
Siemens Healthcare has agreed to sell its Health Services (HS) business to healthcare information systems firm Cerner for $1.3 billion (0.97 billion euros) in cash, transforming the face of healthcare IT by combining the two companies' workforces to become the largest provider in the field.
The deal includes a three-year strategic agreement for the companies to build on Cerner's health IT business and Siemens' medical devices and imaging offerings, the firms said.
"[T]he alliance we're creating will drive the next generation of innovations that embed information from the EMR inside advanced diagnostic and therapeutic technologies, benefiting our shared clients," said Neal Patterson, Cerner chairman, CEO, and co-founder, in a statement.
Siemens' IT business specializes in administrative hospital IT and electronic patient records, and is not part of the IT segment that deals with imaging modalities and laboratory equipment. It is headquartered in Malvern, Pennsylvania, U.S., and employs some 6,000 employees worldwide, with operations in the U.S., Europe, and Asia.
Based on 2014 estimates, Cerner and Siemens Health Services have combined annual revenues of $4.5 billion (3.4 billion euros), $650 million (486.7 million euros) in annual R&D investment, more than 20,000 employees in more than 30 countries, and approximately 18,000 client facilities.
The companies expect the deal to close in the first quarter of 2015, pending regulatory approvals. Under terms of the deal, Siemens' employees will become Cerner employees, and Cerner will provide support for Siemens' products, including its Soarian portfolio, while working to develop technologies for new platforms in the long term, the companies said.
Siemens' proposed divestiture comes just three months after the company announced a broad-based restructuring of its portfolio, called Vision 2020, which will create nine divisions to replace the 16 under which the company previously operated.
The Vision 2020 plan calls for Siemens to emphasize the "electrification, automation, and digitalization" fields, where the company sees maximum long-term potential.
Siemens added that its healthcare business will be managed separately in the future to give the business "greater flexibility on the medical engineering market."
After continuously investing in the HS portfolio and achieving "significant progress on the technology side," Siemens "realized that business success of our hospital information systems could not always keep pace with our competition," Hermann Requardt, Siemens Healthcare's CEO, said in a prepared statement. "Additionally, an increasing number of country-specific requirements, such as resulting from U.S. healthcare reform, make it increasingly challenging to achieve sufficient scale effects."
Going forward, Siemens will focus on the development of information systems that support its businesses in laboratory diagnostics, imaging, and therapy, Requardt said.











![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)





