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

ACOs and Payers leverage KenSci to identify members at high risk, medical fraud & abuse, and levers to optimize care management activities. KenSci’s risk prediction platform for healthcare is engineered to ingest, transform, and integrate disparate sources of healthcare data. KenSci uses machine learning to recognize patterns in large volumes of data, helping view the granular details of members' history and predict future risks for optimal engagement.

See how SCAN Health is doing more with KenSci

Solutions for Health Plans

KenSci’s pre-built ML models enable health plans to build and deploy applications at ease, accelerating their AI journey towards ROI. With KenSci, healthcare payors are able to gain granular insights into the health of their population and adjust for risk accordingly.

  • Avoidable Hospitalization Prediction

    Identify patients who need care at early stages, that allows health systems to provide care prioritization. Improve health outcomes with proactive interventions and treatment,. Estimates of potentially avoidable Medicare spending on 30-day readmissions total $17 billion annually. Based on 2011 data, an estimated 3.3 million patients were readmitted to US hospitals within 30 days of a prior discharge. 5% of all inpatient visits for sepsis result in readmissions with an estimated 75% of rehospitalizations being avoidable and contributing to over $10 billion in excess health care costs for the Medicare population alone. Potentially avoidable hospitalizations have been identified by experts as leading to lower quality of healthcare outcomes as well as expensive care. Potentially avoidable hospitalizations are particularly common among full-benefit dual-eligible beneficiaries.

  • Benefits Optimization

    Identify high-risk cohorts to proactively provide support and guide healthcare decisions. Enhance care coordination and promote treatment and care practices for at-risk members. Health benefit plans are being designed to reduce barriers to maintaining and improving health and to promote higher-value healthcare services. Reducing costs associated with this high-risk and high-cost patient population is necessary to provide optimized benefits. Additionally, providers may have to assume the financial risk when a patient receives subsequent care at another facility, such as a costly visit to the ER at another hospital.

  • HCC Optimization

    Improve HCC coding accuracy and ensure risk-adjusted cost coverage across your health plan. Identify NFLOC eligible members and improve care support and outcomes. Over the years, the HCC model has been refined and significantly expanded to include the risk adjustment of patients in a variety of value-based reimbursement plans, including ACOs, Comprehensive Primary Care Plus (CPC+), and many others. Where HCC provides clear outlines of patient’s health, it has soon been attributed to the financial success of health systems. Under the Value Based Program, HCC has been seen to yield higher reimbursements to cover the costs of providing patients with care. With over 47 million Medicare beneficiaries HCC has played an integral role that has helped include a deep look into the health status in a base year and accordingly identify or predict costs in the following year.

  • Population Cost Prediction Solution

    Identify population health risks early with actionable insights combined across siloed data. Stay ahead of population health needs and improve value in healthcare delivery. Spending for health care services is highly concentrated among a small proportion of people with very high use. In 2009, 20% of all personal health care spending, or $275 billion, was on behalf of just 1 percent of the population. The 5% of the population with the highest spending was responsible for nearly half of all spending This emphasizes the need for population health cost prediction for quality improvement, cost containment and supporting value-based care.

Avoidable Hospitalization Prediction

Identify patients who need care at early stages, that allows health systems to provide care prioritization. Improve health outcomes with proactive interventions and treatment,. Estimates of potentially avoidable Medicare spending on 30-day readmissions total $17 billion annually. Based on 2011 data, an estimated 3.3 million patients were readmitted to US hospitals within 30 days of a prior discharge. 5% of all inpatient visits for sepsis result in readmissions with an estimated 75% of rehospitalizations being avoidable and contributing to over $10 billion in excess health care costs for the Medicare population alone. Potentially avoidable hospitalizations have been identified by experts as leading to lower quality of healthcare outcomes as well as expensive care. Potentially avoidable hospitalizations are particularly common among full-benefit dual-eligible beneficiaries.

Identify at-risk patients

KenSci’s model helps identify patients who need care at early stages, that allow health systems to actively mitigate hospitalization. This solution is utilized by individual physicians and care managers at the bedside and provides risk scores for identifying at-risk patients for prioritization of appropriate multidisciplinary care coordination. An interactive dashboard provides patient risk scores serving as an at-a-glance clinical tool for ease of identification to intervene early for patients who can avoid hospitalization.

Mitigate readmissions early with KenSci's solution.

Here's how

Benefits Optimization

Identify high-risk cohorts to proactively provide support and guide healthcare decisions. Enhance care coordination and promote treatment and care practices for at-risk members. Health benefit plans are being designed to reduce barriers to maintaining and improving health and to promote higher-value healthcare services. Reducing costs associated with this high-risk and high-cost patient population is necessary to provide optimized benefits. Additionally, providers may have to assume the financial risk when a patient receives subsequent care at another facility, such as a costly visit to the ER at another hospital.

Data-driven insights

KenSci benefits optimization solutions will help healthcare organizations gain insight into the actual costs of care to price bundles accurately and appropriately be compensated for the total cost of care for disease or procedure. Payment solutions bring to light the actions needed to reduce cost variation so that organizations can share in the savings of the bundle by proactively reducing costs to below benchmark pricing. KenSci machine learning models enable identification of outliers that result in the highest concentration of costs, especially those propelled by irrational variation.

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HCC Optimization

Improve HCC coding accuracy and ensure risk-adjusted cost coverage across your health plan. Identify NFLOC eligible members and improve care support and outcomes. Over the years, the HCC model has been refined and significantly expanded to include the risk adjustment of patients in a variety of value-based reimbursement plans, including ACOs, Comprehensive Primary Care Plus (CPC+), and many others. Where HCC provides clear outlines of patient’s health, it has soon been attributed to the financial success of health systems. Under the Value Based Program, HCC has been seen to yield higher reimbursements to cover the costs of providing patients with care. With over 47 million Medicare beneficiaries HCC has played an integral role that has helped include a deep look into the health status in a base year and accordingly identify or predict costs in the following year.

Stay ahead with KenSci 

KenSci has developed a comprehensive solution that engages hospital systems with a machine learning-based software to detect and help improve overall HCC optimization. It provides an analysis at an individual patient level determined by the total claimed amount, number of admissions, the total number of services and other relevant factors. The solution can be extended for multiple payers, multiple geographies and add additional geographies. It analyzes a range of factors such as improper coding, phantom billing and unnecessary provision of care to determine the risk involved.

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Population Cost Prediction Solution

Identify population health risks early with actionable insights combined across siloed data. Stay ahead of population health needs and improve value in healthcare delivery. Spending for health care services is highly concentrated among a small proportion of people with very high use. In 2009, 20% of all personal health care spending, or $275 billion, was on behalf of just 1 percent of the population. The 5% of the population with the highest spending was responsible for nearly half of all spending This emphasizes the need for population health cost prediction for quality improvement, cost containment and supporting value-based care.

KenSci’s solution for Population Health 

Learns from a pool of historical clinical data to predict each population Per Member Per Month cost by medical condition. It stratifies 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. KenSci’s Population Cost Prediction Solution: Plumbs together isolated data stores and helps users understand and control costs using descriptive analytics applies algorithms via the KenSci Machine Learning Platform to fully leverage the richness and latent patterns embedded within the claims data to derive accurate and actionable insights. It learns from a pool of historical clinical data to predict each population Per Member Per Month cost by medical condition. It stratifies 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.

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Optimize PMPM margins

Review predicted costs and PMPMs for future months using KenSci’s advanced ML models. KenSci masters predicting high-risk, high-cost population challenges by leveraging a hospital’s existing investments in scalable, value-based healthcare analytics. Learn from a pool of historical clinical data to predict each population Per Member Per Month cost by medical condition. 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.

Predict Population Cost

Identify patterns within data to predict population's costs to better drive clinical and financial outcomes. Fully leverage data fluidity, cloud computing, and Machine Learning to predict population health risk and cost, and deliver value at unprecedented scale and speed now required of the healthcare sector.

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.

Identify future high utilizers

Investigate care pathways that could be cost drivers and proactively help stay within budget. Address the 5% percent that could costs health systems the most and manage their health efficiently by avoiding unwanted hospitalization.

Target them with care programs that are specifically catered to this cohort can minimize their risk of critical illnesses. KenSci’s recommendation systems help keep them active and healthy by understanding their everyday lifestyle patterns.

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

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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.

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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

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

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