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Information Cycle Data Handling in Information Cycle: Collection and Collation

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Define what is data and what is ... Recognize the difference between collecting data and ... do not have to be followed-up (e.g. headcounts, children weighed) ... – PowerPoint PPT presentation

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Title: Information Cycle Data Handling in Information Cycle: Collection and Collation


1
Information CycleData Handling in Information
Cycle Collection and Collation
  • University of Oslo
  • Department of Informatics
  • Oslo - 2007
  • Facilitator Gertrudes Macueve
  • 11th April 2007

2
Learning objectives (1)
  • Define what is data and what is information
  • Identify the different stages of the information
    cycle
  • Explain how to handle data
  • Recognize the difference between collecting data
    and gathering data
  • Identify data collection tools

3
Learning objectives (2)
  • Explain the need for flexibility and
    standardization in data collection
  • Explain the rationale for use of an essential
    dataset
  • Explain the correlation between data elements and
    indicators
  • Define what is data collation
  • Indicate common data collation methods and
    problems

4
Data and information
  • Data
  • observations and measurements about the world,
    e.g.
  • Representation of observations or concepts
    suitable for communication, interpretation, and
    processing by humans or machines.
  • May or may be not useful to a particular task.
  • Information
  • facts extracted from a set of data (interpreted
    data), Meaningful and useful
  • Data brought together in aggregate to demonstrate
    facts
  • It is useful to a particular task.

5
Information Cycle
What do we collect?
What do we do with it?
How do we use it?
Quality information
How do we present it?
6
Information Cycle
Stages Tools Outputs
Decision-making for effective management
What do we collect?
data sources tools
Good quality data
What do we do with it?
How do we use it?
Quality at every stage EDS
Interpretation of information
Process Analysis
feedback
How do we present it?
Data converted to information
Reports graphs
7
Data Handling in the Information Cycle1. Data
collection
8
The starting pointFeeding the information cycle
Presenting Interpreting
Output INFORMATION
ANALYSIS Processing
USE
Collection
Input Raw data
9
Data collection
  • Two ways to obtain data
  • Collect data Physical counting of elements
  • Gather data if data have already been collected
    Requirements
  • The definitions of the data are the same as ours
  • The format in which the data are collected, is
    the same
  • Data are collected reasonably accurately
  • We are able to negotiate access to the data

10
Data collection/gathering guiding principles
  • WHO health care workers at all levels
  • WHAT Essential Data Set
  • WHEN daily collated weekly processed monthly
  • WHERE work sites, facilities, districts (info
    filter)
  • HOW data sources (tally sheets, registers etc)
  • WHY To monitor progress towards goals targets
  • To Plan new policies and changes
  • To evaluate current services
  • To assist health management processes

11
What data elements should be collected?
  • Can provide useful information (affecting the
    management decisions)
  • Cannot be obtained elsewhere
  • Are easy to collect
  • Do not require much work or time
  • Can be collected relatively accurately
  • ESSENTIAL DATA SET based on indicators
    reflecting the health status of the community

12
Essential data set
The of children under one year who are fully
immunised
Drop out rate DPT 1-3 measles coverage
The of children under two years who are fully
immunised
Other programme vaccines given
13
Essential data set at each level
  • Standardised
  • Usefulness
  • Address the needs of all stakeholders
  • User-friendly
  • Dynamic

14
Where do we get data from?
  • Routine data collection
  • Routine health unit and community data
  • Activity data about patients seen and programmes
    run, routine services and epidemiological
    surveillance e.g.
  • Semi-permanent data about the population served,
    the facility itself and staff that run it
  • Civil registration

15
Where do we get data from?
  • Non-routine data collection
  • Surveys
  • Population census (headcounts proportion/facility
    catchments area)
  • Quantitative or qualitative rapid assessment
    methods

16
Example data collected at PHC facilities
Special programme activities Mental reproductive health Child health nutrition HIV/AIDS, STI and TB Chronic diseases
Routine Service Activities Minor ailments Non-priority activities
Epidemiological surveillance Notifiable diseases Environmental health
Administrative Systems Infrastructure, equipment Human resources Drugs, transport, labs, finances, budget, staff
Population Census age, sex, place Births deaths registration
17
Requirement for data collectionStandardised
definitions
  • Essential standardised definitions of both data
    elements and indicators
  • To ensure comparability between different
    facilities, districts and provinces
  • To ensure comparability across years

18
Data collection tools
  1. Client Record Cards
  2. Tally Sheets
  3. Registers

19
A. Client Record Cards
  • Record details of the clients interaction with
    the health service, e.g.
  • Health facility record system (traditional)
  • Associated with misfiling and loss vs
  • Client-held record system (Road to Health Card,
    Child Health Booklet, Womens Health Book, TB
    patient treatment card)
  • Associated with efficiency of the individual
    concern, suitable for mobile community

20
Road to Health card
21
Family planning consultation card
22
B. Tally sheets
  • Easy way of counting identical events that do not
    have to be followed-up (e.g. headcounts, children
    weighed)

23
C. Registers
  • Record data that need follow-up over long periods
    such as ANC, immunisation, FP, TB

24
Assessment of data collection tools(Using SOURCE
criteria)
  • conduct an information audit of all tools type
    number
  • S simple ease of use (layout)
  • O overlap duplication (no overlap)
  • U useful for indicators (relevance)
  • R relevance
  • C clear ease of use (layout)
  • E effective decisions used for (purpose)

25
Data collection Toolscriteria for appropriateness
TOOL PURPOSE LAYOUT RELEVANCE OVERLAP
How many? client cards tally sheets registers reports Effective decision-making for Public health Management Supervision/ support monitoring evaluation Simple, Clear, Easy to understand Priority actions No useless data Missing actions evident Useful for Output/ Outcome/imput/ Process coverage/ Quality incidence/ prevalence no Overlap with other forms What When Where Why How
26
Data Collation
27
Ways of collating data
  • summarising data from the same data elements but
    from different sources
  • 2. summarising data from the same source but over
    a period of time.

28
Common collation problems
  • Incorrect grouping of data
  • Data are incorrectly added
  • Missing data forms
  • Double counting of data

29
Data collation practical methodsUnities method
30
Data collation practical methodsRectangles method
31
Data collation practical methodsZeros Method
(Tally sheet)
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