What exactly is charge capture in healthcare?
In healthcare, charge capture refers to the process of ensuring all potential billable services — such as procedures performed or supplies used — during a patient encounter are accurately reflected in claims documentation. It serves as a critical link between clinical care and financial accuracy, converting provider actions into reimbursable charges.
Why is charge capture so important for a hospital’s financial health?
For most hospitals, charge-based reimbursement represents a critical share of total revenue, balancing alongside diagnosis-related group (DRG) payments to sustain financial performance. Looking at the numbers, when even just 1% of revenue is lost, that can mean a mid-size hospital sees losses of up to $15M. Inefficient charge capture is a significant contributor to that revenue leakage.
How does charge capture typically work, and why not just stick with the status quo?
Typically, health systems capture charges by setting up rules-based systems that surface specific information about patient care and present it to the revenue integrity team. While this is a good framework, it’s not a foolproof one. Rules can’t always account for some of the nuances and complexities inherent in clinical care. Static systems also can’t analyze information throughout the entire patient chart, which can create blind spots and missed opportunities that a more dynamic tool — like clinical AI — is designed to address.
How does AI improve the completeness and accuracy of charge capture?
Clinical AI is dynamic, meaning it can look at the entire patient chart and understand the clinical context behind the data it analyzes. What’s more, because it looks at all of a patient’s data — not just the sections that are tied to legacy systems — it can surface opportunities in advance of claim submission. Plus, clinical AI continually improves, meaning it supports better patient outcomes and surfaces continuing education opportunities.
What types of patient data can AI analyze that might be missed by existing legacy systems?
- Progress notes
- Imaging reports
- After-visit summaries
How will AI change the way revenue cycle teams capture, validate, and defend revenue?
As a dynamic tool that complements and optimizes health systems’ workflows, AI can provide faster and more thorough insights to revenue cycle management (RCM) teams. Teams are empowered with knowledge from the additional clinical context that AI can provide, allowing them to address potential revenue gaps and missed opportunities proactively, submit more defensible claims, and strengthen their compliance with regulatory requirements.
What exactly is charge capture in healthcare?
In healthcare, charge capture refers to the process of ensuring all potential billable services — such as procedures performed or supplies used — during a patient encounter are accurately reflected in claims documentation. It serves as a critical link between clinical care and financial accuracy, converting provider actions into reimbursable charges.
Why is charge capture so important for a hospital’s financial health?
For most hospitals, charge-based reimbursement represents a critical share of total revenue, balancing alongside diagnosis-related group (DRG) payments to sustain financial performance. Looking at the numbers, when even just 1% of revenue is lost, that can mean a mid-size hospital sees losses of up to $15M. Inefficient charge capture is a significant contributor to that revenue leakage.
How does charge capture typically work, and why not just stick with the status quo?
Typically, health systems capture charges by setting up rules-based systems that surface specific information about patient care and present it to the revenue integrity team. While this is a good framework, it’s not a foolproof one. Rules can’t always account for some of the nuances and complexities inherent in clinical care. Static systems also can’t analyze information throughout the entire patient chart, which can create blind spots and missed opportunities that a more dynamic tool — like clinical AI — is designed to address.
How does AI improve the completeness and accuracy of charge capture?
Clinical AI is dynamic, meaning it can look at the entire patient chart and understand the clinical context behind the data it analyzes. What’s more, because it looks at all of a patient’s data — not just the sections that are tied to legacy systems — it can surface opportunities in advance of claim submission. Plus, clinical AI continually improves, meaning it supports better patient outcomes and surfaces continuing education opportunities.
What types of patient data can AI analyze that might be missed by existing legacy systems?
- Progress notes
- Imaging reports
- After-visit summaries
How will AI change the way revenue cycle teams capture, validate, and defend revenue?
As a dynamic tool that complements and optimizes health systems’ workflows, AI can provide faster and more thorough insights to revenue cycle management (RCM) teams. Teams are empowered with knowledge from the additional clinical context that AI can provide, allowing them to address potential revenue gaps and missed opportunities proactively, submit more defensible claims, and strengthen their compliance with regulatory requirements.
