Module 8: HMIS Data Quality Flashcards
Elements of Data Quality
- Accuracy
- Completeness
- Consistency
- Reliability
- Up-to-date
Techniques for HMIS Data Accuracy
The Lot Quality Assessment (LQAS)
The Routine Data Quality Assessment Tool (RDQA)
is a simplified version of the Data Quality Audit
(DQA) which allows programs and projects to verify and assess the quality of their reported data
The Routine Data Quality Assessment Tool (RDQA)
is a tool that allows the use of small random samples to distinguish
between different groups of data elements (or lots) with high and low data quality
The Lot Quality Assessment (LQAS)
analyzes information and identifies incomplete or incorrect data.
Data Quality Tool
refers to the decomposition of fields into component parts and formatting
the values into consistent layouts based on industry standards and patterns and user-defined business
rules.
Parsing and standardization
Enrichment,
Monitoring
,Profiling
,Matching
Generalized “cleansing”
data quality
the enhancement of the value of the data by using related attributes from external
sources such as consumer demographic attributes or geographic descriptors.
Enrichments
refers to the deployment of controls to ensure conformity of data to business rules set
by the organization.
Monitoring
refers to the analysis of data to capture statistics or metadata to determine the quality of
the data and identify data quality issues.
Profiling
is the identification and merging of related entries within or across data sets.
Matching
is the modification of data values to meet domain restrictions,
constraints on integrity, or other rules that define data quality as sufficient for the organization.
Generalized “cleansing”
is a problem solving method that identifies the root causes of problems or events
instead of simply addressing the obvious symptom
Root Cause Analysis.
identifying all possible failures in a design, a manufacturing or assembly process, or a product
or service. Failures are prioritized according to how serious their consequences are, how frequently they
occur, and how easily they can be detected
Failure Mode and Effects Analysis
Pareto analysisis a simple decision-making technique for assessing competing problems and measuring
the impact of fixing them. This allows you to focus on solutions that will provide the most benefit.
Pareto analysis
The main purpose of the fault tree analysis is to help identify potential causes of system failures before
the failures actually occur. It can also be used to evaluate the probability of the top event using analytical
or statistical methods (ASQ, 2002)
Fault Tree Analysis
is an easier method than the Failure Mode and Effects Analysis (FMEA) as it focuses
on all possible system failures of an undesired top event.
Fault Tree Analysis
are fairly simple in structure, at the top of the tree you have an “undesirable effect”
(UDE), below them you have the intermediate effects and at the bottom of the tree you have the root
causes.
Current Reality Tree
also, known cause and effect, is a graphic tool used to
explore and display the possible causes of a certain effect.
Fishbone Diagram
breaks a problem down to its root cause by assessing a situation using
priorities and orders of concern for specific issues. The various decisions that should be made to address
the problem are then outlined. Then, a potential problem analysis is made toensure that the actions
recommended are sustainable
Kepner-Tregoe technique
Another technique for root cause analysis is the rapid problem resolution (RPR problem
diagnosis) which diagnoses the causes of recurrent problems by following the three phases below:
● Discover - data gathering and analysis of the findings
● Investigate - creation of a diagnostic plan and identification of the root cause through careful
analysis of the diagnostic data
● Fix fixing the problem and monitoring to confirm and validate that the correct root
cause was identified
Rapid problem Resolution (RPR Problem Diagnosis)
