Development Intelligent E-Health

Model of

Indonesian

vince

Using

Big

Data

Dedy Prasetya Kristiadi

, Alfredo Pasaribu

, Yoas Arnest Soetopo

, Andri Irawan

Lukman Nulhakim

and Jawahir

Department of Information System, STMIK Kuwera, Jakarta, Indonesia

Department of Information System, Universitas Raharja, Tangerang, Indonesia

lukman.kuwera@gmail.com, jawahir@raharja.info.

Keywords: Health Service Using Big Data, Intelligence E-Health Model, Province E-Health Model.

Abstract: The need for public health services in a country is urgent need because the community is an important asset

in a country. Therefore, the state must be able to guarantee its citizens to stay healthy with maximum ser-

vice. Rapid population growth is the main reason for strengthening the poorly coordinated national health

information system. In addition, the use of inadequate information systems for decision-making requires the

integration of all patient data in medical records, health facilities, health workers such as doctors and experts,

health financing, community participation, and health management. The intelligence model of integrated

health services is a model that will be focused on the field of health services and will build collaboration in

data collection with public and private health centers and hospitals, the Social Security Administering Agency

(BPJS) for Health, the Ministry of Health of the Republic of Indonesia and the World Health Organization

(WHO). Health service development will be built by implementing Data Warehouse, Big Data, Data mining,

machine learning, knowledge management, and Decision Support systems. Application development will be

built on intelligence phones, using the Internet of Things (IoT) and extracting Indonesian health information

from geo-based and up-to-date social media.

INTR

ODUCTION

Indonesia is an archipelagic country that often faces

major natural disasters and has an impact on

casualties. Natural disasters are disasters caused by

natural upheaval. For example, earthquakes, tsunamis,

volcanic eruptions, floods, droughts, hurricanes, and

landslides (Kesehatan, 2022a). Furthermore, the

Indonesian state also often faces social disasters such

as conflicts in certain areas that result in casualties.

Relief for disaster victims often experiences delays in

medical treatment, lack of equipment and

geographical locations that are difficult to reach by

medical personnel (Sosial, 2022). Casualties and

injuries also of- ten occur on roads that require

immediate help. The readiness of health services for

medical personnel and hospitals is an urgent need to

save victims. In addition, patient administration in the

form of medical record administration and health

insurance funding is also an inseparable need

(Kesehatan, 2022b). The integration of patient data

from the lowest level of health services to the central

level must be developed immediately in line with

population growth. Based on population

administration data (Adminduk) the results of the

population census (SP2020) in September 2020

recorded a population of 270.20 million. Meanwhile,

the census as of June 2021, the population of

Indonesia is 272,229,372 inhabitants, of which

137,521,557 people are male and 134,707,815 people

are women (Negeri, 2022) and based on the Health

Profile of Indonesia in 2020 has 2,985 hospitals

consisting of 2,449 general hospitals and 536 special

hospitals (Kesehatan, 2017b) spread across provinces

and regions, cities and districts in Figure 1.

Human resources in hospitals consist of medical

personnel, pharmacy staff, nursing staff, other health

workers and non-health workers (Kesehatan, 2022b;

Kesehatan, 2020). There are 817,145 HRK in the

hospital, consisting of 569,714 health workers and

247,431 health support personnel. The data can be

seen in Figure 2.

Kristiadi, D., Pasaribu, A., Soetopo, Y., Irawan, A., Nulhakim, L. and Jawahir, .

Development Intelligent E-Health Model of Indonesian Province Using Big Data.

DOI: 10.5220/0012444400003848

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Advanced Information Scientiﬁc Development (ICAISD 2023), pages 96-101

ISBN: 978-989-758-678-1

Figure 1: Hospital in Indonesian.

Figure 2: Indonesian Human Resources Health.

At a lower level of government, there is a commu- nity

health service center (puskesmas) with the main

objective of seeking first-rate individual public health

with promotive and preventive actions. The num- ber

of Puskesmas in Indonesia as of December 2020 is

10,205 consisting of 4,119 consisting of inpatient

Puskesmas and 6,086 non inpatient Puskesmas. This

is explained in Figure 3.

Figure 3: Inpatient and Non Inpatient Health Centers.

The ratio of Puskesmas in patients and nonpatients to

sub-districts in 2020 is 1.4. This illustrates that the

ideal ratio of Puskesmas to subdistrict is at least 1

Puskesmas. The data can be seen in Figure 4.

Based on the Regulation of the Minister of Health

Number 43 of 2019 concerning Health Centers, the

types of health workers in the Puskesmas consist of at

least doctors, dentists, nurses, midwives, public

health promotion workers, and behavioral sciences,

Figure 4: Puskesmas in Indonesian.

environmental health workers, nutritionists, pharma-

cists and/or pharmaceutical technical personnel, and

medical laboratory technology experts (Kesehatan,

2017a). The number of health workers on duty at

puskesmas in Indonesia in 2020 is 434,308 people.

The number and types of puskesmas health workers

are calculated based on a workload analysis by con-

sidering several things,

namely

the number of

services provided, the number of residents, and their

distribu- tion. This data is described in Figure 5.

Figure 5: Health Workers at The Puskesmas.

Posyandu (Integrated service post) is a social

institution that accommodates community

empowerment in basic social services. The task of this

com- munity institution is to provide health

promotion assistance to the smallest scope of

government while at the same time providing

preventive measures against disease in the

community. For example, maintaining maternal and

child health, immunization, family planning

programs, immunization, nutrition, prevention, and

control of diarrhea. In Indonesia, there are around

80% active posyandu from 15 provinces that report.

Posyandu data is shown in Figure 6.

Figure 6: Posyandu at Indonesian Province.

Development Intelligent E-Health Model of Indonesian Province Using Big Data

Based on the ratio of data on rapid population growth

and the distribution of health service infrastructure

which includes posyandu, health centers, hospitals,

medical personnel, and health insurance that is not

balanced, big data is needed that allows data

integration to ensure the implementation of maximum

health services. The novelties of this research are:

1. Individual data collection from birth to death is

a must in order to provide optimal health services.

2. Data collection on pregnant women that

produces health data on birth failure, baby

defects, and death. This includes health

promotion, patient medical records, and

outpatient services.

3. In addition, data on drug readiness, facilities

such as ambulances for victims who need

immediate assistance, and the readiness of

health workers are also required.

4. Social media can also support this health service

by providing accurate and up-to-date

information about casualties due to disasters,

accidents, mal- practice cases in hospitals, etc.

5. Data integration in big data can also be used to

validate health insurance claims and information

needed by provincial and central governments in

making decisions.

LITERATURE

BIG

Big Data is a large collection of data that is very

useful in the business world to store, manage, and

manipulate large amounts of data at high speed

according to a predetermined time to get the right

information (Kesehatan, 2017b). there are five

characteristics of big data, namely (Sudarto et al.,

2018):

Figure 7: Big Data Character.

The character of big data consists of:

1. Having the amount of data (volume) that has

been manipulated and analyzed to get optimal

results (Sudarto et al., 2018). Manipulating and

analyzing very large amounts of data requires a

lot of resources so it is only archived in log form

(Sudarto et al., 2018).

2. Increasing the amount of data (Velocity) in a short

time requires fast processing so that the data

results can be used quickly (Sudarto et al., 2018).

3. Types of data (varieties) that are stored, analyzed

and used. Data can be in the form of location co-

ordinates, video files, or data received from var-

ious media. The data must be sequential so that it

can be used maximally and quickly (Kesehatan,

2020).

4. Value is defined as the quality of valuable data,

stored and used for further processing.

5. Veracity is defined as data accuracy and

consistency with data source analysis.

2.1

Hadoop

Hadoop is a software framework to support

applications running on Big Data (Balusamy et al.,

2021). The Hadoop architecture consists of the

Hadoop Dis- tributed File System (HDFS) and the

MapReduce programming framework. Large files on

the machine in large clusters are stored in HDFS with

a sequential block model then each block is sent to the

machine for error checking. The MapReduce method

is used to process data allocated to each node in

Hadoop (Kristiadi et al., 2020; Kristiadi et al., 2018).

Hadoop core services on Big Data can be illustrated

in the following figure 8.

Figure 8: Hadoop on Big Data.

2.2 Proposed

Idea

Figure 9: Intelligence Health Service Model Design with

Big Data Technology.

ICAISD 2023 - International Conference on Advanced Information Scientiﬁc Development

The need for data integration of public health ser-

vices from the lowest levels of government aims to

monitor, prevent and improve the health of citizens. In

addition, the integration of health data supported by

computer technology with internet media can help

residents in remote areas and the government in terms

of:

1. Areas affected information by disease outbreaks

and the availability of medical personnel,

hospitals, medicines, and patients.

2. Clinical Decision Support System (CDSS),

which provides digital information for a health

professional to use in diagnosing or treating

patients in districts in Indonesia.

3. Telemedicine, where the diagnosis and

treatment of physical and psychological

emotely.

4. Citizen health informatics, which uses digital

medical information (Balusamy et al., 2021;

Kristiadi et al., 2018).

5. Knowledge management to health, namely

health care data will be processed in the form of

knowledge and experience that can be practiced

by citizens.

6. At the village level hospital which is integrated

with the provincial hospital, a special

application is built for the Health Information

System to serve appointment scheduling,

patient data management, work schedule

management, and other tasks in health

administration (Kesehatan, 2020; Negeri, 2022).

7. E-Mental health, a web-based application built to

assist patients, the public and health workers in

recognizing and curing mental patients (Kristiadi

et al., 2019; Kristiadi et al., 2021a)

8. Information on accidents that occurred in

residents that occurred outside the area where

they live.

9. Information on the management of deceased

patients.

A medical record is a file that contains records and

documents about patient identity, examination,

treatment, action, and other services that have been

pro- vided to patients and outpatients (Kesehatan,

2022c; Kristiadi et al., 2019) based on medical

practice. While the type of medical record data in

question can be in the form of text (both structured

and narrative), digital images (if digital radiology has

been applied), sound (such as heartbeat), video, or

biosignals such as electrocardiogram recordings. The

medical record consist of Patient Record and

Management. The medical record is information that

is recorded both in writ- ing and electronically about

the health condition and disease of the patient and

Management is the process of processing and

compiling the health condition and disease of the

patient so that it becomes useful infor- mation for

accountability in terms of management, fi- nances,

and the condition of the patient’s health de-

velopment (Kristiadi et al., 2021b; Rui and

Danpeng,

2015).

An information system model that can

accommodate early health services, accident, and

elderly care, death, health workers, available drugs,

funds, and facilities must be the main focus of an

integrated system Kesehatan, 2022c). This model

presents a system consisting of a file containing

identity in the form of health services at the posyandu

(immunization, history of toddlers, etc.), medical

history, physical examination, laboratory, diagnosis,

treatment of patients from accidents to deaths

recorded both in writing and electronically called

files. medical. Data is stored in big data with good

database management. The definition of health

services recorded in medical records

is not just

recording patient registration and services but must

accommodate services before and after serving as an

implementation system (Prabaswara and Saputra,

2020; Rao and Makkithaya, 2088; Rui and Danpeng,

2015). Recording of medical record health services

can be started from registration of pregnant women,

recording of information on health services since

toddlers, health services and treatment actions

received by patients, then the archive is stored. Data

can be used when someone needs it for their needs or

other purposes (Kesehatan, 2022d; Balusamy et

al.,2021).

Integration of health services from smaller levels

to higher health services such as puskesmas, and city

and provincial hospitals is very much needed. The

development of intelligence e-health will start by

collecting patient data from an early age for pregnant

women and birth data in public health services

(Kristiadi et al., 2021a; Kristiadi et al., 2021b). The

medical history of all patients who have been

recorded will be an important document that can be

shared with relevant parties who are referred for

services in the city and province. In addition, patient

data will be linked to the Health Social Security

Administration (BPJS) (Kristiadi et al., 2021b) the

Ministry of Health of the Republic of Indonesia, the

Civil Registry Population Service (disdukcapil), and

the World Health Organization (WHO). Furthermore,

the collaboration will be extended to several local

governments that implement a health system such as

DKI Jakarta Province to treat patients suffering from

special diseases such as cancer, Covid, etc. By

enforcing service administrations such as the Healthy

Development Intelligent E-Health Model of Indonesian Province Using Big Data

Jakarta Card and the Surabaya Province with online

patient

registration (Leng et al., 2013), residents will

also get health services at special hospitals outside the

province. The application of technology involving big

data and data warehouses is a major requirement

(Sudarto et al., 2018) in addition to data mining,

machine learning, knowledge management, and

decision support systems (Kristiadi et al., 2021b;

Kristiadi et al., 2018; Leng et al., 2013).

3 RESULTS AND

DISCUSSION

In terms of data warehouse, this technology will help

to prepare a flexible data warehouse scheme for

Indonesian health services, which in turn will make

decision reports fast and accurate (Kristiadi et al.,

2021a; Kristiadi et al., 2021b). Big Data in the

application of concepts such as capacity, speed, and

variation will help in the case of bigger data volumes

(Leng et al., 2013; Rui and Danpeng, 2015) in public

health services in the province, using Hadoop or

combined with other programs together in terms of

increasing speed as data processing and retrieval

performance. prompt decisions in the province

(Kristiadi et al., 2019; Kristiadi et al., 2021b).

Indonesian health sector. Meanwhile, diversity will

show that intelligence application e-health for the

province of Indonesia will handle various data. There

are such as

images,

text,

sound will

applied

input data that will

studied with intelligent

technology

maintain their

health (Nugraha and

Aknuranda, 2088; Sudarto et al., 2018).

Intelligent technologies in the form of data mining

and machine learning will be chosen to manage and

find the best patterns from various data and find

similar patterns (Kesehatan, 2022d; Nugraha and

Aknuranda, 2088). Furthermore, knowledge

management, especially in intelligence e-health, will

be designed as another method to evaluate this

intelligence e-health.

The use of the Internet of Things (IoT) in

intelligence e-health will provide convenience in the

form of ease and speed of data access, sending

messages between devices containing health

information because one device will be connected to

another device (Negeri, 2022; Sudarto et al., 2018).

Applications based on the development of

Intelligent e-health will be designed for wider e-

health starting from health services in villages, cities,

and provinces where e-health can be seen in

demographic profiles where the community and

government can open content based on their

demographics. Interests such as gender, and various

age groups (infants, children, adolescents, adults, the

elderly). In addition, we can choose content that

contains data based on categories of diseases and

outbreaks such as covid, cholera, and so on. The types

of diseases will be grouped into the most infectious,

the deadliest, and the disease in general (Kristiadi et

al., 2019). In addition, e-health for disabilities is also

developed in e- health intelligence in public health

services.

The development of e-health intelligence in the

provinces of Indonesia will result in an enterprise

architecture that is implemented by building an e-

health website for the Indonesian province. The

implementation of the e-health website using a

personal home page (PHP) and using MySQL is

shown as the implementation of the data warehouse

and database. Furthermore, to my unstructured data as

information on health services and services by using

social media such as Twitter, Instagram, and so on.

The healthcare model will be built into a mobile

app that can be accessed with a personal cell phone.

This health care assistant model will provide

personalized health care advice based on illness

complaints, for example: about activities carried out

and nutrition needed (Sudarto et al., 2018). In

addition, this application model can provide benefits

such as medical test results and treatment, get quick

medical action, and useful information to provide

immediate help to those who are facing disasters and

diseases. This health care assistant model is also

useful for health workers who are helped by the

availability of information about the patient’s medical

record and history of treatment or similar medical

symptoms and can provide appropriate medical

information to patients (Kristiadi et al., 2021a).

Extracting health information from social media can

be used to find information that can be used for further

processing of health information based on user needs.

Data mining from social media such as news mining

must receive serious supervision to avoid

unauthorized data and furthermore, unstructured data

will be converted into structured data that can be used

(Leng et al., 2013).

4 CONCLUSIONS

Rapid population growth has a major impact on the

handling and maintenance of health. This will have an

impact on economic growth and community welfare.

Intelligent e-health development is an urgent need so

that people can prosper and carry out their activities

well. Economic development and health are

synergies, where the health of the population of a

ICAISD 2023 - International Conference on Advanced Information Scientiﬁc Development

100

country will result in economic development and will

ultimately improve their quality of life. The

application of e-health intelligence for provinces in

Indonesia will bridge health services from the

community group, ru-ral, urban and provincial levels

that are integrated into a health application. In

addition, the number of health workers, facilities, and

medicines can be immediately identified and

completed based on the patient’s need for treatment.

Health services for residents who be- come patients

will feel fast, comfortable, and of good quality. The

benefits of having online access to secure personal

health and health workers will be to provide efficient

and effective health services without wrong

treatment. In addition, legitimate information from

social media will provide great benefits for citizens in

terms of securing health services where patients and

health workers will be assisted with valid data to com-

bat health problems.

REFERENCES

Balusamy, B., Kadry, S., and Gandomi, A. H. (2021). Big

Data: Concepts, Technology, and Architecture. John

Wiley Sons.

Kesehatan, K. (2017a). The ministry of health of the re-

public of indonesia, Indonesian healty program with

family approach,2017.

Kesehatan, K. (2017b). The ministry of health of the re-

public of indonesia, teaching materials and medical

records of health information,2017. access 20 jul 2022.

Kesehatan, K. (2020). The ministry of health of the republic

of indonesia.profil kesehatan indonesia. access on,13-

july-2022.

Kesehatan, K. (2022a). Indonesian disaster management

agency, potential threat of disaster. access on 19 july

2022.

Kesehatan, K. (2022b). The ministry of health of the

republic of indonesia,2019. access on.

Kesehatan, K. (2022c). Number of doctor per 1,000 people.

Kesehatan, K. (2022d). Number of nurse and midwife

per

1,000 people,2022. access on 13 july 2022

Kristiadi, D. P., Hasanudin, M., Salim, J., Gunawan, M. G.,

Laurence, A., Chandra, A., Warnars, H. L. H. S., and

Mantoro, T. (2019). Mobile patient application for

outpatient. In 2019 Fourth International Confer- ence

on Informatics and Computing (ICIC, page 1–6. IEEE.

Kristiadi, D. P., Hasanudin, M., Sudarto, F., and Tarmizi,

R. (2021a). Indhci: Indonesian districts health care

information model. Journal of Physics: Conference

Series, 1764:012214.

Kristiadi, D. P., Hasanudin, M., and Sutrisno, S. (2021b).

Mobile application of electronic medical record (EMR)

systems using near field communication (NFC)

technology. International Journal of Open Information

Technologies, 9(10):68–72.

Kristiadi, D. P., Warnars, H. L. H. S., Hidayat, R. T., and

Randriatoamanana, R. (2020). Big data implementation

for searching unstructured data pattern as social media

pattern from a news website. Solid State Technology,

63(6):1277–1289.

Kristiadi, D. P., Warnars, H. L. H. S., Randriatoamanana,

R., Megantara, F., Nulhakim, L., and Zarlis, M. (2018).

Big data implementation for inventory ware- house

systems. In 2018 Indonesian Association for Pattern

Recognition International Conference (IN- APR, page

207–212. IEEE.

Leng, C., Yu, H., Wang, J., and Huang, J. (2013). Secur-

ing personal health records in the cloud by enforcing

sticky policies. TELKOMNIKA Indonesian Journal of

Electrical Engineering, 11(4):2200–2208.

Negeri, K. D. (2022). The ministry of internal affairs, data

penduduk indonesia. access on 13 july 2022 from :.

Nugraha, D. C. A. and Aknuranda, I. (2088). An overview

of e-health in indonesia: Past and present applications.

International Journal of Electrical Computer

Engineering, 7(5).

Prabaswara, I. R. and Saputra, R. (2020). Analisis data

sosial media twitter menggunakan hadoop dan spark. IT

Journal Research and Development, 4(2).

Rao, R. R. and Makkithaya, K. (2088). Learning from a

class imbalanced public health dataset: a cost-based

comparison of classifier performance. International

Journal of Electrical Computer Engineering, 7(4).

Rui, J. and Danpeng, S. (2015). Architecture design of the

internet of things based on cloud computing. In 2015

Seventh International Conference on Measuring

Technology and Mechatronics Automation, page 206–

209. IEEE.

Sosial, K. (2022). Aid/help needed for victims of natural

disasters/humanitarian tragedy,2022. access on 19

july

2022.

Sudarto, F., Kristiadi, D. P., Warnars, H. L. H. S., Ricky,

M. Y., and Hashimoto, K. (2018). Developing of

indonesian intelligent e-health model. In 2018

Indonesian Association for Pattern Recognition

International Conference (INAPR, page 307–314.

IEEE.

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