Presentation to Ryan White Grantee Meeting - dataCHATT

dataCHATT 201:
Introduction to Data Flow and Data
Quality Assessment
Mira Levinson, JSI Research & Training Institute, Inc.
Kim Lawton, Quality and Information Management
Lisa Hirschhorn, JSI Research & Training Institute, Inc.
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Data Quality
• Having quality data is critical for many
program activities
– Clinical care
– Quality improvement
– Planning
– Reporting
• But what do you mean by “quality”, how do
you measure it, and why should you care?
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Presentation Overview
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The Importance of Data Quality for Ryan White Program
Grantees
Essential Steps of Data Flow from Collection to Reporting
and Use
Key Factors for Ensuring Systemic Data Quality
Key Elements of Data Quality
Quality Improvement Techniques to Improve Data Quality
from Collection through Reporting (a really quick tour)
Provide an Overview of HAB-Funded Sources of Available
TA to Support Data Quality
Get Participant Feedback
The Importance of Data
Quality for Ryan White
Program Grantees
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The Importance of Data Quality
for Grantees: Data Reporting
• Grantees need to accurately report HIV
services provided and patients served to
HRSA/HAB
• HRSA needs to accurately reports to
Congress for ongoing support of the Ryan
White Program
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The Importance of Data Quality for
Grantees: Program Management
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Internal monitoring and evaluation
Planning
Quality improvement
Grant writing
Data Quality Concerns
But…
• What if it’s not timely?
• What if it’s not valid?
• What if it’s not complete?
• Why is good data so important to
grantees?
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So where do you start?
• To ensure quality data you need to follow
a series of steps in the collection,
reporting and use of your data
• These form a flow from identifying what
you need to collect through where you will
get it to how you will collect and report
your data
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Essential Steps of Data
Flow from Collection to
Reporting and Use
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Data Flow Steps: An Overview
1.
Identifying and Defining
Data Elements:
What do you want/need to
collect?
2.
Data Sources:
5.
How do you submit the data you
have?
6.
Where can you find what you
need to collect?
3.
4.
Data Validation and Data
Quality Procedures:
How do you know the data you
get is good and accurately
reflects what you are trying to
measure or report?
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Communicating about Data:
How do you use the data you
have to inform our program about
how you are doing?
Data Collection:
How can you get the data you
need to collect?
Data Reporting:
7.
Using the Data:
How do you use the data you
have to inform our program
decisions?
Assessing the Effectiveness of
the Current System
How can you improve our data
system in order to effectively
accomplish steps 1 – 7?
Focus on Data Validation and
Data Quality Procedures
Efforts to measure and improve data need
to happen during all of these steps.
This presentation focuses on Step 4: Data
Validation and Data Quality Procedures
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Key Factors for Ensuring
Systemic Data Quality
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Review and use your data
• Know your data - The best way to improve data
quality is to review and use the data!
• Create a system for data quality assessment
that is routine, comprehensive and reflective
• Define and follow your data flow steps to collect
and report the data
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Involve your staff
• Engage your staff and your contracted providers
in the efforts to ensure data quality!
• Define roles and responsibilities at all levels
– Consider identifying one or more individuals to
oversee data quality procedures (reviewing
definitions, protocol development, training, etc).
• Conduct routine training to review data-related
procedures and learn about any changes
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Develop and communicate your
requirements and expectations
• Provide routine training to internal staff and
contracted providers on reporting requirements,
timelines and expectations (through policies,
procedures, contracts or MOUs)
• Provide written guidance, and make sure
everyone has access to it
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Ensuring Consistency
• Standardize forms/tools across data collection and
reporting efforts
• Develop a written protocol (you own user guide) to
document which explains your procedures for data
collection, quality and reporting
– Includes clear and consistent definitions of the key elements for
data collection
– Provides the details for each variable (data source, how you will
collect it)
– Defines who will be responsible for what
– Is clear and easy to understand
• Develop data review and data cleaning procedures to
be performed at all levels
• Update tools and protocols regularly
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Key Elements of Data
Quality
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Elements of Data Quality
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Validity
Reliability
Completeness
Timeliness
Integrity
Confidentiality
Validity
Valid data are accurate data
defined as “They measure what
they are intended to measure.”
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Validity Questions:
Data Collection
• Does the setting and how the questions
are being asked potentially compromise
their validity?
– For example: asking an adolescent about
sexual activity in front of their parent
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Validity Questions: Data Collection
• How is the primary data collection and entry
being done? Is there potential for error? For
example:
– Client fills out a paper form and misunderstands a
question
– Administrative staff enters form into EMR, and makes
an entry error based on client handwriting
– Databases are not linked, so data must be extracted
and then entered hand into HIV program’s database:
opening opportunity for mistakes.
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Validity Questions:
Data Reporting
• If you are combining data or calculating
rates…
– Are the correct formula and approaches
being applied?
– Are they applied consistently (e.g., from site
to site, over time)?
• Are final numbers reported accurately
(e.g., does the total add up)?
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Validity: Steps to Limit Errors
• Training:
– Are all staff trained on definitions and how to
complete data entry fields?
• Validation Checks:
– Do the data fall within acceptable range?
– Look for outliers
• e.g. age >100
• CD4 count > 4,000
• pregnant men
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More Steps to Limit Errors
• Validation Rules:
– Do you have data validation rules (e.g. can
not enter pregnancy if client is male)
• Validation Activities:
– You can do chart extraction to validate data
entered
– Double entry usually reserved for research or
when data quality is a significant concern or
new staff
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Example: Validation Checks
In this example Specimen Source: cervix/endocervix is
checked against Gender: Male
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Reliability
Reliable data are measured and
collected consistently (i.e.,
repeated measurements using
the same procedures get the
same results)
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Reliability: Key Questions
• Where are there potential gaps in the
data flow which may compromise
reliability?
– The same instrument is not used year to
year or across sites
• Data collected changes without true change in
services
• One site uses a nurse to extract from a medical
record, while another uses an non-clinically
trained data manager
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Procedures to Ensure Reliability
Are steps being taken to limit reliability errors?
• Training
– Do you provide clear and consistent training across all sites?
– Is the instrument always administered by trained staff?
• Guidance/Instructions
– Do you provide detailed procedures and instructions to all sites
and providers?
– Are all providers trained to ask clients to self-identify their
ethnicity, race and gender? Is it possible that some providers
make assumptions based on appearance?
• Consistent tool (across all sites and providers)
• Refer to user manual
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Completeness
Complete data do not have any
missing elements and are collected
on the entire population outlined in
the user manual or guidance.
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Completeness: Key Questions
• Percent of all fields on data collection form
filled in
• Percent of all expected reports actually
received
• Are the data from all sites that are to report
included in aggregate data? If not, which
sites are missing?
• Is there a pattern to the sites that were not
included in the aggregation of data?
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Procedures to Ensure
Completeness
• Develop a procedure to routinely look for
frequency of missing data elements
– Check for completeness and communicate edits on a
routine basis (e.g. monthly)
• Develop and implement procedures follow-up
on missing data
– Volume of missing data often diminishes over time
once staff are aware that someone is looking at it
– Procedures may be different for data received from
contractors versus internally collected data
– electronic data submission vs. paper data submission
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Look for “missing data” trends
Look for trends in missing data,
and ask “why?”
– Are there barriers to capturing or entering the
data?
– Meet with your staff and ask for their insights
– Use this information for data collection
planning
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Timeliness
Timely data are…
• sufficiently current and frequent to inform
management decision-making
• received by the established deadline
• received with adequate time to review for
other elements of quality, and to address
identified gaps
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Timeliness: Key Questions
• Is a regular schedule of data collection in
place to meet program management
needs? When are your established
deadlines?
• Does program staff and contractors know
and understand the reporting deadline? Is
it consistent across all reporting sites?
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Timeliness:
More Key Questions
• Is there adequate time to review data for
other aspects of quality and address
identified gaps before it is needed for
reporting or other use?
• Are data available on a frequent enough
basis to inform program management
decisions?
• Are data being collected and reported
according to your timeline?
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Optimal Timeline for Collecting
Data to Ensure Quality
• Work back from the submission deadline
– include time to review, address identified gaps, etc.
• More frequent collection allows for more time to
review data collected
– Care and services being provided
– Missing data
– Other data problems
• Grantees with subcontractors can request data
submissions more frequently than reporting
requires (more than annually)
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Procedures to Ensure
Timeliness
• Define and set reasonable timelines
• Communicate and stick with timelines
• Include a process for reviewing whether
data was submitted on time, providing
feedback and requesting revisions
• Consider implementing consequences for
lateness, and rewards for timeliness
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Integrity
Data are protected from
deliberate bias or manipulation
for any reasons
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Integrity: Key Questions
• Are there risks that data might be
manipulated for any reasons?
• What systems are in place to minimize
such risks?
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Confidentiality
Clients are assured that their data will
be maintained according to
organization, state and national
standards
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Confidentiality: Key Questions
Do you provide routine training…
• to program staff on the importance of confidentiality,
and on confidentiality requirements and procedures?
• to IT staff on the specific issues of HIV confidentiality
and electronic information storage and transfer?
• to contracted service providers on procedures for
data submission?
• to clients on confidentiality procedures?
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Procedures to Ensure
Integrity and Confidentiality
• Training
– Train all staff and contracted providers on
confidentiality and privacy protocols
• Electronic Data Security
– Document user access to database
– Limit user access to database
– Consider security limitations of laptops,
handheld devices, etc
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Procedures to Ensure
Integrity and Confidentiality
• Security of Paper Data
– Store paperwork in a secure, locked cabinet
and/or user-restricted area
• Inform Clients of Confidentiality and
Privacy Protocols
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Discussion:
How Does This Apply To Me?
• Validity (accuracy)
• Reliability
(consistency)
• Completeness
(all there)
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• Timeliness (there
when you need it)
• Integrity (honesty)
• Confidential
Ensure Quality Through
Assessment
When to assess program data quality:
• Integrate data quality control mechanisms into
standard operating procedures and software
• Integrate data quality checks into routine
supervisory or contract monitoring visits
• Conduct periodic formal assessments
• Provide feedback on submitted data
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Quality Improvement
Techniques to Improve
Data Quality from
Collection through
Reporting (a really quick
tour)
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Application of Basic Data Quality
Improvement (QI) Techniques
• The same concepts apply to improving
data quality as they do to improving
quality of care:
– Measure the quality.
– Explore steps required for quality data and
where gaps may have occurred (flow chart).
– Understand the potential causes of the
identified gap (fishbone or cause and effect).
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A Sample QI Technique:
Plan-Do-Study-Act Cycle
Plan: Develop a QI Project goal (i.e. what you want
to accomplish) based on assessment of data quality
– Decrease missing data, improve timeliness,
– Form a team
– Identify where you think the problem (gap)
may be and develop a potential solution
Do: Carry out the proposed solution
Study: Analyze your data, summarize what was
learned, compare with what you wanted to achievedid the solution work
Act: Determine next steps (if worked, how to
expand, if not as successful what to change ) and
then begin Plan to implement
Graphic adapted from the American Heart Association
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Example: Low Reported Pap
Smear Rates
PLAN
• Identify the problem: A hospital-based site notices that
their Pap rates for HIVQUAL are 75%, but those
reported in the RDR are only 40%.
• Develop a QI Project Goal: They want to improve the
quality of reported data.
• Form a Team: A team is formed including the program
data manager, a nurse provider, and a case manager.
• They define the goal as decreasing the difference
between reported and actual rates to less than 10%.
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Example: Low Reported Pap
Smear Rates
PLAN
• Identify the data steps required for a Pap
smear to be included in the RDR report
– Internally: internal lab results are
automatically entered into the EMR, which is
then used to download data into a program
database for RDR submission
– Versus HIVQUAL: chart review of client
sample and entry into HIVQUAL database
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Example: Low Reported Pap
Smear Rates
PLAN
• Pap results for patients seen by external providers are
not received 25% of the time.
• When these results are received, they are manually
entered into a different field than the one used for Pap
results for patients seen by internal providers (done via
automatic transfer from lab system).
• For HIVQUAL reviews, both fields are manually
extracted, but the automated RDR report only extracts
the data field of the program database of the internallyprovided Pap tests.
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More Plan, Do, and Study
• PLAN: Modify IT systems can be modified so that data
sources are the same OR reporting draws from both
Pap data sources.
• DO: Ask the hospital IT department to reprogram so that
external Paps can go into the same field OR the RDR
report can look at both fields
• STUDY: nothing happens as the hospital EMR is a
proprietary software and takes significant resources to
revise and will take many months
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Act and the Next Cycle
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ACT: Decide to try a different approach for an interim solution
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PLAN: Establish a Log for women getting Paps from external providers
and use to manually enter into program database.
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DO: train a nurse and data manager to use an Excel spreadsheet to
enter any woman getting a Pap from provider external to the clinic and
educate all providers to give the Pap results to the nurse before
sending to medical records
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STUDY: Next RDR rate is only 18% below HIVQUAL data.
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ACT: Continue log and also work with PO to get resources to ultimately
automate capture of externally provided Pap tests.
Provide an Overview of
HAB-Funded Sources of
Available TA to Support
Data Quality
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TA Resources to Support
Data Quality
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Project officer
TARGET Center
– http://www.careacttarget.org
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dataCHATT
– http://www.datachatt.jsi.com/
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Ryan White HIV/AIDS Program Data Report TA
– http://datasupport.hab.hrsa.gov/
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CAREWare TA
– http://hab.hrsa.gov/careware/
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National Alliance of State and Territorial AIDS Directors Cooperative
Agreement (NASTAD)
– http://www.nastad.org/Programs/hivcareandtreatment
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National Quality Center (NQC)
– http://www.nationalqualitycenter.org/
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HRSA Information Center
– http://ask.hrsa.gov/
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Get Participant Feedback
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Data Academy
• dataCHATT is developing a series of webbased training modules.
• This Data Academy will include training
modules on data collection, data quality,
data reporting and using data.
• We need your feedback to make sure the
information is presented effectively.
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Feedback
–Was this content useful?
–Appropriate?
–Did it meet your needs?
–Any suggestions?
–Can we contact you to review
future Data Academy modules?
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Acknowledgements
• The HIV/AIDS Bureau for support of this
Cooperative Agreement
• The 101 Grantees who participated in the
Request for Information
• JSI contributing staff (Julie Hook, Kim Watson,
Michael Rodriguez and the dataCHATT team)
• Positive Outcomes, Inc.
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For more information…
Visit the dataCHATT website:
www.datachatt.jsi.com
For copies of today’s presentation, contact
us at: datachatt@jsi.com
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