1. 1
MODULE 4:
RHIS Data Quality
SESSION 1:
Introduction to Data Quality
ROUTINE HEALTH INFORMATION SYSTEMS
A Curriculum on Basic Concepts and Practice
The complete RHIS curriculum is available here:
https://www.measureevaluation.org/our-work/ routine-health-information-systems/rhis-curriculum
2. 2
Learning Objectives and Topics Covered
Objectives
Understand the data quality conceptual framework
Become familiar with the dimensions and metrics of
data quality
Understand what different RHIS management levels
can do to ensure data quality
Identify the main types of data quality problems
Topics Covered
Define data quality
Link between data quality and quality assurance
Data-quality conceptual framework
Metrics of data quality
Common threats to data quality
3. 3
What Is Data Quality?
Data quality is often defined as “fitness for
use.”
What does this mean?
Data are fit for their intended uses in
operations, decision making, and planning.
Data reflect real value or true
performance.
Data meet reasonable standards when
checked against criteria for quality.
4. 4
Importance of Data Quality
High-quality data help providers and managers:
o Form an accurate picture of health needs, programs, and
services in specific areas
o Inform appropriate planning and decision making (such as
staffing requirements and planning healthcare services)
o Inform effective and efficient allocation of resources
o Support ongoing monitoring, by identifying best practices
and areas where support and corrective measures are
needed
5. 5
Symptoms of Data Quality Problems
Different people supply different answers to the
same question.
Data are not collected in a standardized way or
objectively measured.
Staff suspect that the information is unreliable, but
they have no way of proving it.
There are parallel data systems to collect the same
indicator.
6. 6
Symptoms of Data Quality Problems (2)
Data management operational processes are not
documented.
Data collection and reporting tools are not
standardized; different groups have their own
formats.
Too many resources (money, time, and effort) are
allocated to investigate and correct faults after
the fact.
Mistakes are spotted by external stakeholders
(during audits).
7. 7
What Is Quality Assurance?
“A program for the systematic monitoring and
evaluation of the various aspects of a project,
service, or facility (and taking actions
accordingly) to ensure that standards of
quality are being met” (Merriam-Webster
Dictionary)
8. 8
What Is Data Quality Assurance?
A systematic monitoring and evaluation of
data to uncover inconsistencies in the data
and data management system, and making
necessary corrections to ensure quality of
data
9. What are the roles and responsibilities
that should be carried out at each
level of the health system
to assure production of high-quality
data?
9
Quick Plenary Discussion
10. 10
Maintaining Data Quality by RHIS Management Level
Collect and enter initial data
Summarize patient data and
check quality of registers
Complete, verify, and submit
summary reports on time
Routinely analyze and use data
Review reports received;
submit aggregated reports
Ensure timeliness and
completeness of reporting
Monitor quality of data
captured and reported
Conduct routine supervisory
visits
Routinely analyze and use data
Provide guidelines on data
collection, reporting, and
management procedures
Ensure timeliness and
completeness of reporting
Monitor quality of data
throughout all levels
Monitor quality of data
captured and reported
Conduct routine supervisory
visits
Routinely analyze and use data
Health Facilities
(Service Delivery
Sites)
Intermediate Level
Central Level
12. 12
Metrics of Data Quality
Completeness and Timeliness of Data: Availability of reports and availability of
complete data (up-to-date, available on time, and found to be correct)
Internal Consistency of Reported Data: Plausibility of reported results, trends over
time, and consistency between related indicators and potential outliers
External Consistency with Other Data Sources: Level of agreement between two
sources of data measuring the same health indicator
External Comparisons of Population Data: Consistency between denominators from
different sources used to calculate health indicators
13. 13
Group Work
Instructions:
• In your small subgroups, identify the five most
common problems that you think affect data
quality.
• For each problem, propose actions that could lead
to improvements in data quality.
• You have 15 minutes to discuss in your subgroups
before reporting back for plenary discussion.
14. Most Common Problems Affecting Data Quality
across System Levels
Technical determinants
• Lack of guidelines to fill out the data sources and reporting forms
• Data collection and reporting forms are not standardized
• Complex design of data collection and reporting tools
Behavioral determinants
• Personnel not trained in the use of data sources & reporting forms
• Misunderstanding of how to compile data, use tally sheets, and
prepare reports
• Math errors occur during data consolidation from data sources,
affecting report preparation
Organizational determinants
• Lack of a reviewing process, before report submission to next level
• Organization incentivizes reporting high performance
• Absence of culture of information use
15. 15
ROUTINE HEALTH INFORMATION SYSTEMS
A Curriculum on Basic Concepts and Practice
This presentation was produced with the support of the United States Agency for
International Development (USAID) under the terms of MEASURE Evaluation
cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is implemented by
the Carolina Population Center, University of North Carolina at Chapel Hill in
partnership with ICF International; John Snow, Inc.; Management Sciences for Health;
Palladium; and Tulane University. The views expressed in this presentation do not
necessarily reflect the views of USAID or the United States government.
Editor's Notes
#4:Emphasize the importance of high-quality data for effective decision making: Good decisions cannot be based on bad data.
Explain that bad data may mislead management about the true circumstances of a situation (health status), such as data gaps and gross errors.
Explain the meaning and use of high-quality data (what high-quality data are, and why they are important).
Examine the importance of high-quality data from different perspectives, and how high-quality data provide clues to health status and can help identify problems.
#5:Ask participants: What are some signs that can alert you that there might be a problem with data quality? Listen to their responses. Then discuss the bullet points on the slide.
#6:Here are some more symptoms of a data quality problem.
#7:Facilitator: Read the Merriam-Webster Dictionary definition and add "That means doing the right thing the right way"
#8:This slide adapts the quality assurance definition on the previous slide to data quality assurance. After reading the definition on the slide, explain "This means that the data are fit for their purpose and use.”
#9:Because data quality is a product of systemic M&E of data and data production, ask participants to volunteer their ideas about what needs to be in place to support high-quality data at each management level of the health system.
Discussion should be 10 minutes.
#10:Service delivery site responsibilities:
Make the first data registration. Depending on the indicator type, this can be patient/client records, registers, training participant list, prevention activity participant list, etc.
Summarize patient/client information on tally sheets, when appropriate, and then complete summary reporting forms (monthly or quarterly), and check the data quality of each.
Submit the validated summary reporting form to the next level of the information system.
Analyze and use data on a regular basis to improve quality of care.
Intermediate site responsibilities:
Collate summary reports and their associated quality checks, including review and approval of aggregate numbers prior to their submission to the next level of the information system.
Ensure that all data, whether paper-based or captured electronically, reach the next reporting level for each established reporting period defined by a given indicator.
Conduct regular supervisory visits (at least quarterly) to lower levels.
Monitor the quality of the data captured by the information systems in place.
Analyze and use data on a regular basis for strategic planning and/or any other related activity.
Central level responsibilities:
Provide the subreporting levels with clear written instructions/guidelines on the completion of data collection and reporting forms and tools, to ensure a common standard response.
Collate summary reports provided by subreporting levels (such as aggregation-level sites or service delivery sites), depending on the type of indicator and how the data flow is organized.
Perform quality checks on reports, including review and approval of aggregate numbers prior to their dissemination.
Conduct regular supervisory visits (at least quarterly) to monitor data quality.
Monitor the quality of data captured by the information systems in place.
Provide written guidelines on reporting requirements and deadlines.
#11:A conceptual framework is presented that supports the M&E data management and reporting system. This framework is based on three parts:
Reporting levels (such as service delivery sites), intermediate levels, and national M&E (shown in blue on the slide)
Metrics of data quality (shown in rust and green in the top right part of the slide)
Functional components of data management systems (shown in the green section at the bottom right of the slide)
The quality of reported data is often dependent on the underlying data management and reporting systems at the various levels of the information system. Stronger systems routinely produce better-quality data. Data quality problems can occur at any level of the system and systematic efforts should be made to ensure high-quality data at all levels.
#12:Facilitator: Ask volunteers to read the definition of data quality metrics and to provide an explanation for the whole group.
