Challenges for HIS. Learning objectives Know about a main challenge for HIS: lack of access Know...
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Transcript of Challenges for HIS. Learning objectives Know about a main challenge for HIS: lack of access Know...
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
• “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
This is not the usual case…
Picture: HMN
Picture: HMN
The lack of access to health information
Why?
Multileveled fragmentation
• Health programs• Health information domains• Public/private• Many electronic formats (and paper still very
common)
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…
Dental unit 1PAWC
City HealthClinic 1
54 private medical pract.
GeriatricServices
MOU(Midwife&
obstetric unit)PAWC
23 private dental pract.
12 private pharmacies
Private hospital:31 medical specialists
Day HospitalDNHPD
UWC OralHealth Centre
City HealthClinic 2
City HealthClinic 3
City HealthClinic 4
City HealthClinic 5
Dental unit 2PAWC
Dental unit 3PAWC
12-15 NGOs
SchoolHealth
DNHDPPretoria
Groote SchuurHospital
PAWC
DNHDPWestern Cape City Health
MITCHELL’S PLAIN
Environmentaloffice
MandalayMobile clinic
RSCYouth
Health Services
PsyciatrichospitalPAWC
RSC
Outsidehospitals
BirthsDeathsNotifiable diseases
New /emergingflow of information
Apartheid legacy: a fragmented and top down health structureno local governance & control of information
Example: South Africa in mid 90s
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)
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?
Health Statistics
District - DHT
Facility 1 Facility 2 Facility n
IDSR – NotifiableDiseases
PMTCT
EPI
STD
Home Based Care
Nutrition Nutrition
ARV
MCH
Family Planning
HIV/AIDS
TBSchool Health
Mental HealthAnd more …
Facility 3
Botswana: Pre-intervention – Fragmentation – No shared IST resources “converging” at district level - Fragmentation at central level
/ HISP
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
A possible example: different information domains.
Others
Statistics
Patient data Human Resource data
No linkage!
Public/Private fragmentation
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
How does fragmentation influence data quality?
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
limited capacity to manage or analyse data
Using evidence not perceived as a winning strategy
A vicious cycle A vicious cycle
Data not trusted
Weak demand
Weak HIS
Poor data quality
Limited investment in HIS
Decisions not evidence-basedDonors get their own
Fragmentation
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…
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?
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
Two steps of data exchangeFrom Facility Tally Sheet Total, to MMRCS Summary form, to DHIS
ANC 1 Bednets given ANC 2 ANC 3
Tally Sheet
Sum.Form
DHIS Tally Sheet
Sum.Form
DHIS Tally Sheet
Sum.Form
DHIS Tally Sheet
Sum.Form
DHIS
Jan 26 20 20 26 20 20 10 8 8 7 7 7Feb 40 40 40 40 40 40 12 12 12 10 10 12Mar 12 12 12 0 0 0 15 15 20 4 4 10Apr 24 24 24 0 0 8 8 8 13 13 13 2May 31 30 30 0 0 0 11 10 10 6 6 6June 15 15 15 0 0 0 6 6 6 5 5 5July 12 13 13 0 0 0 6 9 9 2 4 4Aug 8 8 8 0 0 0 13 13 13 12 12 12
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)
More examples
4 deliveries checked off.......but the number recorded is 0!
7 IPT 1st doses....
... recorded as 2
This example is not uniqueWhat are the consequences?
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
HMN study
• Mostly countries from low and middle-income countries
• Main findings– Data management and Resources are areas most
countries struggle
Overall score, 54 countries
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60.0
70.0
Resources Indicators Data Sources DataManagement
InformationProducts
Disseminationand use
Per
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Across income levels...
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Resources Indicators Data Sources DataManagement
InformationProducts
Disseminationand use
Per
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e o
f m
axim
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sc
ore
Low Income
Lower Middle Income
Upper Middle Income
Common problems I
• Policies for HIS– Access– Routines– Ownership– Standards
• Human resources– With right skills?– HIS Staffing not prioritized
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