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Data demand and use in the health system

Comprehensive Training to support the National Integrated TB/HIV Information System Implementation. Data demand and use in the health system. Introduction (1). Define data demand and use (DDU) Introduce DDU framework/processes Contextualise why DDU is important in respective contexts

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Data demand and use in the health system

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  1. Comprehensive Training to support the National Integrated TB/HIV Information System Implementation Data demand and use in the health system

  2. Introduction (1) • Define data demand and use (DDU) • Introduce DDU framework/processes • Contextualise why DDU is important in respective contexts • Provide support to provincial and (sub)district managers on • Processes and skills required to routinely interrogate data • Utilising data to improve patient outcomes and programme management • Addressing issues around data quality

  3. Introduction (2) • Phase 6 engagements conducted by national team • Summarise the phase 6 engagement process • Provinces that have been covered • Findings

  4. What is data demand & use? (1) • Data demand and use (DDU) is a strategy to identify opportunities for, and constraints to, effective and strategic data collection, analysis, availability, and use • It begins with an assessment that helps stakeholders, policy-makers, and monitoring and evaluation(M&E) practitioners determine points of entry for DDU interventions

  5. What is data demand & use? (2) • Once specific needs are identified, DDU can be utilised to • Stimulate routine requests for/use of information to support day-to-day responsibilities • Identify capacity building gaps • Enhance evidence-based decision making through coordination as a collaborative effort • Improve data quality • Improve patient management

  6. DDU: operational imperatives • Facility meetings that feature DDU-specific agenda items • TIER.Net line lists being requested, generated, and acted on per SOP • Gaps identified in facility processes that influence data quality • Conduct facility-level data verification prior to sign-off • Timeous correction of data quality gaps • Data routinely interrogated at all levels • Routine data reviews at facility/(sub)district • Missing data and outliers

  7. DDU Conceptual Framework • What are we doing on these fronts? • Where are breakdowns in this cycle? • Why care about this cycle of data mgmt? • How are these concepts interrelated? Tools for Data Demand and Use in the Health Sector, Measure Evaluation, 2011

  8. Why should we demand and use data? • Planning • Monitoring • Quality improvement interventions • Patient management • Evidence based decision making • Ownership of outcomes • Resource utilisation

  9. How is data being used in districts? (1) • What are managers along the health system cascade doing to drive a DDU agenda? • How are DDU, reporting, and/or feedback different from one another?

  10. How is data being used in districts? (2) • Reporting • Process of communicating results or outcomes for programme activities to reflect the degree/extent to which the objectives have been met • E.g. submission of monthly data in accordance with NIDS or capturing data in webDHIS

  11. How is data being used in districts? (3) • Feedback • Process in which the output of a specific action is communicated to the producer in order to inform, modify, and/or improve on the next action • E.g. following up on incorrect data and providing feedback to facility staff to correct MDI forms

  12. How is data being used in districts? (4) • Data demand and utilisation • Culture embedded within the facility that ensures data is demanded for optimal utilisation in patient management through the use of available tools • Process does include reporting and feedback • However, it is not limited to only these • E.g. clinicians demanding the data to support their day-to-day work • Routine generation of early missed appointment lists for tracing purposes

  13. How is data being used in districts? (5) • What tools/guidance exist to support DDU? • Integrated TB/HIV Data Management SOP • DHMIS policy • TIER.Net line lists and reports • webDHIS • Integrated audit tool • Site visit task list • Other related SOPs and guidance material

  14. How is data being used in districts? (6) • What DDU focused lessons/takeaways have provincial and/or (sub)district managers garnered from regular engagements with facilities? • Status of facility-level DDU? • Barriers to optimal DDU?

  15. Phase 6 engagements – overview (1) • Predicated on supporting the maintenance phase (ART or TB module phase 6) of TIER.Net implementation • Engagement is intended to be an instructive process where technical support is provided • Probe on how TIER.Net maintenance and management processes unfold at the facility • Standard tools readily available to TKIs to utilise • Site Visit Task List and Integrated Audit Tool

  16. Phase 6 engagements – overview (2) • Optimal use of the tools is meant to spark important conversations around management of the health information system • Provide technical support to the facility on best means of maintaining the system • Critical pieces of the maintenance phase that will be a key focus throughout • Utilising THIS to support patient and programme management • Monitoring data quality

  17. Phase 6 engagements – process • Facility selection process for phase 6 engagements • Informed by data from webDHIS • Pull up data for past 12 months using WHO data quality tool • Look for missing data and outliers • Select facility based on issues identified • Communicate with provincial PIT leads on logistics • Members of the NIT work in concert with TKIs, but TKIs largely direct and lead the engagement

  18. Phase 6 engagements – where have they occurred? • Phase 6 engagements have been conducted in various provinces • Provinces visited thus far: • Eastern Cape (Nelson Mandela Bay, OR Tambo) • Free State (Mangaung and Lejweleputswa) • KwaZulu-Natal (eThekwini, iLembe, and uMkhanyakude) • Gauteng (Ekurhuleni) • The plan is to conduct these engagements more frequently and with more coverage nationally

  19. Findings from phase 6 engagements (1) • Barriers to optimal DDU • Inconsistent information sharing with facilities • Guidance documents not shared • Change processes not implemented correctly (THIS impl. guide) • Lack of mentorship • Use of non-standardised clinical stationery and patient management tools • Weak documentation in the clinical stationery - differentiated care • Lack of, and inconsistent adherence to, NDOH guidance

  20. Findings from phase 6 engagements (2) • General mismanagement of lab results • Bulk-capturing of normal laboratory results not following guidance • Challenges with normal and abnormal lab results • Line lists not optimally used for patient management

  21. Thank you

  22. Comprehensive Training to support the National Integrated TB/HIV Information System Implementation Creating a data demand and use culture

  23. What is DDU culture? (1) • A data-driven cultureentails consistent use of data for decision-making on an ongoing basis to improve the management of patients and manage programmes • Adhering to SOPs • Generating, interrogating, and digesting reports • Using and actioning line lists • All levels of staff must be ‘bought-in’ and be part of a DDU culture, including leadership and governance structures • A DDU culture is embedded and institutionalisedin the routine activities of the team or department at hand

  24. What is DDU culture? (2) TKIs, PITs, and DITs are the drivers Improved DDU performance Improved patient & programme management Lead • Scan • Focus • Align/mobilise • Inspire • Stimulate evidence-based decision making • Improved data utilisation • Increased data demand • Improved data availability • Manage • Plan • Organise • Implement • Monitor/evaluate Execute • Prioritise questions • Identify & analyse data • Develop action plans • Implement & monitor Adapted from: Data demand and use tools, Measure Evaluation, 2011

  25. Poll the participants… WHO IS A DATA USER or DATA PRODUCER IN THE ROOM?

  26. What is a data producer? • Data producers include staff responsible for generating routine health information, such as health information officers, data analysts, admin clerks, clinicians and managers • When interrogating data it is useful to first engage the data producers to ascertain any data quality issues before engaging the data users

  27. What is a data user? • Data users include staff who have decision-making responsibilities including managers, clinicians, laboratory and pharmacy staff and counsellors • These are the staff who should have an active role in demanding data/information for use to improve client care

  28. Data users and data producers Information Collect, collate, feedback & report Monitoring & Evaluation Analyse, predict, & plan Programmes Own data, report, manage, use, demand

  29. Roles within the health system cascade Facility-level focus should be on collecting data, actioning line lists and reports for patient/facility management, and facility-level process flow/adherence Sub-district, District, and Provincial focus should be on reviewing data quality and completeness, reviewing performance data, supporting facilities to address performance shortfalls, and providing technical assistance/guidance where applicable DHMIS policy and DHMIS facility-level SOP

  30. Thank you

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