How Hospitals Can Unlock New Revenue Streams Amid OBBBA’s Cuts

On July 4, the One Big Beautiful Bill Act (OBBBA) was signed into law, ushering in Medicaid reforms that present health systems with looming financial challenges. A new analysis from Kodiak Solutions projects hospitals could lose up to $25 billion annually in net revenue. For every hospital already operating on razor-thin margins, this is a significant challenge. But in times of disruption, opportunities emerge.

The Financial Reality Hospitals Face

Kodiak Solutions’ analysis modeled the impact of Medicaid disenrollment scenarios:

  • 5% disenrollment → $1M+ in annual net revenue losses

  • 10% disenrollment → $2M+ drop

  • 15% disenrollment → $3M+ drop

  • 20% disenrollment → nearly $4M drop

Traditional levers – cost reduction, payer negotiations, or Medicaid enrollment outreach – won’t be enough to fill the widening gap. Hospitals need to think differently.

A New Path: Monetizing Medical Imaging Data

What if hospitals could transform an existing, underutilized asset into a steady revenue stream? That’s where Segmed comes in.

Segmed enables hospitals to securely monetize de-identified medical imaging data, turning information already being collected every day into revenue. By connecting hospitals with life sciences, pharmaceutical, and AI developers, Segmed helps institutions generate non-patient-dependent income – without adding operational burden. And, importantly, the data is supporting groundbreaking innovations that could further reduce hospital costs and improve patient outcomes – as long as the patient data is included in the training.

Why Segmed Is the Right Partner for Hospitals

  1. Revenue Diversification: Don’t rely solely on reimbursements – create a future-proof income stream.

  2. Low Operational Burden: Segmed handles the heavy lifting so hospital teams can stay focused on patient care.

  3. Proven Model: Leading life sciences organizations and AI innovators already rely on Segmed to accelerate breakthroughs. Hospitals should be part of this ecosystem too.

Conclusion: Transforming Uncertainty into Opportunity

Health systems can pro-actively navigate the changes. By partnering with Segmed, they can turn uncertainty into opportunity, improve their financial future, and play a pivotal role in advancing healthcare innovation.

Connect with Segmed today to explore how your hospital can unlock a new revenue stream. 

Location: United States

Comments

Post a Comment

Popular posts from this blog

The Future of AI in Medical Devices: FDA Guidelines and Value of Imaging Data

Image
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the medical device industry. These technologies are developing new methods to aid in the process of diagnosis, clinical decision-making, and the formation of treatment plans. AI and ML can identify anomalies and patterns to forecast patient outcomes from intricate data trends. They are making medical treatments more accurate and faster. As such algorithms evolve, they are being applied more and more in medical devices, enabling the devices to learn, adapt, and improve while being used. Medical device companies are applying these technologies to create innovative products that will better aid healthcare professionals and enhance patient care. One of the most significant advantages of AI/ML is the fact that it can learn from practical usage and experience, and improve its performance. Evolving FDA guidance on AI in medical devices The FDA also encourages AI and machine learning-based software as a medical device (...
Read more

Segmed’s Role in WHO Framework for AI Medical Devices

Image
Developing an AI algorithm that will make it into the clinical setting is not easy. The whole process is challenging, from gathering and curating medical data, to training, validating, and testing an AI model, and finally to deploying the algorithm in the clinics. The World Health Organization (WHO) recently published a  framework  that helps developers of AI-based software as a medical device (AI-SaMD) to understand the requirements of evidence generation from development to post-market surveillance [1]. It is expected that AI will significantly improve healthcare delivery and outcomes. The clinical application of AI models is, however, still challenging, not only in low- and middle-income countries, but also in high-income countries. One of the biggest hurdles for clinical application of AI algorithms is access to well-curated and diverse data used for training, validation, and testing. Many AI models are developed in an academic setting, based on homogeneous data from a sin...
Read more

Introducing Segmed’s LLM-based Data De-identification Playground

Here at Segmed, we’re simplifying access to real-world imaging data for AI research and collaboration. Due to this, we regularly process massive quantities of data - both radiology reports and associated DICOMs. To date, we have accumulated 60M+ patient records sourced from 1500+ sites across 5 continents (and this number continues to grow!). Medical data has incredible potential - researchers and developers in medical AI benefit from high-quality, standardized data to train, test and validate their algorithms. However, prior to sharing and collaborating on this data, patient privacy and confidentiality must be maintained. For this, said data must be effectively de-identified. What is data de-identification? Organizations that use patient data for research must prevent the exposure of protected health information (PHI). HIPAA (Health Insurance Portability and Accountability Act) mandates the removal of any information that could potentially lead to the re-identification of a patient....
Read more