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Other Healthcare Solutions

Predict risk to improve operational efficiency

Other Healthcare Solutions
Assimilate various conditions to provide the most optimal outcomes
Delivering ROI in 12 weeks

Assimilate various conditions to provide the most optimal outcomes

Healthcare organization, use the KenSci solution to manage their everyday operations better and in turn improve patient satisfaction scores. 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

Predicting Patient Payment Estimation
Predicting Shoulder Arthroplasty Outcomes
Predicting Patient Payment Estimation
Predicting Patient Payment Estimation
Predicting Shoulder Arthroplasty Outcomes
Predicting Patient Payment Estimation

Predicting Patient Payment Estimation

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 ...

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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. 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.

Predictive cost estimate

Predictive cost estimate

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.

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.

Optimize resource utilization

Optimize resource utilization

Improves accountability in care, and optimizes resource utilization and workflows in the context of healthcare delivery.


End to end support

End to end support

Supports end-to-end revenue cycle management workflow and analytics, and assists providers to scale capabilities and improve margin performance.

Predicting Shoulder Arthroplasty Outcomes

Predicting Shoulder Arthroplasty Outcomes

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 ...

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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. The machine learning model iteratively learns relationships between input and output data to minimize predictive error. In orthopedics, predictive models derived from high-quality outcomes and patient data represent a patient-specific implementation of evidence-based decision-making tools, transforming complex healthcare data into practical knowledge to support more-informed treatment decision making. Machine learning is relatively new to orthopedics, although, its usage in research has increased in recent years. The majority of machine learning applications were image-based and the top three were: 1) osteoarthritis detection, 2) bone/cartilage image segmentation, and 3) spine pathology detection. Applications of machine learning are expected to be ubiquitous in the near-future as manufacturers and hospitals operationalize predictive analytics into their business practices.

ML based predictive modeling

ML based predictive modeling

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.

Insights into better patient outcomes

Insights into better patient outcomes

With more insight into the factors that predict patient-specific improvement, and with better alignment between predicted and actualized outcomes, patient satisfaction may increase.

Operative vs. Non Operative insights

Operative vs. Non Operative insights

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

Predictive surgical outcomes

Predictive surgical outcomes

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.

Stay one step ahead

  • Improve patient outcomes

    Improve patient outcomes

    Identify procedural outcomes based on patient data generated through healthcare sources

  • Manage patient costs

    Manage patient costs

    Know the high utilizers and transition to a system of value based care for better outcomes.

  • Gain insights to optimize costs

    Gain insights to optimize costs

    Know which parts of your operations are costing you to increase margins optimally.

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