1. Challenges for HIS

2. Learning objectives Know about a main challenge for HIS: lack of access Know about the reasons for this Know how this influence data quality Know about some data quality issues

3. The goal of the HIS “is to produce relevant information that health system stakeholders can use for making transparent and evidence-based decisions for health system interventions” (HMN) The challenges here are many: –You need access to data –You need quality data –You need to know what to do with it

4. This is not the usual case…

5. Picture: HMN

8. The lack of access to health information Why?

9. Multileveled fragmentation Health programs Health information domains Public/private Many electronic formats (and paper still very common)

10. Fragmentation of health programs One information stream for Malaria program One information stream for TB program One information stream for… etc etc etc Surveys Data not available for comparison. Double counting, low data quality Country X: three national figures of HIV+ rate. All different…

11. Dental unit 1 PAWC City Health Clinic 1 54 private medical pract. Geriatric Services MOU (Midwife& obstetric unit) PAWC 23 private dental pract. 12 private pharmacies Private hospital: 31 medical specialists Day Hospital DNHPD UWC Oral Health Centre City Health Clinic 2 City Health Clinic 3 City Health Clinic 4 City Health Clinic 5 Dental unit 2 PAWC Dental unit 3 PAWC 12-15 NGOs School Health DNHDP Pretoria Groote Schuur Hospital PAWC DNHDP Western Cape City Health MITCHELL’S PLAIN Environmental office Mandalay Mobile clinic RSC Youth Health Services Psyciatric hospital PAWC RSC Outside hospitals Births Deaths Notifiable diseases New /emerging flow of information Apartheid legacy: a fragmented and top down health structure no local governance & control of information Example: South Africa in mid 90s

12. Why program fragmentation? Health services inherently fragmented due to high level of specialization Donors (both from necessity and ignorance) WHO is highly fragmented itself Interests and ownership Leads to lack of transparency, some people thrive on that (corruption)

13. Many official actors: risk of fragmentation Ministry of Health is not alone… –Central Statistics office (census) –Ministry of Local Government (run the clinics) –Ministry of Education (school health programs) –Ministry of Defence (military clinics) –Ministry of Justice (civil registration) –Special units on for example HIV In Norway?

14. Health Statistics District - DHT Facility 1Facility 2Facility n IDSR – Notifiable Diseases PMTCT EPI STD Home Based Care Nutrition ARV MCH Family Planning HIV/AIDS TBSchool Health Mental Health And more … Facility 3 Botswana: Pre-intervention – Fragmentation – No shared IST resources “converging” at district level - Fragmentation at central level / HISP

15. Health information domain fragmentation Various subsystems deal with different types of data –Patient data: name, address etc –HR data: name, diplomas, employment history –Logistics: drug batch No., expiry date Has (naturally) led to different systems But the link between them has been neglected

16. A possible example: different information domains. Others Statistics Patient dataHuman Resource data No linkage!

17. Public/Private fragmentation

18. Why public/private fragmentation? Taxation reasons Business ”secrets” Lack of capacity at MOH to follow up –Not one private sector, or umbrella organization –Private clinics, traditional medicine, religious organizations, NGOs No incentives for private sector to share Private sector often not very formal Lack of policies and legal frameworks

19. How does fragmentation influence data quality?

20. Fragmentation linked to data quality Vicious cycle: 1.Low data quality 2.Do not trust it 3.Build a new system for your own needs 4.Duplication, and higher workload for those collection data (nurses) 5.Leads to low quality data Lack of access is poor quality itself: missing data (as in example of Western Area above) affects indicators

21. limited capacity to manage or analyse data Using evidence not perceived as a winning strategy A vicious cycle Data not trusted Weak demand Weak HIS Poor data quality Limited investment in HIS Decisions not evidence-based Donors get their own Fragmentation

22. Data Quality Is the data complete? Is the data on time? Is the data correct? (are we collecting the right data?) Surprisingly often the answer is no…

23. A few reasons why data quality is low Fragmentation, which together with excessive amounts being collected leads to –Less time, less interest, in collection process Many manual steps Unclear definitions Lack of use: no incentive to improve quality More?

24. Correct? A real example Data is produced at the service level. That usually means the nurse. For each step of manual aggregation and counting, there is a possibility for human errors There are 4 steps before data is ”safe” in the database: –Nurse ticking off slots in a tally sheet –These ticks counted into a total –This total written on the MMRCS Facility Summary form –The data recorded into DHIS

25. Two steps of data exchange From Facility Tally Sheet Total, to MMRCS Summary form, to DHIS ANC 1Bednets givenANC 2ANC 3 Tally Sheet Sum. Form DHISTally Sheet Sum. Form DHISTally Sheet Sum. Form DHISTally Sheet Sum. Form DHIS Jan 2620 2620 1088777 Feb 40 12 10 12 Mar 12 00015 204410 Apr 24 0088813 2 May 3130 0001110 666 June 15 000666555 July 1213 000699244 Aug 88800013 12

26. Analysis 14 errors from 32 data entries (4 elements, 8 months) 43.75%..... 6 mistakes during entering to DHIS 8 mistakes during exchange of data from tally sheet to summary form Not counting errors in tally sheet aggregation....(or those figures never ending up in the tally sheet in the first place)

27. More examples 4 deliveries checked off.......but the number recorded is 0! 7 IPT 1st doses....... recorded as 2

28. This example is not unique What are the consequences?

29. Key points Lack of access to health information is a major issue Fragmentation is a main reason for this Fragmentation at many different levels Data quality is often a big issue

30. HMN study Mostly countries from low and middle-income countries Main findings –Data management and Resources are areas most countries struggle

31. Overall score, 54 countries

32. Across income levels...

33. Common problems I Policies for HIS –Access –Routines –Ownership –Standards Human resources –With right skills? –HIS Staffing not prioritized

34. Common problems II Data management –Fragmented, no central HIS unit –Appropriate technology Information use –Too much collected, too little used –Little incentive to use information locally