Science Applications International Corp. has entered into a definitive agreement to acquire Unisys Federal, in an all-cash transaction valued at $1.2 billion ($1.025 billion net of the present value of tax assets of approximately $175 million), in a highly strategic and value-creating transaction. This represents a transaction multiple of approximately 10.5x CY2020 adjusted EBITDA, adjusted for the net present value of tax assets.

Unisys Federal, an operating unit of Unisys (NYSE: UIS), is a leading provider of infrastructure modernization, cloud migration, managed services, and enterprise IT-as-a-service through scalable and repeatable solutions to U.S. federal civilian agencies and the Department of Defense.

“With the addition of Unisys Federal, SAIC will be a leading provider of digital transformation services and solutions to the federal government. This exciting opportunity advances our strategy by building on our modernization capabilities, increasing customer access, accelerating growth and enhancing shareholder value,” said SAIC CEO Nazzic Keene. “The financial benefits of acquiring Unisys Federal are compelling, including accretion of adjusted EBITDA margins, non-GAAP earnings per share, and cash generation.” {module INSIDE STORY}

The transaction will further differentiate SAIC in the government services market by deploying technology-enabled, intellectual property-based solutions through a commercial–like service delivery model. The acquisition will further enhance shareholder value through a highly attractive financial profile, enabled through greater customer access and differentiated solutions in areas of higher growth profiles.

Strategic and Financial Benefits

• Enhances capabilities in government priority areas, including IT modernization, cloud migration, managed services, and development, security, and operations (DevSecOpps)
• Expands portfolio of intellectual property (IP) and technology-driven offerings, that enable government-tailored, commercial-based solutions
• Increases access to current and new customers with a strong pipeline of new business opportunities
• Highly accretive across all key financial metrics

Transaction Details

SAIC expects to fund the $1.2 billion cash transaction through a combination of cash on hand and incremental debt. The transaction is expected to close by the end of SAIC’s first quarter of the fiscal year 2021, ending May 1, 2020, following customary closing conditions, including HSR regulatory clearance. The transaction has been unanimously approved by SAIC’s Board of Directors. The businesses will continue to operate independently until the transaction closes.

Could patients experience less pain and possibly have better recovery outcomes if their fears or emotional issues were addressed before surgery?

Three researchers at the University of Tennessee, Knoxville, recently developed a predictive model to examine that question.

Rebecca Koszalinski, assistant professor in the College of Nursing; Anahita Khojandi, assistant professor in the Department of Industrial and Systems Engineering in the Tickle College of Engineering; and Bruce Ramshaw, a physician and adjunct professor in the Haslam College of Business, examined data collected from 102 patients who underwent ventral hernia repair surgery.

A ventral hernia is a bulge of tissue that pushes through a point of weakness in an abdominal wall muscle, requiring surgical correction. Approximately 350,000 ventral hernia procedures occur each year in the US and are associated with an estimated $3 billion in health care costs. {module INSIDE STORY}

The predictive model suggests that the emotional status of the patient prior to surgery--levels of depression, anxiety, grief, or anger--influence recovery outcomes. Patients may experience less pain if their fears or emotional issues are addressed before surgery.

"If we begin prehabilitation, which includes a holistic assessment--not limited to physical and emotional condition--of the person prior to the intervention, then we may be able to affect outcomes," Koszalinski said.

The researchers looked at historical patient data, including demographics and details from the surgical procedures, and examined patterns that led to complications following surgery. By associating the information collected before and during the patients' surgeries to their outcomes, the researchers developed a predictive model to identify future at-risk patients.

The predictive model, generated by Python programming, could be used as a decision support tool, allowing practitioners and patients to more easily assess the risks involved in this type of surgery. Using predictive modeling to examine health data sets is one example of how artificial intelligence can transform modern health care.

"There is a lot of potential for developing decision support tools using data science and artificial intelligence," Khojandi said. "We hear about similar models in the news every day, focused on detecting tumors in chest X-rays, among other things. This is an example of how a tool can be used for shared decision-making and change how individuals interact with the health care system."

The study suggests using the model as a tool for physicians, nurse practitioners, and other clinicians to simulate various scenarios for different patients, examining how the risk factors change for patients. The model could assist in avoiding overtreatment.

The predictive model could help direct efforts on patient education and quantify the impact lifestyle changes have on patients.

"I focus on the person and how they may be better informed and empowered to share in decision-making," Koszalinski said. "The hope is that predictive modeling coupled by empowered patients and expert clinical professionals could result in optimal patient outcomes."