No Now wha hat? t? Presenter: Kate Mullins Co-Author: Hailey - - PowerPoint PPT Presentation

We f e fou

• und

nd a d a dat ata a quali uality ty is issu sue. e. No Now wha hat? t?

Presenter: Kate Mullins

Co-Author: Hailey DuBreuil

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MEE EET T OU OUR R DATA Q A QUAL ALIT ITY Y TEA EAM

Ka Kate e Mullin lins

kmullins@hsri.org

Project Manager

Haile iley y DuBreuil reuil

hdubreuil@hsri.org

Project Coordinator

Ka Kati tie e Howar ard

khoward@hsri.org

Data Scientist

Yuan Zhang

yzhang@hsri.org

Research Analyst

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01 01 02 02

AGE GENDA

03 03 04 04 05 05 06 06

HSRI RI and nd Our ur Approac

• ach

Quest stions ions Presen esentat tation n Objec ectiv tives es Wh What are data a quality ity issue ues? s?

Issue e Resolutio tion n Frame mework

Fut uture ure Directions ections

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HS HSRI RI AN AND OU D OUR R AP APPR PROACH CH

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Human Services Research Institute

We are a nonprofit, mission-driven organization. We use our data expertise—developed over 40+ years—and our understanding of the complete health and human services landscape to help agencies and communities improve the health, well-being, and economic stability of the populations they serve.

Housing & Homelessness | Population Health | Aging & Disabilities Child, Youth & Family | Behavioral Health | Intellectual & Developmental Disabilities

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Population Health Team: What We Do

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We develop and maintain nonproprietary data collection and reporting systems, custom analytics, state-level health data warehouses, data quality improvement procedures, and healthcare transparency websites.

Post-Intake Quality Assessments & Reports Internet Payer Submissions (Commercial/Medicaid) SFTP Server Monitor Process Enclave Server Unzip, Decrypt, Initial Storage Data Intake Validation Passed Files Release Staging Batch Calculated Variables, Member ID, Provider Processing Ingestion Recommendations Release Ingestion Decisions Business Rule Processing - EMPI, Grouper, Provider Index Analytic Layer (DED/Valid Views) Data Mining Client Sign-off Release Medicare Ingestion Newly Detected File Client Review

1.

• 1. Data

Submi miss ssion ion & Stagin ing

• 2. Data

ta Ware rehou

• use

se Process essing ng & Enhancem ncemen ent

• 3. Extracts,

tracts, Analysis ysis-Rea eady y Datase sets, ts, and Reporting ting

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Data Quality Approach

Continual Improvement and Flexibility

• Feedback loop with internal and external

stakeholders

• Regular process improvement procedures with

flexibility to address more immediate issues

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HSRI Help Desk Provides Data Intake Support

• Detailed technical support to resolve validation issues

and ensure data are submitted in a timely manner

• All support requests received during business hours

(Mon.–Fri.) are responded to in 2 hours or less

• Accessible via:
• Toll-free phone number
• Email
• Web contact form in the HSRI Data Submission

and Quality Portal

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PR PRES ESENT ENTATIO TION N OB OBJEC JECTIVES TIVES

Today’s Objectives

• 1. Inform data users on the complexity and

challenges of resolving APCD data quality issues

• 2. Provide a framework for states navigating the

process:

a) Where to focus limited resources b) How to approach decision making and resolve issues

• 3. Make recommendations for future directions

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WH WHAT AR ARE D E DATA QU A QUAL ALIT ITY Y IS ISSUES? UES?

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Post-Intake Quality Assessments & Reports Internet Payer Submissions (Commercial/Medicaid) SFTP Server Monitor Process Enclave Server Unzip, Decrypt, Initial Storage Data Intake Validation Passed Files Release Staging Batch Calculated Variables, Member ID, Provider Processing Ingestion Recommendations Release Ingestion Decisions Business Rule Processing - EMPI, Grouper, Provider Index Analytic Layer (DED/Valid Views) Data Mining Client Sign-off Release Medicare Ingestion Newly Detected File Client Review

• 1. Data

Submission & Staging

• 2. Data

Warehouse Processing & Enhancement

• 3. Extracts,

Analysis-Ready Datasets, and Reporting

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What are data quality issues?

• Inconsistent claim and/or encounter volume over time
• Inconsistent PMPM over time
• Low match rates for Patient/Provider/Encounter

identifiers

• Inconsistent population of fields over time
• Mismatch of results when compared with external sources

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How do we find out about issues?

• Data Submitter Self-Report
• Post-Intake Quality Assessments & Reporting
• Data Mining
• Data Users (internal and external)
• Implementation of Third-Party Tools

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Why are the issues difficult to address?

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AP APCD Admini inist stra rator AP APCD Data Vend ndor

• r

APCD Data Sub ubmi mitt tter er Limited Resources

• Competing Priorities
• Revolving Door of Data Quality Issues
• Staff Turnover and Training
• Varying Requirements Across States
• Tolerance for Issues
• Policies for Resubmission
• Data Submission Platforms

IS ISSU SUE E RE RESOL OLUTION UTION FR FRAM AMEW EWORK ORK

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Framework for Resolution

What questions should be asked to choose the best resolution option?

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Prioritize Issues Identify Resolution Options Make Decisions Implement Resolution

Is this issue worth pursuing? How urgent is it? What are options for resolving the issue?

How can we prevent a similar issue in the future? How can we best communicate the resolution?

Considerations for Prioritizing Issues

Time Periods

Issue impacts multiple months or years

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Priority Payers

Issue impacts a high number of covered lives

• r high percentage of

APCD, payer types, etc.

Key Fields

Used for Member Identification, Claim Versioning, etc.

Status of Data

Data impacted are in use

Options for Resolving Issues

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PROS CONS NS

Up Update e Document mentati tion

• n

(e.g. .g.: : sub ubmi miss ssion ion gui uide e or rul ule) e)

Future standardization across payers No immediate impact Lengthy approval process

Modi dify fy Data Qu Quality ty Iden entif tific icat ation ion Proces cesse ses

Resolution in future submissions Future standardization across payers No resolution in data

Ed Educ ucate Sub ubmi mitt tter ers

Resolution in future submissions Historical issues remain

Request uest Resubm submiss ssion ion from

• m

Sub ubmi mitt tter er

Historical issues resolved Potentially time- and resource-intensive

Remed mediat ate e Data by Admini inist stra rator/ / Vendor ndor

Historical issues resolved Patchwork code

Modi dify fy Data User ser Docume menta ntati tion

• n

Users can work around issue based on use case No resolution in data

Decision-Making Questions

• Is this a significant issue that makes the data unusable?
• Can data users code around the issue easily?
• Is there time sensitivity to resolving the issue?
• How many payers does the issue affect?
• Does the issue occur in recent data (last 5 years)?
• Does the issue span more than a short amount of time

(e.g.: 1 month)?

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Decision-Making Questions

• Can the submitter fix the issue and resubmit?
• Will resubmission cause unintended

consequences/other quality issues?

• Is there other specialty code that needs to be

considered when processing?

• Will remediation cause unintended

consequences/other quality issues?

• Does the issue warrant the resources necessary for

resolution?

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Example 1: Insurance Product Type

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Issue: ue: Al All claims ms are sub ubmit mitted ed with h the e same e Insurance urance Produ duct ct Type e (IPT) ) code de whil ile e the e eligi gibility bility IPT has s variati tion

• n

Identification Method Data analysis and reporting Decision-Making Considerations

• Data released to users
• Payer has a large number of covered lives and makes up large

proportion of APCD

• Issue occurs in recent years (past three years)
• Time constraints to fix the issue for data users
• Data resubmission may cause shifts/unintended consequences

Resolution

• Administrator/vendor data remediation in historical submissions
• Submitter education & resolution in future submissions
• Exploring modification of data quality identification processes
• Timeline from issue identification to resolution implementation:

2 months

Example 2: Provider Network Unknown

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Issue: e: Many payers submitted a high volume of claims with “Unknown” for Provide ider r Netw etwor

• rk

k Indi dicat cator

• r,

, which ich indica icates s if the e servi vicing cing provi vide der r is partic icipat ipating ing in vs.

• s. out

ut of net etwor

• rk

Identification Method Data Mining Decision-Making Considerations

• Data released to users
• Targeted payers with the largest impact
• Issue occurs in recent years of data
• Relatively easy issue to fix if the payer has the data and time

Resolution

• Requested resubmission of historical data from high-impact payers
• Payers without capacity to resubmit files corrected the issue in

future submissions

• Modified data intake validation processes for earlier issue

detection in the future

• Timeline from issue identification to resolution implementation:

8 months

FU FUTUR URE E DI DIRE RECTIONS CTIONS

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Future Directions for the APCD Community

• Continue efforts toward data submission and intake

validation standardization (APCD-CDL™)

• Establish venue for:
• States to share data quality policies
• States to discuss common data quality issues
• An APCD data user learning community

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Th Thank nk You

• u.

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