1. USING THE DHIS TO MAKE A CHANGE
Nirvasha Moodley
KwaZulu-Natal Dept of Health
&
Calle Hedberg
HISP-SA
South Africa

2. SA/KZN adopted DHIS in1999/2000 – Key Facts
Indicator
2001
2016
KZN Population (SA ~55 mill)
PHC Headcount
Public Facilities
748
841
Sub-Facility Reporting Units
~7,000
Monthly Routine Data Records
Daily Captured Data Records
Routine Data Coverage
~90-95%
99.x%
Data Capturing Timeliness (98%)
30 days
<10 days
Cleaned-up Data Available (SA)
90 days
40 days
1. 15% increase in population, but
2. 80% increase in PHC headcount (total attendance)
3. 10% more public health facilities – but shift to sub-facility (level 6) reporting means going from 750 to over 35,000 reporting units.
4. The number of routine data records have gone up from under 300,000 to over 20 million per year
5. Facility data coverage is close to 100% and average data capture time has come down from around 30 to less than 10 days.
6. Data clean-up and verification take considerable time, but deadline for submission to national is steadily decreasing.
PEPFAR’S DEMAND FOR DATA WITHIN 15 DAYS DOES NOT PROVIDE SUFFICIENT TIME FOR DATA QUALITY ASSURANCE!

3. Critical Aspects pursued last 10 years
Systematic use of indicator-driven data sets (DHMIS policy)
SOPs defines tasks and responsibilities at all levels and programs
Shift from facility to sub-facility data capture to improve local data use
Shift from monthly to daily data capture (fewer errors & timeliness)
Hospital wards, OPD clinics, and PHC consulting rooms computerised
Expanded data submission from private and NGO sectors (NHI)
Shift from desktop systems to online systems (e.g. DHIS 1.4 -> DHIS2)
requires data security and integrity to move to a new level
Big advances in systematic use of data/information for planning,
budgeting, and day-to-day management
1. National Indicator/Data Sets and Provincial Indicator/Data Sets are revised every 2 years. Regular reviews and semi-annual audits by auditors are part of the process.
2. Sub-facility data is absolutely critical for local analysis and ACTION.
3. Around 2/3 of all PHC facilities are now computerised, with computerisation started in 2017 for small/medium hospitals – expected complete in
4. National Health Insurance will require better integration of public and private sector information systems
5. DHIS data/information use – a brief outline of a few successful examples from KZN.

4. 3 Year trends – KZN Hospitals
Between 2015/16 and 2016/17, the OPD headcount decreased by 4.3%, inpatient days by 1.5% and patient day equivalent by 8.2%.
The OPD headcount not referred (patients accessing PHC services at hospital level) showed a significant decrease of 177 347 (19.7%) since 2014/15 which is a positive trend indicating a shift towards clients entering the public health system at the appropriate level of care.

5. Number of clients remaining on ART
KwaZulu-Natal has the highest burden of HIV in South Africa, with a stable antenatal HIV prevalence rate of around 40% the last 7-8 years
The number of clients remaining on ART is increasing by around 120,000 per annum to currently around 1.25 mill. Defaulters and lost to follow up are 30-40%.
Case-based data on ART clients are currently captured onto Tier.Net, a desktop application – with monthly and quarterly aggregated data exported to webDHIS
DATIM data is currently submitted to a parallel process – and comparisons with DoH data has revealed considerable discrepancies.
From the Department’s perspective, this should be replaced by exporting data from webDHIS to DATIM (one authorised source of data)

6. How else do we use the data?
Tables, charts, and maps – the KZN Department of Health uses a large range of reporting formats.

7. The webDHIS Eco-system(s)
We have up to now very briefly outlined the expansion of standard routine data collection and use over the last years. South Africa’s use of the DHIS goes way beyond routine data, though, as is seen in this coarse overview:
Routine facility data
Quarterly cohort data for HIV/ART and TB from external desktop systems (ETR.Net and Tier.Net)
Campaign data (HPV, EPI)
Ward Based Outreach community data
Survey data (ANC-HIV survey; District Hospital Survey)
Special client data (MomConnect, Patient Experience of Care, Integrated Disease Surveillance and Response, Patient Safety Incidents, Complaints & Compliments, Waiting Times
Using a generic information system like DHIS for many different usage areas are challenging, but with major benefits in terms of training and maintenance

8. HR – Medical Intern eLogbook
Earlier efforts to build large a HR “data warehouse” within the health sector never took off – too complicated and too expensive
The Department, with support from CDC, instead opted for developing more focused applications on the DHIS platform
Some of these components are more traditional databases with HR data
Among more innovative components are the Internship and Community Service Program – electronic applications and deployment of 10,000 graduates per year
Linked to that is this medical Intern eLogbook – aimed at capturing the experiences of Medical Interns as they go through their internship (and improve them)

9. Surveys – Annual ANC-HIV Surveys
DHIS 1.4 has been used to capture the annual Antenatal HIV and Syphilis Survey data since 2006
From 2017 this data is being moved to webDHIS
The more limited data set captured by the laboratories will be imported and used for data quality checking
A new feature in 2017 is that discrepant values (where Rapid Test and Lab Test show different results) will be fed back to the sentinel sites for follow-up

10. Main Challenges with DHIS2 Switch-over from 1
Main Challenges with DHIS2 Switch-over from 1.4 to webDHIS has been a rough road
webDHIS (DHIS2) has limited reporting flexibility, in particular for large data sets
Limited data quality functions (very strong in DHIS 1.4)
Data sign off (approval) not aligned with Auditor’s recommendations
No helpdesk for webDHIS (DHIS2) – HISP support only
General technical challenges related to internet connectivity, bandwidth, security (cyber-attacks on the increase world-wide)
KZN DHIS users have for many years become used to working with very large data sets and reports in DHIS 1.4
DHIS2 as a server-based system running on PostgreSQL can handle much larger databases – but browser-based outputs as currently designed have limitations
The combination of security demands from the Auditor General and increasing hacker activity world-wide have been challenging
High data prices and private internet providers focusing mainly on wealthier urban areas are also challenging and frustrating for previous desktop system users
Notwithstanding all the challenges, the Department is steadily moving towards more integrated, online health information eco-systems
And we are in general far more advanced that many other departments and sectors 

11. Thank you