Information Revolution (IR)

Submitted by admins on Wed, 10/16/2019 - 12:20


    • Lack of active engagement of the Leadership: owning the data
    • Paper based tally system (EMR in emergency service started (which soon to be upgrading to all case teams)
    • High turnover of staff which calls for continuous capacity building
    • Decision authority
    • Avoiding deadline approach. Data is updated weekly, not compiled at the end of the month.

With this in mind the following should be critically thought for the next period:

  • Data warehousing and data integration
  • Capacity (Human and infrastructure capacity at all levels)
  • Attitude, behavior, culture
  • Leadership role and commitment
  • Trust (Example do we get data from private sector)
  • Digitization
  • Effective coordination platform since data is multi sectoral and multidimensional
  • Accountability and legal framework: Governance system
  • How can we scale up data use at point of care

The panel discussion was followed by group discussion. The group was further divided in to three sub groups.

Sub-group 1: Information use culture

Sub-group 2: Evidence generation and research

Sub-group 3: Relevance of HSTP indicators


Sub group one: Information use culture recommendations

Human capital

  • Staff in MRU should be capable (It shouldn’t be place to dump demotivated and incapable staff)
  • HIT are not playing their role (We lost as a country because of the HIT training) ….. Governance of HIT (example JD for HITs and curriculum with detailed HIS content has to be given)
  • Making data use everyone’s job (Training of program people)
  • Each program team should access their own data, analyze and prepare report rather than PPMED doing it for them (Bridging the gap between data users and data producers
  • Other line sector’s responsibility of the HEW


Organizational factors

  • Design and implement accountability framework for data quality as well as data use
  • Design and implement behavioral intervention
  • Motivational scheme for health workers
  • Improve financing for information revolution
  • Infrastructure and human resource development (Example: Solar empowered, satellite based system for reporting data in areas where telecom internet is not available (To be tested in 900 HPs under Sekota))
  • Improve data sharing and accessibility to improve data quality and information use
  • Solving PMT and QI duplications (Making QI subset of PMT and bringing lessons from QI to PMT)
  • Functionality of PMT (Thoroughness and criticality)
  • Strengthening case team level review meeting and other data use platforms
  • Standardization and partnership (Coordination of support being provided (example: university, partners and GOV are working on IR))
  • IR one of the HE package will improve accountability and data use at community kevel


  • Improve digitization (MRU’s should implement Smartcare, EMR, DHIS-2, e-CHIS etc)
  • Avoid some of the indicators from HEP for improved care and data use (example ANC)
  • Work on simplifying concepts (Avoid jargons, simplify terms, avoid abbreviations etc)


  • Implementing data quality checks at all levels
  • Leadership commitment is key for data use – introduce incentives focused on problem solving and more support to non-performing areas
  • Experience worked in one place may not work in other. So, why the Afar experience worked has to investigated as to why government fails (The Afar experience could be because of the intensive support they provided can government)
  • Robust performance based incentive in a careful way not to encourage false reporting
  • Integrate data use in all health professional training curriculum
  • Instead of supervision, focus on coaching

Sub-group 2 - Data source and Use – lead by EPHI -Drs Alemnesh and Awoke

Discussion points

1.   Access and availability of quality data

  • Revolution on data accessibility)
  • Develop national data guideline
  • Develop data governance
  • Improve data investment (human resource, skill and knowledge)
  • Standardized methods/skills—data quality assurance mechanisms/methodology experts’/data supervision
  • collaboration for measuring indicators (189)
  • Coordinated fragmented surveys coordinated by different groups; develop thematic areas ---Research advisory council -----coordinating Research Advisory Councils ---research review meetings with stakeholders --- cohort data sources (analytical studies) ---triangulation ---
  • Application of technology in data platform: data repository for data access
  • EPHI and Health Sector coordination including routine data
  • Multi-sectoral collaboration for data availability and accessibility

2.       Data sharing policy

  • National health data sharing
  • Open data access/open data portal—define public domain data and insist data sharing 
  • Develop legal framework (Geospatial data sharing for sensitivity)

3.       Evidence to policy translation

  • Evidence to policy translation roadmap
  • Assess demand of stakeholders
  • Identify stakeholders of users
  • Define template for users
  • Executive summary, translated to different language
  • coordinated dissemination with stakeholders
  • Policy dialogue forums/research dialogue translation
  • Human resource on evidence to policy translation 
  • Scientific advisory committee
  • Capacity Building: Collaborate with universities to build capacity ----data analytics …. evidence to policy translation

4.  Impartiality/independence of the research platform – how can we ensure stakeholders (including the public) trust data coming from EPHI, CSA, Universities and various stakeholders?

  • Coordination/collaboration mechanisms
  • Transparency of data sources/create platforms 
  • Accountability and responsibility of sources of data
  • Data quality assurance mechanism ----concept and definition, standardized data collection tools,
  • institutionalizing data/survey/
  • Two pillars: routine data, SSR data 

5.       Data integrity – safeguards for quality data collection and analysis

6.       Discussion needs to address both Survey and HMIS data

7.       How many Program directors are familiar with DHIS-2? To what extent is the platform being used?

  • DHIS2; we need access

8.       Capacity to do Root-cause/Bottle-neck analysis at various levels (regions/zones/districts)

  • Based on need, build capacity


Sub group 3- Relevance of HSTP indicators

  1. Key areas raised/discussed under the sub theme
  • Review of HSTP indicator development
  • Current situation of existing HSTP indicators
  • Factors for unable to measure HSTP indicators
  • Source of data of HSTP indicators
  • The how of monitoring/tracking of HSTP indicators
  • Dashboard and visualization of HSTP indicators
  1. Major challenges raised/discussed  
  • Unable to measure few existing indicators (eg. CRC indicators)
  • Over measured indicators
  • Under measured indicators (eg. Provider competence and patient satisfaction)
  • Unavailability of data source to measure indicators
  • Indicators are not aligned with disease burden
  • Absence of monitoring dashboard for HSTP indicators
  • No responsible unit that would timely monitor HSTP indicators on regular basis
  1. Consideration for EFY 2012 implementation and beyond to be considered in the next HSTP (in the following four categories)
  • Intervention/s that need to be dropped:
    • Too many indicators ==difficult to handle
    • Indicators with no available data sources
    • Indicators that cannot be measured right away
  • Intervention/s that need to be modified:
    • CRC difficult to measure
    • Private facilities should be forced to report data/complete data
    • Multi-sectoral indicators should be aligned with national indicators
  • Intervention/s that need to be continued as is:
    • considering national and - global commitment for defining HSTP indicators
    • Revising indicators on regular basis
  • Intervention/s that need to be newly added:
    • New tools for monitoring including dashboard and visualization tools
    • Be able to be measured on quarterly basis
    • Responsible body/unit to track the indicators on regular basis
    • Favor for systems than surveys



Add new comment

Restricted HTML

  • Allowed HTML tags: <a href hreflang> <em> <strong> <cite> <blockquote cite> <code> <ul type> <ol start type> <li> <dl> <dt> <dd> <h2 id> <h3 id> <h4 id> <h5 id> <h6 id>
  • Lines and paragraphs break automatically.
  • Web page addresses and email addresses turn into links automatically.