23
Amy L. Wood, CPC Yale-New Haven Health System

Computer Assisted Coding Fact or Fiction, A Case Study

Embed Size (px)

DESCRIPTION

Computer Assisted Coding Fact or Fiction, A Case Study. Amy L. Wood, CPC Yale-New Haven Health System. Computer Assisted Coding. Magic Bullet or Marketing Hype? Selling the CAC concept to Administration Do the results change as coders become use to the technology?. Administrative “Buy In”. - PowerPoint PPT Presentation

Citation preview

Amy L. Wood, CPCYale-New Haven Health System

Computer Assisted Coding

Magic Bullet or Marketing Hype?

Selling the CAC concept to Administration

Do the results change as coders become use to the technology?

Administrative “Buy In”Highlight organization benefits

Increased compliance

Increased productivity

Potential for increase in revenues

Increased employee satisfaction

Decrease of the coding productivity gap during

ICD-10 transition

Selection Process for Product

Determine which service lines will be coded

Select visit types to be part of the process

1. Inpatient accounts Determine what documents to be included

Selection Process Continued

Outpatient Accounts to include:

Ambulatory SurgeryInterventional RadiologyHeart and Vascular Center

Selection Process Continued

Facility Infrastructure

Additional Equipment Needs

Cost of Implementation

Coder Staff Buy-In

Non-Threatening Introduction to Process

Calming Fears of Job Loss

Product only a Tool, NOT a Replacement

Stress “Assisted” in CAC Discussion

Coder Staff Buy-In

Outline ICD-10 Benefits

What is Involved in the Learning Process

Coder Reaction to Suggested Codes

Steps to Implementation

HIM must inventory sources of current medical record documentation

Information Technology Department (IT) heavily involved

Detailed mapping of document types to be part of the process

Additional Steps to Implementation

Work with IT to determine infrastructure of Facility

Involved testing and re-testing

Realistic expectations regarding implementation timeline

Post Implementation-Go Live

Monitor coder productivity

Measure and compare pre and post productivity values

After assessment, adjust productivity standards as necessary

Post Implementation Go Live con’t

Monitor impact on Accuracy

Are there additional benefits of CAC?

Can you compare ICD-9 to ICD-10 at this point?

CAC Phase 1 Go-Live Results

Outpatient Surgery implementation process January, 2012

Review conducted April, 2012

10% increase in coder productivity realized

CAC Phase 1 Go-Live Results

Inpatient implementation process December, 2011

Productivity measured over a three month period

Demonstrated 15% increase in coder productivity

Phase 2 Auto-suggested CodesIdentify all possible document typesBuild into test environmentIdentify potential obstacles Define what results wanted

Diagnosis codes onlyDiagnosis and CPT codesService areas or visit types

Phase 2 Auto-suggested CodesKeep a document type library

NLP engine needs to “learn” as product is used

Sample multiple scenarios to cover all visit types

Test and re-test results

Phase 2 Auto-suggested CodesDefine a reasonable timeline

Staff will need additional training

Select go-live date

Prepare for initial reduction in productivity during learning phase

Phase 2 resultsVery little reduction in productivity with go-

live

Staff has the option to use the product or to continue to code historical way

New productivity standards implemented 3months post go-live

Additional increase in productivity most notably in the surgical areasGI proceduresAmbulatory Surgery

ICD-10 Impact

Increased coding challenges

New coding guidelines/regulatory rules

Need for increased specificity of documentation

CAC Fact or Fiction?

Fact based upon our use of the product

Fiction

Not a magic bullet

Lessons Learned from Implementation

Testing and Re-testing a must

Monitor coder use of CAC process

Does one visit type work better than the otherInpatient vs Outpatient

Additional Lessons Learned

All document types not always easily available for use.

Coder training time and resources neededExpectations of implementation timeline and

deadlinesInterface monitoring and EMR changes and

the effect on current system

Questions?

Thank You!!!!!