Information Cycle Data Handling in Information Cycle: Collection and Collation

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Information CycleData Handling in Information
Cycle Collection and Collation

• University of Oslo
• Department of Informatics
• Oslo - 2007
• Facilitator Gertrudes Macueve
• 11th April 2007

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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

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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

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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.

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Information Cycle
What do we collect?
What do we do with it?
How do we use it?
Quality information
How do we present it?
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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
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Data Handling in the Information Cycle1. Data
collection
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The starting pointFeeding the information cycle
Presenting Interpreting
Output INFORMATION
ANALYSIS Processing
USE
Collection
Input Raw data
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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

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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

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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

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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
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Essential data set at each level

• Standardised
• Usefulness
• Address the needs of all stakeholders
• User-friendly
• Dynamic

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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

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Where do we get data from?

• Non-routine data collection
• Surveys
• Population census (headcounts proportion/facility
  catchments area)
• Quantitative or qualitative rapid assessment
  methods

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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
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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

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Data collection tools

1. Client Record Cards
2. Tally Sheets
3. Registers

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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

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Road to Health card
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Family planning consultation card
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B. Tally sheets

• Easy way of counting identical events that do not
  have to be followed-up (e.g. headcounts, children
  weighed)

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C. Registers

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

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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)

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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
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Data Collation
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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.

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Common collation problems

• Incorrect grouping of data
• Data are incorrectly added
• Missing data forms
• Double counting of data

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