Health Intelligence



Data-informed decision-making means that data is converted into usable information through processing, analysis, synthesis, interpretation, and review and discussion. In the case of health data, most of the data is generated from various essential programmes, and it is challenging for health leaders and managers to gather and utilize this information to decide an appropriate course of action. In the subfield of immunization, managing health data or health information system means using data and information on vaccine supply to prevent stockouts or using individual-level patient data to decide which patients or communities to target for vaccine and health outreach.

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Sources of Data Quality Loss


While information systems and tools are available at country level, health programs still face challenges of inaccurate and poor quality of health and immunization data. Most of the sources of data quality loss are identified as follows:

  • Intentional falsification
  • Inaccurate denominator data
  • Poor/ missing/ outdated forms
  • Errors in transcribing, calculation
  • Loss, damaged records
  • Procedural gaps
  • Failure to record properly
  • Private sector not included

At GaneshAID, our psychology and data analysts are dedicated to investigating the causes of data quality loss and supporting the effective implementation of the WHO Framework for assessing and improving data quality.

Challenges of Data Availability and Reporting


Considerable variety of immunization and surveillance data is required nationally, regionally, and globally because of:

  • the increasing demand for disaggregated data (subnational, individual-level);
  • health and immunization equity, high-risk of disadvantaged populations (e.g., migrants, refugees), no integration of the private sector, life-course;
  • and collected data that may be inaccessible to those that need them.

Attributes of Quality Data

Health Intelligence & Engineering for data quality and use


Understanding the crucial need for data quality and use, GaneshAID supports LMIC countries in data intelligence (managing, analysing, and producing data in multiple forms) to cope with the essential needs of in-country evolutive health and immunization services. We design and develop customized solutions for Ministries of Health. We take into account the benefits for health programs, managers, and the human factors. We provide technical assistance in the necessary performance improvement by considering all challenges and constraints to be eliminated for the smooth running of a health intelligence project, including the data-use culture.

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Using dashboards to strengthen data for management.

  • Supply chain dashboards have long been used to improve the performance in both the private and public sectors by visualizing performance assessments, placing them in context and drawing management attention to areas where intervention is needed. Such visualization enable managers to make informed operational and strategic decisions and to take corrective actions, hence contributing to a continuous improvement process.
  • Dashboards gather data from multiple sources and combine them in a single interface for a detailed overview of the program operation while reducing reporting time. For a health professional to gain the best insights into data and analyse it properly, they need to identify the interventions to act upon and streamline workflow.
  • Designing and planninga health supply chain dashboard is essential to address the needs and priorities peculiar to a particular programme, while considering the constraints and limitations of its situation.
  • Dashboards are likely to be used in challenging and imperfect systems. The use of dashboards can drive a process of identifying problems and supporting future changes.

Coordination & Collaboration


GaneshAID supports strong coordination & collaboration across areas and organisations (health systems approach) which are necessary to avoid:

  • Fragmented information systems as some countries are expected to manage several information systems caused by partners introducing duplicative systems, or programme/ geography-specific systems.
  • Inefficiencies from lack of data sharing or non-interoperable systems
    • Lack of private providers’ engagement in immunization and surveillance reporting causes poor representativeness and delayed outbreak detection.
    • Poor agreement between epidemiologic & laboratory, or aggregate & case-based data caused by lack of communication between different units.

Do tools improve data quality and use?


Technological innovation can improve data quality & use BUT not all quality & use problems have a technological solution. Nowadays, there is a large number of tools for immunization information systems, digitization of paper records, decision support tools (e.g. dashboards), logistic management information systems, mHealth, media based approaches, geospatial based technologies, and predictive analytics. These tools may be used individually for vaccines and VPD surveillance or as part of existing health information systems.

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Data quality and use: the human factor


Health workers in LMICs spend 25% to 40% on data recording & reporting process at primary care – (Whittaker et al 2015)


Workforce capacity is cited as limitation in more than 80% of references included in review of barriers to Vaccine Preventable Disease surveillance data quality:

  • Staff shortages (capacity): Poor staff recruitment & retention, inadequate person-time equivalents allocated for data.
  • Skill shortages (capability): Data not included in pre-service training, lack of in-service training, supportive supervision.
  • Poor motivation to collect quality data: Only reporting rather using data, lack of feedback, and competing priorities (clinical duties, other programmes).
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