Skip to content
Microsoft Industry Blogs

As the use of cloud and digital technologies grows in the healthcare industry, they are transforming how providers make everyday decisions and improve the quality of care. Over the coming months, we’ll be blogging about some of the groundbreaking ways that Microsoft and partner technologies are reshaping healthcare. Today’s blog post features KenSci, a Microsoft partner helping providers use machine learning to improve health outcomes and reduce costs.

Healthcare is expensive, which is why care providers continually seek opportunities to lower costs while improving the quality of care. One opportunity is to adopt a more proactive method of identifying and reducing patient risks. While predicting risk with a high degree of accuracy has historically been difficult, today emerging machine learning technology is making it easy. The value of machine learning is that it recognizes patterns in huge volumes of data, and when providers have it at their fingertips it has the potential to transform daily operations in hospitals and keep patients healthier for longer.

One of the most precious resources in healthcare is physicians’ time. From completing paperwork to treating patients, there are a lot of tasks competing for it. Usually, providers only have the time and tools to look at a limited number of variables about a patient to determine a diagnosis and treatment. But machine learning can sift through oceans of data to bring valuable insights to the surface – giving doctors information that maximizes the value of their time. With the right machine learning solution, providers not only get a closer look at each patient’s history, but they can actually foresee future risks and get suggestions on how to provide the most effective care.

Helping physicians prioritize time and make better-informed decisions is exactly what the KenSci Clinical Analytics solution is designed to do. The solution, built on Microsoft Cloud Technology, is the world’s first vertically integrated machine learning platform for healthcare. It delivers risk predictions to providers in the context of their daily workflow, using machine learning models that mine millions of records from public and customer data sets – like claims, EHR, ADT, financial, and patient-generated data. These insights help doctors base decisions on dynamic analysis that gets more precise every day. Read on to learn how Clinical Analytics helps providers identify population health risks, improve care outcomes, and streamline patient flow through the hospital.

Predict risks and reduce clinical variance across your network

Graphic of high utilizers prediction dataBefore patients even come in the door, Clinical Analytics identifies high utilizers so providers know who to keep a close eye on. The solution learns from a pool of historical clinical data to predict each patient’s risk of readmission, comorbidity, 9-month mortality, and risk of developing specific diseases. It stratifies patients into risk categories so that physicians can see who is at highest risk and intervene accordingly. It could, for example, tell a provider that a patient has an elevated risk of heart disease in the next 3 years, giving them the chance to prevent the onset of the disease. The solution also tracks care quality measures and cost drivers across provider networks, giving providers the tools to reduce clinical variance and improve outcomes across the board.

Improve patient outcomes and quality measures

Graphic of discharge plan dataWhen it comes to care delivery, risk analysis helps physicians make data-driven decisions about whether to transfer someone, prescribe antibiotic regimen, or recommend further testing. Clinical Analytics not only breaks down each patient’s modifiable and unmodifiable risks, it also proposes effective treatments based on successful outcomes for similar patients, helping people get healthy faster and stay healthy longer. Next, Clinical Analytics recommends when to discharge patients and what actions to prescribe in the care transition process to prevent readmission or mortality.

 

Streamline patient flow with discharge planning and load prediction

Graphic of patient dataWhile it’s impossible to know with certainty what patient flow will look like across an entire hospital or organization, machine learning can make a significant difference here as well. Clinical Analytics identifies common patterns to predict the inflow of patients at a given time of the week or year. It then combines load predictions with discharge predictions, enabling providers to manage more efficiently manage beds. Moving patients in and out of the hospital efficiently is a high priority for physicians, because it allows them to treat more people in less time. Finally, risk and load predictions help organizations staff the right specialists at the right times across their network, ensuring that clinics are adequately staffed to meet patients’ needs.

Try it today

The KenSci Clinical Analytics solution leverages 180+ pre-built machine learning models, delivering a substantial return on investment in just 12 weeks. Plus, it integrates easily with existing EMR systems – so care managers and care providers aren’t slowed down by having to learn and use a separate interface.

Want to learn more? Try the solution today on Microsoft AppSource.