Intelligent AI based applications for providers

Customers trust KenSci to help them make more of their data

In the report, “Healthcare AI 2019: ACTUALIZING THE POTENTIAL OF ARTIFICIAL INTELLIGENCE.” by KLAS Research, KenSci was given a score of 92.8* out of 100 based on interviews with seven customers. The findings among interviewing customers, corroborate KenSci as an engaged partner providing customers with Data Science expertise. All of the customers interviewed stated that KenSci played a part in their long-term plans and that they would buy again.

Power operational, clinical and care workflows with ROI led insights

KenSci's analytics and AI solutions, built on and for the AI platform. KenSci helps healthcare providers, health plans and device tech organizations improve operational and clinical metrics while consistently delivering better care outcomes. KenSci's Provider Solutions are targeted at health systems and providers, to enable better care provisions, to manage risks and intervene early with data-driven insights that help see patients more efficiently.

See how IU Health is doing more with KenSci

Solutions for Providers

KenSci’s pre-built ML models enable healthcare providers to build and deploy applications at ease, accelerating their AI journey towards ROI. Built by Doctors, Developers and Data Scientists, in partnership with health systems, KenSci's solution helps bring predictive insights to clinical workflows.

Real-time Command Center

With the unprecedented surge in patient volumes, as a result of the COVID-19 pandemic, KenSci stands committed to delivering insights that might aid in flattening the curve through prevention, outreach and better planning. Built in collaboration with pulmonologists, nurses and CMIOs of large health systems handling COVID-19, our Command Center enables health systems to have a real-time view into bed management and capacity planning, to provide novel coronavirus affected patients, with better care.
Remote patient monitoring

Remotely monitor chronic patients with predictive risk identification of ER visits and risk of readmission. Improve patient experience and care outcomes. Gather data from healthcare sources such as EMRs or from medical patient monitoring devices to understand which patients have a propensity for chronic illnesses. Proactively identify the patients who might visit the hospital or the ones at the highest chances of readmission
Patient flow prediction

With actionable insights on Length of Stay, Risk of Readmission, LWBS and No Shows, optimize discharge planning and improve patient experiences without ED or in-patient bottlenecks. Overcrowding in the ED has been linked to decreased quality of care, increased costs, and diminished patient dissatisfaction. Were healthcare administrators and staff able to be alerted prior to severe overcrowding, they might be able to intervene to alleviate increased demand, before health care quality and access become compromised.
ED staffing prediction

Predict future ED demand and optimally staff ED physicians and nurses to accommodate the patient population regardless of ED demand. Were healthcare administrators and staff able to be alerted prior to severe overcrowding, they might be able to intervene to alleviate increased demand before health care quality and access become compromised.

Real-time Command Center

With the unprecedented surge in patient volumes, as a result of the COVID-19 pandemic, KenSci stands committed to delivering insights that might aid in flattening the curve through prevention, outreach and better planning. Built in collaboration with pulmonologists, nurses and CMIOs of large health systems handling COVID-19, our Command Center enables health systems to have a real-time view into bed management and capacity planning, to provide novel coronavirus affected patients, with better care.

How it works

In critical times such as these, we understand the urgency of analytical insights and the impact it might have on your frontline teams as well as patients.

Mobile Census Analytics: Real-time Bed Availability and capacity (with ventilator tracking) across units, facilities, and systems.

Huddle Tool: In-Patient COVID-19 tracker and discharge planning across the entire patient roster and encounter level drill-through.

Risk Stratification: High-risk cohort finder across patients to facilitate remote-care engagement and resource delivery.

Learn more

Remote patient monitoring

Remotely monitor chronic patients with predictive risk identification of ER visits and risk of readmission. Improve patient experience and care outcomes. Gather data from healthcare sources such as EMRs or from medical patient monitoring devices to understand which patients have a propensity for chronic illnesses. Proactively identify the patients who might visit the hospital or the ones at the highest chances of readmission

Stay ahead with KenSci

Pioneer the use of AI to spot trends in patients suffering with chronic conditions such as Chronic Obstructive Pulmonary Disease (COPD), Diabetes, Cancer and more. Reduce costs associated with each unplanned hospital trip and leverage technology to reduce the overall costs required to manage people with long term conditions.

See how NHS is using Remote Patient Monitoring

Learn more

Patient flow prediction

With actionable insights on Length of Stay, Risk of Readmission, LWBS and No Shows, optimize discharge planning and improve patient experiences without ED or in-patient bottlenecks. Overcrowding in the ED has been linked to decreased quality of care, increased costs, and diminished patient dissatisfaction. Were healthcare administrators and staff able to be alerted prior to severe overcrowding, they might be able to intervene to alleviate increased demand, before health care quality and access become compromised.

Do more with data

KenSci offers a locally-tuned, ML model-based solution that predicts patterns in hospital capacity to enable operational staff to plan for staffing, on daily, weekly, and monthly basis. This supports the delivery of enhanced care to patients needing emergency services – resulting in increased ED staff satisfaction, better patient satisfaction and ED throughput quality measures scores, and improved patient outcomes.

Looking to better manage patient length of stay

ED staffing prediction

Predict future ED demand and optimally staff ED physicians and nurses to accommodate the patient population regardless of ED demand. Were healthcare administrators and staff able to be alerted prior to severe overcrowding, they might be able to intervene to alleviate increased demand before health care quality and access become compromised.

See patients better. Everyday

The KenSci ED Demand Prediction tool enables the ED to have the right number of ED physicians and nurses to accommodate the patient population regardless of ED demand. This solution enables the operational team to visualize future demand and act on precise prediction to optimally staff the ED and demonstrate improved sensitivity, specificity within a 4-hour timeliness.

Understanding your ED Demand better, can help you staff more efficiently. See how KenSci can help predict ED Demand.

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Predict risk and reduce clinical variance

Stay ahead of your patients by understanding patterns in your data on which cohorts are at risk. Manage healthcare outcomes proactively to ensure that patients are delivered quality care beyond the four walls of the hospital.

Reduce clinical variance and manage costs better. Minimize variation in surgical outcomes, drug prescription and pharmacy to maintain consistent care outputs. Find the best care pathways to ensure that patients have the highest levels of satisfaction.

Improve outcomes and quality

Leverage the power of data driven insights and predictive AI models to improve the quality of care and reduce costs. Proactively engage patients even before critical illnesses stem to levels beyond cure, and actively engage them to improve their health.

Better align to the quadruple aim to ensure that AI is able to impact the care continuum and drive better patient as well as staff satisfaction scores.

Better discharge planning and load prediction

With the help of the accurate estimation of the stay of patients, the hospital can plan for more efficient patient discharge. Predicting the probable discharge dates can help to determine available bed hours that results in higher average occupancy and less waste of resources in the hospital. Machine learning approach increases the reliability to answer the question of who might return to the hospital within thirty days of discharge. Ensure all bottlenecks in managing patient load are mitigated early to manage patient throughput

Leaders at the core of transformation

Hear from them

Combating COPD at a National Level

KenSci is enabling NHS Greater Glasgow and Clyde to identify patients are the risk of COPD even before they are admitted

Leveraging data to fight COVID-19 with SLUHN

Experts from St. Luke’s University Health Network, Microsoft and KenSci, share how to be more effective while managing hospital operations during COVID-19

WW Business with Kathy Ireland

KenSci and INTEGRIS Health were featured on the Worldwide business with Kathy Ireland, to talk about using AI for better care

Transforming Data to AI

KenSci's platform allows healthcare organizations to tap into the vast amounts of data and transform them into AI led insights

AI Best Practices with IU Health

Hear from three industry experts who are on top of the AI game on what it takes to deploy right, deploy fast and get your AI orchestration to demonstrate a big win.