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Payor Solutions

Predict risk to improve care costs

Payor Solutions
Predicting financial risk to reduce costs and minimize variance
Delivering ROI in 12 weeks

Predicting financial risk to reduce costs and minimize variance

Finance and Acturial teams in ACOs and Payers leverage KenSci to identify cost risks, medical fraud & abuse, and levers to optimize care delivery cost. Healthcare organization, use the KenSci solution to track key metrics and look out for early warnings that could impact fiscal performance. KenSci’s risk prediction platform for healthcare is engineered to ingest, transform and integrate disparate sources of healthcare data, including EHR, Claims, Admin/Finance, streaming and other sources. KenSci uses machine learning to recognize patterns in large volumes of data, helping health systems view the granular details of the patient’s history and predict future risks for optimal care.

Use cases we solve for

Avoidable Hospitalization Prediction
Benefits Optimization
HCC Optimization
Population Cost Prediction Solution
High Utilizer Identification
Avoidable Hospitalization Prediction
Avoidable Hospitalization Prediction
Benefits Optimization
HCC Optimization
Population Cost Prediction Solution
High Utilizer Identification
Avoidable Hospitalization Prediction

Avoidable Hospitalization Prediction

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%...

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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

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.

Singular insight dashboard

Singular insight dashboard

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

Learn from existing data

Learn from existing data

Variables used in the prediction are an aggregate of labs, comorbidity history, demographics, and patient inpatient history from the electronic medical record during the hospital stay.

At risk stratification

At risk stratification

This solution provides risk scores to individual physicians and care managers to identify at-risk patients to enable appropriate care prioritization, and the risk predictions are visualized through user-customized applications or via API’s inputted into the current end-user workflow.

Benefits Optimization

Benefits Optimization

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 for to provide optimized benefits. Additionally, providers may have to ...

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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 for to provide optimized benefits. Additionally, providers may have to assume financial risk when a patient receives subsequent care at another facility, such as a costly visit to the ER at another hospital. Providers need the ability to identify who is high risk to target resources towards those individual to avoid modifiable cost drivers such as readmissions and variation in post-acute care. The ability to target specific patients to tailor discharge plans and enhance care coordination is a vital component of cost containment

Data driven insights

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.

Reduce overall costs

Reduce overall costs

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.

Identify high cost outliers

Identify high cost outliers

KenSci machine learning models enable identification of outliers that result in the highest concentration of costs, especially those propelled by irrational variation.

Prediction and risk stratification

Prediction and risk stratification

Classifies new claims with predictions and risk stratification so measures can be taken to optimize resource utilization and billing process workflows.

HCC Optimization

HCC Optimization

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...

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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 of the market

Stay ahead of the market

KenSci’s systems and infrastructures for data ingestion, extraction, and analysis tackle fraudulent claims, and have evolved to stay current with business expansion and FWA tactics.

ML based risk prediction

ML based risk prediction

KenSci has developed a comprehensive solution that engages hospital systems with a machine learning-based software to detect and help improve overall HCC optimization

Extendable and expandable

Extendable and expandable

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.

Insights at granular levels

Insights at granular levels

It provides analysis at an individual patient level determined by the total claimed amount, number of admission, the total number of services and other relevant factors. 


Population Cost Prediction Solution

Population Cost Prediction Solution

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 ...

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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. Much of the total cost of caring for a patient involves shared resources, such as physicians, staff, facilities, and equipment. The failure to prioritize value improvement in health care delivery has led to ill-advised and variable cost containment. Accurate prediction is necessary to identify the internal costs structures, how to price them, and determine whether they will be profitable for the provider to add value

Ties isolated data sources

Ties isolated data sources

Plumbs together isolated data stores and helps users understand and control costs using descriptive analytics.

Actionable insights delivered

Actionable insights delivered

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.

Learns from existing data

Learns from existing data

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.

Predicted future alerts

Predicted future alerts

Can alert an administrator and provider that a population will have an elevated risk of heart disease in the next three years, giving them the chance to prevent the onset of illness and apply disease risk management in a timely fashion.

High Utilizer Identification

High Utilizer Identification

U.S. emergency departments (EDs) collectively experience 130 million encounters annually, and a large percentage of ED visits originate from a small proportion of patients who keep returning to the ED, commonly known as super-utilizers. In 2016, researchers found that of 1,443 patients presenting to the ED, 71%...

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U.S. emergency departments (EDs) collectively experience 130 million encounters annually, and a large percentage of ED visits originate from a small proportion of patients who keep returning to the ED, commonly known as super-utilizers. In 2016, researchers found that of 1,443 patients presenting to the ED, 71% percent had 2 or more visits and 15% had five or more visits within the previous 12 months. A significant fraction of the frequent ED user population is comprised of patients with complex, unmet needs for interventions, including mental health, substance abuse, transportation and housing, and chronic disease management services.
Frequent readmissions for these conditions can cause ED throughput inefficiencies and overcrowding, resulting in a decreased quality of care and an increase in operational costs and patient dissatisfaction. Accurate prediction of ED readmissions is integral for cost-effective resource allocation planning to improve post-discharge intervention in high-risk patients.
Learn from existing data

Learn from existing data

KenSci’s ML Risk Prediction Platform and ED Readmission Prediction Solution utilizes individual patient information, including the patient’s demographics, diagnoses, and lab values from claims, EHR and other data sources to develop a risk score corresponding to a patient’s likelihood of utilizing the ED frequently..

Know the high utilizers

Know the high utilizers

The doctor can see that a patient has been flagged as having a high risk of becoming a frequent ED high utilizer through the KenSci Risk Prediction Solution that leverages Machine Learning to make this prediction.

Identify at risk patients

Identify at risk patients

This tool enables doctors to better understand the likelihood that a patient may return to the ED for exacerbations of her chronic disease.

Prediction based action

Prediction based action

Following EMR review and ML factor analysis, potentially modifiable situation included missed office visits, diabetes out of control and personal stress, the medical team is able to view the risk prediction.

Stay one step ahead

  • Optimize PM/PM margins

    Optimize PM/PM margins

    Review predicted costs and PMPMs for future months using KenSci’s advanced ML models.

  • Predict population cost

    Predict population cost

    Predict population and costs by identifying patterns of clinical and cost outcomes.

  • Identify future high utilizers

    Identify future high utilizers

    Investigate care pathways that could be cost drivers and proactively help stay within budget.

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