UDDM Training_versionUDDM Training_version

UDDM Training_version two.pptx

1. 1 From Reports to Results: Strengthening Project Reporting and Data-Driven Decision Making From : September 17- 19, 2025 Adama

2. Outline Health information system in Ethiopia Introduction and Overview of DHIS2 DHIS2 Building blocks DHIS2 Data Entry ( Routine data Entry, Disease registration) Data Analysis and Visualization Custom Dataset Report Data Visualization app(Pivot table)

3. The WHO Health System Frame work(Building Blocks) 3

4. Monitoring & Evaluation of Health Systems strengthening

5. Health Information System (HIS) It refers to a system that captures, stores, manages or transmits information related to the health of individuals or the activities of organizations, which improves health care management decisions at all levels of the health system Information is crucial to inform on the performance of the health system and about health challenges Heath Information system is required for timely intelligence on the other building block of the health system: 5

6. Components of HIS Legislative, regulatory, & planning framework, personnel, financing, logistics support, ICT, coordinating mechanisms Measures, usually core set of indicators (determinants, inputs, outputs, outcomes & health status) Making readily accessible to decision makers, ensure information use Population-based sources (censuses, civil registration, surveys) Institution based data (individual, service & resource records) Others: Occasional surveys, research, & information, CBOs, Covers all aspects of data handling, collection, storage, quality-assurance, flow, processing, compilation & analysis, Data translated to information 6

7. What are HIS Data sources? Data Sources components Institution based sources Individual records, service records, supplies, resource records, administrative reports Routine HMIS, supportive supervision, review meetings, inspection, surveillance Facility-based surveys: SARA, SPA+, Population-based sources Census, civil registration, population based surveillance, population surveys ( DHS, MIS, MICS, etc ) and other program specific surveys and researches Data sources by level: Community, Facility (HCs, Hosp. Private Facilities), Woreda, Zonal and Regional levels, and National level 7

8. Health Management Information System (HMIS) Health Management Information System (HMIS) is the routine collection, aggregation, analysis, presentation and utilization of health and health related data for evidence based decisions for health workers, managers, policy makers and others Purposes of HMIS Availing accurate, timely and complete data to support decision making at each level of the health system Strengthening the use of locally generated data for evidence based decision making 8

9. HMIS… Components of HMIS 1. Information management Data collection : Recording of health data using individual and family folder, registers, tally and reporting formats Data processing : is a process of cleaning, entering and aggregation of data. Data analysis and presentation : is a process of interpretation and comparison of generated information in the form of sentence, tables and graphs. 9

10. HMIS 2. Using information for management purposes Problem identification: identifying problems using key indicators Prioritizing problems and decision making : Problems identified should be prioritized and decide what types of actions need to be taken. Action taking: Implementing the agreed action. Result monitoring: Assessing the desired result has been achieved. 10

11. Key Components of the M&E Framework Health Data Collection and Analysis Health Indicators Data Quality Integrated Supportive Supervision Performance Monitoring Team Information use for action Dissemination of Health Information Evaluation

12. Cont.…….Overview 108 core set of Indicators 2003-2006 122 core set of indicators 2007-2010 131 core set of indicators since 2011 177 core set of indicators since 2014 Trends of Indicator Revision The monitoring and evaluation of HSTP II (2020-2025) demands the revision of HMIS indicators

13. Number of indicators by category (2017 versus 2021) SN Indicator Category 2017 2021 1 Reproductive and Maternal health 14 15 2 PMTCT 7 6 3 EPI 13 12 4 Child health 8 10 5 Nutrition 8 8 6 Hygiene & environmental Health 2 10 7 Medical service 12 21 8 HIV/AIDS/Hepatitis viruses 10 15 9 Tuberculosis/TB/ & Leprosy 22 22 10 Malaria 5 8 All Indicators by category(177) SN Indicator Category 2017 2021 11 Neglected Tropical Diseases (NTD) 2 8 12 Non-communicable diseases (NCD) & mental Health 3 10 13 HEP and Primary Health care 3 4 14 Leadership and governance 4 4 15 Health Financing 3 4 16 Pharmaceutical supply and services 4 7 17 Evidence based decision making 3 6 18 Health Infrastructure 4 2 19 Human Resource Development & mgt 4 3 20 Regulatory system 1 2 Total 131 177 13

14. S. No Type of Tools Maintained Modified New Total 1 Registers 13 26 1 8 57 2 Tally Sheets 5 9 7 21 3 Report forms There are changes in data elements (some modified and others added) and NTD, TB & Leprosy quarterly changed to monthly REVISED REGISTERS AND TALLY SHEETS NB: Health Post level tools are not included 14 Registers: for HCs/ Hosp = 42 For Hosp only =13 For HC only= 1 Geographic Areas specific= 2 Special center=1

15. Number of Register and Tally by program area Program area Number of Register Number of Tally Reproductive and Maternal health 7 6 Child and EPI 6 1 Nutrition 8 2 HIV/AIDS & Hepatitis 10 4 TB/Leprosy 6 Malaria 1 NCD & Mental health 3 1 NTD 2 Medical Service 11 7 HSS 3 Total 57 21 15

16. INTRODUCTION TO DHIS2

17. Training Objectives At the end of the training Participants will be: Familiarized with DHIS2 basics Will know DHIS2 building blocks Will be familiar with DHIS2 V40 different features and functionalities Will be familiar on data entry , analysis and Visualization for decision support (DS)

18. DHIS2 is a tool/system for collection, validation, analysis, and presentation of aggregate and individual statistical data. DHIS 2 is developed from an effort by HISP – a University of Oslo program It is an integrated system of tools that help operators and planners – collect, collate and USE health data and information for progressive action . It is a generic tool that can be easily customized to fit to any field * It can work both offline and online What is DHIS2?

19. Introd… Continue It has a flexible user interface and provides easy access for planners, managers and monitoring and evaluation specialists to design the ‘meta-data’ needed to collect important data. It was developed from an effort by Health Information Systems Program ( HISP ) – a University of Oslo program – to design a tool that can collect, process, analyze, and present data

20. What is DHIS2? 20 is an integrated system of tools that help operators and planners – collect, collate and USE health data and information for progressive action . Collection It is generic tool that can easily Validate each field to secure data quality and minimize errors Validation It has a flexible user interface to design different analysis in tables, graphs and Maps Analysis It has a flexible user interface and provides easy access for planners, managers and monitoring and evaluation specialists to design the ‘meta-data’ needed to collect important data. Storing and Presentation

21. What is DHIS2? Self-service analysis tools Integrated warehouse for essential data Capturing, analysis and dissemination of data Handles routine data, events and surveys Communication (messaging and feedback) Open source, web-based software platform D H I S 2 Global network initiative with local nodes

22. Data Dimensions ... The WHERE , Organization Unit: Is Hierarchical ( Example : E.g., FMoH  Harari RHB  Erer Woreda  Errer PHCU Erer HC ) Historical data are safeguarded when changes occur in the org units. The WHAT , Data Element: Can also be an Indicator or a Data Set Either aggregate or individual domain; Data type Number, Text, Yes/No, Date value, etc. Can have Sum, Average, Average [Sum in Org Unit Hierarchy], Count, Standard Deviation, Variance, Minimum or Maximum aggregation operators. The WHEN , Period: Predefined or Relative

23. Data Dimensions in DHIS 2 A data captured in DHIS2 needs at least three dimensions that answer three questions: the WHAT, the WHERE and the WHEN. WHERE The organization/ facility that performed the activity WHAT WHERE WHEN WHEN The period the activity is performed. WHAT capture what data is recorded in the system. e.g. Number of cataract surgeries performed, Number of individuals who swallowed MDA drug for Core building blocks in DHIS2

24. The 3 W’s in DHIS2 Where When What

25. Data Elements and Categories What

26. Organisation Units Location of the data Can be either a health facility or department/sub-unit providing services or an administrative unit representing a geographical area (e.g. a health district) Can be grouped in organization unit groups, and these groups can be further organized into group sets

27. PERIOD Periods answer the “ WHEN ” question. DHIS 2 has 15 pre-defined frequencies/period-types for data collection including: Daily, weekly, monthly, bimonthly, quarterly, six-monthly, six-monthly (and a variant), yearly (and variants) It also has relative periods used for analyses purposes (created at the definition of the period parameters for analyses) – e.g., last 12 months, last 3 quarters, last 6 months. All periods are generated in the database when the first data involving that specific period is created.

28. Fixed Periods v Relative Periods

29. DHIS2 Environment

30. To access the FMOH DHIS 2 application in the Staging Server: URL :https://hmis-staging.moh.gov.et/ dhis Username and password: Your production username and password username Password DHIS 2 Sign in page

31. Access to the DHIS2 staging server URL :https://hmis-staging.moh.gov.et/dhis Username and password: Your production username and password For CBMP University:- Username :-test Password :-Test43_86@9

32. DHIS2 Default Page … Dashboard Apps Profile Key Performance Indicators are presented in the Dashboard (in the form of Tables, Graphs/Charts and Maps)

33. Apps As you click on Apps …

34. DHIS2 Apps and profile Click on Click on

35. Data entry DHIS2 has three data entry apps Routine Data Plan Setting Gross Disease Registration

36. Steps for Data entry

37. select the data entry app 1 Org unit 2 Data Set 3 Period If the data entry box is green shaded it indicates the data entered is saved, otherwise the data is not saved. do data entry in the combo box 4 Click Mark complete if done all Data entry

38. Disease registration 1 Org unit Disease registration 2 3 Period 6 select disease from the list 5 Select outcome(Morbidity /Mortality 4 select Dep.(OPD/IPD) 7 Do data entry from the paper Click Mark complete if done all Data entry 8

39. Tips on Data entry Use yes/no to shut down the remaining fields with the service/programs not given in the facility The data entry box has inbuilt Validation rules, if you see pop up red texts read and take corrective action in consultation with service delivery point. Don’t try to act by yourself without verifying with source document or program expert By double-clicking on any input field in the form a data history window opens showing the last 12 values registered for the current field. Audit trial allows you to view other data values which have been entered prior to the current value

40. Offline data entry data entry module will function even if during data entry the Internet connectivity is not stable Data can be entered and stored locally while being off-line and uploaded to the central server when on-line Remember!!

41. Let us Practice Data Entry!

42. Data Quality … Data Entry Level Validations: Data Types, Validation Rules. Correctness of the data

43. Validation Rules An expression that defines a relationship between a number of data elements Expression has Left side, right side and an operator Common operators are: less than, equal to or greater than You create Validation Rules based on what you know to be true e.g. ‘Malaria Microscope/RDT positive’ should always be LESS THAN ‘Malaria Microscope/RDT tested’

44. Generates dataset reports for multiple periods/org units Has options to filter by other dimensions (Org unit groups, Category option group sets) The disease registration dataset further generates Top lists, filters by sex/age/disease) Integrated completeness/timeliness Introduced by Ethiopia DHIS2 team to address the limitations in the legacy Data Set Report module,

45. Multiple periods/org units Org unit group set filter Category option group set filter (for disease report) Top lists (any number), filters by sex, age, and spelling

46. Reporting Rate Summary

47. Content Completeness Automated Calculation: The system automatically calculates the percentage of content completeness. Provides a clear overview of data comprehensiveness. Downloadable reports in various formats are available for convenient analysis and sharing.

48. Reporting Rate Summary Completeness and Timelines of the data. Like Custom Data set there is new custom reporting rate summary feature with flexibility on the presentation layout.

49. Let us Practice Custom data report ! Follow my Demo first

50. Data Analysis and visualization

51. Data Analysis: Definition Turning (raw) data into information

52. Data Analysis … Analyzed data: Tells us what effects our service delivery has on the health status of the population; Gives direction in decision making; and Shows patterns and trends. Remember! Data use is the purpose of data collection Data use is not accidental, it is planned .

53. DHIS2 Data Visualizer

54. Data Visualizer … Data Visualizer has three dimensions: Series: A series is a set of continuous, elements which you want to visualize in order to emphasize trends or relations in its data. Categories : A Category is a set of elements for which you want to compare its data. Filter: Since most charts are two dimensional, a filter must be used on the third dimension in order to use only a single element for the chart to become meaningful.

55. Data Visualizer … Data Visualizer Chart Options: Show values: Shows the values above chart. the series in the chart. Hide Empty Category : Hides category items with no data from the chart. Show Trend Lines : Will visualize how your data evolves over time—example—whether performance is improving or declining. It makes sense when periods are selected as category. Target Line Value/Title : Displays a horizontal line at the given domain value. It is useful, for example, when you want to compare your performance to the current target. Baseline Value/Title : Displays a horizontal line at the given domain value. Useful, example, when you want to visualize how your performance has evolved since the beginning of a process. Others : Include Sort Order, Aggregation Type, Range Axis Min/Max, Hide Chart Title, Hide Chart Legend, etc.

56. Data Visualizer … When is Data visualizer Preferable? When the purpose is presenting the general performance trends in an attractive way. When we want to present performance to audiences who are not detail-oriented (example, top management). When the nature of the data we present is not complex. When the presentation time is limited and as such we should focus only on core stuff.

57. Pivot Tables Pivot tables can be used to : Generate reports in tabular format Easily compare data based on different time and locations See the development of coverage And, more …

58. Pivot Tables … WHAT WHEN WHERE Presentation Area

59. Pivot Tables …

60. Pivot Tables … Steps to analyze data using Pivot Table … Go to the App and click on Pivot Table Go to the top left and select the Data (DEs, indicators, dataset, etc.) for analysis (Move your selection from Left to Right). Go to the Period and select [relevant] fixed or relative period. Select relevant organizational unit for the data analysis. Click on the Layout at the top-middle of the presentation area,and decide the way the data should be presented. Click Update button. The table appears in the presentation area. Save the Table Favorites to facilitate reuse of the analyzed data.

61. Pivot Tables … Table layout Favorites

62. Pivot Tables … But … When do we use Pivot Tables for Analysis? In general, we use Pivot Tables … When we want to show more than two dimensions. When the data to be presented are complex with multiple data elements/indicators/org units/periods. When we are interested in detail.

63. Let us Practice Data Visualizer and Pivot table ! Follow my Demo first

64. Hands-on Exercise-1 Data Visualizar (Column Chart) Data: Inpatient mortality rate Period : last 12 months Org Unit: Your user org Layout using the column chart canvas. S eries = org unit Category = Period, Filter = Data Org Unit Drill Down: Select the high number of Inpatient mortality rate occurs period. Click on the arrow icon next to the selected period on the column chart canvas. Explore the available potential outlier of sub-levels of organization units (e.g., District Health Offices, Health Centers, Clinics). Save the report as favorite Download it as Excel

65. Hands-on Exercise-2 Pivot table Data: PNC Visit Period : last 12 months Org Unit: zonal(Group) Layout Column = Data, Period Row = org unit Freeze Row and Column Headers: Navigate to Options > Style > Fix Column Headers to Top of Table and Fix Row Headers to Left of Table. Enable both options to freeze row and column headers for easier navigation through large pivot tables.

66. Hands-on Exercise-3 Data Visualizer: (Use Reliable Chart Type) Data: Malaria cases tested; Malaria cases positive Period: last 12 month Org Unit: Your user org Layout Series =data (Malaria cases tested & positive) category dimension= Period Report filter =org unit Convert Malaria cases tested to a bar chart and Malaria cases positive to a Line graph Customize axis labels by providing descriptive names or units for each axis ( e.g Malaria cases tested , Malaria positive cases). Save the chart as a file using initials - What, Where, When, and add Description. Download it as PDF

67. Hands-on Exercise-4 Year over year(line) chart Data : Number of under 5 children treated for pneumonia. Period: 2015,2016, by Months per Year Org Unit: Your user org Layout series)  Period (category dimension)  Period Report filter  Data (Data Element), org unit Save the chart as a favorite using initials - What, Where, When Download it as pdf

68. Dashboards

69. Why Dashboards? Easily view the visualizations most frequently used Allows for holistic view of data Quickly monitor program progress Easily share information with others

70. Dashboard Overview Dashboards have a title , description and dashboard items Dashboard items can be of many different types : Charts, maps, reports, tables, resources, messages, and text items The control bar shows all your available dashboards, including a dashboard search field , and a “create new” button for creating a new dashboard The dashboard has three modes: view, edit/create and Filter Allows for flexible dashboard layout

71. Dashboard Overview Create new List of dashboards Edit Share Filter Switch visualization type

72. Dashboard Features Search Filter Mark dashboard as favorite Sharing Free text items Interpretations Interactive charts and maps

73. Thank You! Keep Practicing and Master DHIS2

74. 74 Techniques of data quality assurance

75. 75 Session Objectives At the end of the session, participants will be able to: Discus data quality and its dimensions Explain the concept of Lot Quality Assurance Sampling (LQAS) Utilize and apply LQAS technique

76. 76 Data Quality It refers to accurate and reliable information collected through a monitoring and evaluation data management system The real concern with data quality is to ensure not that the data are perfect, but that they are accurate enough, timely enough, and consistent enough for the organization to make appropriate and reliable decisions.

77. 77 Dimension of Data Quality Accuracy Accurate data are considered correct; the data measure what they are intended to measure. Reliability The data are reliable because they are measured and collected consistently Completeness- An information system captures all of the eligible persons, services, sites, or other units that it is supposed to measure . A. Content completeness: is the completeness of the data elements expected to be included B. Representative completeness : is the extent to which expected facilities/institutions are included in the report

78. 78 Dimension of Data Quality 4. Precision Data have sufficient detail to measure indicators according to the definition 5. Timeliness Data are timely when they are reported to the next level in time to meet reporting deadlines. 6. Integrity Data have integrity when the information system is protected from deliberate bias or manipulation for political or personal reasons 7. Confidentiality Clients are assured that their data will be maintained according to national and/or international standards.

79. 79 Common cause of poor data quality A. Human element error Transcriptional - Omission (12345 becomes 1234) -Addition (12345 becomes 123456) -Transposition (12345 becomes 12354) Double Counting - Results in overestimation of  individuals, services, and sites. B. Manipulation (Deliberate acts) - Purposive inflation of activity reports C. Under reporting - Activities done at static or outreaches not captured & reported

80. 80 How double counting occurs? One Institution at one site provides the same service (treatment, care, etc.) multiple times to the same individual within one reporting period and counts the individual as having received the service multiple times within the same reporting period.   Example: FP acceptors, ANC clients Solution? Two or more Institutions provide the same service (treatment, care, prevention, etc.) to the same individual at different sites within one reporting period and both Institutions add the individual to their count of the service delivery.   Example: FP acceptors ANC Solution?

81. 81 Data Quality Assurance Lot Quality Assurance Sampling (LQAS) is a technique useful to assess whether the desired level of quality of data being collected and reported has been achieved or not. LQAS methodology compares registers, the tallies made from the registers, and the report. The tallies are included in the comparison so that we can see if the tally process creates errors.

82. 82 Steps for data accuracy check Select the month for which you are doing the data accuracy check Put serial number against the data elements in the Service Delivery or Disease report that you want to include in the data accuracy check. To check for monthly report exclude the quarter basis report in giving serial number. Generate 12 random numbers that lies within the serial number given in step 2 ( eg 1 to 97). You can use Excel program to generate such random number by using the formula = RANDBETWEEN (1,97) or the random number table. List down the 12 selected data elements from the report on to the Data Accuracy Check Sheet in Column 2

83. 83 Steps…… For randomly selected data elements count and fill the corresponding figures from corresponding registers/tallies and the report in Column 3/4 and Column 5 respectively If the data match/mismatch put 'yes' or 'no' in Column 6 or Column 7 respectively Count the total numbers of 'yes' and 'no' at the end of the table Match the total number of ‘yes’ with the LQAS Table and determine the level of data quality achieving the expected target or not.

84. Systematic Random Sampling Option to select sample data elements Procedures: Number the units on your frame 1 to N ( where N is the total data element) Determine the sampling interval ( k) by dividing the number of units in the population by the desired sample size Select a number between 1 and K at random. This number is called the random start and should be the first number included in your sample Select every K th unit after that first number

85. Data Accuracy Check Sheet   Ser. # Randomly Selected Data Elements from the monthly reporting form (Reporting Element) Source & figure Do figures in columns 3, 4,& 5 Match? Register Tally Report Yes No (1) (2) (3) (4) (5) (6) (7) 1             2             3             4             5             6             7             8             9             10             11             12            

86. LQAS table for decision making  LQAS Table: Decisions Rules for Sample Sizes of 12 and Coverage Targets/Average of 20-95%   Sample Size Average Coverage (Baselines)/ Annual Coverage Targets (Monitoring and Evaluation)   <20% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 12 N/A   1 1 2 2 3 4 5 5 6 7 7 8 8 9 10 11 Example: If total number of “Yes” is 6, the data accuracy level is 60% ; if total “YES” number is 7,the data accuracy level is 65-70%

87. Group work LQAS Data Quality Assurance

88. 89 Data Quality Assessment (RDQA) RDQA is an assessment technique that can be used to self-assess and to monitor progress and evaluate the RHIS status. Unlike to LQAS, the RDQA help the Health facilities and administrative health units to verify reported data against to source documents and to look RHIS system implementation. Objective of RDQA : By using the RDQA tool, we can achieve three main objectives. Verify rapidly o the quality of reported data for key indicators at selected sites; o the ability of data management systems to collect, manage, and report good-quality data 2. Implement o corrective measures with action plans for strengthening the data management and reporting system o improving data quality 3. Monitor capacity improvements and performance of the data management and reporting system to produce good-quality data

89. 90 Steps followed to conduct RDQA