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* score 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.

Built by doctors, data scientists and developers… For healthcare

With millions of lives that are being treated every day, technological assistance is augmented by interpretable or explainable models. Interpretability provides medical practitioner with explanation on the factors driving the predictive insight. Explainable AI allows the possibility of new hypothesis generation which can lead to the discovery of new knowledge and makes predictive insights actionable. KenSci’s solutions help medical device and technology companies unlock insights from patient and device data to provide explainable, actionable predictive insights that help improve health outcomes for their users.

Read about our work with Exactech

Solutions for device tech and device manufacturers

KenSci Solutions integrate with medical devices and aggregate data from the devices to deliver AI-based insights to the point of care, assisting physicians and care providers to improve care delivery, patient experience and patient satisfaction.

  • Arthroplasty Outcome Prediction

    Identify arthroplasty fit for patients and leverage clinical observation from patient data of similar clinical history and treatments. Predict clinical improvement outcomes based on historical observations. From 2005 through 2015, the cost of total hip arthroplasty (THA) quadrupled, to $13.43 billion, and the cost of total knee arthroplasty (TKA) increased more than five times, to $40.8 billion. Reliable assessments of readmissions after TJA are varied, with rates ranging from 1% to 8.5% between 7 and 90 days after surgery. Supervised machine learning is a class of artificial intelligence by which the computer learns the complex structure and relationships in large datasets to create predictive models with the help of labeled features.

  • 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

  • Surgery outcome prediction

    Identify operative and nonoperative factors, that post-surgical improve, evaluate gains vs procedure-specific risks such as instability aseptic loosening and infection.

  • Patient Payment Prediction

    Predict expected costs for future years, and identify cost drivers maximizing reimbursement and simplifying patient collections. Optimize revenue cycle workflow and improve margins. The financial viability of a healthcare organization is dependent on medical claims submission and payment. This review process has become very complicated and adversarial. The post-adjudication claims appeal process requires the involvement of the clinical team, treating physicians may need to amplify on clinical documentation and rationale in order get reimbursement approved. Denial of claims is commonplace. Data on medical expenditures or costs of treatment typically feature a strongly skewed distribution with a heavy right-hand tail, including a few intermediate spikes depending on underlying population.

Arthroplasty Outcome Prediction

Identify arthroplasty fit for patients and leverage clinical observation from patient data of similar clinical history and treatments. Predict clinical improvement outcomes based on historical observations. From 2005 through 2015, the cost of total hip arthroplasty (THA) quadrupled, to $13.43 billion, and the cost of total knee arthroplasty (TKA) increased more than five times, to $40.8 billion. Reliable assessments of readmissions after TJA are varied, with rates ranging from 1% to 8.5% between 7 and 90 days after surgery. Supervised machine learning is a class of artificial intelligence by which the computer learns the complex structure and relationships in large datasets to create predictive models with the help of labeled features.

KenSci’s solution for arthroplasty outcomes

Predictive models can help the shoulder surgeon to better identify which patients will benefit from shoulder arthroplasty and also help better-align patient and surgeon expectations for clinical improvement by leveraging the experiences of previous patients with similar clinical history and treatments. With more insight into the factors that predict patient-specific improvement, and with better alignment between predicted and actualized outcomes, patient satisfaction may increase. Non-operative treatment may be best for some patients, and this foreknowledge represents a more efficient treatment and resource allocation for the patient, surgeon, hospital, and payer. An improved understanding of the amount of clinical improvement that can be expected at different post-surgical time points for a given patient will aid both the surgeon and patient in weighing these gains versus the procedure-specific risks associated with aTSA and rTSA, such as: instability, aseptic loosening, and infection. 

Case Study

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

Identify at-risk patients

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

Surgery outcome prediction

Identify operative and nonoperative factors, that post-surgical improve, evaluate gains vs procedure-specific risks such as instability aseptic loosening and infection.

Data driven insights

KenSci’s platform provides a single system of record, integrating all available data sources into a common schema that can then be applied to KenSci’s Machine Learning models for surgical outcome prediction. The ML models have been trained for accuracy on millions of data elements. KenSci’s models factor hundreds of variables simultaneously, resulting in greater accuracy of prediction, at an earlier time point, with increased cost savings. KenSci’s Platform  uncovers insights that lead to information regarding which patients are at highest risk to get patients the care they need sooner. Risk predictions are visualized through user-customized applications or via API’s inputted into the current end-user workflow. With more insight into the factors that predict patient-specific improvement, and with better alignment between predicted and actualized outcomes, patient satisfaction may increase. An improved understanding of the amount of clinical improvement that can be expected at different post-surgical time points for a given patient will aid both the surgeon and patient in weighing these gains versus the procedure-specific risks.

Case Study

Patient Payment Prediction

Predict expected costs for future years, and identify cost drivers maximizing reimbursement and simplifying patient collections. Optimize revenue cycle workflow and improve margins. The financial viability of a healthcare organization is dependent on medical claims submission and payment. This review process has become very complicated and adversarial. The post-adjudication claims appeal process requires the involvement of the clinical team, treating physicians may need to amplify on clinical documentation and rationale in order get reimbursement approved. Denial of claims is commonplace. Data on medical expenditures or costs of treatment typically feature a strongly skewed distribution with a heavy right-hand tail, including a few intermediate spikes depending on underlying population.

ML based risk prediction

Leveraging existing large and varied claims, clinical, and survey data to identify claims that are likely to be protested, as well as collect feedback and lessons learned to take measures for process improvement, and improved data collection from 3rd party payers. Identifies the patients’ structural characteristics that are influencing costs to obtain an estimate of expected costs for future years, maximizing reimbursement and simplifying patient collections. Classifies new claims with predictions and risk stratification so measures can be taken to optimize resource utilization and billing process workflows. Improves accountability in care, and optimizes resource utilization and workflows in the context of healthcare delivery. Supports end-to-end revenue cycle management workflow and analytics, and assists providers to scale capabilities and improve margin performance.

Trying to understand claims denial better?

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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.

Gain insight to optimize costs

Review predicted costs for future months using KenSci’s advanced ML models. Using dynamic, responsive prediction models that progressively incorporate data elements such as claims and EHR data, the tool enables precise allocation of intervention resources to mitigate the cost of high-risk populations. Stratify patients into risk categories for administrators and providers to identify who is at highest risk for increased length of stay and readmission so that providers can develop a precise plan of care.

Leaders at the core of transformation

Hear from them
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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

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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

Integris

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

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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

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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.

Integris

Rush University prepares to combat COVID-19

When the call to prepare for COVID-19 arrived, Rush University turned to KenSci to stand up the Realtime Command Center.

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Use your existing data and get started with your first AI solution… quickly

Our experts can guide you through every leg of the AI journey, and help deliver tangible ROI. Talk to us today

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Access Our Latest Thinking

Research, blogs, and other whitepapers on AI Led healthcare transformation

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Webinar

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PodCast

HIT Like a girl~ Health from Corinne Stroum on Healthcare Informatics

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Press Release

SCAN Health and KenSci are leveraging AI to help the senior members

August 11, 2020