1 / 33

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. HIS goal revisited.

sema
Download Presentation

Challenges for HIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. HIS goal revisited • “is to produce relevant information that health system stakeholders can use for making transparent and evidence-based decisions for health system interventions” • 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… Donead-hoc • 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, many 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) • 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. Data Quality • Is the data complete? • Is the data on time? • Is the data correct? • (arewecollectingthe right data?) • Surprisinglyoftentheanswer is no…

  21. 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?

  22. 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

  23. Two steps of data exchangeFrom Facility Tally Sheet Total, to MMRCS Summary form, to DHIS

  24. 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)

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

  26. This example is not uniqueWhataretheconsequences?

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

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

  29. Overall score, 54 countries

  30. Across income levels...

  31. Commonproblems I • Policies for HIS • Access • Routines • Ownership • Standards • Human resources • With right skills? • HIS Staffing not prioritized

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

More Related