Encounter Data Validation: Review and Project Update August 25, 2015 Presenters: Amy Kearney, BA Director, Research and Analysis Team Thomas Miller, MA

Encounter Data Validation:Review and Project Update

August 25, 2015

Presenters:

Amy Kearney, BA

Director, Research and Analysis Team

Thomas Miller, MA

Executive Director, Research and Analysis Team

1

Welcome

About the presenters Rules for engagement Presentation overview

• Understanding Encounter Data Validation (EDV) studies

• SFY 2014-15 EDV Results and Recommendations

• Encounter data quality improvement activities

2

Meeting Objectives

1. To understand the structure and purpose of Encounter Data Validation studies

2. To review the SFY 2014-15 EDV results and recommendations

3. To review ongoing efforts to improve encounter data quality

3

What’s an EDV?

Encounter Data Validation– Optional EQR activity

Assess completeness, timeliness, and accuracy of encounter data submitted to a state by its MCOs

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Importance of EDV

State Medicaid agencies rely on quality of encounter data submissions to:– Accurately and effectively monitor and

improve its program’s quality of care– Establish appropriate performance measures

and acceptable rates of performance– Generate accurate and complete reports– Obtain complete and accurate utilization

information

5

AHCA’s Annual EDV Studies

2013-14 EDV– Information Systems Review– AHCA Encounter Data File Review– Medical Record Review (MRR)

2014-15 EDV– Encounter Data File Review– Comparative Analysis– MRR

2015-16 EDV– Study design in progress

6

SFY 2014-15 EDV: Study Design

Objectives– Determine extent to which encounters

in Florida’s Medicaid Management Information System (FMMIS) are complete and accurate when compared to plans’ data

– Completeness and accuracy of plans’ encounter data stored in FMMIS through MRR

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Evaluation Components– Encounter data file review– Comparative analysis– MRR

Dates of Service– January 1, 2013 – March 31, 2014

Examined three encounter types– Professional– Dental– Institutional

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Encounter Data File Review– Examined the extent to which data submitted by

AHCA and the plans were reasonable and complete

– Unique encounters were identified by using a combination of plan, recipient ID, provider identification number, and date of service• Unique control number not utilized across plans and

AHCA

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Encounter Data File Review, continued– Key measures

• Volume of submitted encounters over time• Percent of encounter data fields with value

present• Percent of encounter data with valid values• Documentation of anomalies associated with

data extraction and submission were documented

10


Comparative Analysis– Based on encounter data records present in

AHCA’s and plans’ encounter data:

• Element Omission- Were data elements present in plans’ files not present in AHCA’s files?

• Element Surplus- Were data elements present in AHCA’s files not present in plans’ files?

• Element Agreement- For data elements present in both sources, did the values match?

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Comparative Analysis, continued– Three Key Steps:

1. Develop data submission requirements

2. Conduct file review

3. Conduct comparative analysis of encounter data

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MRR– Assessed whether key data elements in AHCA’s

data were complete and accurate when compared to medical records

– Four Key Steps:

1. Identification of eligible population and generation of MRR samples

2. Medical record procurement

3. Medical record abstraction

4. Conduct MRR analysis of abstracted data

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MRR, continued Assessed whether key data elements were complete and accurate

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Key Data Elements for Medical Records Review

Key Data Fields Professional Dental Institutional

Date of Service √ √ √Diagnosis Code √ √CPT/CDT/HCPCS Code/ Surgical Procedure Code √ √ √

Procedure Code Modifier√ √ √


MRR, continued– Four study indicators to report results:

• Medical Record Omission• Encounter Data Omission• Coding Accuracy• Overall Accuracy

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Meeting Objectives

1. To understand the structure and purpose of Encounter Data

Validation studies



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Meeting Objectives


Validation studies



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EDV Results: Encounter Data File Review

Figure 1—Monthly Variations in Professional Encounters for Plans and AHCA

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EDV Results: Encounter Data File ReviewFigure 2—Monthly Variations in Dental Encounters for

Plans and AHCA

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Figure 3—Monthly Variations in Institutional Encounters for Plans and AHCA

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Variation was present in the overall and month-to-month submission of encounters by type and source; the greatest variation was noted with institutional encounters

Required data elements (e.g., Recipient ID, Procedure Code, and Primary Diagnosis) were consistently complete and contained reasonable values


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Recommendations– Investigate differences identified in monthly

encounter data volume and reconcile where appropriate • Review activities should focus on determining whether

differences are due to failed or incomplete submissions or processing parameters

22

EDV Results: Comparative Analysis Record Completeness

– Findings• Record omission and surplus rates varied considerably

across plans– Dental encounters were the most complete (11.9 percent

omission rate and 30.0 percent surplus rate)– Institutional encounters were the least complete (84.7 percent

omission rate and 41.1 percent surplus rate)

– Recommendation• Review and update encounter data submission

standards to ensure they meet current reporting requirements

• Conduct root cause analyses related to low performing encounter types and elements

23

EDV Results: Comparative Analysis Encounter Data Element Completeness

– Overall, encounter data elements exhibited high level of completeness across all encounter types

– Provider-related encounter data elements were most frequently associated with incomplete data• Referring Provider NPI—professional omission and

surplus rates > 10%• Billing Provider NPI—dental omission rate = 13.6%• Rendering Provider NPI—dental surplus rate = 17.5%• Attending and Referring Provider—institutional

surplus rate > 75%

– Encounter data element omission and surplus rates varied by field and plan

24

Study Results: Comparative Analysis Encounter Data Element Completeness, continued

– Dental encounters• Omission rates showed less variation across plans than

other encounter types• Encounter data element surplus rate differences were

greatest for Line Date of Service, Billing Provider NPI, and Rendering Provider NPI

– Institutional encounters• Omission and surplus rate variation

was mixed• Omission rates for nearly half of

evaluated elements exhibited minimal variation across plans

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EDV Results: Comparative Analysis

Encounter Data Element Completeness, continued– Recommendations

• Review State and plan processes for submitting, tracking, and storing provider information

• Continue collaborative activities focused on exploring reasons for incomplete data submissions and developing improvement strategies

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Encounter Data Element Agreement– Professional encounters

• Agreement rates were high for key data elements with Procedure Code, NDC, and Primary Diagnosis Code exhibiting agreement rates of at least 90%

– Dental encounters• High levels of agreement for key data elements with

the exception of Dental Procedure Code

– Institutional encounters• High levels of agreement for one-third of key data

elements

27


Encounter Data Element Agreement, continued– Recommendations

• AHCA should continue working collaboratively with key stakeholders to develop and implement a monitoring strategy to routinely examine claims and encounter volume

• AHCA and the plans should regularly review existing contracts and encounter data documentation to ensure clear expectations surrounding the collection and submission of data

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EDV Results: MRR Medical Record Submission

– 1,234 sample cases requested for MRR• 981 records submitted by plans• 192 records where provider refused to submit• 59 records where provider

was unable to locate• 2 records were

submitted for incorrect patient

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EDV Results: MRR Encounter Data Completeness

– AHCA’s encounter data was only moderately supported by enrollees’ medical records (medical record omission)

– Plans’ encounter data was only moderately supported by AHCA’s encounter data (encounter data omission)

30

EDV Results: MRR Encounter Data Element Accuracy

– Overall encounter data element accuracy was high• Diagnosis codes (95.4 percent)• Procedure codes (82.3 percent)• Procedure code modifiers (99.3 percent)

– Only 1/3 of encounters accurately represented all three elements relative to medical record

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Table 6-2—Encounter Data Element Accuracy Summary for Overall Population

Statewide Rate MCP Range Main Error Type

Diagnosis Code 95.4% 87.0% - 100% -

Procedure Code 82.3% 66.1% - 100% Inaccurate Code (37.0%) Higher Level Service (26.4%) Lower Level Service (36.6%)

Procedure Code Modifier 99.3% 75.0% - 100% - All-Element Accuracy 31.9% 0.0% - 79.2% -

EDV Results: MRR

Recommendations– AHCA should continue working collaboratively

with key stakeholders to develop and implement a monitoring strategy that audits provider encounter submissions for completeness and accuracy

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Questions?

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Meeting Objectives


Validation studies



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Meeting Objectives


Validation studies



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Improving Encounter Data Quality

AHCA explored reasons for incomplete encounter data submissions from plans and began developing strategies to improve rates.– Developed encounter data support process– Worked to improve encounter data submission

issues, timeliness, and accuracy• Dedicated email account• On-site plan visits• Webinars• Conference calls

36


AHCA ensured there was a reliable process for timely submission of data from plans.– Implemented timeliness reports that are provided

to plan managers. – Plan managers work

with the plans to ensure contract timeliness compliance.

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AHCA organized a Webinar explaining comprehensive list of operational edits associated with error categories identified in the feedback/response files. – HP created encounter data reports listing

operational edits the plans are receiving on encounter files.

– Worked with plans by phone, webinar and on site visits to provide information regarding errors and feedback on resolution.

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Meeting Objectives

1. To understand the structure and purpose of Encounter Data Validation studies



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Questions

SFY 2015-16 EDV Study: Next Steps

Study Design – July/August Data Request – September Data Collection – October Conduct Analysis

– Encounter Data File Review – November/December– Comparative Analysis – December - March– Medical Record Review – January - May

Reporting – May/June

Contact Information

Amy KearneyDirector, Research & Analysis [email protected]

Tom MillerExecutive Director, Research & Analysis [email protected]

Mary WileyDirector, State & Corporate [email protected]

mailto:[email protected]



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Documents

Encounter Data Validation: Review and Project Update August 25, 2015 Presenters: Amy Kearney, BA Director, Research and Analysis Team Thomas Miller, MA