CASE STUDY
Ensemble's AI prevents $80 million revenue loss for health systems in 12 months.
EIQ, Ensemble’s revenue cycle intelligence engine, improves billing accuracy to help health systems reinvest millions in patient care.
Challenge
Efficient revenue cycle management is crucial for healthcare organizations to maintain operations and reinvest in patient care. Yet, challenges such as inaccurate coding, documentation mistakes and limited account visibility lead to significant financial losses.
While traditional manual audits may work for small samples, they fall short when managing large volumes of patient accounts or uncovering systemic issues across facilities. To address these challenges, healthcare leaders require a scalable, accurate and automated solution that ensures both precision and efficiency.
While traditional manual audits may work for small samples, they fall short when managing large volumes of patient accounts or uncovering systemic issues across facilities. To address these challenges, healthcare leaders require a scalable, accurate and automated solution that ensures both precision and efficiency.
Solution
Ensemble’s proprietary revenue cycle intelligence engine, EIQ®, helps hospitals and health systems avoid lost revenue, reduce claim denials and lower administrative costs.
Powered by adaptive machine learning models trained on billions of transactions, EIQ enables pre-bill claim analysis and error detection at scale to improve billing accuracy and identify anomalies that traditional human audits often miss.
Powered by adaptive machine learning models trained on billions of transactions, EIQ enables pre-bill claim analysis and error detection at scale to improve billing accuracy and identify anomalies that traditional human audits often miss.
Ensemble is investing in technology at a much greater rate than any hospital system is doing, and that includes AI and learning models. I like the idea that they are investing in the business because the insurance companies sure as hell aren’t, and keeping up in the battle of the bots is getting close to feeling like a game of Ping–Pong now. The machines are talking to each other, and Ensemble's investment in technology is second to none in the business.
How it works
- EIQ analyzes 100% of inpatient accounts using natural language processing (NLP) and advanced machine learning (ML) models.
- Account scores are assigned using a predictive model trained on 80,000 data points to identify possible coding errors, missing documentation and query opportunities.
- High-risk accounts are prioritized and routed to experienced coding and clinical auditors for targeted reviews and corrections.
- Outcomes from more than 5,000 daily transactions enhance the model, adding to insights from a comprehensive, multi-facility dataset to continually improve accuracy and efficiency.
Results
recovered + reinvested
into patient care
into patient care
$
0
M
With the power of EIQ, we helped our clients secure $80 million in rightful payments that would have otherwise gone unclaimed for the services they delivered.
of accounts
are flagged for correction
are flagged for correction
0
%
In-depth analysis prevents even the smallest errors from going undetected, routing 15% of accounts for pre-bill intervention to address errors or improve documentation.
saved per account
$
0
+
By preventing errors early, Ensemble’s AI helps safeguard over $5,000 per account from being lost to inaccuracies, translating into millions of dollars saved across facilities.