ICTer Journal PID294 Vol13 No3


International Journal on Advances in ICT for Emerging Regions 2020 13(3): 

December 2020                                                                             International Journal on Advances in ICT for Emerging Regions 

A Multi-layered Data Preparation Model for Health 

Information in Sudan 
Ahmed Mustafa Abd-Alrhman#1, Love Ekenberg #2 

 
Abstract— Data quality is a major challenge in almost every 

data project in today’s world, especially when the required data 

has a national or global look and feel. However, data 

preparation activities dominate the efforts, cost, and time 

consumption. Nowadays, many data collection approaches are 

continuing to evolve in the era of big data to accommodate 

revolutionary data flows, especially in the health sector, which 

contains many different levels of data types, formats, and 

structures. The lack of qualified and reliable data models is still 

an ongoing challenge. These issues are even magnified in 

developing countries where there is a struggle to make advances 

in health systems with limited resources environments, and to 

adopt the advantages of ICT to minimize the gaps in health 

information systems.  This article introduces a geo-political 

multi-layered model for data collection and preparation, the 

model enables the health data to be collected, prepared and 

aggregated by using data attendance approach and address data 

challenges such as data missing, incompetence and format. The 

currently used data collection method in health sector in Sudan 

was analysed and data challenges were identified, with respect 

to geo-political structure of the country. The result of the model 

provides structured datasets framed by time and geographical 

spaces that can be used to enrich analytical projects and 

decision-making in the health sector. 

 

Keywords—Data preparation, Data quality, Sudan, Health 
Information systems 

 

I. INTRODUCTION 

In today’s world, the use of data is a common necessity in 

almost every domain, including individuals, groups, 

enterprises, and governmental entities; however, different 

usages and needs characterize the data type, format, and 

volume. Data management introduces an entire span of 

sequential processes from the generation of a particular 

dataset until archiving or in some cases deletion; this includes 

generation, collection, recording, processing, transforming, 

and loading of data, known as Data Lifecycle Management 

(DLM) [1]. Many developing countries, including Sudan, 

suffer from data collection problems, particularly in the 

health sector [2-4].  In the case of Sudan, the governance 

body for health, which is the Federal Ministry of Health 

(FMoH), obligates health providers (private, public) to record 

and submit a monthly manual health report which 

summarizes different medical and management statistical 

data in any health facility. The report is structured into 

subsections in a unified fashion for all health service 

providers. Each section contains a list of heading titles and 

blank spaces for required data entries, for the purpose to 

record the existence of the services in the facility, in addition 

to numerical values that represent: services, tests, patients, 

equipment, and personnel. The analysis of the data herein 

pointed out many challenges, for instance, some reports have 

35% incomplete data, other reports had more than 25 manual 

corrections, in addition to 5-7% of missing data and 

inconsistencies between working electronic laboratory 

systems and the corresponding manual aggregated data in the 

monthly reports for one health facility. With this reservation, 

the results generally showed many challenges in the data 

collection and management, which are categorized into three 

problematic themes: data, user, and management errors.  

• The main data errors are (1) missing data: some 
columns are left entirely blank, without a trace for 

default values; (2) incompleteness: some patient 

files were found not fully recorded; (3) inconsistent 

data: for example, this was found when comparing 

existing software results with designated data 

elements in the report and variances were identified; 

and (4) unavailability: in some cases, the data was 

not ready in data collection points at reporting time.  

• The interaction of the user (data reporter) with the 
medical report in terms of collection, recording, and 

submission of data indicates issues such as (1) 

manual data correction or cancellation using 

stationary tools; (2) collection of data being done 

verbally and not documented in some cases; (3) the 

collected data  were not supervised in some cases, as 

the collection department consists of one person 

who is responsible for all reporting activities; and 

(4) harmonization problems between medical and 

management staff in data collection.   

• The management of data suffers from many 
obstacles including: (1) manual collection of data, 

which might be exposed to human mistakes; (2) the 

absence of validation or verification mechanisms; 

(3) poor time constraints; for instance, data may be 

reported as monthly aggregations, and reports can be 

delayed for 10 days from submission dates; (4) poor 

data structure, with no standard coding mechanisms 

and no data links, which overburdens future 

analysis. 

 

The overall results indicate poor data quality, which is 

considered one of biggest challenges facing those who 

working with data preparation and management. Despite the 

notion that the time spent in collecting and preparing data to 

ensure its quality dominates total efforts and represents 

almost 60% of the duration of data analysis projects [5], 

applying ICT in health domain can have a positive impact on 

healthcare provision in developing countries, especially with 

the increases in network coverage and data processing 

Manuscript received on 22 Sep. 2019. Recommended by Dr. Manjusri 

Wickramasinghe on 20 November 2020. 

 
Ahmed Mustafa Abd-Alrhman,is from Sudan University For Science 

and Technology. (ahmedabudi@hotmail.com).  

Love Ekenberg is from the Department of Computer and Systems 
Sciences, Stockholm University (lovek@dsv.su.se) and the International 

Institute for Applied Systems Analysis (IIASA) (ekenberg@iiasa.ac.at) 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     2 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

capacities and the decreases in hardware costs. However, 

challenges remain outstanding to develop data models that 

can satisfy data quality measures and management, to support 

and strengthen health information systems.  

Data usually needs further processing in order to be ready 

for use, that is, to transform the collected data into other 

shapes and formats to meet new requirements such as data 

analysis. This has become even more necessary in the case of 

non-electronic data forms, which put and additional 

preliminary step to transform the data into a soft version as an 

essential requirement to enhance the upcoming data 

management [6], as noted by Sarkies et al. [7] who estimated 

that an improvement of more than 30% could be achieved by 

using an electronic patient recording program rather than 

manually recording patient data. 

In this article, we suggest an innovative approach to build 

structured data collection systems for health information in 

Sudan and elsewhere whit similar conditions. We are 

suggesting a geo-political data hosting approach to 

systematically organize the data according to demographical 

dimensions with political control mechanisms. The reason for 

choosing such a structure is to better support the hierarchy of 

official health authorities, which are responsible for 

maintaining policies, regulations, and management activities. 

We introduce a data model to structure data sources using 

geo-political settings to represent health organizations in 

Sudan and provide a mechanism for exchanging transactional 

and aggregated data, between health facilities and governing 

health bodies. We introduce a different approach in the data 

preparation process by moving the processes of data 

preparation activities downstream to data collection points, 

compared to traditional approaches that process and qualify 

the data after completion of data collection phase. The 

objectives of the proposed approach mainly focus on: 

• Integrating data preparation techniques in early 
stages at the data entry point to increase the 

efficiency and speed of data preparation process;  

• Early detection of errors and isolation of low data 
quality and fix data errors at data entry points; 

• The new approach transfers part of the process of 
Extract, Transform, Load (ETL) to data entry points 

in order to enhance the load balance, process 

throughput, and data production time.  

 

The next section will discuss data management in the health 

sector, and highlight the data quality requirements and its 

impact on health information, after, we will describe the 

current status of ICT implementations in Sudan and identify 

the requirements from health data models in this developing 

country. The third section introduces the Multi-layered Data 

Attendance Collection Model (MDACM) and its 

configuration. Lastly, a discussion is presented to show the 

model’s ability to function in an environment with a poor 

ICT infrastructure. 

II. METHODOLOGY 

The intention was to understand the data collection 

problems inside the health facilities as well as the 

communication mechanisms and challenges with health 

authorities and we have primarily used a qualitative approach. 

An analysis of the monthly manual reports was conducted to 

investigate the report structure, contents, and user interaction 

possibilities. The analysis included five large hospitals in the 

capital city, Khartoum, whereof two were public and three in 

private ownership. The selection of health facilities was 

constrained in terms of the service capacity and frequency of 

patients. Monthly reports were analysed for three months 

(June - August 2018) for each hospital. To that, digital 

database analysis was conducted in the health facilities using 

digitalised systems to verify the manual monthly report data 

and to identify any possible discrepancies. Furthermore, 

interviews were conducted with management employees, 

responsible for generating the monthly reports. Moreover, 

interviews with medical staff were conducted to identify the 

possible gaps and obstacles in the data collection processes. 

We also monitored the actual data collection, verification and 

exchange to validate this information.  

III. LITERATURE REVIEW 

A. Data Management Challenges in Health Sector 

It is obvious that data management in the health sector is 

crucial because of the sensitivity of the domain and its tight 

relationship with society, economics, security, and public 

health. However, the importance of data preparation extends 

the perception of data usage to decreasing operational cost for 

organizations and better resource management. A cost-benefit 

analysis of data quality conducted by Redman [8] estimated 

that data errors in the range of 1–5% can cause revenues 

losses by 10% in industry. This can also be a burden in 

developed countries; for instance, Eckerson [9] estimates that 

the cost to US businesses of poor data quality estimated by 

600 billion USD per year. It is not only the financial cost that 

is negatively impacted by poor data quality, data analytics 

and the IT industry also share such concerns; for instance, a 

survey conducted by a professional data preparation company 

[10] showed that data preparation costs almost 450 billion 

USD per year. Even in such circumstances, the survey 

indicates that data analysts prefer to work with modelling and 

analytic activities rather than spending time preparing data. 

The impact of poor data quality can affect health 

provision, as noted by Yawson and Ellingsen who 

investigated the implementation of an electronic health record 

system in Ghana, where they concluded that recording huge 

amounts of data in health information systems does not 

necessarily indicate an improvement in health provision 

quality because of the bad quality of the data [11]. This 

reveals that data models should be strengthened with quality 

measures in the data collection and acquisition phases to 

produce a valid and qualified dataset. Collected data might 

contain errors and the quality of data can be negatively 

impacted by, for example, incompleteness, inconsistency, and 

duplications, which can be a challenge for data projects in 

various phases. Rahm and Hai Do noticed the problem of 

data quality when uploading data into data warehouses that 

can contain highly probable quality issues occurred at their 

original sources [12]. This is shown in the process of data 

cleansing, which occurs after data collection. The problem 

observed here, in the concentration and delays of the work 

burden until the collection process is finished. In the same 

context, Zhang highlight the importance of the data quality in 

data preparation [13], which can speed up performance of the 

data analysis and mining processes. The authors identify the 

needs to purify and clean the collected data, which might be 

collected from different sources with multiple issues such as 

incompleteness and inconsistency [13]. Moreover, Kwak and 

Kim pointed the problems of contextual missing values and 



3                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

the significance of outliers’ affection in statistics estimation 

[14]. Aguinis et.al discussed and recommended the best data 
preparation techniques and addressed data issues, such as 

outliers management, data correction methods and data 

transformation [15]. 

The density of data in health is always characterized by 

the accumulation of a huge amount of data which is batched 

as datasets. Many researchers recognize probable obstacles in 

managing big datasets and many solutions and approaches 

have been proposed in that context. For instance, Kanchi et al. 

identify the challenges when managing big data including 

health records and recommended optimization methods to 

reduce data problems by concentrating on data management 

practices, techniques, and infrastructure, which can have a 

positive impact on data quality [16]. In the same context, 

Levant et al. suggest that data quality of systems can be 

improved significantly by addressing the data problems and 

the consequent remedies, in particular, data correctness, data 

completeness, precision, timeliness, and usability [17]. From 

that perspective, they suggest the enhancement of systems’ 

data quality by building semantically rich data models, 

increasing the database rules and constraints, and using a 

predefined process for data usability. The size of data, 

complexity and data sources can be viewed as a significant 

challenge as was pointed by Kristian [18]. 

 

B. Data Quality Management 

Different approaches have been introduced to measure 

data quality. For instance, Heinrich et al.  introduced a 

metric-based approach to quantify the data quality by adding 

data correctness and a timeline to meet the system 

requirements [19]. Rather than using pre-defined 

requirements as recommended by Heinrich [19], Cappiello 

et.al introduced another approach which uses scoring 

processes to compare the result outputs to match the 

evaluation of pre-specified objects [20]. In addition, an 

assessment of data quality was introduced in 2018 using the 

Data Quality Framework (DQF) by creating quality 

properties and provenance of data with respect to user’s 

experience of the quality, to make it possible to assess the 

data quality and track possible data errors [21]. Svetlozar 

introduced some new approaches to combine data preparation 

with data visualization and clustering techniques to support 

decision-making processes [22]. Furthermore, it has been 

highlighted that feedbacks on collected data can prevent, e.g., 

incomplete data and consequently can be used for mapping 

and completion processing [23]. 

Management of health data at national levels is gaining 

more global attention, especially in developing countries, by 

bringing forward compliance with international goals [24]. 

Many countries including low- and middle-income countries 

have adopted national approaches for health data collection. 

For instance, in 2010, India started identifying the population 

eligible for state healthcare for by using Aadhaar cards with 

biometric verification for citizens. Similarly, Côte d’Ivoire 

introduced a data-sharing mobile application to monitor 

epidemics nationwide in 2013 [25]. The international 

community encourage countries for utilization of data models 

to improve the countries health information systems, and thus 

health status, which can positively enable decision makers to 

achieve better health provision and proper resource 

management, especially in severe conditions like catastrophic 

events and disease outbreaks. 

 There is global consensus on migrating from paper-based 

information systems to electronic format. This is especially 

emphasized by WHO, which describes health information 

systems as “the health information system provides the 

underpinnings for decision-making and has four key 

functions: data generation, compilation, analysis and 

synthesis, and communication and use” [26]. 

ICT adoption models for health information systems have 

been presented as systematic guidance to implement 

information systems based on electronic medical records. For 

instance, the Capability Maturity Model (CMM) is used to 

measure the current status of ICT implementation in health 

institutions by focusing on initiating and standardizing the 

processes with continuous improvements [27], however, the 

adoption of the CMM in Australia can be viewed as an 

enhanced version which scaled into five steps to assess the 

increasing capability of the e-health system in Australia. 

Enterprise Architecture (EA), introduced earlier by Spewak 

and Hill, follows a top-down approach for instantiating and 

improving process cycles, starting by defining enterprises 

requirements frameworks and governances [27]. In addition, 

the EMR adoption model focuses on the adoption of 

paperless EMR in health facilities by using seven progressive 

stages including a clinical decision support system, which 

was adopted in US and Canadian hospitals with different 

degrees of implementation [27]. An observation on such 

adoption models is that, they require a reasonable degree of 

stability of the ICT infrastructure, in particular, processing 

and storage capacities, network coverage, and personnel 

skills, which are the major obstacles faced by developing 

countries compared to developed countries. In the same 

context, a study by the Gates Foundation in 2009 involving 

19 developing countries showed a poor ICT status, which 

requires these countries to adopt an alternative approach that 

considers the difficulties and obstacles facing ICT 

infrastructure. 

Cost of implemention and infrastructure requirements had 

been identified as a real challenge in developing counties to 

adopt ICT in health information systems [27]. On one hand, 

we find the existence of ICT infrastructure in these countries; 

however, it is still poor and not widespread, especially 

network coverage; on the other hand, almost all developing 

countries have reasonable paper-based health information 

systems, which vary in terms of strength and effectiveness. 

Another obstacle is that without the support of reliable health 

information system, it is difficult to perform health 

management and planning nationwide [27]. 

Many implementations of health information systems 

focus on electronic patient records, although a significant 

improvement in this methodology can be achieved [28], 

however, health management, planning, and policy making; 

depend on disease aggregation datasets that focus on better 

support for medical researches and patient treatment, instead 

of individual patient records.      

One of the biggest challenges in building electronic health 

information systems in developing countries is the cost of 

implementation [8], which requires moving paper-based data 

collection systems into electronic format to enable 

aggregation and analysis of data for the purpose of decision 

making, health planning, and resource management. To 

tackle cost and technological obstacles in developing 

countries, Bram et al. suggest that health data should be 

collected by hiring community health workers who interact 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     4 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

directly with the population and deliver health care and 

consultations [28]. The approach is constructed based on the 

relationship of trust between volunteers and their respective 

communities; however, the approach is heavily dependent on 

a non-stable concept, the human factor, which can be 

unreliable and subjective; moreover, the coverage and data 

management could become issues in terms of incompleteness, 

inconsistency, accuracy, and traceability. 

Data management in developing countries, including 

Sudan, is therefore facing a common challenge including 

poor infrastructure, cost of implementation, lack of data 

models, and consequently poor health information systems 

and data quality for use it in health planning, management, 

and decision making.  

 

C. Current Health Information System in Sudan 

Sudan is federated as 18 states, with each state being 

divided into a number of localities. The population is around 

40 million, distributed over 17,765,048 Km2. Four mobile 

operators are licensed to operate in Sudan and they cover all 

the big cities and almost 83% of inhabited areas with 28 

million subscribers and provide internet connectivity 

including 3G and 4G speeds. 

Healthcare delivery in Sudan is divided between the 

public (governmental) and private sectors. The health 

management structure is divided into four organization levels. 

At the top is the Federal Ministry of Health (FMoH); the 

second level is the health ministries in the 18 states, which 

control – at the third level – the medical units in localities 

(189 localities), and health facilities are regulated at the 

fourth level by localities (almost 6100). There is no central 

record of registration of health facilities; however, states 

ministries are responsible for registering and licensing each 

health facility within their boundaries, in addition, there are 

referential public hospitals in the capitals of states, with extra 

presence in the country’s capital, Khartoum.  

The health information system in Sudan is an old system 

dated back to the twentieth century. The data has been 

collected from health facilities manually until the current date 

(June 2019). This is done by a unified monthly report for all 

health facilities including the private sector. The monthly 

form is organized as aggregated data that summarize statistics 

about clinical information on diseases and patient frequencies 

in a given facility. Each locality is responsible for 

aggregating and reporting, monthly, the collected medical 

forms under its authority to the state health ministries. 

Likewise, the state ministries summarize localities’ 

aggregated data, and send it to federal ministry of health as 

state report in monthly or quarterly frequencies. 

Data collection in the Sudanese health sector faces many 

challenges which negatively affect its quality. This includes 

many incidents of data incompleteness, inconsistency, 

inaccuracy, and delays in reporting time, although a   

prescheduled reporting times was found. In addition, the 

health system struggles from fragmentation and lack of a data 

integration mechanism between different administrations 

even in the FMoH. Data management is still facing major 

challenges, especially the process of data repairs and follow-

up.    

The use of ICT in the health system in Sudan is still an 

ongoing struggle and it has a poor status. For instance, FMoH 

indeed has an IT department equipped with servers, storage, 

and a local network with fibre optic connectivity used to 

connect the government’s central data centre as part of an 

ongoing e-gov project. However, moving down the health 

organizational hierarchy, states and localities are poorly 

equipped with computers and networks, especially the 

medical units that are far from the central capital, with 

additional challenges in network coverage and power supply. 

Many attempts to implement electronic health information 

systems have failed. However, the FMoH started to use 

District Health Information Software (DHIS) in 2016 for the 

purpose of aggregating health data from the level of localities. 

Implementation of the electronic system did not cover all 

areas, including the capital state, Khartoum, which did not 

adopt DHIS system because of an ongoing separate 

Enterprise Resource Planning (ERP) system for Khartoum 

state that includes a dedicated health information system for 

the state. In addition, other localities face technical and 

managerial obstacles, for example network coverage and 

power supply; however, the health units in the other localities 

vary in the level of system implementation too, and they are 

still reporting their monthly data twice, manually and using 

DHIS.    IT department in FMoh prepares an annual report 

that reflects country’s health status uses special electronic 

system. The report is sent to health officials, Ministry of 

finance, donors and international organizations. The data 

contained in the annual report is collected from DHIS and 

completed by the data collected from other health units that 

does not uses this system.   

Sudan started to deploy a national information system to 

register new born in hospital, however, the system is very 

poor in terms of implementation and coverage.   The system 

was implemented in private and public hospitals, but despite 

the presence of the system, users are still issued with birth 

certificates manually and some technical issues are 

continuing to emerge including network disconnections.  

Health administration performs regular programmes of 

staff training, including training on the current ongoing DHIS 

system; however, the instability of staff is still a challenge. 

On the other hand, management and medical students take 

courses in using computers and basic operation software in 

universities as part of their studies. Although health workers 

have the basic knowledge to operate computers and basic 

software, capacity building is another challenge that needs 

more attention. 

Implementation of health information systems and 

electronic patient records in health facilities is poor and 

limited. For instance, one of the three biggest public hospitals, 

Omdurman public teaching hospital in the capital, only uses 

the electronic health information system in daily work, while 

the other two hospitals have faced a failure to implement e-

health solutions. 

Private sector succeeded to use electronic systems for 

financial, administration and statistics purposes, especially in 

capital and big cities, however, there is limited attention to 

use applications for medical purposes.  No national patient 

record system has been identified. 

The overall status of ICT in the health sector in Sudan, as 

a developing country, is reasonable for it to start the adoption 

of a data collection model to strengthen health information 

and achieve continuous improvement; however, there is 

much that can be done to improve the use and adoption of 

ICT in the health sector in Sudan, especially in capacity 

building and IT infrastructure. To improve the quality of data 

and minimize data errors in the health sector in Sudan, the 



5                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

data must be prepared and managed in order to be ready for 

analytical activities and decision making. 

IV. MULTI-LAYERED DATA ATTENDANCE 
COLLECTION MODEL (MDACM) 

A. Design Motivation 

The data organization is intended to represent the geo-

political organization of the country. The background here is 

that Sudan geographically is divided into 18 states, where 

each state has a local government, consisting of a number of 

local ministries. Each state is further subdivided into a 

number of departments, each characterized by demographic 

properties, where services, people, and offices are located and 

systematised in pre-specified geographical areas governed by 

the locality authorities. Therefore MDACM organizes and 

visualizes data in multiple layers, where each layer represents 

a governance level (see Figure 1). The proposed model is 

mainly concerned with medical data, and for that reason the 

model will focus mainly on providing a hierarchal data model 

for health organizations in Sudan.    

The proposed model is intended to minimize health 

system obstacles by using a flexible data structure design that 

benefits from the current ICT infrastructure, that is, existing 

networks and processing and storage capacities. It is also 

suggesting an enabling mechanism to allow electronic data 

entry wherever possible while at the same time accepting data 

transformation from paper-based records and appropriately 

integrating it into the data model. 

Building a national health information system should 

reflect the country’s current health status. Health information 

systems in countries require data to be structured into two 

dimensions: time and geographical space. While a timestamp 

identifies the occurrence of the data, the geographical area 

articulates the incident locations; therefore, a time-space data 

representation can be provided for decision makers, health 

planners, and health administrators in that context. 

In addition, the design of the data model is motivated by 

many requirements and challenges, such as support decision 

making, interpretation of data, and identification of patterns. 

Its primary focus is to collect and organize medical 

information to reflect the health status in the country; 

however, this implicitly requires modelling data to match the 

geo-political structure of the country, in particular, the four 

governing layers that are used in Sudan and represent 

authorities’ hierarchy. By considering this outer design 

approach, it will provide flexibility to organize and manage 

health information and data flows. 

Another aspect considered by the model is the concept of 

combining different objectives and criteria, in particular: (1) 

health information criteria which focus on diseases and 

patient medical records; (2) data management criteria, which 

focus on: data preparation, data quality and decision making; 

and (3) health management, which concerned about health 

provision, control health status, and better resource 

management. On the other hand, health information is 

distributed in many sources with commonalities and 

disparities; thus the model should organize the data to reflect 

the status of different health medical sections, data types, 

formats, and health programmes.  

Stakeholders in health domain- include governmental 

bodies, health workers, health providers, international 

organizations, and donors- use health information from 

different perspectives, and for that reason, it is a necessity 

from the proposed model to fulfil these different 

requirements.  

New advances in ICT provide additional opportunities for 

utilization of technology to enhance data collection, 

processing, and exchange; however, developing countries 

like Sudan is still suffer from a lack of capabilities such as 

integrated networks. In this model we argue that it is still 

possible to use existing ICT infrastructure to enhance health 

information system in Sudan, by using a hybrid data transfer 

approach including online and offline data communication 

mechanisms. 

 

B. Governance Organizational Structure – Country (Layer 
1: L1) 

The Federal Ministry of Health (FMoh) represent the first 

layer (Layer 1: L1), and governing body which will contains 

the further sub layers (see Fig. 1). The required data format, 

and structure will be created in this layer, including the 

disease coding system and data template structure. In addition, 

this layer represents the final destination of data after the 

collection and aggregation processes were completed in the 

descendent states’ layers, to reflect the health status as a 

national representation format.  In addition, the data in this 

layer could be used for national interventions, analysis, and 

decision making. 

 

C. States (Layer 2: L2) 

The second layer (L2) represents the geo-political division 

of the country in term of states. Each state in Sudan has a 

state government ruled by a governor and state ministries. 

Not all federal ministries have corresponding state ministries; 

however, all states have ministries of health which are related 

to the FMoH technically and report medical data to the 

federal ministry. The data template structure is inherited from 

the parent country layer (L1) and forms the data definition 

that is required from each state; furthermore, this definition 

will be spread downstream to the third layer (L3) for each 

locality under the corresponding state. The collected data in 

the state layer reflects the health status in a given state and 

represents the resultant data aggregation from governing 

subunits (Localities L3); furthermore, interventions, analysis, 

and decision making are limited to the state authority only.   

D. Localities (Layer 3: L3) 

The third layer (L3), the localities, is the organization of 

governance activities in each state, which is responsible from 

applying policies and regulations in a specified geographical 

area and interacting directly with the population and business 

bodies including health facilities. Although localities are 

obligated to supervise public health and interventions, they 

are however, loosely tied to maintaining technical health 

information. the states’ ministries are more related to health 

providers and facilities, but even in that case, ministries of 

health in states organize the health data based on localities, 

and thus this layer can be considered as an organizational 

data layout at that level. The data template is inherited from 

the state layer (L2) to form the locality data structure 

definition. Data in this layer is collected directly from data 

sources, which are the health facilities (L4), and the 

aggregated data represents the health status in the specified 

locality.    

At this point, the proposed data model defined how the 

data should be hosted, structured, collected, and aggregated, 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     6 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

and the data levelling mechanism was introduced; 

furthermore, the design determines the data usage, authority, 

and representation. Using a layered hierarchy will provide 

more flexibility if the data in each layer needed to be 

compiled with other datasets; for instance, we can compile 

the model data with a civil registration system, medical 

insurance data, or budget data, because the national data is 

structured in the same design, and in that context the model 

output will enhance future data compilation and integration 

needs and reduce the time consumption of further data 

preparation activities. 

 

 
Fig.1: An overview of the Multi-layered Data Attendance Collection Model 

(MDACM) 

Legend: 

L: Layer 

DT: Data Template 

NDC: National Disease Code 

DAL: Data Aggregator in Layer x 

HF Register: Health Facility Register 

Mini-EMR: Minimized Electronic Medical Record 

E. Medical Data Management 

Data in the medical sector is shared and used by different 

stakeholders for different purposes and interests, which 

include: patient medical records, disease information and 

management data. However, data links, relations, and 

semantics are always challenging, especially technical health 

information concerned with diseases. For example, in a 

health facility, a patient who enters the health facility for 

clinical consultation may be redirected to a medical 

laboratory for investigation of a specific disease such as 

malaria. A positive laboratory result of the requested disease 

test will support the physician’s order for medicine placed 

with the pharmacy, which will finally be issued by the 

pharmacist. In this example, three medical sections (clinic, 

laboratory, pharmacy) interact with   the mentioned disease 

from different perspectives, in particular, the data format and 

type of that disease; however, the common denominator is 

malaria, which is processed differently in each section and 

needed to be mapped and linked in all medical sections, and 

thus, building a basis for disease codes becomes crucial for 

disease analysis and control. 

The relations between medical data records can be 

identified using diseases’ standard codes, by unification of 

the language between different medical stakeholders, which 

facilitates data links and semantics even in different 

environments and usages; moreover, further data analysis can 

be enhanced if the data has been properly standardized, 

referenced, and normalized. This should be considered in 

early data design stages and thus diseases need to be 

modelled and standardized. 

F. National Disease Code 

Top health management is responsible from producing 

and maintaining National Disease Code (NDC) records for 

each identified disease in the country. An NDC is a unique 

numerical or string value that defines a specific disease (see 

Table 1), and this code will be used to link the data for all 

data processing activities concerning this disease. In addition, 

Health facilities will integrate and report their data regarding 

this disease using the NDC as a key, and furthermore all data 

templates use the NDC in the template’s definition structure 

to refer to the required disease. 

An example of NDC records could look like the following 

table: 
TABLE 1 

 DEFINED NATIONAL DISEASE CODES (NDCS) 

No. NDC Disease Name 

1 10001 Malaria 

2 20001 Cholera 

3 30001 Flu 

 

NDCs can be further nested to accommodate many types for 

a specific disease; however, we are going here to use a simple 

single disease code in this model. 

G. Medical Sections Organization 

The health system in a country is constructed and 

managed by different specialized bodies inside the ministries 

of health; for instance, in Sudan clinical data are maintained 

by sub-administration (disease control) in the health ministry, 

drugs are managed by the National Fund for Medicine, and 

medical laboratories are supervised by laboratory 

administration in the ministries of health. On examining 

medical data for diseases particularly, we can identify the 

relations between these medical entities concerning a given 

disease, for instance, diagnosis, laboratory testing, and curing 

of the same disease, and we notice that data from different 

entities can be collected and mapped to that disease. This 

relation can gain significant importance when the data is to 

be collected at national levels and the integration between 

different sections can enhance the data analytical process. To 

create such a relation, we integrate NDCs with designated 

medical entities (sections) by listing the sections that are 

involved in each disease (see Table 2); in addition, medical 

sections are used to link health facilities’ internal medical 

sections to corresponding layered health bodies; for example, 

pharmacies are linked and report to the drugs and medicines 

administration. 

H. Data Templates 

When data is collected from different data sources, it is 

normally represented in different data formats and structures, 



7                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

which puts an extra burden on data preparation activities in 

terms of transforming different datasets from different data 

sources into a unified data-ready structure to enable data 

analysis operations. The time and effort consumed in this 

process always dominate almost every analytical project; 

however, this problem can be optimized by instantiating pre-

defined and constrained data templates and install these 

templates in data sources before data collection starts. 

 
TABLE 2 

 INTEGRATION OF NDC IN MEDICAL SECTIONS 

No. NDC Medical Section 
1 10001 Clinic 

2 10001 Medical Laboratory 

3 20001 Clinic 

 

In this model, the data template implementation 

mechanism is introduced for the data collection process 

inherited by data sources. It is designed to constrain the 

required data format, type, and values in lower stages (data 

entry points), unlike traditional approaches which collect data 

and then optimize it later. This mechanism transfers and 

distributes the burden of data preparation activities to the start 

point; moreover, data cleansing, transformation, integrity, 

and constraints are implemented and corrected at the data 

source at the time of collection, which enhances the 

collection process in terms of time, effort, and cost. 

 

 
Fig. 2: National Disease Code 

At this point, an abstract of the data (template) is 

created and a definition of the data is structured in terms of 

format (how), reporting dates (when), data source (who), and 

the required data (what), in addition to injecting the pre-

defined reporting layer hierarchy to insure proper data 

organization (where). An instance of a data template 

represents the reporting of data (medical data) from a specific 

health facility (data source) at a specific date (attendance of 

data). To enhance the management process, the data template 

is organized into two sections, first, the management section, 

which is concerned with the organization and administration 

of the data-reporting templates, and secondly the medical 

section, which focuses on the medical data results and values.  

 

1) Data Template Management Section: 

The management section of a data template starts by 

providing identification of a data source (health facility). As 

each health facility is registered in the system, the 

identification will directly lead to mapping the data source 

location to the corresponding parent layers (locality –> state 

–> country); moreover, it implements an authentication 

mechanism (registered and authenticated facility). 

Health information analytics and statistics use time and 

space dimensions in reporting data. The space (geographical 

area) was already inherited previously by using health facility 

identification in this model, and the time of data reporting is 

managed by the reporting-date property in the data template. 

The reporting date not only acts as a timestamp for the data 

but can also be used to identify missing (absent) data, in 

addition to the attendance status property which provides the 

status of the data template. The following values of 

attendance status are used: 

- Pending: The data template reporting date has not 
been reached. 

- Present: The data template date has been reached 
and the data has been uploaded. 

- Missed: The data template date has been reached 
and the data has not been uploaded. 

 

Constraints are applied to the data entry time to check for 

possible data errors, for example, incomplete data, format, 

translation, and conversion, and correction takes place. Each 

row in the template is referenced by a data source identifier in 

addition to the date field, which enables the collection 

process to update the data row rather than creating a new data 

record. 

The proposed model introduces a mechanism to collect 

and upload data in term of the date (timestamp) of the data. 

This mechanism can be used to enhance the collection 

process from monthly reporting (monthly aggregate data) to 

daily reporting and even real-time reporting by continuously 

updating the template’s instance-specific date. The collected 

data is aggregated in the data sources daily, which can 

optimize the analysis and decision-making process in 

emergencies and catastrophic events which need instant 

feedback, for example disease outbreaks. 

2) Data Template Medical Section 

Medical data is concerned with disease-related 

information and is organized in this section, first, by 

identifying the reporting unit (medical section) within the 

health facility which is the data owner, for example the clinic, 

laboratory, or pharmacy. Although the medical section will 

provide additional data aggregation criteria in parent layers, it 

also enables a data traceability feature by determining which 

medical unit is responsible and has reported what data; 

moreover, it enhances the data correction process if needed. 

The NDC is used to constrain the collected data about a 

specific disease. 

Although the main objective of the model is to properly 

collect accurate medical information about specific diseases, 

medical data about patients is usually represented in the form 

of age groups or ranges, for instance, infants, adults. In 

addition, the gender also characterizes and classifies the 

medical representation, especially if the data is required in 

medical analysis. For that reason, the model structures the 

values of medical results in two extra levels: age and gender. 

A matrix organization is formed by adding age groups as 

rows and gender types as columns. A summation of rows will 

provide the total number of patients for the data template. 

To enable more insight into the data results, optional 

reference values are attached to the template data; for 

instance, laboratory results are often supported by a normal 

range, which can be standard default values and can be 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     8 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

updated by local reference values for the health facility to 

justify its result values.  

Different data sources may use different results formats to 

report their data. For this purpose, a translation mechanism 

should be implemented in the data template to map the local 

data format into unified format and data types; for example, 

the result can be represented as [Found|Not Found], [Yes|No], 

[True|false], or [0|1], which all represent Boolean values, and 

the string results can be coded and mapped to numeric values 

to make quantitative analysis easier. 

The proposed model provides, at this point, the necessary 

infrastructure for the data collection process by determining 

how the data will be collected and aggregated; in addition, it 

provides a constrained data template model for the health 

facilities.  

 

I. Positioning and Registration of Health Facilities (Layer 

4: L4) 

Health facilities are registered and licensed to be able to 

provide healthcare services in Sudan, while the FMoH 

supervise this process, it is actually practiced by   sub-state 

ministries; as an advantage, the model will use current 

registration process to identify health facilities. Registration 

of health facilities is integrated in the model as the last layer 

(Layer 4: L4) and positioned under the locality layer (L3), 

where the health facility is physically located. The data in this 

layer is aggregated locally on a daily basis for each disease 

and represents the local statistics for a specific health facility. 

To properly organize and link any health facility 

organizational structure with the data model, medical sections 

in the health facility should be identified and integrated with 

the corresponding master medical sections in the master layer 

(L1). This step provides a mechanism to manage subsets of 

data, for instance the laboratory data for a specific disease. In 

addition, the medical section in the health facility will inherit 

a full set of concerning NDCs and the health facility will be 

able to exchange data with the model.  

J. Mini-Electronic Medical Record (Mini-EMR) 

In this section, we introduce a minimized version of the 

Electronic Medical Record (mini-EMR) to address the 

problems of data collection in health facilities which occur 

between medical and management staff, who are mutually 

responsible for recording and reporting health information 

and statistics in the health facilities. While management 

personnel experience problems due to incomplete data, 

missing data, reporting delays, and unavailability in some 

cases, the medical staff on the other hand, prioritize service 

provision instead of data management, which as a 

consequence, affect their data recording commitments, in 

addition to other challenges such as: lack of medical staff, 

training skills, and a preference for using the data recording 

time to provide health care to waiting patients. Apparently 

achieving a balance between the provision of healthcare and 

strengthening of the health information system is a difficult 

trade-off and an intermediate solution should be introduced 

with continuous optimization. Medical staff are obligated to 

record clinical information while management staff are 

responsible for aggregate medical and management 

information, and from that perspective, a simplified and 

structured medical patient record with a data flow model can 

enhance the data collection process by reducing the number 

of fields in the EMR to create a smaller version. The mini-

EMR is structured into two sections: the first contains the 

patient’s personal information and management data and the 

second contains the medical data. In addition to a simple 

process flow between the two sections, the mini-EMR 

addresses and adopts the model organization and criteria by 

incorporating data links and interfaces for the data 

aggregation process such as dates, the medical sections, and 

the NDCs. An example of a mini-EMR could be introduced 

as follows: 

Mini-EMR 

Data Structure  

- Management Section 
I. Patient name 

II. Gender 
III. Age 
IV. ID (optional) 
V. Date 

VI. Health facility ID  
- Medical Section 

I. Medical section name (clinic, laboratory, 
pharmacy, etc.) 

II. NDC 
III. Description (clinical data, test name, 

medicine name, etc.) 

IV. Result (diagnosis, laboratory result, drug 
dose, etc.) 

Data Flow 

- Management 
I. Create mini-EMR 

II. Record management section data 
III. Send to medical section 

- Medical 
I. Receive new mini-EMR 

II. Record medical section data 
III. Send for management and aggregation 

- The minimized EMR can be used as a starting point 
for implementing a full and comprehensive 

electronic medical record in the future or according 

to the development of the health facilities toward 

full ICT adoption in the health information system; 

however, the proposed structure is used to enable 

data aggregation from health facilities (DAL4) while 

at the same time reducing the data collection 

problems between medical and management staff 

and introducing the data sharing mechanism inside 

the health facility in compliance with the general 

data model requirements. 

- Data aggregation in the health facility layer (DAL4) 
uses the medical section data to aggregate the data 

into the data template row (DTR) by transforming 

and aggregating the patient records into a disease 

aggregation record using mini-EMR parameters 

such as date, NDC, gender, and results. 

K. Data Configuration 

In order to start the data collection process, the system 

should be initialized and configured. The configuration 

process starts by determining the target collection period, for 

instance, annual collection, which constrains the model by 

start and end dates. The next step is selecting the required 

data template from the templates list, which identifies which 

disease is targeted and in which medical section; for example, 



9                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

the objective may be to target malaria disease test results in 

laboratories annually. The model generates data template 

rows for all dates for the collection period, and default values 

are initialized for quantitative attributes, in addition to 

performance indicators such as the attendance status.   

The data template, at this point, was created in the master 

layer (L1) and instantiated subsequently in Layers 2 (States), 

Layer 3 (Localities), and Layer 4 (Health Facilities) to 

produce instances of datasets.  

 

L. Data Transaction 

Datasets are collected from health facilities according to 

referenced data template. Each registered health facility uses 

a combination of a health facility identifier and reporting date 

to authenticate its reporting data. The complexity of the 

model structure is simplified in the health facilities by 

mapping the registration ID to the entire structure (see Figure 

3), and the main focus of the health facility is to accurately 

collect and report the required medical data. The data 

aggregator in the fourth level (DAL4) is responsible for 

aggregating (summation, counts) the health facility’s data by 

using date attributes. The process is organized as follows:    

• Collect and aggregate data in the health facility 
(DAL4) daily. 

• Use the date key to match data to the DTR and the 
health registration ID to identify the health facility. 

• Identify missing and incomplete data. 

• Correct data errors and assign default values if needed. 

• Translate the result type to match the template codes. 

• Provide the results to the user and ask for 
confirmation. 

• Update the DTR result data and attendance status. 
 

M. Data Aggregation in Organizational Layers 

Ready datasets from health facilities are used to generate 

upper layers’ datasets, in particular, three additional data 

aggregators (DAs) are generated as follows: 

• Data is aggregated from all health facilities in a 
locality and the attendance performance is assigned in 

the localities layer (DAL3). 

• Data is aggregated from all localities in a state and 
the attendance performance is assigned in the states 

layer (DAL2). 

• Data is aggregated from all states and the attendance 
performance is assigned in the country layer (DAL1). 

 

At this point, the model provides a structured dataset for a 

specific disease with the ability to verify, validate, and trace 

the data to the data sources; moreover, the datasets are 

represented in multiple dimensions, which are:  

• Geographical distribution (space dimension): provided 
by the layered structure;  

• Periodical distribution (time dimension): provided by 
articulating the data template by date ranges; 

• Disease-specific dimension: provided by standardizing 
and linking the reported data using the NDC;  

• Gender distribution: provided by structuring the 
dataset values by gender; 

• Age distribution: provided by using the age group 
categorization for datasets. 

 

•  
 

Fig. 3: Data Transaction Legend: DTR: Data Template Row 

V. DISCUSSION 

Developing countries -including Sudan- are making 

significant steps in adapting ICT in governance; however, 

many challenges are still outstanding, which can be found in 

the infrastructure, cost, and capacities. The health sector 

always has the dilemma of assigning the budget between 

health care provision and development to improve data 

collection, quality, and decision making. Our model provides 

an optimization, in that regard, by addressing these 

challenges with respect to data preparation issues as well. 

The model provides a comprehensive solution for health data 

collection in the country with respect to the global consensus 

and guides of health information systems, for example 

managing the disease code system and structuring records for 

health facilities and patient data. Many approaches to national 

data management focus on patient records like the Aadhaar 

card in India and Côte d’Ivoire [25]; however, these models 

which focus on patient records rather than the health service 

providers – the health facilities – may suffer from data 

analysis problems if it is necessary to reflect the country’s 

geo-political status, because of instability of the moving 

human patient versus the fixed health facilities, which can 

have a significant impact on health planning, resource 

management, and decision making. The ability to manage 

diseases, using the NDC for example, can help decision 

makers and health programmes in a country, by providing a 

mechanism to monitor all diseases in one national data 

warehouse with the ability to split datasets or even entire data 

marts at the same time as focusing on a special disease or 

health programme. 

Another contribution of the proposed model, it combines 

different analysis properties early in the model design; for 

instance, the timeline, gender, and group ages. By 

considering such important parameters in the data gathering 

phase, the analysis process can be enhanced with rich 

datasets that could be analysed with practical health 

indicators combined with geographical distribution. In 

addition, the model is prepared to manage historical and 

archival data using dates and attendance data, which can 

improve decision quality and data-pattern discoveries. 

As many researchers point to the need for data quality in 

big data management [1,15], developing data models that can 

adopt data quality measures and practices to strengthen health 

information systems is becoming an outstanding challenge. In 

this regard, the model provides a mechanism to adopt such a 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     10 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

recommendation in data preparation by distribution of the 

workload at different levels, in particular the ETL process, 

which can improve the processing speed, data quality, and 

error correction. 

Application of the model is flexible from the data 

exchange point of view. The data can be transferred using 

different methods including online and offline connections, 

depending on the availability of networks and 

communications capabilities in the country. Configuration of 

data templates with dates can allocate data on arrival even in 

the situation of unavailable connectivity, and thus, overcome 

the necessity for a pre-existence of communication for 

immediate data exchange, which can offer an aspect of cost 

reduction. However, in good economic situations, the model 

can be further improved using fast online networks without 

the need to make significant changes. 

-Model Implementation 

Implementation of the proposed model should 

systematically follow a top-down setup approach. First, the 

health authority in the country is requested to provide the 

required framework, policies, and procedures in order to set 

up the health information system for the country, for example 

Sudan. Setting up the system includes, definition of the geo-

political structure of the country by dividing the country into 

matching states and localities. A tree view hierarchy will start 

to emerge, which provides a visual layout of the upcoming 

data and represents geographical areas with political 

governance. 

Secondly, the health authority should keep a national 

registration records for health facilities in the country, and 

each health facility should be allocated using the geo-political 

organization that was defined previously. The allocation 

should determine the geographical position of the health 

facility inside the locality layer and state layer respectively. 

This step will enable health managers to visualize the health 

capacity and provision status in the structured area. Not only 

is this useful to organize health data; it will also provide the 

benefit of giving insights about health resource allocation, 

gaps, and needs. 

At this point, the framework was implemented with four 

layers: country (FMoH), states, localities, and health facilities. 

At the bottom the data sources (health facilities) were 

identified and defined as data collection points; moving up 

the hierarchy, three data aggregation levels were identified. 

Moreover, data links, flow, interfaces, and traces paths were 

clearly visible. 

The third step is to define a standard disease coding 

system, in particular, the NDC. The health authority in the 

country should create and code a unique list of diseases in the 

country in order to create a foundation for data links for 

health data. The NDC will be the reference key that will be 

instantiated, shared, and inherited between all stakeholders in 

the system. Master medical sections should be created to 

match different medical sections in health facilities, for 

example, laboratory, medicine, and clinics (disease diagnosis). 

Of course, health management has specialized administration 

and subsections that manage each medical domain; however, 

a virtual representation of theses sections will simplify data 

classification in the upcoming data management activities; 

moreover, it will map the actual medical section with the 

health facility directly to the collection model and hide the 

actual complex distribution of the real medical administration.  

In the fourth step, to start using the system, a data 

template should be created at the health authority level (Layer 

1). The data template creation will define what disease is 

targeted by using NDC for the data collection process, and 

this will automatically identify the targeted medical sections 

which is previously configured in the master medical section 

records; in addition, the data template will contain the time 

duration of the targeted data, empty result fields, and data 

attendance status flags. Data is structured in the data template 

to represent different data criteria such as patient’s age 

intervals, gender, and result transformation references. The 

creation of a data template enables health administrators to 

define their expectations of the model output in addition to 

data rules and constraints. The quality management measures 

can be created and applied, and in addition, the data template 

illustrates (Layer 1) the expected output shape of the data. 

In the second layer (the state layer), the data template 

should be instantiated by the number of states. In the context 

of Sudan, an 18 layers (L 2) data templates will be created. 

Each targets one state. In the same context, each Layer 2 data 

template (state template) will be further instantiated by the 

number of localities in the designated state, and thus the 

Layer 3 data templates are created (locality layer). Each 

health facility should obtain a copy of the descendant Layer 3 

data template, which defines the data required from the health 

facility and its format and constraints. The data template for 

the health facility is linked with the health facilities register 

and can be used as an authentication mechanism to identify 

the source of the data. At this point, the data templates in the 

model are created and sent to data sources, to be filled by 

health data. 

Adoption of the system depends on the available ICT 

infrastructure and available resources, in order to work with 

optimal performance. At the health facility, a daily 

aggregation of the data for a specific disease will form the 

output for a specific record in the health facility’s data 

template – the DTR. However, it can be flexible in order to 

accept many forms of collection and aggregation mechanisms 

for health data depending on the currently available ICT 

resources at each health facility. For instance, the following 

scenarios may occur: 

• the health facility has an electronic HIS and the 
system can be directly integrated with the DTR;  

• the health facility adopts the Mini-EMR to collect and 
aggregate data and upload it to the DTR; 

• the health facility uses manual records, performs 
manual aggregation, and enters the output in the DTR; 

• the health facility has no ICT resources and manually 
aggregated data is provided to the locality layer; 

• the locality layer has no ICT resources and manually 
aggregated data is provided to the state layer; 

• the state layer has no ICT resources and manually 
aggregated data is provided to the country layer. 

 

The data is exchanged between multiple layers in bi-

directional mode. On the creation of the data template in the 

first layer (L1), instances of the state layer are created and 

sent down the hierarchy to the states (L2). In the same 

fashion, instances of data in each L2 are created and sent to 

L3. Each locality creates and sends data templates to all 

health facilities in that locality. The exchange of data can 

benefit from the available network connectivity to automate 



11                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

the data transfer and provide online data exchange; however, 

in the case of unavailability of network coverage, the 

instances are created in the corresponding layer and 

maintained in that layer. In addition, in the case of 

disconnection of the network, the system can operate in 

offline mode and should provide a synchronization 

mechanism to upload or download the data when restoring 

the connection. Depending on the available resources and 

technologies, the system implementation can adopt many 

other forms of data exchange, for example, cloud computing 

and shared storage; however, in limited-resources 

environments, additional extracting and loading tools could 

be developed; for instance, a tool can be provided to extract 

and upload the template data stored in spreadsheets or other 

file formats. 

Data quality management should be considered in the 

design of the data templates in the first layer, and quality 

rules and constraints should also be defined and equipped 

with the data template. This will enable the system 

administrators to implement shared quality policies early, to 

minimize the burden of the data preparation load. When 

collecting the data from health facilities, translation rules 

should be applied so that the results match the required 

output format of the model. Next, the data aggregator is 

implemented inside the health facility – the data aggregator 

Layer 4 (DAL4) – and the quality rules are checked. In the 

case of data errors, the system should provide a repair 

mechanism or report the errors to the user for correction. 

The system will continue to aggregate the data in a 

backward (folding) fashion from localities (DAL3: from all 

health facilities in the locality) to states (DAL2: from all 

localities to states) to country level (DAL1: from all states to 

the health authority, FMoH). The aggregated data in all levels 

should maintain quality status measures which report the 

level of availability (attendance), accuracy, and error ratios in 

order to determine the degree of reliability of the collected 

data. After the process has been completed, the model will 

provide hierarchal datasets that match the organizational 

structure (geo-political), which will be ready for analysis and 

usage. 

Datasets that will be produced from the data model can 

have multiple usages and advantages. For instance, they can 

show the status of specific disease in the country, it can be 

used to analyse specific interventions for a disease, and 

moreover, it can enhance the allocation and management of 

resources and of course increase the visibility of the decision-

making process. 

The implementation of MDACM can have many shapes 

depending on the ICT resources available and upgrades, 

moreover, a phase-based implementation can take place 

without the need for significant modification of the data 

model. In addition, the model can also operate in a limited-

resources environment; however, the model will continue use 

and enhance the quality measures of data in the health 

information system and ensure proper data representation of 

the health status in the country by considering the ICT 

resources and availability – even if limited – as an interim 

process toward health information system optimization rather 

than as an impossible obstacle. 

The implementation of electronic health information 

systems at national scale is directly affected by the country’s 

financial resources and capacities, which leads to an 

observable distinction between lower and higher income 

countries. As a consequence, many system adoption models 

[27] have been introduced to systematically advance the 

process of health system automation in developed and 

developing countries. One solution to minimize the time 

delay in this process is to use flexible data models, like the 

one which is introduced in this article. Besides providing a 

structured mechanism toward the adoption of e-health in 

developing countries like Sudan, the model contributes by 

facilitating the transformation process between medical 

patient records and aggregated data: while the former are 

required to manage patients’ medical treatment and are used 

by medical and management staff in health facilities, the 

aggregated data is mainly used for public health management, 

resource management, and decision-making processes. Our 

model provides a solution to enhance health information 

system by: (1) structuring an effective e-health system at 

country scale, (2) it provides a mechanism to transform 

patient medical data into aggregated data for upper 

management, (3) the model facilitates the evolution of an 

adoption mechanism for an e-health system with 

consideration of the implementation cost, infrastructure, and 

capacities of lower income countries, (4) it provides a starting 

point for data quality management to enhance the quality of 

collected data in the health system in the country, and 

lastly,(5) it provides a mechanism to comply with 

international health recommendations like standardization of 

disease codes and profiling health facilities in the country[24].  

-Example 

The example demonstrates the applicability of the proposed 

data model. The simulation uses MCDAM for collecting and 

analysing the data for the large Malaria outbreak in Sudan 

during 01-Jan-2019 to 31-Mar-2019. The model targets all 

states and is based on collected laboratory medical results. 

First a new data template is created and template instances 

are generated for all states. This is done in the first layer (L1) 

(see fig.4). The empty data templates are then sub-

instantiated in the next layer (L2, L3 and L4). The actual data 

collection process starts from the health facilities, using the 

proposed Mini-EMR model (see fig.5), where the data is 

aggregated daily for each health facility (see fig. 6). After 

collecting the data from the health facilities, it is aggregated 

in a similar way from the health facilities in the locality layer 

(fig. 7), thereafter in the state layer (fig. 8) and finally in the 

master layer (fig. 9). 

In this simulation, a data status field is attached 

representing the data attendance. The successful data 

collection for all specified period is indicated by a “Present” 

status. The contextual, partial collection is indicated by 

“Partial Completed” stating also the last date of the data 

collection (Data Date). Finally, absence of data is designated 

an “absent” status. 

VI. CONCLUSIONS 

 In the article, we tackled the manual data collection 

problem in the health sector in Sudan. Many challenges were 

discussed including disease coding models, data collection 

processes, data preparation, and data quality. The proposed 

model provides a mechanism to collect health data in a multi-

layered fashion that represents the geo-political layout of the 

country and thus enables data compilation with similar 

governing structures that were found at national levels. 

Moreover, it provides an efficient and structured data 



A Multi-layered Data Preparation Model for Health Information in Sudan                                                                                                                     12 

International Journal on Advances in ICT for Emerging Regions                                                                                                                              December 2020 

 
 

 

 
 

 

 
 

 

 
 

 

 

 

 

 

 

 

 
 

Fig. 4: Data Template for Malaria Disease for All States in Sudan from 01/01/2013 to 31/012019 

  

  

Fig. 5: Use Mini-EMR to Collect Data for Malaria Disease Khartoum State-Bahry Locality- Bahri Teaching Hospital in Sudan for 01/03/2019 

  

 
Fig. 6: Aggregated Data for Malaria Disease Khartoum State-Bahry Locality- Bahri Teaching Hospital in Sudan from 01/03/2019 to 12/03/2019 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

 
 

 

 

 

 
 

 

 
 

 

 
Fig. 7 Collected Data Template for Malaria Disease Khartoum State-Bahry Locality in Sudan from 01/01/2019 to 31/03/2019 

 



13                                                                                    M. Ahmed#1, L. Ekenbereg#2 

December 2020                                                                                                                              International Journal on Advances in ICT for Emerging Regions  

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 

 

Fig. 8 Collected Data Template for Malaria Disease Khartoum State in Sudan from 01/01/2019 to 31/03/2019 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Fig. 9 Collected Data Template for Malaria Disease for Selected States in Sudan from 01/01/2019 to 31/03/2019 

 

 

collection method with implementation of data quality and 

preparation measures to minimize the processing efforts, cost, 

and time consumption in this regard. The concept of 

measuring the attendance data is used to detect the status of  

the data early on and thus to improve data analytics and 

decision making. 

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