DATA ANALYTICS FOR SMART HMIS

Dhivyalakshmi.S1, Umamakeswari.A

2, Aishwarya.V

3, Subramanian E.R.S

3,

Sri Gurubaran.B3, Shailesh Dheep.G3

1M.Tech-CSE, 2Associate Dean-CSE, 3B.Tech-CSE,

School of Computing, SASTRA University, Thanjavur.

lakshmi.dhivya2@gmail.com

Abstract: With the arrival of the emerging trend in the

development of IoT enabled solutions in every business

as well as service sector, this manuscript proposes a

Data Analytics for Smart Hospital Management and

Information System that is aimed at keeping one

objective in mind which is a better patient care at an

affordable cost. The system will not only automatically

monitor and track sick people, hospital personnel and

biomedical devices but will also help the doctors in

better decision making to treat sick people effectively.

There is research taking place around the world to

bridge the gap between technology and healthcare so

that diseases can be easily and quickly diagnosed. So,

the goal of this project is to design and develop a

Hospital Management and Information System (HMIS)

which make use of the Data Analytics concept. The

proposed system will be used by clinicians and

laboratory personnel to diagnose emerging diseases as

early as possible and treat the same with appropriate

medications.

Keywords: Data Analytics, Hospital Management

and Information System, Linear search, Sorting,

Decision Support System, JIPMER Database etc.

1. Introduction

In today’s world, technology is growing at a healthy

pace for making everyone’s life simpler. Technological

advancement can be observed in almost every sector as

it is much evident in our day-to-day life. Even though

there is a rapid growth in technology on one side, there

is also a gradual and noticeable increase in the

evolution and emergence of new diseases. Hence, there

are more research works that are taking place around

the world to bridge technology to fill the gaps of

healthcare systems so that diseases can be easily and

quickly diagnosed.

Data Analytics (DA) is the learning of exploring

raw data for the purpose of getting conclusion. Data

analytics is used in many sectors since it helps

organization to make enhanced business conclusions

and to verify or disprove existing systems in various

science fields.

Data Analytics helps healthcare organizations to

combine their clinical and laboratory data and use them

to quickly diagnose existing diseases as well as to

identify new diseases. That can lead to smarter

treatment moves, more efficient operations, higher

patient turnover and better patient care. Moreover, the

healthcare organizations can find better value in

reduction in cost, relevant and better Decision Making

and providing New Remedies or solutions.

There are more research works that are taking place

around the world to bridge technology to fill the gaps

of healthcare system so that diseases can be easily and

quickly diagnosed. So, the aim of the project is to

design and develop a Hospital Management and

Information System (HMIS) which make use of the

Data Analytics concept. The proposed system will be

used by clinicians and laboratory personnel to diagnose

emerging diseases as early as possible and treat the

same with appropriate medication.

2. Related works

Authors, [1] proposed a robust model for big healthcare

data analytics. The purpose of this learning is to

discourse the recent growths in big data analytics with

medical application field. The first step contains in

deliberating the concept of problem domain with its

importance with 4 V’s (volume, velocity, variety and

veracity). The second step evolves how these purposes

can help healthcare organization for analytical

approaches. The third step comprises assigning the

transmitted task to team for proper execution of

objectives. The fourth step is to position big data

platform for implementation and assessment of big

data. The last step confers the saved results and its

inference for future healthcare medical diagnostic.

Results are conferred with healthcare practitioners and

scientific committee for validation.

In [2], an outline of loading and recovery procedures is

given. Big Data techniques used in medical clouds,

importance and need of Big Data Analytics in medical

field and its merits, viewpoints in promising domain of

predictive analytics, difficulties faced and the cure

methods in medical domain. They carried out the trials

on clinical data by Open source web interface with

Horton works Data Platform. In these trials, they

examined the hospital’s over-all information such as

common obstacles, sicknesses and clinical knowledge.

The authors tried to research on sicknesses, which

affects the patients and the type of hospital where the

International Journal of Pure and Applied MathematicsVolume 115 No. 7 2017, 167-173ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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patient should join. They have also investigated on the

types of difficulties encountered by the hospital(s).

Authors [3] have proposed a novel learning to

improvise the Hip Breakage Maintenance Processes in

a Provincial Restoration System using a Business

Intelligence. This paper defines methodology

considered and outcomes attained, utilizing data

management to simplify structure alteration from out-

dated physical structure to programmed BI analytic

answer. This paper converses the summary of Hip

fracture flow, which is a grave life alerting, event for

older adults. The General flow is given as the patient

with hip fracture is admitted to the hospital; clinical

operation should takes place within 48 hours, the

patient need to stay in the hospital for 7 days which is

the least target time, finally therapy at home. The

results produce tangible outcomes in better-quality time

of surgery, reduces the length of hospital stay and

access to rehabilitation.

Volker, Marc, Markus [4] have addressed the trend

towards continuous healthcare where health is

continuously monitored by wearable and immobile

devices. They also discussed recent initiatives toward a

personalized medicine, based on advances in molecular

medicine, data management and data analytics.

Authors [5] have proposed a novel architecture which

advises in using the Hadoop, Apache Storm, Kafka and

NoSQL Cassandra. The authors proposed a basic

architecture by merging the merits of the batch-based

and real-time based computing to improvise the big

data computing in the medical domain. In the proposed

structure, as the authors mentioned earlier, big data

analytics in the medical can be categorized into two

equivalent layers called real-time based computing and

batch-based Computing. Stream Computing integrates

Kafka to enable real-time computing, on the other

hand, in batch computing the output data are produced

by the Hadoop cluster and used to store them in the

HBase Database.

In this paper, authors [6] have proposed a novel

approach in Big Data with Integrated Cloud Computing

with Healthcare Analytics. In this article the authors

have specified to fuse the two technologies such as

Cloud Computing and Big Data to get improvement in

the area of healthcare organization. In the proposed

approach the authors have conferred different

application domain of integrating these two

technologies. This combination is expected to increase

the proficiency and usefulness for patient outcome of

disease. There are several advantages for this approach.

It is very much helpful in tracing and handling the

population health more competently and successfully. It

also improves the ability to deliver preventive care etc.

In this paper [7] a Platform for interactive healthcare

analytics under simulated enactment is proposed. The

authors established a platform called Healthcare Big

Data Analytics (HBDA). The proof-of-concept was

done and the result of the test matched with the patient

data representative of the hospital system. The authors

did cross check with the available data, profiles and

metadata with the already available medical report. The

outcomes proved that processing time for one billion

records took 2 hours when Apache Spark is used.

Apache Drill outstripped Spark/Zeppelin and

Spark/Jupiter.

Authors [8] proposed security solution for big data

analytics in medical domain. This paper discusses four

main security models which are De-Identification

model, Data centric approach, Walled Garden approach

and Jujutsu model. The first model deals with the

unknown customer data which allows deeper analysis

on the facts by the scientists without providing any

disrespect to the industry and to the government data

privacy policy. The second model protects the

information at the information level itself. The third

model keeps the whole cluster under its private linkage

and strongly controls the coherent entrance via the

firewalls and the access controls. The final model is the

traditional martial art technique, which has the

capability to design and implement an engine called a

vibrant reference engine.

Author [9] discusses various applications provided by

big data analytics in medical domain. The aim was to

review few applications in medical domain and their

related outcomes. The first application called

Diagnosis, discussed is integrated software which helps

health resorts and the treatment center to use health

care analytics. The second application called

Treatment, tracks the consequences and the effects of

several patients. IBM’s Supercomputer allows the

medical organizations to create their own software,

yields and information analytics [10]. The third

application called Readmission, avoids re-admitting

patients which can be caused by mishandling the data,

confusion and shortage of right to use the relevant data

and aggregating the status of individual patient.

3. Implementation

The algorithm is designed to be implemented at any

major hospitals across the nation. JIPMER, Puducherry

is India’s one of the largest central government hospital

after AIIMS in terms of infrastructure, patient care and

research. Hence, JIPMER was chosen as a site for

requirement analysis, testing and implementation. All

the workflow of the project has been designed and

developed as per the database structure of JIPMER.

Before proceeding with the proposed solution, a

research ground work was carried out in order to build

more efficient system. The drawbacks of the existing

Hospital Information System of the hospital was

analyzed and conducted a comparative study with the

proposed system to make sure the solution that is

proposed provides a concrete solution to the existing

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drawbacks of a real time hospital information system of

any hospital.

Authors [11] proposed a system with an idea of

creating a smart architecture that was aimed for

meeting two prime objectives like keeping the track of

hospital personnel and monitoring patients in times of

emergency and critical situations. The proposed system

provides a data analytics system that will be helpful

even in treating the patients in addition to monitoring.

That is, through the data obtained via patient

demographics and general examination the proposed

system will additionally input data like symptoms to

proceed for further data analytics.

Initially, the consultant will obtain the patient’s case

sheet and will enter his/her hospital number into the

system. The system will use a linear search algorithm

to validate whether the entered hospital number is a

valid one or not. If it is a valid hospital number then

another linear search algorithm will check if the patient

is a follow-up patient or a new patient to the hospital

and system. If he/she is a follow-up patient his/her

previous visit data would be fetched from the database

including his persisting symptoms from the proposed

system’s database. Otherwise, if the patient is a new

patient, only the patient’s basic demographics and

general examination alone will be pulled from the

hospital’s database. This will further let the consultant

to check the patient and enter the findings and

symptoms into the system to check the possible

provisional diagnosis for the patient.

For obtaining the provisional diagnosis, Decision

Support System algorithm was used as there are more

possibilities for a patient to have more than one disease

based on his/her symptoms. Decision support system

will first examine the patient’s general examination to

figure out if there are any chances for a general fever or

any other critical complications for any disease. If the

examination turns out to be very sceptic for any major

disease then algorithm will take the symptoms into

account and performs a linear search algorithm to find

out if there are any matching diseases with the input

symptoms. As per the standard rule of medical

practices, only if more than input three symptoms

matches with the symptoms of any disease the

algorithm will consider them as provisional diagnosis.

If more than one diagnosis is found then a Selection

sort algorithm is performed to list out the diagnosis in

the order of maximum number matched symptoms.

That is, disease in the descending order of the number

of matched symptoms displayed the most possible

diagnosis on the top and the least possible diagnosis on

the bottom.

The possible provisional diagnosis will be displayed

along with the necessary laboratory investigations that

can be carried out to confirm one or more of the

suggested provisional diagnosis as the final diagnosis.

The consultant will advise all the relevant

investigations for the patient and will suggest him/her

to turn up for a follow-up review on a particular day.

Once, the investigation results are ready and the patient

turns out for his follow-up the decision support

algorithm will analyze out from the list of

investigations to confirm one or more diseases as the

final diagnosis. Once a final diagnosis has been

confirmed, the system will display the list of

medications that need to be prescribed for the patient

and the advice that need to be provided to the patient

for a better and speedy recovery.

3.1. Complexities

• JIPMER has classified patient general patients

and hospital staffs and students and are storing the

details in separate tables of their DB. So, in order to

validate a patient’s hospital number the search

algorithm should go through both the tables.

• Decision Support System algorithm can only be

defined and designed for every single

disease/diagnosis. Hence, within a short span of time

the algorithm was defined only for three similar

symptoms oriented diseases.

• JIPMER did not have a symptoms based

database to carry out the data analytics and hence the

proposed system though being an analytics system was

in a condition to input symptoms as a free text instead

of drop-down values that could be populated from

database. Hence, for the same reason, a sample

symptoms and diseases table has been designed for

carrying out analytics.

• JIPMER has a separate vast database for

pharmacology. But the major drawback was that

JIPMER have not gone online for prescriptions and

have not associated diagnosis with medications. Hence,

for the same reason, a sample table was created to

associate diseases with medications and advice.

• JIPMER’s database tables were highly and

neatly normalized. This has increased the complexity of

pulling out investigations results from the database.

There were almost 8 tables that were associated with a

patient’s laboratory report. Hence, complex join queries

have been written to pull out each investigation for

even a single patient.

3.2. Algorithms Used

The system has been built in such a way that project

would be tested and implemented in a real time

scenario with efficiency. Hence, standard algorithms

have been used for completing the project. Some of

them include:

• Linear search: Linear search techniques have

been used throughout the project wherever searching is

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carried out. For instance, to validate a patient’s hospital

number linear search algorithm is used. Similarly to

find out the list of provisional diagnosis the same

algorithm has been used.

• Selection Sort: Selecting sort technique is one

of the most standard sorting techniques that have been

used across many products. The same has been used for

list out the possible provisional diagnosis in the order

of matching symptoms. That is, the diagnosis with

maximum number of matched symptoms will

obviously have the maximum probability of being

diagnosed as the final diagnosis. Hence, the list of

provisional diagnosis has been sorted using selection

sort technique.

• Decision Support System Algorithm (described in fig 1): One of the world’s famous

healthcare algorithms that are used for making a

decision for confirming a diagnosis is decision support

system algorithm. The same has been used in the

project to analyze the results and confirm one or more

of the provisionally diagnosed disease as the final

diagnosis.

Figure 1. Overall view

3.3. Decision support system –Algorithm for

malaria

Methodology used here is Machine learning. Machine

learning is a category of Artificial Intelligence (AI)

which offers, the ability to learn without being

programmed openly, to computer. Machine learning

concentrates on the development of computer programs

that can help them to teach themselves to grow and

change when exposed to new data. Machine learning is

a technique of Data Analytics that powers the analytical

model construction.

Using some existing algorithms, Machine learning

permits computer to interpret the buried meaning

without being programmed openly where to look. With

the dawn of wearable devices and sensors which will

use records to assess a patient’s health in real time,

helps machine learning a faster emerging technology in

the hospital industry. This technology can also help

medical specialists to analyze the data which helps to

identify the trends that may lead to enhanced diagnoses

and treatments.

Popular Machine learning approaches are supervised

learning, unsupervised learning, Reinforcement

learning and Semi supervised learning. But, the

machine learning method adopted here is Unsupervised

learning. Unsupervised learning is used on the data

which has no chronological marker. Popular techniques

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of unsupervised learning include self-organizing maps,

k-NN mapping, k-means clustering and singular value

decomposition.

Decision-Tree learning is an algorithm that uses a

decision tree as a predictive model which maps

observation to conclusion. It has advantages such as

easy to understand and interpret, needs only little data

preparation, robust and uses white box testing etc. It

also has limitations such as Decision-tree is based on

heuristic algorithm which will not provide optimal

decision-tree. Learners of decision-tree will create

over-complex trees which will not generalize well from

the data set. Concepts in decision-tree are complex to

learn etc.

The system has been built in such a way that project

would be tested and implemented in a real time

scenario with efficiency.

4. Output/Results

The typical outcomes of the proposed system will be

broken down into multiple stages like providing the list

of possible provisional diagnosis for the list of given

symptoms, providing the list of laboratory

investigations that can be advised for the patient in

order to diagnose one or more of the suspected diseases

from the list of provisional diagnosis, providing the

appropriate final diagnosis based on the laboratory

values obtained and providing the list of appropriate

medications for curing the disease(s). The proposed

system also aims in providing the clinicians with the

list of possible procedures that can be performed for the

patient in case the disease has been found to be

requiring an admission and surgery. Patient details and

symptoms of the patient are entered (in fig 2) . The

provisional diagnosis details are displayed for reference

(in fig 3).

Figure 2. Fetching Patient Details and Entering

Symptoms after Authentication Process

Figure 3. Displaying Provisional Diagnosis details

Figure 4. Displaying Laboratory Result

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Figure 5. Displaying Final Diagnosis and Treatment

Details

The laboratory results and final diagnosis and treatment

details are finally displayed (in fig 4,5).

5. Conclusion

The paper is aimed at developing a complete Hospital

Management and Information system that result in

better patient care as well as optimizes the utility of the

hospital resources to the maximum. The system will

reduce the routine effort put by the hospital personnel

at different stages like registration, general

examination, consultation review, laboratory findings

and treatment. The entry point of the system will start

from registration and the exit point of the system will

either be the procedure to be performed along with

medications or medications alone. The data are

categorized and normalized properly in order to ensure

data security and indexing. Overall, the system will

meet the objectives of a complete HMIS which will not

only used for diagnosing and treatment but will also

ensure data security, reusability and better patient care.

6. Acknowledgments

The authors wish to express their sincere thanks to the

Department of Science & Technology, New Delhi,

India (Project-ID:SR/FST/ETI-371/2014) and

SASTRA University, Thanjavur, India for extending

the infrastructural support to carry out this work.

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