This blog was created by a human and reviewed with AI
In the rapidly evolving world of healthcare, Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) are not just buzzwords but powerful tools transforming the way we deliver care. Cognome has developed a suite of AI / ML models designed to dramatically improve quality outcomes, drive innovation, reduce healthcare costs and, humbly put, save patient lives. In this blog series, we will provide an introduction to these innovations, setting the stage for deeper dives into our various ML & LLM based models.
Cognome’s journey into AI began over a decade ago, with a vision to leverage cutting-edge technology to solve real-world healthcare challenges. Our models are built using advanced algorithms and trained on extensive real-world datasets, ensuring high accuracy and reliability. From predicting sepsis and preoperative risk levels, and even surgical case cancellations, to managing the optimal length-of-stay (LoS) for a patient, each model addresses a specific clinical or operational need within the healthcare system. With the ability to “bundle” to create novel model sets and a robust backlog, Cognome aims to continuously innovate and introduce new solutions to the market. Stay tuned as we explore each of these models in more detail in upcoming posts.
One of the primary goals of our AI initiatives is to improve patient care and outcomes. Probably the most important model that we as an industry are working on is Sepsis prediction. This has been a focus for Cognome for several years. That hyperfocus and a lot of great collaboration has resulted in a model with the best false-positive rate (2%) of any published model. Another example is the RESP-RISK model, which predicts respiratory failure, allowing clinicians to intervene early and prevent complications. Similarly, the ARDS-RISK model aids in the early detection of Acute Respiratory Distress Syndrome, improving survival rates. Our ASA-AUGMENT model enhances preoperative risk assessments, ensuring that patients receive the most appropriate care based on their risk profile. Lastly, the PACU-LOS model helps prevent prolonged stays in the Post-Anesthesia Care Unit by identifying patients at risk for complications, which ensures smoother patient transitions and recovery.
In addition to improving patient outcomes, our models are designed to drive cost efficiencies across healthcare systems. The CASE-CANCELLATION model, for example, significantly reduces the impact of surgical case cancellations, utilizing 29 predictors ensuring better resource utilization and patient scheduling resulting in $500-$1M in retained revenues and cost saving.
Operationally, our LLM ensemble we proudly call “ALBERT” addresses high ROI health system use cases such as automating nurse chart abstraction and CPT code generation (AUTOCHART-LLM). This LLM has various use cases that either eliminate or drastically reduce the need for nurses to perform manual chart abstraction. One of the key use cases here is to start automating time consuming, resource depleting submissions to healthcare registries.
Other LLMs address hyperscaling Clinical Trials matching (CLINICALTRIALS-LLM), automating de-identification of PHI (DEID-LLM) and extracting key terms from the diverse and complex clinical texts in millions of patient records (QIPI-INSIGHTS-LLM).
The PACU-LOS model helps decrease the length of hospital stays, leading to significant cost savings for healthcare providers. By leveraging these models, healthcare organizations can not only enhance patient care but also streamline operations and reduce overall costs, making quality healthcare more accessible and sustainable.
AI has the potential to transform healthcare, bringing about significant improvements in innovation, patient outcomes, and cost efficiency. Our AI models exemplify this potential, offering tangible benefits to healthcare providers and patients alike. We invite you to join us on this journey as we delve deeper into each model in future blog posts. Stay tuned, stay informed, and let's revolutionize healthcare together!
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