An Ethical Framework for Big Data and Smart Healthcare

Victor Chang

, Rahman Olamide Eniola

, Ben Shaw-Ching Liu

and Mitra Arami

Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K.

Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering and Digital Technologies,

Teesside University, U.K.

Department of Marketing, Lender School of Business Center, Quinnipiac University Hamden, CT 06518, U.S.A.

Pardis Limited, London and EM Normandie Business School, France

Keywords: Ethics for AI and Data Science, Ethical Framework, Ethics for Smart Healthcare.

Abstract: There has been significant growth in big data technology in healthcare in recent years. However, the potential

of big data analytics is affected by various ethical and security concerns, which have hampered the application

of big data analytics in healthcare. Recently, numerous studies have been conducted on the emerging big data

ethical issues in healthcare. While most of the journal reflects on privacy and security questions, it did not

examine; objectively the possible discriminatory impact of big data analytics has no. This mixed-method

project aims to highlight various ethical problems in big data analytics while also providing an in-depth insight

into the biased results derivable from big data analytics and the effects of such outcomes.

1 INTRODUCTION

Higher healthcare investment in a nation can provide

better health prospects that can enhance human

capital and increase productivity, thus contributing to

economic performance (Raghupathi and Raghupathi,

2020; Cutillo et al., 2020). However, the exponential

growth in the world's population presents a critical

threat to current medical and healthcare systems (Zhu

et al., 2019). The change in population demographics,

the increase in the number of aged people, and the

drastic increase in the cost of in-hospital services all

lead to realizing the value of effective healthcare

systems (Demirkan, 2013). The professional-to-

patient ratio is another factor that led to the rise in

demand for an efficient healthcare system (Borodin et

al., 2016).

With the explosive growth of disruptive

technologies in recent years, the speed and quantity

of digital data collected have expanded steadily and

rapidly (Chang, Shi and Zhang, 2019).

Correspondingly, the evolution of information

technology and the introduction of digitized computer

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systems has resulted in the transition of conventional

hard copy medical data to Electronic Health Records

(EHR) and Electronic Medical Records (EMR)

systems (Rehman, Naz and Razzak, 2021). These

systems resulted in exponential data expansion

(Razzak, Imran and Xu, 2020), which has contributed

to the growth of big data analytics, especially in

healthcare.

According to a 2021 Grand View Research, Inc.

study, the worldwide healthcare analytics market was

valued at USD 23.6 billion in 2020, projected to rise

at a Compound Annual Growth Rate (CAGR) of 23.8

percent from 2021 to 2028 (‘Healthcare Analytics

Market Size Industry Report: 2021-2028’, 2021). See

Figure1.

This massive increase fulfills the growing need

for improved healthcare, aided by innovative

technology.

Administrative claim reports, hospital registries,

electronic records of health, biometric data, patient

data, the internet, medical imaging, biomarkers,

prospective cohort studies, and clinical trials are

possible medical big data sources in healthcare

(Hermon and Williams, 2014; Luo et al., 2016).

Chang, V., Eniola, R., Liu, B. and Arami, M.

An Ethical Framework for Big Data and Smart Healthcare.

DOI: 10.5220/0011030900003206

In Proceedings of the 4th International Conference on Finance, Economics, Management and IT Business (FEMIB 2022), pages 65-74

ISBN: 978-989-758-567-8; ISSN: 2184-5891

Figure 1: Healthcare Analytics market in the USA, by end

user, 2018 – 2028 (USD Million).

These sources; are aggregated to produce fast and

cost-effective prescriptive, descriptive, and

diagnostic insights for the healthcare stakeholders.

While strategically analyzing data for insightful

analysis is crucial, the existence of different data

types accessible from numerous sources makes big

data management extremely difficult (Nair, 2020).

Despite the aforementioned benefits of big data

technology, it is worth remembering that big data

analytics has its drawbacks due to its intertwinement

with people's sensitive personal information, daily

behavioral patterns, and potential prospects. The most

pressing concerns of big data analytics are privacy

(Francis, 2014), confidentiality and informed consent

(Ioannidis, 2013), epistemic hurdles (Floridi, 2012),

and the analysis of monitoring in a growing

datafication of the society (Ball et al., 2016). Indeed,

the assurance of privacy and safety of subjects

through the application of big-data analytics are of

significant importance and high priority. The study by

IBM (IBM, 2019) in Figure 2 shows that the health

sector has suffered an average overall cost of data

breaches considerably higher than other sectors such

as hospitality, media, and research. Healthcare data

should be securely kept, and big data analytics

performed ethically (Mittelstadt, 2019).

Figure 2: Trend of Industry Average Data Cost (IBM,

2019).

The possibility of potential discrimination is

among the most alarming yet understudied issues of

big data technology. There is no universally accepted

definition of discrimination. The term generally refers

to acts, practices, or policies that impose a relative

disadvantage or treat a person or specific group of

people differently, especially in a worse way than

treating other people because of their skin color,

gender, sexuality, language, or other factors (Reinsch

and Goltz, 2016).

The research (Obermeyer et al., 2019) that

revealed pervasive racism in decision-making

systems utilized by US clinics is an excellent

demonstration of discrimination in healthcare

analytics. Participants who self-identified as black

were rated lower risk scores than equally ill white

people in the study. Consequently, black individuals

were less likely to be referred for more personalized

medical care (Obermeyer et al., 2019).

The emergence of these instances describes why

discrimination in big data analytics has become an

emerging topic in a variety of fields, from data

science and artificial intelligence to psychology,

culminating in a dispersed and fractured

interdisciplinary corpus that tends to make

thoroughly accessing the foundation of the problem

difficult (Favaretto et al., 2019).

This study summarized big data and its use in

healthcare, addressing current ethical and security

issues relevant to big data application in healthcare.

Moreover, we suggest several alternative solutions to

compromise between the application and the ethical

obligation.

2 LITERATURE REVIEW

Big data and big data analytics are arguably the pillars

of other disruptive technologies, providing the

necessary business insights for patients, experts, and

government (Wong, Zhou, and Zhang, 2019). Big

data analytics is the method of storing, processing,

and analyzing vast collections of data to find trends

and other valuable knowledge (Heyman et al., 2004).

These massive and complex big data collections are

manipulated and managed using various

computational methods such as machine learning and

artificial intelligence (Ward and Barker, 2013). The

advent of advanced technology has provided

conditions and procedures for voluminous databases

to be compiled and processed, resulting in informed

decision-making in addressing health problems (Raja

et al., 2020).

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Big data has emerged as a promising option with

the potential to revolutionize the healthcare system by

lowering costs and optimizing treatment process,

delivery, and management (Patil and Seshadri, 2014).

The application of big data comes with some ethical

issues that demand careful consideration (Camilleri,

2020). Suresh and Guttag (2019) explain how bias

problems occur, how they apply to specific

applications, and how they inspire various solutions.

They also present a framework for understanding

analytical bias at a higher level of abstraction to

facilitate constructive dialogue and solution

development.

Notwithstanding the amount of data generated in

healthcare, the underlying challenge remains in the

integration, of structured and unstructured health

data. According to Dridi et al. (2020), approximately

80% of clinical data is unstructured: and widely

underutilized, once generated. Different clinical data

formats, such as scanned canned medical documents,

prescriptions, patient registries, and clinician notes,

result in poor standardization of healthcare data,

making it more difficult to handle by EHR systems

and more prone to bias from data preprocessing (Cave

et al., 2019; Dridi et al., 2020).

Patient privacy invasion is an emerging problem

in big data analytics. Patients' behavior and sentiment

data can be obtained from various online sources. For

example, an online drug retailer may have recorded

the purchase of a particular medication, a ride-hailing

app may have recorded a visit to a clinic or lab, or a

social media app may have recorded patients'

interactions with a medical web page. Furthermore,

patients' data can also be extracted unethically via

health-care-specific applications and wearable

devices.

Also, we studied several publications to grasp

better the potential discriminatory effects and popular

drivers of discrimination or inequality in big data

analytics on subjects. Different writers arrived at

different conclusions. Big data analytics may result in

unintentional discrimination (Žliobaitė, 2017;

Sonawane and Irabashetti, 2015). Žliobaitė (2017)

established that discrimination is indirect, not by the

analyst's intention but because of the structure and

noise of experimental data. Such algorithms may

systematically disfavor persons belonging to

particular groups or categories, rather than depending

purely on individual merits.

Conversely, other academic studies emphasized

intentional discrimination (e.g., Kuempel, 2016;

Sonawane and Irabashetti, 2015). According to

Kuempel (2016), data brokers frequently combine

raw components of personal data in a discriminatory

way, leaving customers exposed to exploitative and

distasteful marketing techniques. The effect of

utilizing such a biased dataset with sensitive

information is that such individuals or groups of

people would lead to direct discrimination.

Suresh and Guttag (2019) explain how bias

problems occur, how they apply to specific

applications, and how they inspire various solutions.

They also present a framework for understanding

analytical bias at a higher level of abstraction to

facilitate constructive dialogue and solution

development.

3 RESEARCH QUESTIONS AND

BIG DATA ANALYTICS

ARCHITECTURE

The first step of the research was to identify relevant

research questions. The main research question is,

"given the many applications and benefits of big data

and big data analytics in healthcare, do the ethical

risks overshadow the benefits?"

To answer this main question, we need to find

answers to the following sub-questions:

1. What are the applications of big data in

healthcare?

2. What are the current ethical issues of healthcare

big data analytics?

3. What is the cause of discrimination in big data

analytics?

The big data analytics framework utilized in this

project is a blend of many steps that explains the big

data Analytics procedure (shown in Figure 3 above).

The first phase in the framework is data preparation,

which involves the ETL, i.e., Extraction,

Transformation, and Loading of the data. Extraction

is the process of determining the data type to be

utilized and collecting it from different data sources,

such as existing databases and repositories, APIs, and

the cloud. Data transformation is the next step in

which data is transformed, aggregated, and loaded

into the Power Business Intelligence (BI) dashboard.

Figure 3: Power BI architecture.

An Ethical Framework for Big Data and Smart Healthcare

The transformation step is to ensure the: (1) handling

of inconsistencies and missing values in the data; (2)

elimination of duplicate data; (3) removal of useless

data; and (4) sorting of data into the appropriate type.

Figure 3 below illustrates the overview of the Power

BI analytics procedure.

The visualization step involves taking the

processed outputs and transforming them into

meaningful insights by viewing the results in

diagrams, KPIs, or other easy-to-understand formats.

It is crucial to ensure that results can be interpreted by

those with no previous experience or expertise.

Unlike other tools, Power BI allows the integration of

different programming languages. Applying Python

and R functionalities while using the DAX and M-

language formulas is the advantage of Power BI. It

gives a better result due to the combined strengths of

different programming languages.

4 APPLICATION AND BENEFITS

OF BIG DATA ANALYTICS

4.1 Preventive Medicine

Preventive medicine is arguably the most innovative

application of big data analysis which employs

cutting-edge data analytics methods: for disease

detection and classification, association analytics,

and clustering, with the promise of efficiently

discovering valuable patterns by analyzing large

amounts of unstructured, heterogeneous, non-

standard data (Razzak et al., 2020). Appropriate

disease prevention involves identifying and treating

at-risk patients. To increase therapeutic adherence,

several preventative strategies are employed.

Pertinent data, such as body temperature, pulse, and

blood pressure, are electronically collected, enabling

automated risk prediction. Consonantly, the increased

usage has contributed significantly to the

appropriateness of big data analytics in healthcare

(Rehman et al., 2021). The aggregate of these data is

analyzed to assist patients with diets, reminders of

preventative care, personalized medical care, follow-

up on prior consultations and medicines, and

counseling (Razzak et al., 2020).

Due to the considerably broad customer base,

relatively few regulatory obligations and ease of

access to wearable devices and medical apps,

personalized medical care has significantly increased

its market size, as shown in Figure 4.

Figure 4: Trend of Personalized Medicine (2012 -2022).

4.2 Evidence-based Healthcare

Traditional healthcare is changing from expedient

and discretionary decision-making to evidence-based

medical practices (Piai and Claps, 2013).

Evidence care is a healthcare practice where we

base the patients' conditions on scientific proof.

Through consolidating data from various outlets, big

data offers evidence-based treatment. The data trends

and patterns would provide sufficient support for

diagnosis and treatment (Piai and Claps, 2013).

4.3 Enhancement of Public Health

Monitoring

The analysis of healthcare data with ground-breaking

methods aids in the epidemic trends analysis, disease

outbreaks monitoring, and the spread of disease. This

approach improves public health monitoring,

education, and reaction time. An excellent example is

the Covid 19 pandemic surveillance system in the

United Kingdom which offers a daily update of a

postcode district-based location with infection rates

in that district, generates a risk score, and

communicates it to the user. Furthermore, the app

allows users to check into a specific place, recording

their presence at that particular time and date. The app

also stores an individual's check-ins with the name

and IDs of such locations, which work with the test

and trace teams to inform users on association with a

particular area at a given time. For example, suppose

someone visits a local bar and is tested positive with

Coronavirus. In that case, the app alerts everyone who

has also checked in the same place to self-isolate or

quarantine.

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Figure 5: Emerging Ethical Issues in Healthcare Big data.

4.4 Improves Interaction between

Healthcare Providers and Patients

Big data technology also improves collaboration

between healthcare providers and patients. For

example, on social media, people with common

health conditions and healthcare professionals with

similar specialties across the world can share

information on the treatment and cure of some

illnesses, thereby promoting interaction within health

systems.

5 METHODOLOGY

5.1 Systematic Literature Review

The complete literature review of the paper deals with

'big data in healthcare' based papers and studies

published in scholarly journals focuses on the

following objectives:

• Understanding the concept of big data for

healthcare.

• Recognizing tools and techniques for big data

analytics in healthcare.

• Underlining the future benefits and uses of big

data in healthcare.

• Reviewing emerging ethical concerns of big

data systems in healthcare.

We obtained most of the pertinent papers used for this

study from Research Gate, IEEE, and Google Scholar

research sources, which we used to explore for the set

of specific articles related to the proposed research.

We used an inclusion basis to choose big data and

healthcare papers to find relevant papers to answer

research questions based on predefined keywords.

Our aim is to support developing an emerging ethical

framework for Healthcare big data, as shown in Figu

5.2 The Diabetes Dataset (UCL

Repository)

Since millions of healthcare data points are created

and shared daily, a central data repository that

aggregates the entire dataset in one location is needed

(Luo et al., 2016). We also need powerful tools to

extract information rapidly and analyze the selected

data effectively. While Power BI will give healthcare

organizations visibility into their data and help them

gather many insights, other more effective analytics

tools should also be considered. Furthermore, even

though the data has been de-identified, there are other

ethical issues and concerns that we will discuss in the

subsequent section of the article (Durcevic, 2020).

5.3 Research Surveys

In this project, we used primary data collected by the

authors' team from randomly selected respondents

and a pre-processed dataset originally obtained from

Health Information National Trends Survey HINTS 4

Cycles 1 (NCI, 2012).

5.3.1 Primary Research Survey

We used Sogo Survey to conduct the primary

research questionnaire to extract respondents'

concerns with big data analytics and ensure that the

required data is retrievable intelligently.

Unlike the traditional approach, an online survey

makes retrieval and analysis of the relevant

information more accessible. Power BI visualization

is appropriate because it can display complex data in

an interactive and user-friendly manner. To move this

forward, 53 people filled the survey, and we will

address the results in the following segment.

An Ethical Framework for Big Data and Smart Healthcare

5.3.2 Secondary Research Survey

We used the pre-processed first cycle HINTS 4

survey, conducted on 3959 responders between

October 2011 and February 2012, with a response rate

of 36.7 percent. Five questions, labeled A-E, were

listed, and are discussed further below.

A. Concerns of unauthorized access to their

health records as they are transferred

electronically between healthcare facilities.

B. Concerned about unauthorized access to

their records as they are faxed between

healthcare professionals.

C. Satisfied that protections were in place to

shield their patient records from unwanted

access.

D. Satisfied that they had a voice in collecting,

using, and exchanging their medical records.

E. Hidden details from a healthcare provider

out of respect for the patient's safety?

We used the following concepts in this work.

6 ANALYSIS AND FINDINGS

6.1 Ethical Problems of Big Data

Analytics in Smart Healthcare

As mentioned earlier, this project discusses some

emerging ethical concerns of big data in healthcare,

including discrimination, data breaching and privacy

issues as delineated in the following. We also discuss

further how some ethical issues could lead to

potential discrimination.

6.1.1 Discrimination

Big data analytics can potentially exacerbate pre-

existing demographic gaps in healthcare by

presenting biased results from the algorithm used

(Cahan et al., 2019; Cutillo et al., 2020). The data

used to train these algorithms contributes more to

such generalization or stereotypes against a group.

Racial biases embedded in typically biased training

datasets are more likely to yield racially

discriminatory predictive models (Cutillo et al.,

2020). For example, the predictive models derived

from the Framingham Heart Study and precision

medicine protocols centered on European ancestry

(Paulus et al., 2018). The causes of discriminatory

bias in a dataset could occur at different phases of an

analytical pipeline (Suresh and Guttag, 2019).

As observed in Figures 6 & 7 below, the Diabetes

readmission dataset used in this project is highly

imbalanced. The dataset has an overrepresentation of

the Caucasian race, leading to a false generalization.

Additionally, there is an aggregation bias in the

dataset as it is hard to know which group (race) is

others. While the gender feature is well represented,

the LGBT populations can feel unfairly aggregated

with the two genders.

Figure 6: Number of Readmissions by Race.

Figure 7: Number of readmissions by Gender.

On the other hand, we cannot say there is an

underrepresentation based on age because it is rare for

people below 20 years to be diabetic (as shown in

Figure 8 below).

Figure 8: Number of Readmissions by Age.

The model analysis with Power BI Key Influencer

(shown in Figures 9 & 10 below) identified

evaluation bias. It revealed that Asians, Hispanics,

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African Americans, and people weighing between 0

and 25kg are unlikely to be readmitted.

Figure 9: What factors Influences Readmission to be No.

However, Caucasians and people weighing more

than 200kg are more likely to be readmitted due to

Diabetes. This finding could lead to a misleading

generalization of readmitting Caucasian patients

weighing more than 200kg even though they are fine.

Figure 10: What factors Influences Readmission to be Yes.

Conversely, it might also lead to refusal of

admission for patients who do not identify as White

or do not weigh up to 200kg.

The aforementioned analytical outcome might

lead to social exclusion, marginalization, and

stigmatization. Because some persons may be picked

out and excluded or included due to the bias, the

revelation and application of this study may result in

stigma and discrimination. The possible implication

could be prioritizing hospital spaces for some patients

or refusing to readmit other patients due to their racial

identity. The possible implication could also be

prioritizing hospital spaces for some patients or

refusing to readmit other patients due to their body

weight. This finding is consistent with Obermeyer et

al.'s (2019) research, identifying how big data

analytics could be discriminatory, affecting patients'

treatment plans.

6.1.2 Data Breach

Breach of protected health information (PHI) security

substantially impacts individuals and healthcare

institutions (Agaku et al., 2014). The annual cost of

stolen or compromised PHI in the US healthcare

sector is estimated to be up to $7 billion. According

to research conducted by IBM Security (2019),

healthcare data is the most cost of all sectors, with

continuous growth in the number of breaches. Figure

11 illustrates that healthcare has the highest average

cost of a data breach, almost twice the global average.

Figure 11: Average cost of data breach by industry.

Big data Ethical challenges are not isolated issues

as data breaches could result in the disclosure of

personal health information and financial or medical

identity theft. In some cases, it can result in severe

health consequences on patients (Agaku et al., 2014).

Furthermore, a data breach may result in disclosing

hitherto undetectable behavioral or psychographic

tendencies (Winter, 2018). Data from seemingly

insignificant daily routines is gradually being pooled

and utilized to uncover behaviors or patterns,

clustering or associating individuals into separate

groups, resulting in unfair generalizations against

such groups. Unauthorized access to private

information or activities, such as medical data, could

be used to discriminate against persons seeking

immigration eligibility, medical treatment, education,

banking, and jobs (Winter, 2018).

6.1.3 Privacy

Privacy is a fundamental human right that allows one

to choose between exposing or not to expose

themselves to others and the rest of the world (Chang,

Shi and Zhang, 2019). From the primary survey

results shown in Figures 12 & 13, most people agree

that big data analytics technologies are functional in

healthcare. However, respondents are concerned

An Ethical Framework for Big Data and Smart Healthcare

about the sensitivity of healthcare data, which may

jeopardize their privacy.

Figure 12: Big data and AI in Healthcare.

Figure 13: Sensitive Data.

The HINT (NCI, 2020) survey results (shown in

Figure 14 below) indicate that, while the majority of

respondents are concerned about unauthorized access

to their health records, they have confidence that

medical providers and institutions would value their

voice and therefore keep their data secure.

Figure 14: HINT Survey Results.

Personal data can be retrieved at different stages

of analytics (Dev Mishra and Beer Singh, 2017).

Since modern healthcare services demand patients to

provide private and sensitive information to access

medical services, clients lose control over the

confidentiality of their data when they hand over

personal information to third parties and rely on the

organization to safeguard its security. Such

dependence increases the risk of information leakage

if the trusted entity does not implement proper

security measures to secure client data (Mariani,

Mohammed, and Mohammed, 2015). Protection of

patients should be prioritized by avoiding any type of

surveillance or unauthorized identification.

7 EVALUATION AND

DISCUSSION OF FINDINGS

7.1 Conclusion and Implications

Removing private information to increase patients'

anonymity is a powerful method of protecting patient

data. The difficulty faced is determining the

removable feature with high sensitivity from the data.

While, in cases such as the coronavirus pandemic,

the use of sensitive patient data such as location may

improve governments' and research institutions'

ability to combat the threat more quickly by a

surveillance system that provides location data used

to curb the current crisis. The diabetes data, on the

other hand, has features that could give

discriminatory and stereotypical generalizations.

Data scientists must be mindful that utilizing these

large amounts of data comes at the expense of human

liberty and social autonomy. Lessening the risks of

using these data must be monitored by established

legislative measures, such as the General Data

Protection Regulation (GDPR). The Human-

Centered Design approach must be the intent and

goals of data usage, including its processing, analysis,

warehousing, and dataset sharing.

The following are the main conclusions observed

from these principles and criteria for operational use

of data-driven healthcare analytics:

7.1.1 Data Sensitivity Is Relative

The description and decision of feature sensitivity

vary from project to project, and it also depends on

the social value and regulations. For example, the

outcome from diabetes data analytics is

discriminatory and stereotypical. Can we say that

Caucasian white women weighing more than 200 kg

are more likely to be diabetic than other ethnic

groups?

7.1.2 Discrimination Is Just as Severe

Understanding the significance of data privacy and

security is crucial. Most data science ethics journals

are concerned with privacy and security and their

implications. Notwithstanding, there are

discriminatory and racist submissions arising from

big data analytics, which also have grave

consequences. Furthermore, to ensure a fair model,

we must measure analytics discriminatory tendencies

against respective advantages.

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7.1.3 Human-Centered Design (HCD) Must

Be Ethically Compliant

Each phase in the Machine Learning and big data

analytics design process should consider the data

citizens impacted by models, methods, and

algorithms developed by data scientists. Biases in

defective datasets, algorithms, and human users are

numerous and discussed in depth. We must not ignore

that, owing to the vulnerability of data subjects and

groups, the risk of discrimination is more severe.

Furthermore, data scientists are also data citizens,

asides from developing big data insights, they are also

affected by such techniques. As a result, maintaining

ethically acceptable data processing and analytics is a

win-win scenario for all parties involved.

ACKNOWLEDGEMENT

This work is partly supported by VC Research

(VCR0000158) for Prof Chang.

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