2. Using Information for Health Management; Part I
- Health Information Systems Strengthening

3. Learning objectives
the information cycle; tools and processes for turning data into action
the relationship between data use and data quality
hierarchy of standards / essential data set
common reasons for compromised data quality, and various counter measures
different information products for communicating different meanings

4. What do you think are the steps that have been taken to get the data into the Tanzania HMIS?
Discussion….. Get to the point of identifying registers, tally sheets, monthly summary forms, and the data capture process…. – Once we get these answers, go to the Information Cycle.

5. Collection Processing Presentation Action Data set based on
minimum indicator set
Standard definitions
Data sources & tools
Collection
Processing
Presentation
Action
Interpret information:
comparisons
trends
Decisions based
on information
Actions
Data quality checks
Data analysis:
indicators
The information cycle is about understanding how collected data is converted into information that will be useful to determine health actions (Garrib 549). The relationship between data and information is better seen by considering a diagram, that is called info cycle. So, information cycle is a diagrammatic way of looking at data and information (Heywood and Rhode 21). The Inf Cycle enables one to see the links between the different stages of information. What are these stages or phases? As you can see through this diagram, phases of information handling include the process of data capturing, through observation of real events (collecting), processing data (process means ensuring data quality by auditing data (3Cs), collating (aggregating) data… so that one can analyse it and turn it into information. The information is then presented, interpreted and used for decision-making.
E.g how do we come to measure an indicator from collected data
Source: Garrib et al (pg. 550)
Tables
Graphs
Reports
Heywood and Rhonde Ch.2-7
Garrib et al 2008: 550
Braa and Sahay 2012 ch 2

6. Data collection Input:
Using data sources and tools to collect quality data
Should be standardized for comparability
Output:
Timely and relevant data
Collection
Processing
Presentation
Action
Heywood and Rhode ch.2

7. Common problems during data collection
Poor supply of tools
Timeliness of reporting
Poor understanding of data collection tools
Duplication/overlap of data tools
Too much to collect
Low data quality

8. Example of Data Sources and tools
Collection
Processing
Presentation
Action
Example of Data Sources and tools
Client record cards
Record details of the client’s interaction with the health service
Personal data
Clinical history-Diagnosis and treatment
Road to Health card
Record details of the client’s 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, Women’s Health Book, TB patient treatment card);
Associated with efficiency of the individual concern, suitable for mobile community 8

9. Tally sheets
Collection
Processing
Presentation
Action
Easy way of counting identical events that do not have to be followed-up (e.g. headcounts, minor ailments, number of children weighed etc.)
Data for understanding frequency of conditions and number of services provided

10. Registers Point to the CoC register for ANC/Delivery
Collection
Processing
Presentation
Action
Point to the CoC register for ANC/Delivery
Record data that need follow-up over long periods such as ANC, immunisation, Family Planning, Tuberculosis (TB)

11. Collection
Processing
Presentation
Action

12. Reports weekly, monthly, quarterly Collection Processing Presentation
Action
Reports
weekly,
monthly,
quarterly

13. Why do we need this information?
Epidemiology is the study (or the science of the study) of the patterns, causes, and effects of health and disease conditions in defined populations. It is the cornerstone of public health, and informs policy decisions and evidence-based medicine by identifying risk factors for disease and targets for preventive medicine.

14. Data Collection and Collation in a Health Facility (Zambia HMIS Procedure Manual)

15. Data collection and collation from the community
Primary data collection
Collation of anonymized aggregated data
3. Facility summary report.
Data register
Tally sheet
Aggregated data on services provided
Activity Sheet
Collate all activity sheets
DHIS2 data entry and validation

16. Data collection – at the source of data creation (point of care)
Service data collected by nurses and doctors in-between attending to patients
Usually several (manual) steps before it is in any database/storage
Tally sheets
Tally sheet totals at end of month
Monthly summary forms which are reported to the next level
*Often too much data to collect for overburdened staff
Clerks with limited understanding of public health/HMIS
Collection
Processing
Presentation
Action

17. What data elements should be collected?
Collection
Processing
Presentation
Action
What data elements should be collected?
Cannot be obtained elsewhere (e.g. survey)
Are easy to collect (cost vs usefulness)
Do not require much additional work or time
Can be collected relatively accurately
Is part of one or more indicators
Because we are dealing with thousands of data elements, we have to select exactly the data we need, the data which will contribute for decision-making.

18. Collection
Processing
Presentation
Action
Data Set Definitions
Have standardized definitions of data elements and indicators
Comparability
Data elements for various levels
Challenging process
Braa and Sundeep ch. 3

19. Essential data sets (EDS) Pyramid depicting hierarchy of standards
Collection
Processing
Presentation
Action
As data flows from lower levels to higher levels, less data is required. Data collection normally begins at the community/facility level. It  should then be sent to the district office. From there it may go to a regional office and then to a provincial level before being sent to a national level. The national level will have international reporting requirements. Each level has different requirements. The facility might want to know how many swabs are used on a daily basis. This information would be irrelevant to the national level. However, information such as the infant mortality rate is of international concern and thus important for all levels of the health system.
Braa and Sundeep ch 3

20. EDS Example: vaccination data
Collection
Processing
Presentation
Action
EDS Example: vaccination data
international
Decreased mortality, healthier children
national
Decreased incidence of vaccine preventable diseases
province
Coverage of child immunization
district
# Children Vaccinated
community and facility levels
Staff attendence, vaccines, to whom, when, where

22. Example of a National Data Dictionary
Key issues
Top-down vs bottom-up approaches
Who to involve in discussions
Maximalist vs minimalist approaches
Kenya HMIS indicators 2008
Kenya HMIS indicators 2012

23. Balancing varying information needs
Collection
Processing
Presentation
Action
Nice to know: Data on tally sheets
Explain: political action and health system action and types of
information to know
Balancing varying information needs
Braa and Sundeep ch. 2
Heywood and Rhode ch. 1

24. Essential data sets… Must know % children under one-year fully
immunised
Useful to know Drop out rate DPT 1-3; measles
coverage
Nice to know Other program vaccines given
Dangerous to know All doses given over 1 year; vaccine
wastage rate; DPT2, polio 2; non-program vaccines (MMR, flu,
etc).
Dangerous information distracts health workers from their essential work of seeing patients and making decisions

25. Comparability of collected data
Collection
Processing
Presentation
Action
Comparability of collected data
Stable standardised definitions
To ensure spatial comparability between different facilities, districts, provinces and nations
To ensure comparability over time
Note:
Revising poor indicators /data sets /data elements may not be advisable due to cost and loss of backward comparability
- Comparability necessary to ensure Essential data sets are to the needs of health workers and should be regularly reviewed

26. Where do we get data from?
Collection
Processing
Presentation
Action
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
Semi-permanent data about the population served, the facility itself and staff that run it
Civil registration (vital events being integrated with health)
Non-routine data collection
Surveys
Population census (headcounts proportion/facility catchment’s area)
Quantitative or qualitative rapid assessment methods
Special registers (birth registers, TB registers, mental health registers)

27. Data Sources in the HMN data warehouse concept
Collection
Processing
Presentation
Action
Data Sources in the HMN data warehouse concept

28. Data Processing Input: Relevant data
Collection
Processing
Presentation
Action
Input: Relevant data
Output: data converted to information
Process data to ensure quality, consistency and accuracy
Processes:
Data Quality checks [complete, correct and consistent, timely, reliable, accurate, comparable]
Visual scanning e.g. Zambia
Computer quality checks
Errors-go back to data collector
Aggregation to relevant levels
To aid analysis and reporting both vertically and horizontally
Analysis of data = information
Calculation of indicators towards targets
Heywood and Rhode ch 3

29. Data Processing:
What observations can you make about your experience in processing the data so far?
Get comments about consistency, completeness, quality

30. Data, information, knowledge
observations and measurements
Information (processed data)
facts extracted from a set of data (interpreted data),
data brought together to demonstrate facts
Meaningful and useful
Knowledge
contextualized information
actionable

31. 42 immunized children <1y
Example
42 immunized children <1y
"Answer to the Ultimate Question of Life, the Universe, and Everything".

32. 42 / 60 = 70% Fully Immunized Children < 1y
Where 60 is the target population

33. 42 / 60 = 70% Fully Immunized Children < 1y For month May 2010
Organization Unit: Whatever
With additional contextual knowledge we may act upon this information
The numerator could be wrong due to counting errors etc.
The denominator may be wrong due to census data (not collected with health org units in mind) data divided by org. Units.
Collected for the nation or district and split into lower org units according to some algorithm –
Algorithm can be improved by involving geography etc.

34. Processing; assuring data quality and calculate indicators
Turning data into information
How to assess data quality?
What are indicators, and why do we need them?
Collection
Processing
Presentation
Action
Review the situation of overloaded health workers who collect a range of stats without knowing why (% nursing time spent on stats collection)
Comment on how health workers are often expected to collect stats and send them up without expectation of analysis, feedback & use of information
Reinforce how the aim of a DHIS, to improve coverage & quality of local health services, is facilitated by only collecting data that can be analysed & used at the local level
Indicators: “Variables with characteristics of quality, quantity and time used to measure directly or indirectly changes in a situation and to appreciate the progress made in addressing it” (WHO 1981)
Convert raw data to information
Describe situations and measure changes over time
Give information on conditions

35. Why checking data is vital?
Collection
Processing
Presentation
Action
Why checking data is vital?
Use of inaccurate data leads to
Wrong priorities (focus on the wrong data)
Wrong decisions (not applying the right actions)
Garbage in = garbage out
Decision-making bias
Producing data is expensive
Waste of resources to collect poor data

36. Routine data should be.. Reliable: Correct, Complete, Consistent
Collection
Processing
Presentation
Action
Routine data should be..
Reliable: Correct, Complete, Consistent
Timely: fixed deadlines for reporting
Actionable: no action = throw data away
Comparable:
same numerator and denominator definitions used by all data processers BUT
striving for comparability can compromise local relevance
*Comparability across the same level

37. Complete data? Spatial: submission by all (most) reporting facilities
Collection
Processing
Presentation
Action
Spatial: submission by all (most) reporting facilities
Timely: is the data available within the required time
Temporal: can you do analysis over time?
Many developing countries have low service utilization – many people do not use offical health serives or live out of reach

38. Timely data? Late reports weaken the potential for comparison,
Collection
Processing
Presentation
Action
Timely data?
Late reports weaken the potential for comparison,
action can be too late,
but still useful for documenting trends;
Better to use the data that you have even if incomplete: “Perfection is the enemy of good”
Sierra Leone

39. Correct data? Are we collecting the data we need?
Collection
Processing
Presentation
Action
Correct data?
Are we collecting the data we need?
The data values seems sensible/plausible?
The same definition applied uniformly?
Are there any preferential end digits used?
JAN
FEB
MARCH
APRIL
MAY
JUNE
JULY
250
230
245
225
240

40. Collection
Processing
Presentation
Action
Consistent data?
Data in the similar range as this time last year or similar to other organization units
No large gaps or missing data
No multiplicity of data (same data from multiple sources –which one to trust?)

41. What are the causes of poor data quality?
Collection
Processing
Presentation
Action
What are the causes of poor data quality?
Too many forms to fill out that are not useful to health workers
Absent data collection tools (not distributed)
Data collection tools are poorly designed and hard to understand
Too many steps of manual aggregation and transfer of figures
Limited feedback on data quality to those who collect it
Data is not used

42. Two manual steps of data exchange
ANC 1
Bednets given
ANC 2
ANC 3
Tally Sheet
Sum.
Form
DHIS
Jan 26 20 10 8 7
Feb 40 12
Mar 15 4
Apr 24 13 2
May 31 30 11 6
June 5
July 9
Aug
From Facility Tally Sheet Total, to MMRCS Summary form, to DHIS

43. Accuracy enhancing principles
Collection
Processing
Presentation
Action
Accuracy enhancing principles
Capacity building through training
(90% of HISP activities)
User-friendly collection/collation tools
Feedback on data errors (but not only!)
Availability of processed data
Local Use of information
Clear definitions; standards
Essential Data Set
Information sharing

44. What can be done to improve data quality?
Collection
Processing
Presentation
Action
What can be done to improve data quality?
1. Assess the cause by using the
Information Cycle as the basis
2. Programmatic Issues
Essential dataset
Feedback routines
Use of Information
3. Database validation mechanisms
Min/Max rules in software
Data validation rules, check for consistency in logic of data
Completeness and timeliness reports

45. Indicators - measure service COVERAGE and QUALITY
Collection
Processing
Presentation
Action
Indicators
- measure service COVERAGE and QUALITY
Calculated by combining two or more pieces of data, so that
They can measure trends over time
They can provide a yardstick whereby facilities / teams can compare themselves to others (spatial, organizational)
monitor progress towards defined targets
Good for measuring change
Note: convert raw data into information
To do this, indicators need to have a numerator and denominator
Heywood and Rhode pg: 55

46. numerator indicator = = % denominator X 100 Maternal mortality ratio?
Collection
Processing
Presentation
Action
numerator
indicator =
X 100 = %
denominator
Maternal mortality ratio?
“the number of maternal deaths per 100,000 live births in same time period.”
Numerator: Number of deaths assigned to pregnancy-related causes during a given time interval
Denominator: Number of live births during the same time interval
Multiplier: 100,000
Sustainable Development Goals have a set of proposed targets and indicators
The maternal mortality rate, the number of maternal deaths per 1,000 women of reproductive age in the population (generally defined as years of age) .
The maternal mortality ratio, the number of maternal deaths per 100,000 live births in same time period.

47. a count of the event being measured
Collection
Processing
Presentation
Action
Atop the line – numerators (activities / interventions / events / observations / people)
a count of the event being measured
How many occurrences are there:
morbidity (health problem, disease)
mortality (death)
resources (manpower, funds, materials)
Generally raw data (numbers)
Unpack the meaning & use of numerators (illustrate with appropriate examples)

48. Under the line – denominators (population at risk)
Collection
Processing
Presentation
Action
Under the line – denominators (population at risk)
size of target population at risk of the event
What group do they belong to:
general population (total, catchment, target)
gender population (male / female)
age group population (<1, >18, 15-44)
cases / events – per (live births, TB case)
Unpack the meaning & use of denominators (illustrate with appropriate examples)
Explore population groups used most frequently – age group cohorts

49. Indicator Operationalization
Collection
Processing
Presentation
Action
Indicator Operationalization
Defining the sources of the data – both numerator & denominator (how is it to be collected?)
Determining the frequency of collection and processing of the indicator (How often should it be collected, reported, analyzed?)
Determining appropriate levels of aggregation
(To where should it be reported and analyzed?)
Setting levels of thresholds and target
What will be the nature of the action (decision) once the indicator reaches the threshold?

50. Common problems - Much irrelevant data Low data quality
Limited knowledge of data needs and analysis
Poor understanding of indicators
Limited or no feedback

51. Presentation
Input: Analysed information, to be presented to communicate meaning
Common problems:
Importance of information is lost through poor presentation
Data can be misrepresented or misinterpreted
Output: various information products
- Display Information
Charts
Tables
Maps
Collection
Processing
Presentation
Action
Heywood and Rhode ch: 5

52. Action Input: Information products
Process: Interpretation of information, prioritizing action, making action plan, and execute action through budgets and plans with goals and targets
Common problems:
Incomplete information
Lack of good targets
No plan for evaluation
Output: Decision-making for health management
Collection
Processing
Presentation
Action
Heywood and Rhode ch: 6 &7

53. Summary: two important models
Collection
Processing
Presentation
Action
Summary: two important models
Collection
Processing
Presentation
Action
As data flows from lower levels to higher levels, less data is required. Data collection normally begins at the community/facility level. It  should then be sent to the district office. From there it may go to a regional office and then to a provincial level before being sent to a national level. The national level will have international reporting requirements. Each level has different requirements. The facility might want to know how many swabs are used on a daily basis. This information would be irrelevant to the national level. However, information such as the infant mortality rate is of international concern and thus important for all levels of the health system. Become familiar with the information flows within and outside your district.