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

Perception of the data’s
appropriateness to serve its
purpose in a given context

A

data quality

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3
Q

aspects of data quality

A
accuracy
accessibility
appropriate presentation
completeness
consistency
relevance
reliability 
update status
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4
Q

LQAS means

A

LOT QUALITY ASSESSMENT SAMPLING

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5
Q
Tool that allows the use of small
random samples to distinguish
between different groups of data
elements with high and
low data quality
A

LOT QUALITY ASSESSMENT SAMPLING

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6
Q

RDQA means

A

ROUTINE DATA QUALITY ASSESSMENT

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7
Q
Simplified version of the Data
Quality Audit (DQA) which
allows programs and
projects to verify and
assess the quality of their
reported data
A

ROUTINE DATA QUALITY ASSESSMENT

RDQA

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8
Q

RDQA OBJECTIVES

A
  1. verify rapidly
  2. implement
  3. monitor
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9
Q

the quality of reported data for key indicators at selected sites and the ability of data-management systems to collect, manage, and report quality data

A

verify rapidly

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10
Q

corrective measures with action plans (one of RDQA Objectives)

A

implement

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11
Q

capacity improvements and performance of the data management and reporting system to produce quality data (under RDQA objectives)

A

monitor

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12
Q
A project
management tool
that shows how a
project will evolve at
a high level
A

IMPLEMENTATION PLAN

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13
Q
Helps ensure that a
development team is
working to deliver
and complete tasks
on time
A

IMPLEMENTATION PLAN

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14
Q

IMPLEMENTATION PLAN KEY

CONCEPTS

A

(1) Define Goals/Objectives
(2) Schedule Milestones
(3) Allocate Resources
(4) Designate Team Member Responsibilities
(5) Define Metrics for Success

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15
Q

Answers the question

“What do you want to accomplish?”

A

Define Goals/Objectives:

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16
Q

Outline the high level

schedule in the implementation phase.

A

• Schedule Milestones:

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17
Q

Determine whether you
have sufficient resources, and decide how you will
procure what’s missing.

A

Allocate Resources

18
Q

Create a general team plan with the overall roles that each team member will play.

A

Designate Team Member Responsibilities

19
Q

How will you determine if you have achieved your goal?

A

Define Metrics for Success:

20
Q

ANALYZES INFORMATION AND
IDENTIFIES INCOMPLETE OR
INCORRECT DATA

A

DATA QUALITY TOOL

21
Q

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 rule

A

Parsing and Standardization

22
Q
  • Modification of data values to meet domain restrictions

- Constraints on the integrity of other rules that define data quality as sufficient for the organization

A

generalized “cleansing”

23
Q

This is the identification and merging related entries within or across data sets

24
Q

Refers to the analysis of data to capture statistics or metadata to determines the quality of the data and identify data quality issues

25
The deployment of controls to ensure conformity of data to business rules by the organization
monitoring
26
Enhancing the value of the data by using related attributes from external sources such as consumer demographic attributes of geographic descriptors
enrichment
27
``` Focus on Data Quality Management (DQM), which generally integrate profiling, parsing, standardization, cleansing and matching processes ```
APPLICATION / SCOPE OF DATA QUALITY TOOLS
28
``` A class of problem solving methods aimed at identifying the root causes of the problems or events instead of simply addressing the obvious symptoms ```
ROOT CAUSE ANALYSIS
29
Useful for getting to the underlying causes of a | problem
5 WHYS ANALYSIS (ASK WHY 5 TIMES)
30
• By identifying the problem, and then asking "why" five times - getting progressively deeper into the problem, the root cause can be strategically identified and tackled
5 WHYS ANALYSIS (ASK WHY 5 TIMES)
31
Aimed to find various modes for failure within a system. It requires several steps for execution: 1. All failure modes (the way in which an observed failure occurs) must be determined. 2. How many times does a cause of failure occur? 3. What actions are implemented to prevent this cause from occurring again? 4. Are the actions effective and efficient?
FAILURE MODE AND EFFECTS ANALYSIS | FMEA
32
Operates using the Pareto principle (20% of the | work creates 80% of the results)
PARETO ANALYSIS
33
Utilized when there are multiple potential causes to a problem
pareto analysis
34
Uses boolean logic to determine the root causes of an undesirable event.
FAULT TREE ANALYSIS
35
This technique is usually used in risk analysis and safety analysis.
FAULT TREE ANALYSIS
36
Used when many problems exist and you want to get to the root causes of all the problems
CURRENT REALITY TREE | CRT
37
Analyzes a system at once
CRT
38
``` will group causes into categories including: ▪ People ▪ Measurements ▪ Methods ▪ Materials ▪ Environment ▪ Machines ```
FISHBONE OR ISHIKAWA OR CAUSE-AND-EFFECT DIAGRAMS
39
KEPNER-TREGOE TECHNIQUE
Also known as rational process is intended to break a problem down to its root cause
40
deals with diagnosing the | causes of recurrent problems
RPR PROBLEM DIAGNOSIS
41
Information culture affects the information use outcomes
SUSTAINING CULTURE OF INFORMATION USE
42
3 Phases: • Discover - team members gather data and analyze their findings • Investigate - a diagnostic plan is created and the root cause is identified through careful analysis of the diagnostic data • Fix - the problem is fixed and monitored to ensure that the proper root cause was identified.
RPR PROBLEM DIAGNOSIS