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Healthcare Operations and Patient Outcomes

There is a staggering amount of data at play in the healthcare and life sciences space, and limitless opportunities to leverage that data to improve patient care, reduce costly errors, and predict critical outcomes.

This suite of accelerators leverages data science, machine learning, AI, and Snowflake capabilities to lighten workloads for front-line care professionals, eliminate common administrative pain points, and synthesize data from multiple sources into actionable insights.

Targeted Solutions for Real Care Delivery Challenges

Patient Early Warning System and Readmission Prediction

This set of project accelerators leverages early warning system scores and the Charlson Comorbidity Index (CCI) to identify patients who are at risk of adverse events or readmission upon discharge.

Appropriate Length of Stay Prediction

This data science model flags a patient if they are anticipated to experience an avoidable hospital day during an inpatient encounter. Because the model runs on Snowpark compute, it can be set up automatically and linked directly with patient charts or operational dashboards for more informed resource allocation.

Health Equity Alerts

Health Equity Analysis and Representation is a complex process with the goal of identifying and addressing systemic inequities to achieve equitable health outcomes across populations. This individualized health assessment accelerator uses Snowpark to provide targeted interventions to better address these inequities at scale.

Social Determinants of Health Analytics

Social Determinants of Health (SDOH) data drives 80% of healthcare outcomes. This solution seamlessly and securely merges SDOH data from external sources with clinical information in Snowflake to power insight and predictive modeling across the patient journey.

Driving Better Outcomes for Patients and Professionals

Healthcare Data Mapping

Hakkoda’s Healthcare Data Mapping solution leverages a healthcare-trained classification engine to process data through three distinct layers of machine learning, saving you hundreds—or even thousands—of hours of labor while eliminating costly human errors during data set integration.

Healthcare Price Transparency

This set of accelerators ingests CMS Price Transparency Data, transforms it into a fully structured format, and accelerates analysis with a set of easy-to-use dashboards.
Hakkoda’s ML Classification engine is trained on large healthcare data sets and processes data through three layers of machine learning.

Nursing Staff Retention

This collection of dashboards provides healthcare administrators with descriptive analytics of employee data, including demographic information, Workday data, and employee satisfaction scores, with the ability to overlay predictive models and LLM models that can help anticipate and address employee burnout before it is too late.

Patient Engagement Tool

A data science model that helps determine how engaged a patient is in their healthcare to improve the delivery of personalized medicine and cue care providers for appropriate outreach. The model runs on Snowpark, can be set up automatically, and can link directly to patient charts or an operational dashboards for easy accessibility.

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