Objective data that health systems already have and Atalan’s machine learning models, hospitals can now:
Identify who is at risk of leaving
Receive seamless interventions
Zero in on exactly why
40%
Turnover Reduction
$29M
Protected Annual Revenue
* Based on a $14 billion, multi-state U.S. health system, for the first 6 months of usage.
Atalan’s CRI platform is the first built to predict, explain, and prevent clinician resignations before they happen.
Here’s how it works:
Your Data
Atalan securely extracts the data that health systems already have including EHR and HR sources
Intelligence Engine
Atalan automatically analyzes signals such as EHR usage, patient volume, patient complexity, length of workday, and more than 100 other departure triggers.
Actionable Insights
From this analysis, Atalan delivers turnover predictions up to 12 months in advance, provides a 360° view of departure triggers, recommends targeted actions, and tracks their effectiveness over time.
When clinicians stay, patients thrive. When patients thrive, everyone wins.
Predictive insights that look ahead, not back
Move beyond lagging indicators, intervene before disrupting care and revenue
Data-driven, objective & actionable solutions
No more subjective guesswork, interventions that are easy to implement, and work
Protect financial and clinical outcomes
Keep your top talent, lower recruitment costs, and prevent revenue loss from departing patients
“Atalan sets us up to have positive conversations with those folks who are at the most risk by being able to bring that data to the table”
Chief Clinical Officer, BSMH
Dr. Matt Owens
“Atalan sets us up to have positive conversations with those folks who are at the most risk by being able to bring that data to the table”
Chief Clinical Officer, BSMH
Dr. Matt Owens
“Atalan sets us up to have positive conversations with those folks who are at the most risk by being able to bring that data to the table”
Chief Clinical Officer, BSMH
Dr. Matt Owens
ACT FASTER. PREDICT AND PREVENT BURNOUT AND TURNOVER.
We give healthcare leaders the foresight to intervene before clinician stress turns into resignation. Backed by peer-reviewed research and proven results in the field, our Clinician Retention Intelligence (CRI) platform transforms workforce data into actionable strategies that protect teams, margins, and patient care.
Explore the journal of healthcare management, real-world case studies, and industry conference appearances to see how evidence-based intelligence is reshaping clinician retention and building a stronger future for healthcare.
How Bon Secours Mercy Health Supports Clinician Well-being Using Objective Data When clinician well-being and engagement are supported, better patient care and outcomes typically follow. Bon Secours Mercy Health (BSMH), the fifth largest Catholic health system in the United States, with headquarters in Ohio, is committed to the wellness of its trainees
How a Multi-State Health System Enhanced their Clinician Retention Strategy with a Data-Driven Approach Clinician burnout is a known challenge in healthcare, aggravated by both the standard stresses of working in a high-intensity caretaking field, and also by recent regulatory, pandemic, and financial pressures. The costly ripple effects of burnout
The $11 billion, multi-state U.S. health system partnered with Atalan to implement a predictive, data-driven approach to clinician retention. By leveraging machine learning and predictive analytics, the organization aimed to proactively identify clinicians at risk of leaving, intervene long before a resignation occurred, and make those retention efforts measurable in ways they had never been before.
Clinician turnover is one of the most pressing threats facing U.S. health systems today. The financial impact is staggering. Replacing a single physician can cost $800,000 when accounting for recruitment, sign-on bonuses, onboarding, and lost patient billings. It can even go as high as $2.3 million per physician, depending on the specialty. Beyond dollars, turnover drains staff morale, interrupts patient relationships, and destabilizes entire care teams.
Predict and Prevent Clinician Burnout Before It Impacts Care or Revenue.
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