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Health Informatics > HMIS Data Quality > Flashcards

HMIS Data Quality Flashcards

1
Q

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.

A

Data Quality

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2
Q

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

A

Data Quality

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3
Q

2 Techniques to check HMIS data Accuracy:

A
  1. Lot Quality Assurance Sampling (LQAS)
  2. Routine Data Quality Assessment (RDQA)
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4
Q

Aspects of Data Quality:

A
  • Accuracy
  • Completeness
  • Update Status
  • Relevance
  • Consistency
  • Reliability
  • Appropriate
  • Presentation
  • Accessibility
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5
Q

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

A

Relevance

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6
Q

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

A

Timeliness

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7
Q

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

A

Accuracy

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8
Q

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

A

Completeness

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9
Q

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

A

Missing Data

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10
Q

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

A

Duplicate Data

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11
Q

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

A

Thumb-stuck

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12
Q

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

A

Unlikely values for variable

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13
Q

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

A

Contradictions between variables

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14
Q

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

A

Calculations Errors

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15
Q

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

A

Typing Error

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16
Q

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

A

Capture in wrong box

17
Q

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

A

Data is not relevant

18
Q

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

A

Data is not timely

19
Q

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

A

Data is not complete

20
Q

The decision making based on evidence will be
hampered.

A

overall

21
Q

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

A

LOT Quality Assessment

22
Q

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.

A

LOT Quality Assessment

23
Q

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

A

Routine Data Quality Assessment Tool

24
Q

It aims to strengthen their data management and
reporting systems

A

Routine Data Quality Assessment Tool

25
Q

Objectives of the RDQA

A
  1. Verify Rapidly
  2. Implement
  3. Monitor
26
Q

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

A

Profiling

27
Q

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.

A

Parsing and standardization.

28
Q

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.

A

Generalized Cleansing

29
Q

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

A

Matching

30
Q

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

A

Monitoring

31
Q

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.

A

Enrichment

32
Q
  • 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
A

Root Cause Analysis

33
Q

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

A

Failure mode and Effect Analysis

34
Q

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

A

Pareto Analysis

35
Q
  • 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
A

Fault Tree analysis

36
Q
  • 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
A

Fishbone/Ishikawa/Cause and Effect Diagram

37
Q
  • 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
A

Kepner-Tregoe Technique

38
Q

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

A

Rapid Problem Resolution (RPR Problem Diagnosis)

39
Q

(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

A

Information Culture

Health Informatics (5 decks)

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