The Projects

We're helping forward thinking partners revolutionize healthcare.

From volume forecasting in the Emergency Department to planning for hospital bed capacity.  Explore our most recent projects to see how your team can benefit from working with LKS-CHART.

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Developing A 3D convolutional neural network (CNN) that will classify CT scan images of the cervical spine as containing or not containing a fracture.

Automated Analysis of Cervical Spine CT Scans

Developing an algorithm that predicts length of stay and discharge disposition following total joint arthroplasty (TJA).

Predicting Discharge Disposition and Length of Stay Following TJA

Working with the Tuberculosis (TB) clinic to implement Natural Language Processing that extracts patient information from dictated notes, making it readily available for analysis.

Tuberculosis (TB) Clinic Database

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Building a simulation model based on the current workload of the internal medicine residents, to re-assign residents to match patient volumes. 

General Internal Medicine Resident Staffing Redesign

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Developing a machine learning algorithm to accurately predict the risk of severe hypoglycemia for patients. 

Risk of Unintentional Severe Hypoglycemia in Hospital

(RUSHH)

Creating analytical models for the Emergency Department (ED), including volume forecasting, alerting when full capacity will be reached, and optimizing patient flow.

Emergency Department Analytics

Applying advanced analytics to determine the optimal number of Nursing Resource Team (NRT) nurses to hire for the up-coming year

Determining the Size and Skill-Mix of a Nursing Resource Team

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Working with the Interprofessional Practice Based Research (IPBR) team to create an automated tool to identify patients who are eligible for oral antibiotics.  

CHART-IPBR Fellowship Optimizing Antimicrobials 

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Explore the feasibility and initial performance of an algorithm for the detection of inpatients with atrial fibrillation or flutter who are candidates for oral anticoagulation.  

Improving inpatient rates of oral anticoagulation for stroke prevention in Atrial Fibrillation (IMPROVE-AF)

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Building a mathematical model that produces a repetition-minimizing assignment, and designing an interactive web application to  facilitate the daily assignment process.

ED Nurse Location Assignment Tool

Tracking patient outcome measures in vascular surgery, including in-hospital stroke, discharge medications, and follow-up encounters, so surgeons can identify areas for improvement.

A Vascular Surgery Quality Improvement Initiative

Using near-real-time hospital data to help clinicians identify high-risk patients so they can improve patient care and reduce the chances of mortality.

An Early Warning System for General Internal Medicine 

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Using a discrete event simulation model, it will analyze historical volumes of patients needing recovery beds, as well as, predict volume growth, to project the number of needed recovery beds for now and the next 10 years.

Cath Lab Recovery Bed Capacity Planning 

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Using available inpatient admission notes, a Latent Dirichlet Allocation (LDA) model was used to extract features from raw text data to predict patient length of stay.  

Length of Stay (LOS) Predictions using Text Notes

A PROGRAM OF

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