PreSquared:
Medical Analytics Software Technology

Our products help health organizations prioritize patient care based on levels of risk. PreSquared delivers predictive, machine learning solutions into the provider workflow, creating actionable data that drives interventions and prevents major health events.

Pro-Active Patient Prioritization:

Easily identify patients in need of interventions based on risk scores prior to major health events.

Data Driven Implementation:

Our software provides an invaluable toolset to assist caregivers. The user interface promotes a clear understanding of patient risks and reasons for risk.

How it Works:

Operating as a cloud-based SaaS, our software integrates seamlessly into existing care management platforms. Built using cloud-agnostic components, PreSquared can be deployed as a stand-alone product or implemented within an existing care management system. Our data has been deployed through health information exchanges, managed care entities, and directly to provider groups.

Why PreSquared?
Our software is the most statistically robust in the industry.

Trained on hundreds of millions of health records, our models bring a superior level of expertise to the field. Utilizing a wide variety of systems and provider types brings unsurpassed reliability to our services.

Founded by university faculty and data scientists, our models have been in production informatics environments for many years, garnering praise from providers, care coordinators, payers, and policy makers. The ongoing research and continued development of these algorithms has led to increasingly efficient solutions unmatched in the healthcare industry.

2

MILLION+

patients already benefit from our services

15

YEARS+

of development by University Faculty & Data Scientists

Our Mission:

Improve the health of patients and prevent major events.

Our products provide simple, valid, and trustworthy metrics for driving patient priority lists supported by underlying causes to deliver interventions ahead of major events.

Reduce overcrowding in the emergency department.

Avoidable emergency visits, inpatient admissions, and re-admissions have a significant toll across the entire healthcare ecosystem. Our products provide a solution to ease this burden on hospitals with proactive measures.

Assist care managers with an invaluable toolset.

PreSquared helps nurses and caregivers with limited resources to focus on patients that need help the most. The user interface and data visualizations provide a clear path to enhance the efficacy of their care interventions.

Reduce healthcare costs for patients, insurers, and Medicare.

The cost of unnecessary emergency visits and bed occupancy is a major strain on medicare, patient, and insurance expenses. Our platform aims to make a more efficient system that benefits everyone.

Pre-AH: Avoidable Hospitalization

As our flagship prediction model, Presquared's Avoidable Hospitalization algorithm provides an intelligence and early warning system to assist healthcare workers in preventing major events with proactive care measures. The Pre-AH algorithm takes into account claims from inpatient, physician, pharmacy, as well as demographics and environmental factors to create a unique predictive model that gives risk scores, reasons for risk, and even a timeline built into the technology.

Pre-AH uses advanced machine learning algorithms to predict each beneficiary's likelihood of experiencing an avoidable inpatient hospitalization or emergency department visit during the next 30 days, giving providers the risk rankings they need to accurately maximize the value of their care.

PreAH- PreSquared Avoidable Hospitalization Infographic

Diverse Data Sets:

PreSquared’s predictive algorithms represent a diverse sample across the healthcare industry. Our data sources span both Medicare and Medicaid populations and include inpatient and outpatient hospital records, primary care and specialist claims, and information from pharmacy and laboratory encounters. We also include a wealth of data on social determinants of health, including micro-level environmental risk factors that affect the health and wellbeing of the population.

Adaptive Modeling:

Our models are constantly adapting to changes in the healthcare landscape. In all, we have over 200 risk factors that our discrete-time survival models can choose from, which allows the models to re-rank each reason for risk according to the changing healthcare landscape.

This has been extremely important in ensuring that our algorithms are statistically robust even in the face of dramatic utilization shocks, such as the one driven by the COVID-19 pandemic. Our scores, and the risk factors driving those scores, were able to keep pace with the utilization shifts that occurred during each phase of the pandemic, and allowed providers to keep prioritizing their riskiest patients even through the worst surges.

PreSquared's Impact:
More Efficient Healthcare at Reduced Costs

Our risk scores benefit a multitude of users across the provider spectrum, from primary care physicians and nurse managers to care coordinators and front office managers. By “working the list”, relevant interventions are provided to the highest-risk beneficiaries, improving quality rankings, increasing the patient’s quality of life, and decreasing the total cost of care, while also allowing patients to remain at home and out of emergency departments.

PreSquared:Medical Analytics Software Technology