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Challenges for HIS

Challenges for HIS. 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. The goal of the HIS.

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Challenges for HIS

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  1. Challenges for HIS

  2. Learningobjectives • Know about a mainchallenge for HIS: lackofaccess • Know aboutthereasons for this • Know howthisinfluence data quality • Know aboutsome data qualityissues

  3. The goal oftheHIS • “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 theusual case…

  5. Picture: HMN

  6. Picture: HMN

  7. The lackofaccess to healthinformation Why?

  8. Multileveled fragmentation • Health programs • Health informationdomains • Public/private • Manyelectronic formats (and paper still verycommon)

  9. Fragmentationofhealth programs • One informationstream for Malaria program • One informationstream for TB program • One informationstream for… etcetcetc • Surveys • Data not available for comparison. Double counting, low data quality • Country X: threenationalfiguresof HIV+ rate. All different…

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

  11. 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)

  12. Manyofficialactors: risk offragmentation • Ministryof Health is not alone… • Central Statisticsoffice (census) • MinistryofLocalGovernment (run theclinics) • Ministryof Education (schoolhealth programs) • MinistryofDefence (militaryclinics) • MinistryofJustice (civilregistration) • Special unitson for example HIV • In Norway?

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

  14. Health informationdomainfragmentation • Varioussubsystemsdealwithdifferent types of data • Patient data: name, addressetc • HR data: name, diplomas, employmenthistory • Logistics: drug batch No., expiry date • Has (naturally) led to different systems • Butthe link betweenthem has beenneglected

  15. A possibleexample: differentinformationdomains. Statistics Others No linkage! Patient data Human Resource data

  16. Public/Private fragmentation

  17. Whypublic/privatefragmentation? • 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

  18. How does fragmentation influence data quality?

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

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

  21. Data Quality • Is the data complete? • Is the data on time? • Is the data correct? • (arewecollectingthe right data?) • Surprisinglyoftentheanswer is no…

  22. A fewreasonswhy data quality is low • Fragmentation, whichtogetherwithexcessiveamountsbeingcollected leads to • Less time, less interest, in collectionprocess • Many manual steps • Uncleardefinitions • Lackofuse: noincentive to improvequality • More?

  23. Correct? A real example • Data is produced at the service level. Thatusuallymeansthenurse. • For eachstepof manual aggregation and counting, there is a possibility for human errors • Thereare 4 stepsbefore data is ”safe” in the database: • Nurseticking off slots in a tally sheet • Thesetickscountedinto a total • This total writtenonthe MMRCS FacilitySummary form • The data recordedinto DHIS

  24. Two stepsof data exchangeFrom Facility Tally Sheet Total, to MMRCS Summary form, to DHIS

  25. 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)

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

  27. This example is not uniqueWhataretheconsequences?

  28. Key points • Lackofaccess to healthinformation is a major issue • Fragmentation is a mainreason for this • Fragmentation at manydifferentlevels • Data quality is often a bigissue

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

  30. Overall score, 54 countries

  31. Across income levels...

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

  33. Common problems II • Data management • Fragmented, nocentral HIS unit • Appropriatetechnology • Informationuse • Toomuchcollected, toolittle used • Little incentive to useinformationlocally

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