Blockchain Technology in Medical Data Processing: A Study on Its

Applications and Potential Beneﬁts

Olga Siedlecka-Lamch

1 a

and Sabina Szymoniak

1 b

Department of Computer Science, Czestochowa University of Technology, Dabrowskiego 73, 42-200 Czestochowa, Poland

Keywords:

Blockchain, Medical Data Storage, Data Modelling.

Abstract:

Blockchain is a digital ledger technology that uses a decentralised, distributed database to record and validate

transactions. It allows multiple parties to access the same information and make changes to it securely and

transparently without the need for a central authority. The potential of blockchain technology can streamline

and improve efﬁciency in many industries and sectors. One such application is the processing of medical

data. The use of blockchain is associated with the need to meet many challenges related to the scalability of

processing and storing large amounts of medical data, their security and interoperability. This article presents

an original idea for storing and processing medical data by combining blockchain technology with relational

databases. Such a combination will bring positive effects in terms of protecting patients’ privacy, increasing

trust in the system and increasing the efﬁciency and effectiveness of medical data management. In the pro-

posed model, blockchain technology will ensure security, immutability and transparent medical data storage.

A relational database, on the other hand, will facilitate the processing and sharing of data. The model includes

patient data, their insurance, bills, healthcare workers, doctors, nurses, and data related to the treatment pro-

cess: visits, referrals, releases, test results, diagnoses and medications.

1 INTRODUCTION

Blockchain is a digital ledger technology that uses a

decentralised, distributed database to record and vali-

date transactions. The underlying technology enables

the creation of digital currencies such as Bitcoin, but

it can also be used for a wide range of other appli-

cations. The critical feature of blockchain is that it

allows multiple parties to access the same informa-

tion and make changes to it securely and transparently

without the need for a central authority. This makes

it well-suited for use cases such as supply chain man-

agement, digital identity veriﬁcation, and many more

(Peck, 2017; Raikwar et al., 2020; Wang et al., 2020).

Blockchain technology has the potential to bring

about signiﬁcant changes in a wide range of indus-

tries and sectors. Some of the key areas where

blockchain is being used or has the potential to be

used include Financial Services, where it can stream-

line and improve the efﬁciency of ﬁnancial transac-

tions such as payments, remittances, and securities

trading (Patel et al., 2022). In Supply Chain Manage-

https://orcid.org/0000-0001-9820-6629

https://orcid.org/0000-0003-1148-5691

ment, blockchain can create a tamper-proof and trans-

parent record of transactions across the supply chain,

improving traceability and reducing fraud. Addition-

ally, it can be used to create digital identities that are

secure, private, and veriﬁable, which can be used to

improve access to services and reduce fraud (Queiroz

et al., 2019). In healthcare, it can be used to se-

curely store and share medical data, improving patient

outcomes and reducing administrative costs (Rom

an-

Belmonte et al., 2018). In the Internet of Things, it

can be used to create secure, decentralized networks

of connected devices, which can enable new applica-

tions and services (Miller, 2018; Wang et al., 2019).

Finally, in Government and Public services, it can be

used to create more transparent and efﬁcient systems

for voting, tax collection, and other public services

(Hou, 2017). It should be noted that blockchain tech-

nology is still relatively new and many of the use

cases are still in the development and testing phase,

so the full extent of its impact is yet to be seen.

There are several reasons why blockchain technol-

ogy is well-suited for use in the medical data pro-

cessing. Some key considerations include security,

where blockchain technology allows for secure and

tamper-proof medical data storage, which is particu-

664

Siedlecka-Lamch, O. and Szymoniak, S.

Blockchain Technology in Medical Data Processing: A Study on Its Applications and Potential Beneﬁts.

DOI: 10.5220/0011991400003464

In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2023), pages 664-671

ISBN: 978-989-758-647-7; ISSN: 2184-4895

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

larly important for sensitive information such as per-

sonal health records. The following reason is interop-

erability, where blockchain enables the secure sharing

of medical data between different systems and orga-

nizations, improving patient outcomes and reducing

administrative costs. Transparency, where blockchain

can give patients greater control over their medical

data and who has access to it, can increase trust in

the healthcare system. Auditability, where blockchain

creates a transparent and audible record of all transac-

tions, which can be used to improve trust and compli-

ance in the medical data processing. Decentralization,

where blockchain can enable the creation of decen-

tralized networks of medical data, can reduce the de-

pendence on centralized systems and increase system

resilience.

Several challenges must be considered when using

blockchain technology for medical data processing.

Some of the key challenges include scalability, where

processing and storing large amounts of medical data

on a blockchain can be challenging due to current

technology limitations. Privacy and regulatory com-

pliance, where ensuring that medical data is stored

and processed in a way that complies with privacy

and regulatory requirements can be complex. Inter-

operability, where ensuring that different blockchain

systems can work together and share data can be chal-

lenging. Standardization, where ensuring that med-

ical data is stored and processed in a way that is

consistent across different systems and organizations

can be difﬁcult. Adoption, where getting healthcare

providers and patients to adopt blockchain-based so-

lutions can be a challenge. Cybersecurity, where

blockchain technology is not immune to cyber at-

tacks, and some of the vulnerabilities that are spe-

ciﬁc to blockchain technology can be exploited by

attackers. Integration with existing systems, where

integrating blockchain-based solutions with existing

systems and processes in the healthcare industry can

be a complex task. It is important to note that these

challenges are not unique to blockchain, and similar

issues are faced when implementing new technolo-

gies in the healthcare sector. Additionally, ongoing

research and development efforts are being made to

address these challenges and make blockchain more

suitable for healthcare.

Our study presents an original idea for storing and

processing medical data by combining blockchain

technology with relational databases. Combining

these two technologies can positively impact patient

privacy protection, increasing trust in the system and

increasing the effectiveness and efﬁciency of manag-

ing medical data. Blockchain can provide secure, im-

mutable and transparent medical data storage, while a

relational database can facilitate data processing and

sharing.

The rest of this paper is organized as follows. Sec-

tion 1.1 presents the related works. In Section 2, we

provide theoretical fundaments that are base of our

work. In Section 3, we propose a model for medi-

cal data processing including conceptual and logical

models. Section 4 provides an implementation of our

model. In the last section, we present the conclusions

of the entire article, and ﬁndings from the research.

1.1 Related Work

There are many articles and publications on

blockchain technology, and new research is being

published regularly. Some of the most important and

inﬂuential articles and publications include:

• ”Bitcoin: A Peer-to-Peer Electronic Cash Sys-

tem” by Satoshi Nakamoto - This is the original

white paper that introduced the concept of Bit-

coin and blockchain technology (Nakamoto and

Bitcoin, 2008).

• ”The Business Blockchain: Promise, Practice,

and Application of the Next Internet Technology”

by William Mougayar - This book provides a

comprehensive overview of the business potential

of blockchain technology (Mougayar, 2016).

• ”Blockchain Basics: A Non-Technical Introduc-

tion in 25 Steps” by Daniel Drescher - This book

provides a clear and concise introduction to the

key concepts of blockchain technology (Drescher,

2020).

• ”A Next-Generation Smart Contract and De-

centralized Application Platform” by Ethereum

Foundation - This white paper introduces the

Ethereum blockchain platform and its smart con-

tract functionality (Buterin et al., 2014).

• ”Blockchain: Blueprint for a New Economy” by

Melanie Swan - This book provides an overview

of the potential of blockchain technology to dis-

rupt a wide range of industries and sectors (Swan,

2015).

• ”Blockchain Revolution: How the Technology

Behind Bitcoin Is Changing Money, Business,

and the World” by Don Tapscott and Alex Tap-

scott - This book provides a detailed look at the

potential of blockchain technology to disrupt a

wide range of industries and sectors (Tapscott and

Tapscott, 2016).

These are just a few examples, and many other ar-

ticles and publications provide valuable insights into

Blockchain Technology in Medical Data Processing: A Study on Its Applications and Potential Beneﬁts

665

the technology and its potential applications. It is es-

sential to remember that blockchain technology is still

relatively new and continuously evolving, so new re-

search and publications are coming out regularly.

Many articles and publications focus on using

blockchain technology in the healthcare industry.

Some of the most important and inﬂuential articles

and publications include:

• ”A Survey of Blockchain-Based Strategies for

Healthcare” by De Aguiar et al. - This article re-

views concepts of blockchain in the medical area

including the management of information, drug

tracking, and data security and privacy (De Aguiar

et al., 2020).

• ”Blockchain-Based Electronic Health Record

Systems: A Review of the Current Landscape” by

K. K. Chan, J. Y. Lee, and B. K. Ng - This arti-

cle reviews current efforts to use blockchain for

electronic health record systems.

• ”Blockchain in Healthcare: The Future is Here”

by P. J. Neuman - This article provides an

overview of the potential beneﬁts of blockchain

in healthcare and potential use cases.

• ”Blockchain Technology in the Pharmaceutical

Industry: The Future of Traceability and Sup-

ply Chain Management” by N. Kshetri - This ar-

ticle explores the potential of blockchain in the

pharmaceutical industry, speciﬁcally in traceabil-

ity and supply chain management.

• ”Blockchain in Healthcare: How Blockchain

Technology Can Improve the Health Care Sys-

tem” by R. Kshetri - This article provides a

detailed overview of the potential beneﬁts of

blockchain technology in healthcare, including

improved data security and interoperability.

• ”Blockchain platform for industrial healthcare:

Vision and future opportunities” by Farouk, Alah-

madi, Ghose, and Mashatan - This paper provides

an overview of the blockchain and IoT technolo-

gies usage in healthcare (Farouk et al., 2020).

• ”Blockchain for Health Data and Its Potential Use

in Health IT and Health Care Related Research”

by Linn et al. - This article provides an overview

of the potential of blockchain for storing and shar-

ing health data (Linn et al., 2016).

• ”Blockchain for healthcare data management: op-

portunities, challenges, and future recommenda-

tions” by Yaqoob et al. - This article demon-

strates the practicality of blockchain technology

for healthcare applications (Yaqoob et al., 2022).

• ”Blockchain-enabled supply chain: analysis,

challenges, and future directions” by Jabbar et al.

- This article provides an overview of challenges

and future directions in pharmaceutical supply

chain intervention (Jabbar et al., 2021).

2 THEORETICAL FUNDAMENTS

Blockchain is a technology used in many application

domains. It is deﬁned as a registry of decentralised

data that is securely shared. Thanks to this technol-

ogy, a group of participants can share data and in-

put it into the network simultaneously. Additionally,

blockchain enables accessible collection, integration

and sharing of transactional data from many sources.

Blockchain ensures data integrity by eliminating du-

plication and ensuring security. The data, in turn, is

divided into shared datasets made up of blocks and

a chain of data packets. Each block includes mul-

tiple transactions or entities and metadata. In addi-

tion, each block contains a timestamp, the previous

block’s hash value, and a nonce, a random number to

verify the hash. Blockchain can be extended with ad-

ditional blocks, thanks to which the blockchain con-

tains a complete history of transactions (Rajasekaran

et al., 2022; Bashir, 2018).

Figure 1: Hash calculation sequence.

Blockchain is an electronic list of data blocks. In-

tegrity constraints exist between these blocks. Also,

each block stores the cryptographic hash of the pre-

vious block. The data stored in the block is used to

calculate the hash using appropriate hashing meth-

ods. The hash is the equivalent of a checksum. If

later there are changes in a given block, the hash can

be used to verify whether there has been an error or

an attempted forgery. Each subsequent block uses its

data and the previous block’s hash function to com-

pute the result of the hash function. This action cre-

ates integrity constraints between the blocks. Thus,

any interference with the block results in both its hash

and each subsequent block’s hash is incorrect. Figure

1 shows the idea of the mentioned hash calculation

sequence (Bodkhe et al., 2020).

The functions used to generate the hash should

primarily be one-way and collision-resistant. If the

function is one-way, ﬁnding an argument to perform

the reverse operation will be impossible. An ad-

ditional advantage of one-way hash functions is to

increase the level of data security because one-way

makes it much more challenging to try forgery. Colli-

ENASE 2023 - 18th International Conference on Evaluation of Novel Approaches to Software Engineering

666

sion resistance means generating two arbitrary inputs

with the same hash is not practical. The hash func-

tion makes it much more difﬁcult to interfere with al-

ready created blocks, but it does not prevent it. If the

block is modiﬁed, the result of the hash function no

longer matches. Therefore, it is necessary to calculate

its new value and repeat the same operation in each

subsequent block (Sheik and Muniyandi, 2023).

We can divide blockchains into public and private

chains due to their availability. Private chains are only

open to the group, which means that the users of such

chains are known, and there is usually no need to im-

plement additional security measures. On the other

hand, public chains usually provide free network ac-

cess for all internet users. The use of public chains

increases the risk of abuse and contributes to the need

for additional blockchain security measures, e.g., im-

plementing a consensus algorithm. Reaching a con-

sensus in the network is a critical situation because

the blockchain is usually uniform, which means that

there is one global instance of the blockchain. The

occurrence of branches is undesirable, meaning that

not all nodes make the same decision (Zheng and Lu,

2022).

We can indicate several methods of reaching con-

sensus, i.e. situations in which nodes in the network

agree to accept a new block consisting of a speciﬁc list

of entities or transactions. The consensus mechanism

should guarantee data integrity between nodes and re-

duce the risk of errors and illegal transactions. One of

the most popular mechanisms for reaching consensus

is Proof of Work. This mechanism requires proof that

adequate computational resources were used to gen-

erate the new block, and the PoW algorithm charac-

terises by the high computing power necessary to gen-

erate subsequent blocks. Another consensus-building

algorithm is Proof of Stake. In this case, the algorithm

selects the person who can create the next block based

on certain factors. For example, the criterion for se-

lecting a person may be the number of tokens held and

their age [21]. Thus, a new block can be created by a

person with such a large amount of assets that prohib-

ited activities are unproﬁtable for him, as they carry

the risk of losing the funds collected so far. Proof of

Stake consumes less power than the Proof of Work al-

gorithm. Proof of Importance uses information about

the user’s activity on the network to assess the level of

trust of a given person user. On the other hand, Proof

of Activity combines the features of Proof of Stake

and Proof of Work. Thanks to this, it maintains an ap-

propriate level of security while reducing the required

computing power (Ferdous et al., 2021).

3 MODEL FOR THE MEDICAL

DATA PROCESSING

In this section, we will present a model that combines

the advantages of a relational model and blockchain

technology. For personal data of patients, doctors,

and healthcare workers, as well as descriptions of in-

surance and payments, we will use relationships. On

the other hand, we will utilise blockchain for diag-

nostic information such as test results, diagnoses, and

prescribed medications.

3.1 Conceptual Model

A conceptual model was built in the ﬁrst step of

database modelling, resulting in an entity relationship

diagram (Figure 2). For the article, we limited the

visibility of each entity’s properties to the basic ones

due to the size of the entire diagram. It should also be

noted that the most important entities for analysing

the problem have been shown.

In the project, we distinguished entities closely

related to patient data: patient, insurance, and bill,

where we describe information about the patient,

what their insurance covers, and information about

their payments related to treatment. The subsequent

entities relate to medical staff: doctor, nurse, and

healthcare worker, where information about the listed

employees and their specialisations will appear. The

next group of entities describes the treatment process,

so we have a visit that is ﬁrst reserved and then takes

place. Finally, we have referrals for procedures, to

other specialists, or for tests. There will also be an en-

tity regarding medical leave and completed tests. The

last group of entities is signiﬁcant for the treatment

process, including test results, diagnosis, and pre-

scription. Here we will describe the patient’s health

status at a given time, the diagnosis made, and the

prescribed drugs with the dosing instructions. These

data are complex and will require nesting, and collec-

tions, in short, complex structures. In Figure 2, we

have marked them with the term: Data.

Most of the relationships we identiﬁed between

entities are simple one-to-one or one-to-many re-

lationships. However, attention should be paid to

relationships that occur in three entities describing the

treatment process. We wanted to emphasise the causal

relationship and the sequence of subsequent tests,

diagnoses, and medications. Therefore, each entity

instance should refer to its predecessor in time. This

requirement is shown in the form of relationships be-

tween entities with themselves. Importantly, in medi-

cal history, there will always be the ﬁrst and last test

Blockchain Technology in Medical Data Processing: A Study on Its Applications and Potential Beneﬁts

667

Figure 2: Conceptual model of healthcare data.

or the ﬁrst and last diagnosis; hence, the relationships

are bidirectionally optional.

3.2 Logical Model

After the ﬁrst design phase, entities can be identiﬁed

as possible to implement in a relational (optionally

relational-object) form. In Figure 2, we have high-

lighted them with a blue background in the header.

We would particularly like to secure three entities,

which are sensitive data important in terms of the or-

der of occurrence, time, and immutability. Therefore,

we have highlighted them with an orange background.

In the case of diagnostics, the system beneﬁcia-

ries: doctors, staff, and patients themselves, will have

access to an immutable history of diagnoses, prescrip-

tions, and test results. Each of the mentioned enti-

ties contains a record of the key to the previous one

(the result of the hash function), a precise timestamp,

metadata, and the actual data related to the diagnosis.

As we mentioned, the data related to these entities

are complex. We will not be able to use regular re-

lationships. We propose a hybrid model that will use

blockchain technology to secure the order and data

and protect them from changes after approval. In ad-

dition to blockchain, we will use a relational-object

model that will allow us to store both atomic data (pa-

tient number, doctor, date) and complex data (test re-

sult, prescribed drugs with dosing) that can take the

form of a collection in rows.

ENASE 2023 - 18th International Conference on Evaluation of Novel Approaches to Software Engineering

668

4 IMPLEMENTATION

The designed database can be implemented using Or-

acle 19i or a newer server. In the above distribution,

we can use the existing solution - Oracle Blockchain

Platform. This platform provides blockchain tables.

Access to them is assigned in the form of individual

permissions. Hence, all blockchain technology in Or-

acle is a permissioned blockchain.

Relational tables are implemented traditionally

with a standard set of types. However, in the case of

patient or visit data, it is necessary to consider using

partitioning, as these may be large data sets. All of

these tables will be able to undergo insertion, mod-

iﬁcation, and deletion of data according to the ac-

tual state. Authorised healthcare workers will make

changes.

Blockchain tables designated for data insertion

only will be used for test results, diagnoses, and pre-

scriptions. These tables organise rows into a certain

number of chains. Each row in the chain, except for

the ﬁrst, is linked to the previous row in the chain us-

ing a cryptographic hash (Figure 3).

An interesting feature of blockchain tables is the

ability to add certiﬁcates used to sign rows. Sign-

ing a row makes it clear who inserted the row. The

signature is optional and provides additional security

against manipulation. Another important aspect is the

ability to set the expiration time of the data. The total

inability to delete outdated data could be too restric-

tive. Therefore, a storage period can be set during the

table creation.

Unfortunately, in the case of an Oracle server,

blockchain tables also have signiﬁcant limitations re-

garding the allowable types of rows. They only allow

for simple types and do not permit object or collection

types. As a result, assumptions made during concep-

tual and logical modelling must be altered. For ex-

ample, the results of tests that may involve multiple

factors being studied must be broken down into indi-

vidual key-value entries, where the key is the name of

the factor being studied and the value is the obtained

result. The same approach should be taken in the case

of a diagnosis. If more than one illness is detected

during a visit, each illness will be allocated a separate

row. Similarly, in the case of medications, individual

rows will describe individual drugs and their dosage.

In Figure 3, among the data, metadata inserted

into each row in the chain is highlighted. These meta-

data will include database instance identiﬁers, row

identiﬁers, consecutive row numbers, data insertion

timestamp, user number who inserted the data, row

hash value, signatures, and certiﬁcates.

Figure 3: Blockchain model of diagnosis table.

We must add many constraints to our blockchain

tables to facilitate their management and security. The

ﬁrst step will be establishing a period for storing rows

in the table. In the case of medical data, it can be set

for a period of several years, after which older data is

archived. In the next step, because we will be break-

ing down the results of tests and prescribed medica-

tions so much, the tables will quickly grow in size. Fi-

nally, to optimise and avoid full table scans, we must

implement a data partitioning mechanism during the

implementation stage.

We want each entry to have a speciﬁed and

signed author, so we must also add certiﬁcates to

the tables during the implementation stage. We will

use the DBMS USER CERTS.ADD CERTIFICATE

procedure for this purpose.

After the database structure implementation

phase, it is necessary to implement supporting

database triggers, stored procedures and functions,

additional indexes on large tables and their most im-

portant columns in the case of data search. These

are essential steps from the view of application op-

timisation rather than from the point of view of the

blockchain model. What will also be necessary is the

selection of users and their privileges.

Among users, we will distinguish those who have

access to traditional tables; these will be healthcare

professionals who can enter patient, visit, and pro-

cedure data. Laboratory technicians will have cer-

tiﬁcates and can sign off on test results recorded in

the blockchain table. There will be doctors who

have access to patients, visits, procedures, and re-

ferral data but also can sign off on entries related to

diagnosis and prescribed medications using a certiﬁ-

cate. Finally, there will be patients without modiﬁca-

tion rights but only with the ability to read their own

data.

Of course, throughout the entire implementation

phase, we have only described the database side here.

Simultaneously it is necessary to add an API allowing

easy database handling by individual users.

Blockchain Technology in Medical Data Processing: A Study on Its Applications and Potential Beneﬁts

669

5 CONCLUSIONS

In our article, we have outlined the ideas behind

blockchain technology and subsequently developed

and demonstrated a data model for the patient treat-

ment process. The model includes patient data, their

insurance, bills, healthcare workers, doctors, nurses,

and data related to the treatment process: visits, re-

ferrals, releases, test results, diagnoses and medica-

tions. The last three are designed using blockchain

technology, guaranteeing data integrity and maintain-

ing a precise entry sequence. Additionally, each en-

try is accompanied by a certiﬁed signature. Overall,

our article illustrates how blockchain technology can

bring security and transparency to the healthcare in-

dustry by allowing tamper-proof records of sensitive

patient data and medical procedures.

Blockchain technology, in the case of medical

data, offers certain beneﬁts, including:

• High level of security;

• Reduced reliance on external intermediaries;

• Real-time record allowing detection of manipula-

tion that can be shared with all interested parties;

• Facilitation of authenticity and integrity of entries

throughout the treatment process;

• Building trust between patients and healthcare

providers by offering credible, shared data;

• It enables seamless tracking of the treatment pro-

cess.

When implementing a hybrid data model for med-

ical data using blockchain technology, we may en-

counter challenges such as complexity in implemen-

tation, scalability issues, data standardisation needs,

difﬁculties in integrating with existing systems, and

regulatory compliance. These challenges vary de-

pending on the speciﬁc use case and the current in-

frastructure in place. Therefore, it is essential to con-

sider these potential challenges and have a plan to ad-

dress them to ensure a successful implementation of

the hybrid data model.

ACKNOWLEDGEMENTS

The project ﬁnanced under the program of the Pol-

ish Minister of Science and Higher Education under

the name “Regional Initiative of Excellence” in the

years 2019–2023 project number 020/RID/2018/19

the amount of ﬁnancing PLN 12,000,000.

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