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Provider Solutions

Predict risk to manage care outcomes better

Provider Solutions
A proactive method of identifying and reducing patient risk
Delivering ROI in 12 weeks

A proactive method of identifying and reducing patient risk

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

Discharge planning
Capacity Planning
Realtime analytics for IP and ED
Variation Analysis
Discharge planning
Discharge planning
Capacity Planning
Realtime analytics for IP and ED
Variation Analysis
Discharge planning

Discharge planning

Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based ...

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Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based on volume and increased treatment costs.

 

The deficit

Executives and physicians face the menacing task of controlling care variation to control spending in healthcare organizations. Due to the multiple complicating factors contributing to this cost and quality driver, sophisticated prediction to proactively manage costs related to unstandardized care is an immediate need.

Length of Stay Predictions

Length of Stay Predictions

The KenSci Hospital Length of Stay Solution provides a view into how healthcare facilities can use information such as patient’s vitals, their history, and current symptoms and accurately predict how long they need to be treated and what type of care they need during their stay by applying machine learning.

Readmission Prediction

Readmission Prediction

The KenSci solution identifies patients at risk for a preventable readmission with 71% accuracy using a weighted classifier to strengthen predictability among large sets of inpatient data real- time. This machine learning approach increases the reliability to answer the question of who might return to the hospital within thirty days of discharge.

Discharge Disposition Predictions

Discharge Disposition Predictions

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.

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 for patients at- risk for readmission.

Capacity Planning

Capacity Planning

Lack of capacity planning is recognized as a national problem that hinders the delivery of both emergency and downstream medical services. Overcrowding in the ED has been linked to decreased quality of care, increased costs, and diminished patient dissatisfaction. Were healthcare administrators and staff able ...

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Lack of capacity planning is recognized as a national problem that hinders the delivery of both emergency and downstream medical services. 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.
Delays, interruptions, and cancellations are so common that patients and clinicians regard them as an inevitable part of the process of healthcare. Hospitals provide a prime example of the fact that waiting is intrinsic and almost intractable. Obtaining actionable data from patient flows is challenging. It requires data integration from multiple systems to support comparison across departments and workflows.
Improve operational efficiency

Improve operational efficiency

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.

Provide better care

Provide better care

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.

Insight into the future

Insight into the future

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.

Manage staffing better

Manage staffing better

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.

Realtime analytics for IP and ED

Realtime analytics for IP and ED

Delays, interruptions, and cancellations are so common that patients and clinicians regard them as an inevitable part of the process of healthcare. Hospitals provide a prime example of the fact that waiting is intrinsic and almost intractable. Obtaining actionable data from patient flows is challenging. It ...

+ read more
Delays, interruptions, and cancellations are so common that patients and clinicians regard them as an inevitable part of the process of healthcare. Hospitals provide a prime example of the fact that waiting is intrinsic and almost intractable. Obtaining actionable data from patient flows is challenging. It requires data integration from multiple systems to support comparison across departments and workflows.
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.

Improve operational efficiency

Improve operational efficiency

KenSci offers a locally-tuned, ML model-based solution that predicts patterns in Emergency Department Demand to enable operational staff to plan for staffing, on daily, weekly, and monthly basis.

Provide better care

Provide better care

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.

Manage staffing better

Manage staffing better

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.

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.

Variation Analysis

Variation Analysis

Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based ...

+ read more

Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based on volume and increased treatment costs. Executives and physicians face the menacing task of controlling care variation to control spending in healthcare organizations. Due to the multiple complicating factors contributing to this cost and quality driver, sophisticated prediction to proactively manage costs related to unstandardized care is an immediate need.

Integrates data sources

Integrates data sources

Integrates disparate data sources and helps users understand and control care variation using predictive modeling, and applies algorithms via the KenSci Machine Learning Platform to fully leverage the rich and underlying patterns embedded within the claims data to derive accurate and actionable insights.

Variance insight alerts

Variance insight alerts

Can alert an Executive and physician that an increased population cost is attributed directly to care variance from the use of certain kinds of supplies, providing insights that allow for opportunities to consider standardizing supply chain and logistics for products moving forward.

Continuous learning

Continuous learning

Learns from a pool of historical clinical data to drill down to determine Month over Month variation in cohort characteristics and treatment for diseases and procedures

Maximize efficiency

Maximize efficiency

Reduces clinical variance and care utilization critical to maximizing the efficiency and efficacy of the healthcare system – ultimately bringing down costs and making care more affordable and accessible for everyone.

Stay one step ahead

  • Predict risk & reduce clinical variance

    Predict risk & reduce clinical variance

    Physicians and Care managers who are dedicated to scale their mission to save lives and are frustrated by state of healthcare.

  • Improve outcomes and quality measures

    Improve outcomes and quality measures

    Machine Learning experts and computational researchers driven by a burning desire to tease life-saving insights out of data.

  • Better discharge planning & load prediction

    Better discharge planning & load prediction

    ML Engineers who have shipped Cloud OS and Search Engines. Now creating the most meaningful platform of their career.

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