lessons learned for creating a process improvement performance management system

Reduce Waiting & No-Shows Increase Admissions & Continuation www.NIATx.net

Reduce Waiting & No-Shows Increase Admissions & Continuation

Lessons Learned for Creating a Process Improvement Performance

Management System

Jay Ford, Ph.D.Director of Research, NIATx

March 2010


Some is not a number, soon is not a time.

-- Don Berwick, MD


Institute of Medicine Report• Promote patient centered care

• Foster the adoption of evidence based practices

• Develop and using process and outcome measures to enhance quality of care, and

• Mandate the use of quality improvement measures


Improving the quality of care requires

• Identifying problem areas accurately

• Implementing creative interventions

• Understanding customer data needs


Design Data Systems that

• Capture and track essential measures of performance and quality

• Teach staff how to collect, analyze and learn from data and

• Develop systems to support organizational data needs


Is This Your Data System?


Inquiring minds want to know• What factors were related to successful

adoption of process-focused data?

• What barriers were encountered developing data expertise and focus?

• What attributes do we need to think about when developing a data system?


PIPM Hierarchy of Needs


How Much Data to Collect?


Key Lessons Learned: Data Collection

• Key process improvement variables are often not available (Date of first contact) or may not be adequately captured (e.g., no-shows) within existing systems.

• Conduct a data walk-through of your system to assess capabilities.– Identify currently available PI Data Elements– Flowchart of the provider submission process.– Evaluate the data submission instructions– Pilot test the process with a small sample of

records


Data Walk-through Questions

• Could the data easily be pulled from the state system?

• What barriers were encountered?• How complete and accurate was the data?• Were there significant missing gaps in the

data?• Did you notice any errors in the data?• Write-up and share the lessons learned

with key stakeholders.


Is this Data Quality?


Key Lessons Learned: Data Quality

• Establish a process for verifying and checking data accuracy.

• Failure to verify data entry for accuracy will limit the validity of performance management feedback reports related to process improvement.

• Approaches toward ensuring data integrity include – Automatic linkages (e.g., Washington)– Built-in quality checks (e.g., Ohio and Maine)– Feedback mechanisms (e.g., New York, South

Carolina and Oklahoma and – Ongoing training and technical assistance


Examples of Ongoing Training and Technical Assistance

• Oklahoma created a Data Integrity Review Team (DIRT) to provide on site review and technical assistance on all data issues for any provider.

• Maine created a change team to monitor data and performance of the contracted agencies and developed FAQs.

• New York developed a series of data entry and report analysis training modules for the STAR-QI system.

• Ohio offers technical assistance and follow-up through site visits, telephone calls, or conferences.


What type of Feedback?


Comparative Feedback

• Organizational Performance versus – a target (internal) or – a benchmark (external)

• Types of comparisons– Internal comparisons over time– External performance comparisons to other

similar organizations– External performance comparisons to other

agencies within a state



• Measurement Comparisons– Performance vs. Outcomes– Business Process vs. Treatment

Performance/Outcomes Importance of comparisons

• Types of Feedback Reports– Data Quality– Performance Reports– Pay for Performance



• Understand the whole picture

• Select a few key outcome measures

• Use of reports to guide questions

• Benchmarks vs. Targets

• Focus on the comparison (internal vs. external)


Key Lessons Learned: Performance Management

• Do not skimp on data quality efforts.• Ensure access to all persons who need the

reports. • Create performance feedback loops that

include, not isolate, the provider data coordinators.

• Provide only reports that help providers effectively use data to make decisions.

• Use pictures or graphs, but remember: one graph, one message.

• Update reports over time as data is corrected.


State Examples• New York generates data warehouse reports by

provider or in the aggregate.• Ohio links STAR-SI performance measures to

departmental Performance Target Outline (PTO).• South Carolina facilitates provider comparisons by

preparing & disseminating monthly comparative reports.• Maine provides public access to the TDS reports and

allows agencies to access the secure system and to request specialized reports.

• Oklahoma provides feedback through the Integrated Client Information System (ICIS), allowing monthly access to feedback reports.


Key Lessons Learned: Pay for Performance

• Building the system

• Pilot testing

• Offering the right type of incentive

• Overcoming potential obstacles

• Implementing strategies for long-term success and sustainability


For further information, please visit

www.niatx.net

www.odmhsas.org/data

Contact Information

Jay Ford, [email protected]

Mark [email protected]

lessons learned for creating a process improvement performance management system

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