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eHealth AFRO Conference, Johannesburg

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Presentation on theme: "eHealth AFRO Conference, Johannesburg"— Presentation transcript:

1 eHealth AFRO Conference, Johannesburg
Swaziland HIS Overview and Real-Time Monitoring And Data Use eHealth AFRO Conference, Johannesburg 2-4 October, 2017 Kelvin Sikwibele IHM Bhekumusa Lukhele IHM Danicia Phiri MoH

2 Presentation Outline MOH HIS Vision
& Rationale CMIS CMIS Conceptual framework & concept of operation Use of primary clinical data at facility level CMIS Data Flow & Sharing Framework, and CMIS DW and DHIS/DATIM data exchange Achievements/Progress

3 Comprehensive, Effective and Efficient HMIS Health Service Delivery
MoH HIS Vision VISION Comprehensive, Effective and Efficient HMIS Health Service Delivery A Health Information System that will be comprehensive, effective and efficient in supporting the health sector and other relevant stakeholders in the delivery of health services

4 Burden of Data Collection
Rationale for CMIS CMIS: Unique Client ID – Patient Linkage, Electronic Patient Record, Unified Systems Parallel Inv. In HMIS Parallel Inf. Sys. Burden of Data Collection

5 Swaziland CMIS Conceptual Framework
 People’s Trust Positive Health Clients Knowledge, Attitude and Practice on CMIS Government Willingness, Ownership and Effective Leadership Performance of Financial, Administrative and HR Systems High National ID High Coverage and Use Enabling Environment Infrastructure Adequate Skillful and Willing DM & IT Systems Technical Staff CMIS Driving Factors Data Collection, Mgmt and Analysis Strategic Information at the Fingertips of Clinicians, Progs and Decision Makers Adequate and Performing Hardware, LAN & WAN Motivated and Adequately Trained Clinicians and Facility Staff Interoperable, Scalable, Integrated, Extensible and Flexible and Inclusive CMIS Software Application High Quality Data Prod. CMIS Policies, Standard Operating Procedures, Guidelines and Training Manuals Facility Level Staff Ownership and Willingness for CMIS Data Management Warehouse and Business Intelligent Routine Data Quality Assessments and Data Quality Improvement Practices  Regular Data Mining Sound Analysis Plan Information Use Data Quality Positive Health Outcomes

6 CMIS Areas of Operation
Patient Unique Identification & Registration System A IT Network Infrastructure & Operations B Electronic & Paper Based CMIS C Policy, Guidelines & SOPs D Human Resources E Financial Resources F Government & Partner Relations G Communications H

7 HIS Concept of Operation

8 Real-time Application at Facility Level

9 CMIS Data Flow & Sharing Framework

10 CMIS DW and DHIS/DATIM data exchange
Facility Level Regional Level National Level National Level PEPFAR Level

11 CMIS and DHIS/DATIM data exchange technology & protocol
REST API CSV XML Available options in CMIS for interoperability Real-time update FTP or File upload or Real-time Data transfer state External systems want consume information from CMIS HL7 FTP or File upload DATIM LIS DHIS 2

12 Data use at Facility level
This is for the use of data at facility level dashboards were the developed (CMIS & HMIS) To improve data quality at Facility Level Real time data access Identification of gaps ASAP

13 Patients’ enrollment dashboard: At Facility Level

14 HTS Program performance dashboard: At Facility Level

15 PMTCT Program performance dashboard: At Facility Level

16 ART Program performance dashboard: At Facility Level

17 Facilities running CMIS in Swaziland

18 Progress Achieved as per FY 17 Target
Indicators Baseline FY YYYY Annual Target Q1 FY17 Q2 Q3 Q4 Annual Performanc e Achieved to the End of Reporting Period (%) On Targe t Y/N CMIS deployed and operational in 134 health facilities Number of facilities with LAN 134 121 130 97% Y Number of facilities with LAN & physical security in place 46 63 98 73% Number of facilities with Complete Foundation in place; Server/PC hardware deployed 26 55 84 63% Number of facilities with LAN & WAN in place 40 74 20 55% N Number of facilities with LAN, WAN, & physical security in place 28 69 Number of facilities with foundation in place & server/hardware deployed and 90%+ of staff are trained 25 54

19 Progress Achieved as per FY 17 Target
Number of facilities LIVE on CMIS –Patient Registration only 134 20 44 80 60% Y Number of facilities fully LIVE on CMIS 15 42 Number of clients registered in CMIS 80, 000 150,207 189,426 336, 333  420.4% Number of clients with UPID among those registered in CMIS 64, 000 93,627 115,113 196, 088  306.4% Number of PLHIV registered in CMIS 21,000  13866 21,041 46,249  220.2% Estimated number of PLHIV knowing their status registered in CMIS 18,900  9147 46,243  244.7% Number of PLHIV on ART registered in CMIS 17,010 11124 19731 44131 259.4 % % of PLHIV known to be alive 12 months after initiation of antiretroviral therapy in CMIS 90% 98.01 % 98.91% 98.91 % % of PLHIV on ART with suppressed VL registered in CMIS 92.42% 94.13 % 93.34%

20 Progress Achieved Developed a Real time system that securely and accurately stores patient level data Developed Dashboards which address specific challenges faced by target audience and enhances real time monitoring of operations at facility level Strengthened Network infrastructure ( LAN and WAN) Developed a Data Warehouse for data mining as a National Information Repository. Capacity building of HMIS data management team on data utilization using Dashboards (on an online data sharing platform)

21 Current Innovations and Future Prospects
Current Innovations and Future Prospects Real –Time data to management of Decision making. Real-Time Dashboards are interactive thus enabling absolute control of data. Secure access to health data Solution Expansion: Data shall be automatically cleaned and staged on a data warehouse before being shared on an online data sharing platform (SharePoint)

22 Current Innovations and Future Prospects
Solution Expansion: Interfacing U-Report dashboards with SharePoint Solution Expansion: Implementation of tracker indicators on already developed dashboards Solution Expansion: Quality measurement/assurance mechanisms on dashboards Solution Expansion: Built in data mining/data analysis on all standard indicators using the Data Warehouse

23 Thank You….


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