HMIS Data Quality

Question 1

The overall utility of a dataset(s) as a function of its ability
to be processed easily and analyzed for a database, data
warehouse, or data analytics system.

Answer 1

Data Quality

Question 2

It involves data rationalization and validation. “Fitness for use”

Answer 2

Data Quality

Question 3

2 Techniques to check HMIS data Accuracy:

Answer 3

1. Lot Quality Assurance Sampling (LQAS)
2. Routine Data Quality Assessment (RDQA)

Question 4

Aspects of Data Quality:

Answer 4

• Accuracy
• Completeness
• Update Status
• Relevance
• Consistency
• Reliability
• Appropriate
• Presentation
• Accessibility

Question 5

Data collected and reported by HMIS is relevant to the
information needs of the health system for routine
monitoring of program performance.

Answer 5

Relevance

Question 6

Data is collected, transmitted, and processed according
to the prescribed time and available for making timely
decisions

Answer 6

Timeliness

Question 7

Data that is compiled in databases and reporting forms is
accurate and reflect no inconsistency between what is in
the registers and what is in the databases/reporting
forms at facility level. Similarly, in case of data entered in
the computers, there is no inconsistency between the
data in the reporting forms and the computer files

Answer 7

Accuracy

Question 8

At service delivery point, it refers to all the relevant
element in a patient/client register are filled.
At health administrative unit – data completeness has
two meanings

Answer 8

Completeness

Question 9

Common Sources of Data Errors in HMIS reports: Data items for whole months missing.

Answer 9

Missing Data

Question 10

Common Sources of Data Errors in HMIS reports: Multiple counting of a fully immunized child.

Answer 10

Duplicate Data

Question 11

Common Sources of Data Errors in HMIS reports: When data collection tools are not used routinely, staff just fills in a likely-looking number.

Answer 11

Thumb-stuck

Question 12

Common Sources of Data Errors in HMIS reports: A man being pregnant; low birth weight babies exceeding number of deliveries.

Answer 12

Unlikely values for variable

Question 13

Common Sources of Data Errors in HMIS reports: 100 births in a month when there are only 2,000 women in childbearing age.

Answer 13

Contradictions between variables

Question 14

Common Sources of Data Errors in HMIS reports: Mistakes in adding

Answer 14

Calculations Errors

Question 15

Common Sources of Data Errors in HMIS reports: Data is wrongly entered into the computer

Answer 15

Typing Error

Question 16

Common Sources of Data Errors in HMIS reports: TB cured in the place of treatment completed.

Answer 16

Capture in wrong box

Question 17

it won’t have ay added value in
monitoring the program performance. It only adds
burden on data collectors

Answer 17

Data is not relevant

Question 18

– it will not help us to make timely
decisions to fix the problem

Answer 18

Data is not timely

Question 19

we will not be able to see the
complete picture of the performance at different levels

Answer 19

Data is not complete

Question 20

The decision making based on evidence will be
hampered.

Answer 20

overall

Question 21

Tool that allows the use ofsmall random samples
to distinguish between different groups of data with high and low data quality

Answer 21

LOT Quality Assessment

Question 22

Is a technique useful for assessing whether the
desired level of data accuracy has been achieved
by comparing data in relevant record forms (i.e.
registers or tallies) and the HMIS reports.

Answer 22

LOT Quality Assessment

Question 23

Simplified version of the Data Quality Audit
(Which allows programs and projects to verify
and assess the quality of their reported data

Answer 23

Routine Data Quality Assessment Tool

Question 24

It aims to strengthen their data management and
reporting systems

Answer 24

Routine Data Quality Assessment Tool

Question 25

Objectives of the RDQA

Answer 25

1. Verify Rapidly 2. Implement 3. Monitor

Question 26

The analysis of data capture statistics (metadata) that provide insight into the quality of the data and help to identify data quality issues.

Answer 26

Profiling

Question 27

The decomposition of text fields into component parts and the formatting of values into consistent layouts based on industry standards, local standards (for example, postal authority standards for address data), user-defined business rules and knowledge bases of values and patterns.

Answer 27

Parsing and standardization.

Question 28

The modification of data values to meet domain restrictions, integrity constraints, or other business rules that define when the quality of data is sufficient for the organization.

Answer 28

Generalized Cleansing

Question 29

Identifying, linking, or merging related entries within or across sets of data

Answer 29

Matching

Question 30

Deploying controls to ensure that data continues to conform to business rules that define data quality for the organization

Answer 30

Monitoring

Question 31

Enhancing the value of internally held data by appending related attributes from external sources (for example, consumer demographic attributes or geographic descriptors). In addition, these products provide a range of related functional capabilities that are not unique to this market but which are required to execute many of the data quality core functions, or for specific data quality applications.

Answer 31

Enrichment

Question 32

* Problem solving method that identifies the root causes of the problems or events instead of simply addressing the obvious symptoms * Aim is to improve the quality of the products by using systematic ways in order to be effective (Bowen, 2011) * A tool for identifying prevention strategies * Identification and analysis of factors that are contributing to a specific outcome or problem = QUALITY IMPROVEMENT

Answer 32

Root Cause Analysis

Question 33

It aims to find various modes of failure within a system and addresses the following questions for execution.

Answer 33

Failure mode and Effect Analysis

Question 34

The idea that by doing 20 of the work one can generate 80 of the advantage of doing the entire job * finding the changes that will give the biggest benefits * useful where many possible courses of action are competing for attention * lays down the potential causes in a bar graph and tracks the collective percentage in a line graph to the top of the table

Answer 34

Pareto Analysis

Question 35

* used in risk and safety analysis * uses Boolean logic to determine the root cause of an undesirable event * Upside Down Tree o Undesirable result= top of the tree o Potential causes = down tree

Answer 35

Fault Tree analysis

Question 36

* shows the categorized causes and sub causes of a problem * useful in grouping causes (measurements methods, materials, environment, machines) into categories * categories should be the 4 Ms (Manufacturing) the 4 Ss (Service) or the 8 Ps (also service) depending on the industry * 4Ms Method

Answer 36

Fishbone/Ishikawa/Cause and Effect Diagram

Question 37

* breaks a problem down to its root cause by assessing a situation using priorities and orders of concern for specific issues * various decisions are outlined * potential problem analysis is made to ensure that the actions recommended are sustainable

Answer 37

Kepner-Tregoe Technique

Question 38

Diagnose the causes of recurrent problems by three phases: A. DISCOVER – Data gathering and analysis of findings B. INVESTIGATE – Creation of a diagnostic plan and identification of the root cause through careful analysis of the diagnostic data C. FIX – Fixing the problem and monitoring to confirm and validate that the correct root cause was identified

Answer 38

Rapid Problem Resolution (RPR Problem Diagnosis)

Question 39

(organization's values, norms, and practices with regard to the management and use information) affects outcomes of information use (Choo, Bergeron, Detlor and Heaton, 2008) * Determined by mission, history, leadership, employee traits, industry, and national culture * Sets of identified behaviors and values can account for significant proportions of the variance in information use outcomes * Management should continuously work on maintaining and improving the quality of data and information used in daily operations

Answer 39

Information Culture