SHIFT through LinkedIn and reach out directly with a two-page concept note before requesting a meeting. The concept note should mention PANCAIM by name and position REDI’s pancreatic cancer algorithm as the translational complement to PANCAIM’s imaging and genomics work.

Entry Point B — Diagnostics Segment Digital Strategy Leadership. Following the “Elevating Health Globally” announcement, Diagnostics received its own strategic mandate with explicit digital growth targets. The relevant executive is the Head of Diagnostics Digital Strategy, or an equivalent role. This person needs algorithm content for their roadmap and owns the revenue target that a REDI partnership would support. Reaching them requires either a direct LinkedIn approach with a brief, commercially framed proposal or an introduction through a mutual contact in the diagnostics industry leadership.

Entry Point C — PANCAIM Project Leadership. The Siemens project lead on the PANCAIM consortium is a direct scientific peer to REDI’s pancreatic cancer team. A co-investigator relationship for that specific cancer type, framed as a blood-panel complement to PANCAIM’s imaging work, could act as the entry point for a broader platform partnership discussion.

Step 3: Frame the Proposal in Siemens’s Commercial Language

Do not lead with REDI’s mission or the humanitarian argument in the first meeting. These are important and belong in the conversation, but they are not the best way to start a discussion with Siemens. Lead with what Siemens needs: a recurring digital revenue product that runs on their installed Atellica and ADVIA base, differentiates their diagnostics offering from Roche and Beckman Coulter, and gives their hospital system customers a clinically validated cancer risk stratification tool unavailable elsewhere.

The proposed commercial structure includes:

REDI retains algorithmic intellectual property.

Siemens provides co-development funding and pilot access to their hospital network for validation studies.

Siemens receives a preferred or exclusive distribution window for a defined period in exchange for the co-development investment.

Revenue is generated from per-patient licensing fees paid by Siemens customers, structured as recurring software revenue consistent with their transition to digital services.

The model mirrors what Roche built with EarlySign before transitioning to navify integration—a proven commercial template Siemens’s business development team will recognize.

Step 4: Bring a Clinical Champion From a Siemens-Connected Institution

Siemens has strong existing ties with major academic medical centers through their Value Partnerships program, including institutions in the RHÖN-KLINIKUM network, several German university hospitals, and large United States integrated health systems. If REDI can bring a chief of oncology or laboratory medicine at one of those institutions into the conversation as a co-investigator, someone who already has a relationship with Siemens, the introduction shifts from a vendor pitch to a peer clinical conversation. That framing matters significantly at a company whose senior leadership positions itself as a scientific and clinical partner, not a software distributor.

Step 5: Make the First Ask a Feasibility Study, Not a Commercial Agreement

The initial proposal should not be a commercial agreement. It should be a jointly funded feasibility study: can REDI’s existing validated CBC-AI architectures for colorectal and lung cancer, both carrying published performance data, be adapted to run natively on Atellica output data and validated against a Siemens-connected hospital’s retrospective electronic medical record cohort?

That is a six-month, low-commitment proof of concept that requires a modest co-investment from Siemens and gives them a defensible internal rationale to evaluate REDI’s technology before committing to a platform. It also produces a peer-reviewed publication, which Siemens values for scientific credibility, and generates external validation data needed by REDI for the next cancer algorithm in the series.

If the pilot works, the commercial conversation writes itself. If it underperforms, both parties gain valuable insights at low cost. This risk-based approach is how large medtech companies evaluate novel algorithm partnerships, and framing the first ask this way shows that REDI understands how Siemens operates.

Who to Contact and How

Siemens Healthineers Diagnostics headquarters: Siemens Healthineers AG, Henkestraße 127, 91052 Erlangen, Germany. The Diagnostics segment operates partly from this base and partly from sites in Tarrytown, New York (United States Diagnostics) and Marburg, Germany.

SHIFT Innovation Ecosystem: shiftinnovation@siemens-healthineers.com—listed on their public SHIFT page as the submission and inquiry channel for collaboration proposals.

Initial approach document: A two-page executive concept note, not a full deck, is the correct format for an unsolicited approach to either SHIFT or Diagnostics Digital Strategy. It should cover the problem (no CBC-AI cancer detection platform at Siemens), the proof (ColonFlag/LungFlag performance benchmarks and REDI’s validated architectures), the commercial structure (co-development, distribution, per-patient licensing), and the ask (feasibility study on Atellica output data). The deck follows the meeting, not the first email.

Endnotes

Mordor Intelligence. “Hematology Analyzers Market Size & Share Analysis- Growth Trends And Forecast (2026 - 2031).” Accessed March 2026.

Siemens Healthineers identified as one of five major players commanding approximately 65% of the global hematology analyzer market, with significant installed base in hospital laboratories worldwide.

Klover.ai. “Siemens Healthineers’ AI Strategy: Analysis of Dominance in Medical Tech AI.” Accessed March 2026.

In 2023, Siemens Healthineers held the highest market share in the critical North American diagnostics segment.

PortersFiveForce.com. “What is Sales and Marketing Strategy of Siemens Healthineers Company?” Accessed March 2026.

Services and software revenue reached €5.3 billion in fiscal 2024, representing 24.4% of total company revenue, with Siemens actively transitioning from hardware sales to recurring digital services.

Roche Diagnostics. “Roche Introduces navify® Algorithm Suite, a digital library of medical algorithms that enhances clinical decision-making to optimise patient care.” Accessed March 2026.

Siemens Healthineers AG. “Siemens Healthineers Announces New Strategy Phase ‘Elevating Health Globally’, Mid-term Targets.” Press release. Accessed March 2026.

Diagnostics segment given its own strategic mandate, with mid-single-digit growth targeted through 2030 and explicit focus on AI and custom-built partnerships for the most prevalent diseases.

MatrixBCG.com. “What is Growth Strategy and Future Prospects of Siemens Healthineers Company?” Accessed March 2026.

Long-term value-based partnerships of 10 to 15 years have produced several multi-hundred-million-dollar deals; adjusted EBIT margin guided at 15–17% as high-margin digital services and SaaS contribute more to the mix.

Medical Product Outsourcing. “Siemens Healthineers: Top Company Profile.” Accessed March 2026.

Documents Siemens’s AI-based COVID-19 Severity Algorithm, which ingested routine lab values and age to generate a clinical severity score predicting progression to ventilator use and 30-day in-hospital mortality, developed in partnership with Hospital Universitario La Paz, Spain.

Siemens Healthineers. “SHIFT Innovation Ecosystem.” Accessed February 2026.

Active projects include PANCAIM (a European consortium applying AI to pancreatic cancer treatment) and a collaboration agreement with Charité Berlin on innovative concepts in preventive medicine.

Siemens Healthineers. “Value Partnerships.” Accessed March 2026.

The Value Partnerships program is structured around enterprise services, operational efficiency, and technology management for existing hospital customers—distinct from Siemens’s innovation ecosystem programs, which are the appropriate channel for novel algorithm development proposals.

Siemens Healthineers. “SHIFT Innovation Ecosystem.” February 2026.

Contact and project submission information for the SHIFT program, which orchestrates scientific partnerships and collaboration consortia across the Siemens Healthineers portfolio.

Goshen R, Choman E, Ran A, et al. “Computer-Assisted Flagging of Individuals at High Risk of Colorectal Cancer in a Large Health Maintenance Organization Using the ColonFlag Test.” JCO Clin Cancer Inform. 2018;2:1–8.

ColonFlag area under the curve 0.82; 10–20x odds ratio at 0.5% false positive rate. The foundational CBC-AI validation study, now deployed at Geisinger and distributed through Roche navify^®^—the direct commercial precedent for a Siemens distribution partnership.