Towards Providing Full Spectrum Antenatal Health Care in Low and

Middle Income Countries

Muhammad Abubakar

, Amina Bibi

, Rashad Hussain

, Zohra Bibi

, Asma Gul

, Zahid Bashir

Salman Noshear Arshad

, Momin Ayub Uppal

and Safee Ullah Chaudhary

Department of Biology, Lahore University of Management Sciences, DHA (Phase 5), Lahore, Pakistan

Gynaecology and Obstetrics Department, Shalamar Institute of Health Sciences, Lahore, Pakistan

Department of Forensic Medicine and Toxicology, Shalamar Institute of Health Sciences, Lahore, Pakistan

Department of Chemistry, Lahore University of Management Sciences, DHA (Phase 5), Lahore, Pakistan

Department of Electrical Engineering, Lahore University of Management Sciences, DHA (Phase 5), Lahore, Pakistan

Keywords: Antenatal Care, Mobile Health, Smartphone Health Monitoring, Decision Support Systems.

Abstract: The provision of Antenatal Care (ANC) for pregnant women plays a vital role in ensuring infant and maternal

health. Limited access to antenatal care in Low and Middle Income Countries (LMIC) results in high Infant

and Maternal Mortality Rate (IMR and MMR, respectively). In this work, we propose a cloud-based clinical

Decision Support System (DSS) integrated with a wearable health-sensor network for patient self-diagnosis

and real time health monitoring. Patient assessment is performed by evaluating the human-input coupled with

sensor-generated symptomatic information using a Bayesian network driven DSS. High risk pregnancies can

be identified and monitored along with dispensing of consultant advice directly to the patient. Patient and

disease incidence data is stored on the cloud for tuning probabilities of the Bayesian network towards

improving accuracy of predicting anomalies within the epidemiological context. The system therefore, aims

to control IMR and MMR by providing ubiquitous access to ANC in LMICs. A scaled-up implementation of

the proposed system can help reduce patient influx at the limited tertiary care centers by referring low-risk

cases to primary or secondary care establishments.

1 INTRODUCTION

Every year over 210 million pregnancy cases are

reported worldwide (Shah and Åhman, 2004). About

2.6 million of these pregnancies conclude in

stillbirths with 98% of these deaths occurring in Low

and Middle-Income Countries (LMIC) (Hogan et al.,

2010, WHO and UNICEF, 2012, UNICEF, 2012,

May 2014). Nearly 800 women lose their lives every

day as a result of pregnancy related complications

with 99% of all maternal deaths occurring in

developing countries. The high fertility rates amongst

LMIC women (Sathar et al., 1988) predisposes them

to an enhanced risk of pregnancy related

complications. The resource-limited health-care

infrastructures in the LMICs further aggravates the

situation, with women from remote poverty-stricken

areas being the most disadvantaged.

Antenatal care (ANC) has been proven to be a key

determinant of infant and maternal health (Inam and

Khan, 2002). Provision of the most basic ANC has

been shown to significantly reduce the astronomical

IMR and MMR in LMIC countries (Inam and Khan,

2002). A simple but timely measurement of high

blood pressure can help pre-empt several

complications including but not limited to

preeclampsia, ectopic or molar pregnancy, placenta

previa, placental abruption, miscarriage, hyperemesis

gravidarum, preterm labour as well as problems with

the foetus itself (Burrow et al., 2004, Norbeck and

Tilden, 1983, Sattar and Greer, 2002, U.S.

Department of Health and Human Services,

September 2010.). In Pakistan, like other LMICs, the

leading causes of maternal mortality include

haemorrhage, followed by eclampsia and sepsis

(Jafarey, 2002). Regularity in ANC can pre-empt

such obstetric and postnatal complications thereby

averting major emotional and financial stress for the

patients and their families.

The major impediments in provision of ANC in

478

Abubakar, M., Bibi, A., Hussain, R., Bibi, Z., Gul, A., Bashir, Z., Arshad, S., Uppal, M. and Chaudhary, S.

Towards Providing Full Spectrum Antenatal Health Care in Low and Middle Income Countries.

DOI: 10.5220/0005823104780483

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 478-483

ISBN: 978-989-758-170-0

LMICs include poverty, illiteracy, inadequate

governmental healthcare support, obstacles in timely

access to available services and cultural barriers.

Moreover, with the absence of health insurance safety

nets in LMICs accessibility to ANC becomes directly

proportional to the economic status of the patient and

her family. Besides the non-availability of essential

health information, ill-equipped primary care centres

and under-trained primary health care workers also

contribute to the high IMRs and MMRs. (Penfold et

al., 2013, Bloom et al., 1999, Ransom et al., 2002,

Titaley et al., 2010). As a result, a large number of

patients in developing countries fail to access, obtain

and implement ANC, essential for the welfare of both

the foetus and the mother.

In this work, we propose a cloud-based ANC DSS

for an assistive medical diagnosis and monitoring of

pregnant women. The salient features of the system

include: (i) patient self-diagnostics; (ii) automated

monitoring; (iii) patient prioritization; (iv) real-time

case tracking and intervention by patient-opted

tertiary care consultants; and (v) on request consultant

advice dispensation. The structural components of

our clinical Decision Support System (DSS) include

a multilingual tablet and smart phone front-end

application that is usable by both patients and

consultants; a wearable health sensor suite interfaced

with front-ends via Bluetooth (Miller and Bisdikian,

2001); a web application for dealing with PC-based

patient requests; a desktop application for hospital

administration and a backend database server for

storing patient and doctor incidents data.

At the heart of the system is a DSS which is

compliant with the ANC guidelines devised by the

Merck® Manual Professional Version (Merck, 2015)

and is designed to emulate the decision making

capabilities of a professional medical advisor. A total

of 16 pregnancy-related abnormalities (Table 1) are

included and elaborated by Merck along with their

risk factors and symptoms. Pre-eclampsia has been

selected and a probabilistic Bayesian network

(Friedman et al., 1997) is constructed involving its

symptoms (Figure 1). Clinical data from Shalamar

Institute of Health Sciences (SIHS, Pakistan) is used

to calculate the probabilities in the network. Patients

may answer a series of systematically-posed

questions, developed in accordance with the Merck®

guidelines for self-diagnosis, in tandem with inputs

from patient-worn sensors. Sensor-based monitoring

can be performed in a continuous or periodic manner

depending on the type of health marker to be

measured. The probability of an abnormality is then

progressively calculated by employing the Bayesian

network and the patient provided symptomatic

information.

The system can thereby keep track of and assess

the health state of the respondent in real time

irrespective of her physical distance from a hospital.

Users can also choose to subscribe to certified

consultants who are registered in the system, based on

the tertiary hospital that they’re working in. These

consultants may then choose to participate and

receive the patient’s data followed by intercession in

the recommendations dispensation process. On the

basis of the diagnostic information, a health support

or emergency response may be orchestrated at the

primary, secondary or tertiary care centres. The

system is currently being deployed at SIHS, where it

is undergoing functional testing.

Taken together, the proposed system ensures a

timely provision of optimal quality ANC to LMIC

populace; thus, providing significant assistance in

reducing the IMR and MMR.

Table 1: List of pregnancy disorders enlisted in the Merck®

ANC guidelines.

1 Oligohydramnios 9 Abruptio Placentae

2 Preeclampsia and

Eclampsia

10 Cervical

Insufficiency

3 Spontaneous

Abortion

11 Intra-Amniotic

Infection

4 Pruritic Urticarial

Papules & Plaques of

Pregnancy

12 Hyperemesis

Gravidarum

5 Pemphigoid

Gestationis

13 Erythroblastosis

Fetalis

6 Septic Abortion 14 Placenta Previa

7 Stillbirth 15 Ectopic Pregnancy

8 Vasa Previa 16 Polyhydramnios

2 RESULTS

2.1 Elicitation of Risk Factors,

Symptoms and Diseases from

Merck® ANC Guidelines

The comprehensive ANC guidelines provided in the

Merck® Manual Professional Version were used to

elicit pregnancy-related abnormalities, the risk factor

to which a patient may be predisposed and the

presented disease symptoms. A total of 16

abnormalities (Table 1) were extracted along with

respective risk factors and symptoms. These included

pre-eclampsia (MacKay et al., 2001) which is

amongst the leading contributors to IMR (Basso et al.,

2006) and MMR (Ghulmiyyah and Sibai, 2012). A

Towards Providing Full Spectrum Antenatal Health Care in Low and Middle Income Countries

479

Table 2: Symptoms and Risk Factors Associated with

Preeclampsia and Eclampsia.

mptoms and si

ns Risk factors

Proteinuria Nulliparity

High blood pressure

and hypertension

Pre-existing

chronic hypertension

Severe headache Vascular disorders

Visual disturbances

(blurred vision, scotomata)

Pre-existing or

gestational diabetes

Coagulopathy (Petechiae) Age>35 or Age<17

Non-dependent oedema

(facial & hand swelling)

Family history of

preeclampsia

Hepatic ischemia

(right upper quadrant

abdominal, epigastric pain)

Preeclampsia or poor

outcome in previous

pregnancies

Enhanced reflex reactivity Multifetal pregnancy

Nausea Obesity

Confusion Thrombotic disorders

Vomiting

Dyspnea

Cyanosis

Stroke

Serum > two times normal

(AST, ALT)

Thrombocytopenia

Reduced urine output

detailed tabulation of 10 risk factors and 17

symptomatic presentations associated with pre-

eclampsia are provided in Table 2.

2.2 DSS for Pre-eclampsia using a

Bayesian Network

All symptoms of pre-eclampsia are orchestrated into

a Bayesian Network (Figure 1). Bayesian

probabilities for each disease outcome are

progressively calculated upon input of symptomatic

information by the user and wearable health sensors.

The probabilities in the Bayesian Network are

populated in line with the diagnosis pathways in the

Merck® Manual and will later be further tuned in

light of regionally localized patient data (both manual

and sensor inputs). The DSS encapsulates the

Bayesian probability calculation engine and

transforms user inputs into probable outcomes,

thereby also prioritizing high risk pregnancies for

evaluation by a remote clinical consultant. The output

to the user includes primary care advice and

recommendations while detailed user profile and

assessment information is uploaded to the cloud for

ready reference and intervention of the consultant.

2.3 System Architecture for

Cloud-based ANC

A three-tier architecture is employed to construct the

foundation of the proposed system (Figure 2). The

first tier comprises of Graphical User Interfaces

(GUI) for the targeted client audience. These GUIs

include a smart phone/tablet app and a web

application for patients, primary health professionals

and, consultants. A desktop application is also

provided for user administration by the hospital

administrators. Middle tier is a subscription-driven

Web API (Microsoft, 2014) which embodies the

functional logic including the DSS and Bayesian

network. The third tier includes a MySQL (MySQL,

2001) relational database server with a normalized

schema of all system entities. User triggered

operations and requests are transported in JSON

(Peng et al., 2011) between the wearable sensors, GUI

applications, the Web API and the database.

Microsoft® MVC (Masound and Halabi, 2006)

has been employed to develop the Web API. Agile

Development (Martin, 2003) methodology is used to

incrementally develop the system’s overall software

manifestation. The system has been tested and

verified using white-box testing at the component

level. Integration testing has been performed to

validate the functional coupling of the overall system.

2.4 Security and Privacy

Confidentiality of patient data is ensured by

controlled access and its on-demand availability from

isolated logical data storages at hospital level. It can

be further enhanced by continuous risk assessment

and updates to the security policies. Furthermore, we

are considering to deploy Health Level-7 (HL-7)

Healthcare Privacy and Security Classification

System (HCS), and Role-Based Access Control

Healthcare Permission Catalogue (RBAC). Towards

implementing data segmentation for access across

distributed systems, we are also considering to use

HL-7 Data Segmentation for Privacy (DS4P) as a

standard.

2.5 ANC System Software Prototype

2.5.1 Smartphone and Tablet Application

for Patients and Consultants

A smartphone application is developed for users

including patients, primary care operatives, doctors

HEALTHINF 2016 - 9th International Conference on Health Informatics

480

Figure 1: Bayesian Network of Symptoms Leading to Pre-eclampsia and Eclampsia.

and consultants (Figure 3B). Heuristic evaluation for

user interfaces has been undertaken and the smart

phone app has been localized to rural locales along

with a text-to-speech feature in the DSS.

Furthermore, elicitation of potential users’

characteristics such as reasoning ability, gender, age,

spatial memory and learning style is underway

towards creating an enabled user experience.

Windows Phone Development Kit (Microsoft, 2013)

is used to develop the application and XAML

(Schmidt, 2010) is used to construct the user

interfaces. The application encompasses features

such as user registration, profile creation, patient self-

diagnosis using a multi-lingual GUI, consultant

subscription based on patient choice, and public

forums for discussions and sharing ANC experiences.

Figure 2: Overall System Architecture.

2.5.2 Web Application for PC-based Patient

Assessment and Diagnostics

Since smartphone penetration is still low in rural and

sub-urban localities of LMICs, the proposed system

also includes a web application with the complete set

of system features (Figure 3A). An extensive exercise

for user interface design and development has been

undertaken on the lines of the smart phone application

interface (mentioned above). Microsoft® ASP.NET

Web Forms (Sheriff, 2001) has been employed for the

software. This application can be leveraged by using

a personal computer with an installation of standard

web browsers.

Figure 3: (A) Web Application and Desktop Administration

Console, (B) Smart Phone App View.

Towards Providing Full Spectrum Antenatal Health Care in Low and Middle Income Countries

481

2.5.3 Desktop Application for System and

Data Administration

A stand-alone desktop application has been

developed to act as the administration console (Figure

3A). Windows Presentation Foundation (WPF)

(Chappell, 2006) has been employed to develop this

component. Using this console, authenticated doctors

and consultants are enlisted with the system by a

hospital designated administrator.

2.6 Deployment of Prototype at

Shalamar Institute of Health

Sciences (SIHS)

The system has being test-deployed at clinical site in

SIHS. Patient and consultant views are being

acquired to fine tune the GUIs as well as the

probabilities for pre-eclampsia’s Bayesian network.

A data warehouse has been established to act as

patient and self-diagnostics registry.

3 DISCUSSION & CONCLUSIONS

The work outlines the design and development of a

cloud-based ANC health support system which can be

employed by patients for self-diagnosis and by

clinical consultants to provide timely support to high

risk cases. The salient objective of this project is to

provide optimal clinical advice and support to

pregnant women in LMICs. Towards its popular

acceptance, limited financial resources of LMIC

women limits affordability of prevalent smartphone

devices and tablets. However, with a foreseeable drop

in the device costs (Hamblen, 2014), a significantly

improved affordability may be less than a decade

away. Additionally, limited availability of 3G and 4G

spectra for fast communication between the users and

the cloud may be another impediment in scaling the

system in rural and sub-urban areas of LMICs. The

relevance of the system is however anticipated to

improve with rapid penetration of communication

technologies in LMICs (PTA, 2013).

Another significant challenge in wide-spread

application of the proposed system may come from

the inability of the potential LMIC users to read non-

native languages. This has been catered for in this

work by a multilingual support (English and Urdu) in

the GUIs. For world-wide acceptance, an adequate

design allocation has been made for supporting other

native languages. Furthermore, the limited

experience of LMIC women to execute software is

also pertinent and may require tailored human-

computer interaction techniques for ensuring an

enabling user experience. Furthermore, hesitation on

part of the patients can be circumvented by providing

hands-on training to primary health care workers on

the proposed system. Since primary healthcare

networks are established in most LMICs, they can act

as vehicles of delivery and execute the diagnostics in-

tandem with the patient.

At the clinical end, a rapid increase in patient turn-

over may lead to hesitation on part of the consultants

to enrol in the system. However, an encouraging

response has been received after the system’s test-

deployment at SIHS. It is envisaged that with due

enhancements in GUIs, a streamlined patient-

consultant communication, the proposed ANC

system can gain wider subscription in Pakistan and

worldwide.

In conclusion, the proposed system aims to use

pre-existing clinical knowledge and employ

communication technologies to deliver much needed

ANC in LMICs towards reducing high IMR and

MMR.

REFERENCES

May 2014. Maternal mortality.

[Online]. Who-Media Centre Fact sheet. Available:

http://www.who.int/mediacentre/factsheets/fs348/en/.

Basso, O., Rasmussen, S., Weinberg, C. R., Wilcox, A. J.,

Irgens, L. M. & Skjaerven, R. 2006. Trends in fetal and

infant survival following preeclampsia. JAMA, 296,

1357-1362.

Bloom, S. S., Lippeveld, T. & Wypij, D. 1999. Does

antenatal care make a difference to safe delivery? A

study in urban Uttar Pradesh, India. Health policy and

planning, 14, 38-48.

Burrow, G. N., Duffy, T. P. & Copel, J. A. 2004. Medical

complications during pregnancy, Saunders.

Chappell, D. 2006. Introducing Windows Presentation

Foundation. Microsoft Developer Network. Chappell &

Associates.

Friedman, N., Geiger, D. & Goldszmidt, M. 1997. Bayesian

Network Classifiers. Mach. Learn., 29, 131-163.

Ghulmiyyah, L. & Sibai, B. Maternal mortality from

preeclampsia/eclampsia. Seminars in perinatology,

2012. Elsevier, 56-59.

Hamblen, M. 2014. Smartphone prices are dropping, and

will continue to dip through '18. Computerworld.

Hogan, M. C., Foreman, K. J., Naghavi, M., Ahn, S. Y.,

Wang, M., Makela, S. M., Lopez, A. D., Lozano, R. &

Murray, C. J. 2010. Maternal mortality for 181

countries, 1980–2008: a systematic analysis of progress

towards Millennium Development Goal 5. The lancet,

375, 1609-1623.

HEALTHINF 2016 - 9th International Conference on Health Informatics

482

Inam, S. & Khan, S. 2002. Importance of antenatal care in

reduction of maternal morbidity and mortality. Journal-

Pakistan Medical Association, 52, 137-137.

Jafarey, S. 2002. Maternal mortality in Pakistan–

compilation of available data. J Pak Med Assoc, 52,

539-44.

Mackay, A. P., Berg, C. J. & Atrash, H. K. 2001.

Pregnancy-related mortality from preeclampsia and

eclampsia. Obstetrics & Gynecology, 97, 533-538.

Martin, R. C. 2003. Agile software development: principles,

patterns, and practices, Prentice Hall PTR.

Masound, F. & Halabi, D. H. Asp. NET and JSP

Frameworks in Model View Controller

Implementation. Information and Communication

Technologies, 2006. ICTTA'06. 2nd, 2006. IEEE,

3593-3598.

Merck 2015. Merck Manuals Professional Edition.

Gynecology and Obstetrics.

Microsoft 2013. Windows Phone SDK. Microsoft

Developer Technologies. Microsoft.

Microsoft. 2014. ASP.NET Web API Framework [Online].

Available: http://www.asp.net/web-api.

Miller, B. A. & Bisdikian, C. 2001. Bluetooth revealed: the

insider's guide to an open specification for global

wireless communication, Prentice Hall PTR.

MySQL, A. 2001. MySQL.

Norbeck, J. S. & Tilden, V. P. 1983. Life stress, social

support, and emotional disequilibrium in complications

of pregnancy: a prospective, multivariate study.

Journal of Health and Social Behavior, 30-46.

Penfold, S., Shamba, D., Hanson, C., Jaribu, J., Manzi, F.,

Marchant, T., Tanner, M., Ramsey, K., Schellenberg,

D. & Schellenberg, J. A. 2013. Staff experiences of

providing maternity services in rural southern

Tanzania–a focus on equipment, drug and supply

issues. BMC health services research, 13, 61.

Peng, D.-L., Cao, L.-D. & Xu, W.-J. 2011. Using JSON for

data exchanging in web service applications. Journal of

Computational Information Systems, 7, 5883-5890.

PTA 2013. Annual Report. Pakistan Telecommunication

Authority.

Ransom, E. I., Yinger, N. V. & SASS, J. 2002. Making

motherhood safer: overcoming obstacles on the

pathway to care.

Sathar, Z., Crook, N., Callum, C. & Kazi, S. 1988. Women's

status and fertility change in Pakistan. The Population

and Development Review, 415-432.

Sattar, N. & Greer, I. A. 2002. Pregnancy complications

and maternal cardiovascular risk: opportunities for

intervention and screening? BMJ: British Medical

Journal, 325, 157.

Schmidt, W. 2010. XAML Overview. Microsoft Developer

Network. Microsoft.

Shah, I. & Åhman, E. 2004. Age patterns of unsafe abortion

in developing country regions. Reproductive Health

Matters, 12, 9-17.

Sheriff, P. D. 2001. Introduction to ASP.NET and Web

Forms. Microsoft Developer Network. Microsoft.

Titaley, C. R., Dibley, M. J. & Roberts, C. L. 2010. Factors

associated with underutilization of antenatal care

services in Indonesia: results of Indonesia

Demographic and Health Survey 2002/2003 and 2007.

BMC Public Health, 10, 485.

U.S. Department of Health and Human Services, O.

September 2010. "Pregnancy,".

UNICEF, W. 2012. UNFPA, World Bank (2012) Trends in

maternal mortality: 1990 to 2010. WHO, UNICEF.

UNFPA and The World Bank estimates. Geneva:

World Health Organization.

WHO & UNICEF 2012. Trends in maternal mortality: 1990

to 2010: WHO, UNICEF, UNFPA and The World Bank

estimates.

Towards Providing Full Spectrum Antenatal Health Care in Low and Middle Income Countries

483