Proceedings of the 9 th Health Informatics in Africa Conference HELINA 14 PART 2. [Preview]

Proceedings of the 9 th Health Informatics in Africa Conference HELINA 14 PART 2 [Preview]

2014 HELINA. ISBN: 978-3-9816261-2-4 Publisher Koegni ehealth, Innovation for Development e.v. Germany D-22071 Hamburg Germany www.koeghi-ehealth.org E-mail: info@koegni-ehealth.org

Table of Contents Editorial TBA iv FULL PAPERS Implementation of an enhanced inter process communication for health information systems Soriyan, H.A, Ajayi, A.O,Famutimi, R.F, Ikono R.N. Utilization of routine health information system to monitor the United Nation s Millennium Development Goals (MDGS) for 2015 in South Sudan Ayub Shisia Manya, Jørn Braa, Petter Nielsen and Owino Wasunna Conceptual view of a data model for nursing process Yange Terungwa Simon, Soriyan Hettie Abimbola and Olaogun Adenike A viable architecture for the integration of a recommender system and mobile solution for the management of HIV/AIDS Adekunle O. Afolabi, H. Abimbola Soriyan, Juha Mykkänen, Pekka Toivanen, Mikko Korpela Une stratégie d implémentation de systèmes intégrés pour la gestion d informations des structures de soins en République Démocratique du Congo Shamashanga Pierrot, Amisi Clémentb, Karara Gustave, Verbeke Frank Design of mobile appointment reminder and counselling system Theresa O. Omodunbi, Rhoda N. Ikono, Ishaya P. Gambo, Abisola Oyekunle, Hetite A Soriyan Modelling software agents: web-based medical decision support system for malaria diagnosis and therapy Eustache Muteba Ayumba Reporting in the health systems: case study of Ghana Ivy de-souza, Marc Nyssen The need for telemedicine and e-health in Haiti Olayele Adelakun A survey of the barriers to interoperability of Nigeria healthcare systems Ikono Rhoda, Soriyan Abimbola, Iroju Olaronke, and Gambo Ishaya ehealth strategy situation assessment in Malawi Maganizo Monawe, Shawo Mwakilama, Gibson Kapokosa A successfully handed over Master of Public Health in Health Informatics Program at University of Gondar, Ethiopia Atinkut Zeleke, Binyam Tilahun, Desalegn Zegeye, Mulusew Andualem, Mihretu Molla, Solomon Assefa, Tesfahun Melese Le rôle des NTIC dans le développement des mécanismes d assurance maladie en RDC Karara Gustave, Amisi Fataki Clément, Shamashanga Pierrot, Verbeke Frank, Nyssen Marc 1 10 17 26 32 39 46 52 58 63 67 79 86

Technology assimilation in community healthcare in the Western Cape, South Africa Ralitsa D. Debrah, Sophie V. Bhebe, Retha de la Harpe, Ephias Ruhode, Doreen K.M. M Rithaa, Vilma Vainikainen Peer based reviews as a strategy for strengthening the health information systems: a case study from Malawi Christon Moyo, Tiwonge Manda, Petter Nielsen Towards Problem-Based Health Informatics Education and Research: Ten Years of Experience and Future Directions in Ethiopia Binyam Tilahuna, Atinkut Zeleke, Fleur Fritz, Mihretu Mola, Mulusew Andualem, Solomon Assefa, Tesfahun Melese,Desalegn Zegeye, Abebaw Gebeyehu, Sisay Yifru Considerations for the design of relevant health informatics courses for the African context: introducing informatics for nurses Sophie V Bhebe, Retha de la Harpe, Doreen KM M'Ritha, Kaija Saranto, Ephias Rhuode 95 106 114 119 Who s data is this anyway? Understanding relationship between power, empowerment and technology: Case from Mother and Child Tracking System (MCTS) in India Arunima Mukherjee SHORT PAPERS The Architecture Landscape of Electronic Health Information Systems in South Africa Boniface Kabaso, Gillian Khan, David Makola Implementing data warehouse solutions in the health sector: lessons from the Kenya HMIS project Mwenda Gitonga, Ali Karisa, Brian Wakhutu, Mysha Sissine, Joshua Oiro, Donna Medeiros, Bobby Jefferson A mobile platform to facilitate counselling as well as health information and education dissemination to people in resource-restricted communities de la Harpe, R., Kabaso, B., Debrah, R.D. Empowering the Tanzania health system and the community it serves through a stakeholder Centric approach to a District Health Profile (DHP) standardization process Rita Sembajwe The use of standardized tools for monitoring of a Laboratory Information System in Ghana Bernard Nkrumah, Philip Boakye, Anthony Ofosu, Sidney Akuro, Samuel Duh, Ava Onalaja, Patrick Njukeng, Reshma Kakkar, Amitabh Adhikari, Celia Woodfill 137 138 139 140 141

Editorial of HELINA 14 TBA

FULL PAPERS

9 th Health Informatics in Africa Conference (HELINA 2014) Peer-reviewed and selected under the responsibility of the Scientific Programme Committee Implementation of an enhanced inter process communication for health information systems a Soriyan, H.A, a Ajayi, A.O, b Famutimi, R.F, a Ikono R.N. a Computer Science and Info. Technology Dept. Bowen University Iwo, Osun State b Computer Science and Engineering Dept.Obafemi Awolowo University, Ile Ife, The development in technology which resulted in continuous availability of ever increasing processor speeds has contributed greatly to the success of distributed network systems employed in many domains. This paper explored the advantages of distributed network systems in the implementation of an enhanced inter-process communication for health information systems. In Health information systems, timely retrieval of relevant information is very important for life saving systems. This paper implemented an enhanced inter-process communication to improve the performance of information management systems that are based on traditional (normal) remote procedure call for accessing health information systems database file servers. The performance parameters used are the response time and the throughput. The implementation was carried out such that tasks are not implemented immediately, but are first examined to determine the weight of the tasks (query) and then allocate rank to the query. The rank obtained was used to determine the node that will implement the query. Both traditional and the enhanced inter-process communications were implemented and the result showed that there was a better performance in the implementation of the enhanced inter-process communication model when using response time and throughput as parameters. Keywords: task weight, ranking, inter-process, communication, query task, remote procedure call. 1 Introduction The combination of computing and networking technologies gave birth to the new paradigm of computing called parallel and distributed computing in the late 1970s. Sabu, M. (2009). Parallel computing is embedded in distributed computing. The computing method has now changed, because there is demand for fast response to database enquires. A parallel and distributed computing system is the architecture that makes it possible for a collection of computer workstations (nodes) either heterogeneous or homogeneous to behave as a single computing system. In this type of computing, users can utilize resources from any of the workstations, execute processes or programs anywhere in the system. Hariri, S. & Parashar, M. (2004). The continuous demand for increased computing power and different requirements has brought about a situation whereby a single computing platform will find it increasingly difficult to meet these demands. As a result of this, computing environments now make effective use of the existing heterogeneous or homogeneous computing resources that are available in a network of systems. This integration is only possible with the use of parallel and distributed system technology; hence its emergence. A distributed system is a computing system in which a number of components cooperate by communicating over a network. It is a collection of autonomous computers linked by a computer network that appear to the users of the system as a single computer (Petru, 2010). Many researchers, Vincent, O.R. et al.(2010); Mangalwede, S.R. & Rao, D.H. (2009); Yousry, E., Khalid, E. & Magdy, S. (2007); Aderounmu, G., Adekiigbe, A. & Iyilade, J. (2004); Kunal, S. (2003); Robert, S.G. et al. (2001); Ismail, L., Hagimont, D. & Mossiere, J. (1999) have proved that there is a challenge in the use of traditional remote procedure call. Notable among the challenges of traditional remote procedure call in a distributed system is Load Imbalance on client s nodes while making request from file *Corresponding author address: Email: ranti.famutimi@gmail.com, hasoriyan@yahoo.com

2 Soriyan et al. / Enhanced inter process communication for health information systems servers. Traditional Remote Procedure Call has resulted in an improper balancing of tasks among nodes in the network. Task requests are executed as they come and without consideration of the complexity (weight) of the tasks involved. This often resulted in poor or degraded performance of the distributed systems. Most of the previous works made use of traditional Remote Procedure call (RPC) when evaluating the performance of remote procedure call. In this model, calls are made to remote objects in order to carry out tasks, immediately the tasks are available. The model does not take into consideration the weight of the tasks to be carried out, which will normally result to load imbalance and hence a reduced performance in the use of the model. Many health information systems make use traditional Remote Procedure Calls (RPC) in retrieving data from file servers, Korpela et.al.(2005). Many health information systems based on traditional RPC that are implemented in network environments can be re-engineered so that inter-process communications among nodes in the network are made faster when accessing patients medical information by utilizing the computing powers of the different nodes in a distributed computing paradigm. According to Famutimi et al. (2012), the response time and throughput of an RPC based inter-process communication can be improved for better performance. In the paper, an enhanced inter-process communication model for health information systems was presented. The aim was to achieve a faster patients information retrieval than the traditional inter process communication system without upgrading the available hardware. The paper is an implementation of a popular model in a health information based system. A Made in Nigeria Primary Health Information System (MINPHIS) database was used as the file server. 2 Literature review Brandy, T. et al. (2010) worked on SmartGridRPC; The new RPC model for high performance Grid computing. The authors here worked on multiple tasks that are to be completed in parallel. The tasks compared here are grid computing (that is rows and columns computing). The more the number of rows to be computed the higher the weight of that task. After introducing this intelligence into the Remote Procedure Call it was found to be efficient for high performance than the traditional Remote Procedure Call. This work was only based on grid computing of rows and column alone. It was also based on parallel tasks; it did not consider a situation where tasks are to be processed individually. In Manwade, K.B. & Patil, G.A. (2008), the authors here worked on parallel computation of tasks that can be computed in parallel. They applied mobile agents (MA) and traditional remote procedure call on parallel tasks. The performance Analysis of parallel RPC versus parallel MA model was obtained. The authors found out that RPC is better for small amount of data. Mobile agent is better for large amount of data. The authors did not consider an enhanced remote procedure call. A typical architecture a health information system, using a java based RPC scheme for inter-process communication between client nodes and server systems by Korpela, M. J. et al. (2005) is shown in Figure 1. The proposed model is to improve on the performance of the communication between the clients and the server that is using the transmission control protocol and the internet protocol (TCP/IP). Famutimi et.al (2012) proposed an enhanced interprocess communication model, which we are now implementing in this paper. 3 Methodology Framework. In the implementation of the model, all nodes can both request for a query to be carried out on its behalf as well as carrying out a query task on behalf of another node. Database fields are taken as part of query keywords. In the distributed system, a program in node k, can be executed from any other node in the distribution. The complexity of a query is to a great extent proportional to the size of the keywords in the query (task). The keywords in a query are used to obtain the rank of a query. The processing capability of all nodes in the distribution has been earlier on determined through the use of system utility and ranges of query ranks allocated to each node in the node capacity registry. Approach. The implementation consists of sequence diagram given by Figure 1, Use case diagram shown in Figure 2. class diagram in Figure 3 and Figure 4 as the flow chart of the implementation.

Soriyan et al. / Enhanced inter process communication for health information systems 3 4 Results When the enhanced model was evaluated with the traditional remote procedure call, the results were shown in table 1, table 2, table 3 and Figure 5. Based on these results, the implemented enhanced procedure model resulted in an improved speed of patient health information retrieval. In a distributed health information system, the speed of retrieval of patient medical information can be improved tremendously without resulting to frequent upgrade of system hardware whenever there is degradation in speed of patient information retrieval. Figure 1. Sequence Diagram of the implementation (Famutimi et.al.,2012)

4 Soriyan et al. / Enhanced inter process communication for health information systems Figure 2. Use Case Diagram of the Implementation (Famutimi et.al.,2012)

Soriyan et al. / Enhanced inter process communication for health information systems 5 Figure 3. Class Diagram of the implementation (Famutimi et.al.,2012)

6 Soriyan et al. / Enhanced inter process communication for health information systems Figure 4. Flowchart of the implementation

Soriyan et al. / Enhanced inter process communication for health information systems 7 Table 1. The result on traditional RPC RANK/resp times(mill secs) Runs IV V VI VII VIII IX 1. 10 20 100 331 480 902 2. 10 30 100 310 450 812 3. 30 30 91 310 420 961 4. 50 30 100 311 450 952 5. 20 30 90 331 420 911 6. 10 30 108 311 440 881 7. 30 20 100 300 441 912 8. 10 30 100 310 441 912 9. 20 30 100 300 471 921 10. 20 20 100 331 490 931 Table 2. The result on Enhanced RPC Runs RANK/resp times(mill secs) IV V VI VII VIII IX 1. 13 60 51 168 241 564 2. 36 23 45 94 237 528 3. 14 63 44 96 238 594 4. 11 18 46 95 260 528 5. 12 18 54 95 241 555 6. 11 21 41 96 239 556 7. 12 17 40 96 236 555 8. 12 17 43 97 231 513 9 10 18 45 96 241 523 10 10 16 43 93 242 526 AVRG 14 27 45 103 241 544 Table 3. Comparative speed of Traditional RPC model and Enhanced RPC Rank Traditional RPC Enhanced model Speed improvement Improvemetn(%) model (Millisec) (Millisec) (Millisec) 4 21 14 7 50% 5 27 27 - NIL 6 99 45 54 54% 7 315 103 212 67% 8 450 241 209 46% 9 910 544 466 51%

8 Soriyan et al. / Enhanced inter process communication for health information systems Figure 5. Comparison of Enhanced Remote Procedure Call with Traditional Remote Procedure Call using Response Time 5 Conclusion In this paper, an enhanced inter-process communication model for health information systems has been implemented. Its aim was to show that a faster rate of patients information retrieval can be achieved using the proposed model. Significant improvement in speed was observed when the rank (items of retrievable data) was getting bigger. Implications to practice. The speed at which patients information can be retrieved in an RPC based Health Information system can be speed up to an additional 50% or more of the present rate. The rate at which RPC based Health Information Systems Hardware are being upgraded can be reduced in a distributed system when using this model, since high speed processing nodes can be made used of by other nodes. Implications to policy. In a distributed system, upgrade of specific computer nodes can be made use of by other nodes rather than embarking on general upgrade of all nodes whenever performance degradation is noticed. This will result in the conservation of resources used in distributed health information systems. Future research direction. The enhanced model which was RPC based, will be evaluated with a mobile agent that has been proven to be superior in performance to the traditional RPC model. Acknowledgements. We thank all members of Health Information Systems (HIS) research group of Obafemi Awolowo University Nigeria and INDEHELA-ICI project that gave opportunity to enhance the work. References [1] Aderounmu, G., Adekiigbe, A,and Iyilade, J.(2004). An evaluation of mobile agent paradigm. The Journal of Computer Science and its Applications 10(2);107 116. [2] Brandy, T., Doganae J., Guidolin, M., Lastovetsky, A., and Seymour, K. (2010) SmartGridRPC: The new RPC model for high performance Grid computing; 81 90. [3] Famutimi, R.F, Soriyan, H.A, Ajayi, A.O (2012). An Enhanced Inter-Process Communication Model For Distributed Systems. International Conference on ICT for Africa 2012. 21-24th March Makere University Business School Kampala, Uganda. [4] Hariri, S and Parashar, M. (2004). Tools and Environments for Parallel and Distributed Computing. John Wiley and Sons Inc. New York. IEEE, IEEE Standard 601 12 1990.

Soriyan et al. / Enhanced inter process communication for health information systems 9 [5] Korpela M.J.,Soriyan H.A., Afolabi A.O.,Fatusi O., Mursu A., and Akinde A.D.(2005). Development of a computer based Primary health care Information System: Towards a holistic system. Paper Submitted to the IFIP 2005. [6] Kunal, S.(2003). Performance analysis of mobile agents in wireless Internet applications using simulation, An M.Sc Thesis Presented to The Faculty of the College of Graduate Studies, Lamar University, Beaumont TX, United States. [7] Manwade, K.B., and Patil, G.A. (2008). Performance Analysis of parallel client server model versus Parallel mobile Agent. World Academy of Science, Engineering and Technology. Vol 38 pages 374 377. [8] Mangalwede, S.R,and Rao, D.H. (2009). Performance Analysis of Java based Approaches to Distributed Computing. Vol 1. No 1 pp 556 559. [9] Robert, S.G, George, C., David, K, Ronald, A.P. and Daniel R. (2001). D Agents: Applications and Performance of a Mobile Agent System, Using bandwidth, pass ratio (percentage of data passing an application filter) and the number of client machines. Thayer school of Engineering / Department of Computer of Computer science, Dartmouth college, Hanover, New Hampshire Technical paper No 03755. [10] Sabu, M. T. (2009). Introduction to Distributed Systems. arxiv publishers. 1 23. Schroeder, M; and Burrows,M(2006) Paper: Performance of Firefly RPC. Digital Equipment Corporation, Systems Research Center, Palo Alto, California. 4 18. [11] Ismail, L., Hagimont, D.; and Mossiere, J. (1999). Evaluation of the mobile agents technology: Comparison with the Client/Server paradigm. ACM SIGPLAN Notices, vol. 34, no. 10:306-313. [12] Vincent, O.R., Folorunso, O, Akinwale, A.T., and Akinde, A.D. (2010). Transaction flow In card payment systems using Mobile Agent. Interdisciplinary journal of information Knowledge and management. Vol 5,153 166. [13] Yousry, E, Khalid, E., and Magdy, S. (.2007), A Comparative Performance Evaluation Model of Mobile Agent Versus Remote Method Invocation for information Retrieval, World Academy of science, Engineering and technology. Vol 27, pages 286 297.

9 th Health Informatics in Africa Conference (HELINA 2014) Peer-reviewed and selected under the responsibility of the Scientific Programme Committee Utilization of routine health information system to monitor the United Nation s Millennium Development Goals (MDGS) for 2015 in South Sudan a Ayub Shisia Manya, a Jørn Braa, a Petter Nielsen, b Owino Wasunna a University of Oslo,Norway b Health Systems Strengthening Project (HSSP), Sudan Background and Purpose: A national health information system is useful in monitoring both national and global indicators. South Sudan, the youngest nation in the world established their health information system using the District Health Information software in 2010. As a newly established system, we wanted to assess whether it was capable of monitoring international indicators. We analysed the data in the system with the objective of determining its ability to give proxy measurements for the United Nation s Millennium Development Goals (MDGs). Methods: Data in the District Health Information Software (DHIS1.4) for the years 2012 and 2013 were analysed with emphasis on the maternal safety (Fifth Millennium Development Goal (MDG5)). Prior to the analysis, data were cleaned for errors by the county monitoring and evaluation officers (the officers that enter data from paper into the DHIS). Data were evaluated for reporting rates, facility maternal mortality rates, antenatal clinic attendance and the proportion of pregnant women who were delivered by skilled health workers. The results were compared across the various states in the country and presented in graphs and tables. Results: With an average reporting rate of 60%, it was established that 38.5% of pregnant women attended the first antenatal clinic, while 20.4% attended up to four clinics. In terms of deliveries, only 48.5% of all women, who delivered in the country s health facilities were assisted by skilled birth attendants. The statistics on health facility deaths indicated that the facility maternal mortality ratio was 1,060 and 630 per 100,000 live births in 2012 and 2013 respectively. Conclusions: The results show that even though the maternal safety indicators in South Sudan below were below the international targets, the health information system was well developed and capable of providing information on both national and global indicators. Keywords: Health information systems, Public health informatics, Medical records, Data collection 1 Introduction Health information systems form part of the six building blocks identified by the World Health Organization (WHO) s framework for health systems strengthening [1]. They are important in generating useful data on health determinants and health system performance. National program managers need data from different geographical areas within the country to plan for and monitor improvements in service coverage, capacity for care and treatment of various diseases. They need to ensure equitable access to services by those in need [2].The rising importance of health information systems has generated significant global, regional and national investments in this area. Against the realization of the need for an efficient Health Information System (HIS), the Government of South Sudan adopted the use of District Health Information software (DHIS 1.4) as the main reporting system. DHIS 1.4 is an open source health information management software written in Microsoft Access and Visual Basic for Applications [3]. The implementation of DHIS was gradual; starting with two states in 2010, six states in 2011 and covering the entire ten states by 2012[4]. Even though the health information system in South Sudan is still in its early development stages, it is important to start evaluating its performance in order to give feedback to the stakeholders for further refining. *Corresponding author address: Email: ayubmanya@gmail.com

Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan 11 According to the 2014, statistics from World Health Organization, nearly 800 women die globally every day due to complications in pregnancy and childbirth [5]. The United Nations Development Programme (UNDP) states that South Sudan has the highest maternal mortality rate in the world 2,054 per 100,000 live births. This is an astronomical figure representing a 1 in 7 chance of a woman dying during her lifetime from pregnancy related causes [6]. This paper highlights the maternal safety indicators as part of evaluation of the health information system in the country. 1.1 Information Flow in South Sudan A Health information system is a comprehensive and integrated structure that collects, collates, analyses, evaluates, stores and disseminates health and health-related data and information for use by all stakeholders [7, 8, and 9]. In South Sudan, data management is both paper and electronic based. Health workers at health facilities (hospitals, Primary Health Care Centres (PHCC), Primary Health Care Units (PHCU)) record data in paper registers (Antenatal Care, Delivery, Outpatient Department for Adults and Children, and Expanded Programme of Immunization) as they deliver their services. The registers are quite detailed, showing all the patient personal identities (name, age sex), address (village of residence), the diagnosis and treatment given. Data from these registers are then summarised and entered into predefined summary forms on a regular basis (weekly, monthly and quarterly). The summarized forms are then transported by road to the County Health Departments (CHDs), where they are transformed into the electronic formats through entry into the DHIS. Designated officers at the county level are charged with the responsibility of scrutinising the data received from health facilities for completeness and correctness before submitting them to the state Ministries of Health (SMOH) electronically by email. Similarly, State health monitoring and evaluation officers compile all data from the counties and submit their compilation to the national level at the republic of South Sudan (RSS) headquarters. Non-Governmental organizations (NGOs) operating at county and state levels report to these levels respectively. Feedback follows an inverse path: from RSS to SMOH, CHDs to health facilities [10]. The flow of data is shown in figure 1 below. Figure 1. Data Flow from Primary Health Facilities to the National Level in South Sudan Millennium Development Goals (MDGs) Millennium development goals were established at the Millennium Summit in 2000 whereby 192 United Nations member states agreed to achieve them by 2015. The eight Millennium Development Goals (MDGs) which range from halving extreme poverty to halting the spread of HIV/AIDS and providing universal primary education, all by the target date of 2015, form a blueprint agreed to by all the world s countries and development institutions [11]. Target 5A of the fifth millennium goal is to reduce by three

12 Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan quarters, between 1990 and 2015, the maternal mortality ratio while target 5B is to achieve, by 2015, universal access to reproductive health [11]. To achieve the goals, UNDP recommends that more pregnant women should receive antenatal care, teenagers should not get pregnant and there should be an increase in family planning [11]. A crucial factor in reducing perinatal, neonatal and maternal deaths in health facilities is the presence of skilled health personnel during delivery. Unfortunately, the proportion of births attended by skilled personnel remains less than 50 % in most African countries [11]. In 2003 the World Health Organization estimated a global death of 289,000 women during pregnancy and childbirth [11]. Most of them died because they had no access to skilled health workers and emergency care. While many women are being encouraged to attend health care services during pregnancy, it is apparent that the optimal care may not be easily available in developing countries. For instance, it is estimated that there is only one qualified midwife per 30,000 people in South Sudan [12]. Reports from the World Health Organization show that globally, the proportion of women receiving antenatal care at least once during pregnancy was about 81% for the period 2006 2013, while around 56% of women attended the recommended minimum of 4 visits or more[12]. 1.2 Main Objectives The main objective of the study was to evaluate data in the South Sudanese health information system in relation to monitoring of the fifth millennium development goals. Specific objectives were: 1. Determine the l reporting rates for the commonly used data sets in the national health information system 2. Determine the coverage of the fifth millennium development goal indicators Antenatal care, Delivery under skilled workers, facility maternal deaths 2 Materials and Methods Data from the District Health Information software (DHIS) for the entire country of South Sudan were analysed for two years; 2012 and 2013. These were the only years with reliable data since the system was adopted in 2011. Analysis was based on the following indicators: women accessing antenatal clinic, those delivered by skilled health personnel and facility maternal deaths. Prior to analysis data cleaning was performed to reduce errors. 2.1 Data Cleaning In order to correct mistakes in the DHIS database, all the monitoring and evaluation officers from the County Health Departments (CHDs) were invited for a data cleaning workshop at a central venue. These are the officers who normally enter data for their respective counties into the DHIS. The objective of the workshop was to clean the 2012 and 2013 routine data, while at the same time training the participants on data management. The process of data cleaning involved deleting, correcting or leaving the data values unchanged. To correct the data, participants used two techniques; interpolation for missing data; and regression for impossible values. For missing data, the DHIS calculated likely values for isolated absent values provided the health facility had been operative. Participants used regression or simple arithmetic calculations to rectify values out of range. Fictitious values identified as services not provided by the facility, e.g. Caesarean-sections reported by primary health care units or family planning reported by units without the services, were simply deleted. At the end of the data cleaning workshops, clean data were exported into the DHIS data base, ready for analysis. 2.2 Analysis of Coverage on MDG 5 Prior to analysis of specific maternal health indicators, the reporting rates for the datasets during the year 2012 and 2013 were calculated using the number of available reports against those expected to report. This was done using inbuilt data completeness reports in the DHIS. Maternal indicators were analysed by first establishing the number of clients for each care before calculating the specific rates based on

Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan 13 population. The Indicators that were evaluated included: the number of pregnant women attending antenatal clinic, delivery by skilled personnel and facility maternal deaths. Analysis was disaggregated by the states of the country. Absolute numbers were first evaluated before converting them into indicators using the population figures in the system. The results were displayed in graphs and tables. 3 Results The section of the results provides the reporting rates for the period and the specific maternal health indicators. The indicators start with antenatal clinic attendance, health facility delivery, and facility deaths across the country. 3.1 Reporting Rates for Health Facilities The average reporting rate for the data collection tool for maternal morbidity/mortality for the two years was 60%. However, it was noted that even though the number of health facilities remained the same in the two years, the reporting rates for 2013 were higher than those of 2012 by 11%. The bulk of the reporting was from primary health facilities with very little coming from major hospitals. Table 1 below shows the reporting rates. Table 1. Reporting Rates for Facilities in South Sudan, 2012-2013 Year 2012 2013 Total Number of facilities 1,131 1,131 Expected reports per year 13,572 13,572 Total No. of reports received per year 7,673 9,272 Reporting rates 57% 68% 3.2 Results of Antenatal Care and Delivery at Health Facilities In general, most of the data elements reviewed showed a steady increase from the year 2012 to 2013. The highest increase was noted in the number of women attending the first antenatal clinic which increased by 32% from 2012 and 2013. Looking at the overall trend of births in a facility attended by a Skilled Birth Attendant (SBA) in the country, there was a marked improvement from 2012 to 2013. Despite this increase, the results indicate that still more deliveries in health facilities were conducted by unskilled workers (Traditional Birth Attendants or village midwife). Table 2 below shows the number of clients attending maternal care services in South Sudan during 2012 and 2013. Table 2. Number of ANC and Deliveries in 2012 and 2013 Data Element Name 2012 2013 %Increase from Number of Antenatal client 1st visit 169,846 225,041 32% Number of Antenatal client 4th or more visit 94,938 114,937 21% Number of Antenatal client IPT 2nd dose 90,467 111,591 23% Number of Delivery in community 43,599 53,296 22% Number of Delivery in facility by skilled birth attendant 23,339 28,142 21% Number of Delivery in facility by TBA MCHW CHW CM or Village Midwife 26,987 27,577 2% Family planning new user 21,639 23,738 10% In terms of antenatal care, the coverage for women attending the first antenatal care clinic was 38.5% for the two years while the coverage for four antenatal care visits was 20.4%. There were wide ranges of coverage between the states (with Eastern Equatoria having the lowest coverage of antenatal care). Figure 2 below shows the comparison of first antenatal care coverage across the states. 2012

14 Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan Figure 2. Comparison of First Antenatal Care among the States of South Sudan, 2012-2013 Other measures of maternal health care like family planning and caesarean section rates were extremely low. Table 3 below shows indicators of safe maternity in South Sudan for the years, 2012 and 2013. Table 3. Indicators of Safe Maternity in South Sudan 2012-2013 Indicator Name 2012 2013 Average ANC 4th visit rate 55.9 51.1 53.1 Antenatal coverage 1st visit (annualised) 33.6 43.2 38.5 Antenatal coverage 4th visit (annualised) 18.8 22.1 20.4 Caesarean Section rate (annualised) 0.1 0.2 0.1 Delivery rate by skilled health worker in facility 46.4 50.5 48.5 IPT 2nd dose coverage to antenatal client 53.3 49.6 51.2 3.3 Facility Maternal Mortality Rate A total of 335 and 500 deaths were recorded for the entire country during 2012 and 2013 respectively. This translated to the facility maternal mortality ratio of 1,060 deaths per 100,000 live births in 2012 and 630 deaths per 100,000 live births in 2013. In 2012, Lakes state had a very high facility maternal death ratio of 7,850 per 100,000 live births, but this drastically reduced to 670 in 2013 as shown in table 4.

Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan 15 Table 4. Comparison of Facility Maternal Mortality Rates across the States of South Sudan, 2012-2013 Number of deaths Facility maternal mortality ratio per 100000 States 2013 2012 2012 2013 Central Equatoria 26 47 520 290 Eastern Equatoria 29 57 1240 840 Jonglei 50 81 1110 930 Lakes 23 116 7850 670 Northern Bahr 27 20 1000 470 Unity 18 20 490 550 Upper Nile 68 62 1310 880 Warrap 10 17 650 330 Western Bahr 37 11 320 690 Western Equatoria 47 69 860 720 Total 335 500 1060 630 4 Discussion Health data constitute a valuable resource to plan for and monitor improvements in health service delivery capacity [2]. The benefits of a standardized health information system are clear although the downside is that providers in developing countries are often overworked, underpaid and have low motivation to keep records[13]. My study showed that the data in the national database had so many inconsistencies that it required cleaning before it could be used. The mistakes fixed during the data cleaning exercise included: missing data, inappropriate service data in a facility that did not offer the said service and very erroneously high numbers. Cleaning was a big indication that the data quality is a major problem that requires attention. Despite the poor data quality, it was remarkably noted that the average reporting rate for the reproductive health data set was 60%, allowing extrapolation of data during analysis. While there were significant improvements in reporting rates and other indicators from 2012 to 2013, the reporting rates from major hospitals remained low. Maternal and child care are some of the key health items on the agenda for many developing countries, particularly South Sudan where many women die during delivery. The study showed that it was possible to obtain data from the system about women attending antenatal clinic although the numbers were much lower than those recommended by WHO. For instance, only 38.5% women attended the first antenatal care clinic compared to the recommended figure of 81%. Despite the low antenatal clinic attendance, it was exciting to note that at least half of the women who attended the first antenatal clinic attended up to four clinics during their pregnancy. This could be a pointer to good services offered leading to repeat visits or could be attributed to the provision of mosquito nets and preventive treatment for malaria during the clinics. While the results show that less than half of the deliveries in the health facilities were conducted by skilled birth Attendants (SBA), the proportion of births attended by SBA rose from 46.4 per cent in 2012 to 50.5% in 2013. These findings indicate that the value a skilled birth attendant brings to health outcomes is improving the health-care-seeking behavior of the population. It also means that the number of skilled personnel is increasing in the country. Other indicators were extremely low especially those requiring surgical interventions like caesarean sections (an average rate of 0.1%). This could be attributed to lack of requisite medical professionals in the country and low reporting from hospitals. Considering the low numbers of deaths recorded across the country, it was more difficult to draw major conclusions on maternal mortality ratio. The average facility mortality ratio of 1,060 in 2012 and 630 per 100,000 live births in 2013 were too low for a country known to be having the highest maternal mortality ratio in the world. However, the low rates may be explained by the major gaps in data including missing data from hospitals.

16 Manya et al. / Use of routine HIS to monitor the MDGS for 2015 in South Sudan 5 Conclusion The republic of South Sudan, being a new state has made big steps towards the development of a routine health information system using the District Health Information Software version one (DHIS1.4). With adequate investment in personnel and other technological infrastructures, the health information system in South Sudan is capable of providing data for performance monitoring for both national and global indicators. References [1] Bulletin of the World Health Organization August 2005, 83 (8) [2] Wolf, C. R., Bicego, G., Marconi.,K, Bessinger, R., van Praag, E., Noriega-Minichiello, S., Pappas, G., Fronczak, N., Peersman, G., Fiorentino, R.K., Rugg, D., and Novak, J. 2004. Developing and implementing monitoring and evaluation methods in the new era of expanded care and treatment of HIV/AIDS, New Directions for evaluation(fall 2004:103), pp81-100 [3] http://www.hisp.org/html/dhis14.htm. (accessed on 25 th September, 2014) [4] HMIS annual report 2012 [5] World health statistics 2014 : available at: http://apps.who.int/iris/bitstream/10665/112739/1/who_his_hsi_14.1_eng.pdf?ua=1 (accessed on 25 th September 2014) [6] United Nations Development Program (UNDP): available at http://www.ss.undp.org/content/south_sudan/en/home/mdgoverview/overview/mdg5/ accessed on 25 th September 2014 [7] Chilundo, B., and Aanestad, M. 2004. "Negotiating multiple rationalities in the process of integrating the information systems of disease-specific health programmes," Electronic Journal on Information Systems in Developing Countries (20:2), pp. 1-28. [8] Kimaro, H. C., and Sahay, S. 2007. "An Institutional Perspective on the Process of Decentralisation of Health Information Systems: A Case Study from Tanzania," Information Technology for Development (13:4), pp. 363-390. [9] Madon, S., Sahay, S., and Sudan, R. 2007. "E-Government Policy and Health Information Systems Implementation in Andhra Pradesh, India: need for Articulation of Linkages between the Macro and the Micro," The Information Society (23:5), pp. 327-344. [10] SSMJ Volume 5. No. 1. www.southsudanmedicaljournal.com [11] http://www.un.org/millenniumgoals/bkgd.shtml [12] WHO, 2014 Fact sheet N 290-Updated May 2014 [13] Latifov, M. A., and Sahay, S. 2013. Challenges in Moving to Health Information for Action. And Infrastructural Perspective from a Case Study in Tajikistan, Information Technology for Development (19:3).

9 th Health Informatics in Africa Conference (HELINA 2014) Peer-reviewed and selected under the responsibility of the Scientific Programme Committee Conceptual view of a data model for nursing process a Yange Terungwa Simon, b Soriyan Hettie Abimbola and c Olaogun Adenike ab Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife c Department of Nursing Science, Obafemi Awolowo University, Ile-Ife Object-Relational Database (ORDB) techniques are developed rapidly in recent years and widely used for temporal data storage and manipulation. The ORDB has strong basis both in theories and applications, and if well extended, it can also be used to handle temporal data. Although ample research on temporal data modeling has been performed, very little work exists on how to implement a temporal information in an ORDB. This paper aims at building a temporal data model for nursing process via extending object-relational database. The nursing process is a framework used by nurses during care delivery and consists of assessment, nursing diagnosis, planning of care, implementation of the care and evaluation phases. These steps are progressive and successive which forms a continuous cycle generating data that varies with time. Managing the data garnered during the implementation of this process is expedient. Hence, the computerization of the nursing process becomes difficult as the existing systems do not support time varying data. Keywords: Nursing, Nursing Process, Temporal, Object-relational. 1 Background Nursing as a discipline is dynamic and so is the data generated in the course of the activities. Time is very key in nursing since all the activities varies with time and hence, the data produced is temporal e.g. objective (signs) and subjective data (symptoms) collected by nurses. Nursing is a profession with a unique perspective on people, environment and health, and has moved from the medical model which focuses on the treatment and care of pathological illness/disease to a nursing model which emphasizes a holistic care [1]. Nurses are therefore accountable for what they do during client care delivery. This underpins the need for the effective implementation of the nursing process in clients care delivery. Though not all trained nurses use this framework especially in Nigeria [2] like in other developing countries of the world; since their activities are greatly monitored by physicians. The nursing process is a modified scientific method of clinical judgment used by nurses in clients care [3]. The nursing process is adapted from a problem solving technique used by nurses in their daily activities to help clients improve their health and assist physicians in administering care to clients. The nursing process is central to all nursing care; it encompasses all steps taken by the nurse in caring for a client. Its primary aim is to know the health status and the problems of clients which may be actual or potential. It is made up of a series of stages (assessment, nursing diagnosis, planning, implementation and evaluation) that are used to achieve the objective - the health improvement of the client. The use of nursing process can stop at any stage as deemed necessary provided the client need no care or can be repeated as needed [1]. As this process is repeated, new data is collected at every point in time; hence, its temporal nature. Representing the transition states of the data collected during the implementation of client care, over different intervals of time, using the existing data models becomes difficult due to the fact that these models do not support changes in the states of data. Although nurses require both the past, the present and the future data about the client health status when making decisions, these systems faced rejection thereby forcing them to continue with the manual system. Efforts to incorporate the temporal domain into conventional database management system for the purpose of addressing some of these issues have been ongoing for more than a decade, and dozens of *Corresponding author address Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria. Email: lordesty2k7@ymail.com, GSM Number: +2348064067803, +2347056808523

18 Yange Terungwa et al. / Conceptual view of a data model for nursing process temporal models have been proposed and a few or none of them have been implemented [4]. Hence, designing effective, secure and useful nursing information system which handles temporal data is desirable despite its attending challenges for software developers. This paper aims at designing a temporal object-relational data model called nursing care temporal object-relational (NC-TOR) data model. 2 Related Work The five phases of the nursing process are assessment, nursing diagnosis, planning, implementation, and evaluation of the implemented care. These five phases are not discrete entities but overlapping continuing sub processes. This implies that these phases are not isolated but are intertwined and build on one another to achieve client needs. These five steps are the core of nursing actions in which quality nursing care is delivered to clients [5]. It should be noted that nursing diagnosis is different from the medical diagnosis in that the medical diagnoses focuses on the treatment of diseases, while the nursing diagnoses focuses on the client s health care needs [1, 3]. The steps of the nursing process are progressive and successive and this forms a continuous cycle of thought and action as shown in figure 1 [6]. It is client-oriented, goal-oriented, dynamic and cyclic, universally applicable, problem-oriented and cognitive processed. The nursing process is continuous for every client problem and care; each step is built on the previous steps and influences subsequent steps. For example, the nurse cannot develop a plan of care and select nursing interventions from that plan until valid nursing diagnoses have been formulated from an adequate database, which includes the client s perspective and input. Thus, the nurse cannot evaluate outcomes of care unless the desired outcomes have been specified as client goals or objectives in the care plan [7]. This implies that, while the nurse implements a planned intervention, such as pain relief measures, additional data may be collected that further validate and support the nursing diagnosis of pain, while also leading to the development of additional nursing diagnoses, such as a disturbance in the client s usual sleeping patterns. Thus, during any nurse-client interaction, the nurse is continuously collecting data that may then be helpful in planning, implementing, evaluating, or revising/modifying the plan of action (nursing care plan). The nursing care plan is the blueprint for directing nursing activities, as a written guideline for implementing client care. This provides a mechanism for communication among health team members which can help to ensure coordinated, effective care for the client. By writing the care plan, a permanent record is made of the care the client should receive and what he/she actually has received. The nursing care plan, when properly written, will provide direction for the nurse in terms of the type and frequency of observations to be made, what nursing measures to implement and how often, as well as what to teach the client and family. The nursing care plan indicates what should be documented in the nurse s notes or progress notes. It also guides the nurse in evaluating the effectiveness of the care given to the client. Care plans facilitate nurses in delivering high-quality, consistent, and effective care [8].

Yange Terungwa et al. / Conceptual view of a data model for nursing process 19 Figure 1. Nursing Process (Source: [6]) One of the most important applications of computers is storing and managing data. The manner in which data is organized can have a profound effect on how easy it is to access and manage. In every business, keeping daily transactions records is very important. Nursing is not an exception to this; much of the world s computing power is dedicated to maintaining and using databases to manage this effectively. Databases of all kinds pervade almost every business. All kinds of data, from emails and contact information to financial data, medical data and records of sales, are stored in databases [9]. The existing nursing information systems like Computerized Patient Information Systems-CPIS [10], Computer-based nursing Information System-CBIS [11], etc. handles only a single state of data. This makes it difficult for nurses to maintain and provide valuable care plan information throughout the nursing care process using such systems since this information is unstable due to the dynamic nature of the nursing process. Despite the problems, nurses still prefer managing their data manually. The reasons according to [12] are the inability to maintain reliable and accurate data for the planning of clients care, no proper education of nurses on this aspect of technology; many hospitals do not have computers, etc. She further stressed that poor nursing information systems have delayed the progress of nursing services both within Nigeria and the world at large. According to [11,10,13,14], the existing systems are not acceptable or adopted by nurses because they do not include time dimension and also do not capture real nursing. These researchers further affirmed that the computer-based information systems available lack the following features that contribute to quality care delivery: 3. Prompts or reminders within the period of care process (i.e., that is when to carryout assessments, diagnosis, plan for care, implement the care planned and evaluation) 4. Ability to collect real time nursing data 5. Standardized and streamlined diagnoses and interventions 6. Keeping of past and future records 7. Information retrieval from past visits 8. Provide reminders when part of the nursing process is missed. By this, it is quite obvious that data collected during nursing process changes state with time as shown in figure 1; and most computer-based nursing information systems do not have the capability to manage this