The contributions of the Internet of Medical Things (IoMT), cloud services, information systems, and smart devices are useful for the healthcare industry. With the help of digital healthcare, our lives have been made much more secure and effortless and provide more convenient and accessible treatment. In current, the modern healthcare sector has become more significant and convenient for the purpose of both external and internal threats. Big data breaches affect clients, stakeholders, organisations, and businesses, and they are a source of concern and complication for security professionals. This research examines the many types and categories of big data breaches that companies face. In addition, the study’s main purpose is to investigate and draw conclusions from healthcare big data breaches, with the goal of improving healthcare big data confidentiality. From the beginning of 2020–21, both years, practically our entire world moved online due to the COVID-19 pandemic. The coronavirus pandemic dramatically increased the extent of use of technology. Hacking/IT incidents, followed by unauthorised internal disclosures, are the most typical attacks behind healthcare data breaches, according to the report. This has become a main enticement for healthcare data theft and misuse. The number of healthcare big data breaches, the number of records exposed, and the resulting significant commercial losses are increasing. In the report, they analyze and investigate the number of healthcare big data breaches, the number of records exposed during the COVID-19 pandemic. Also, the study assesses the correctness of the datasets in order to achieve a dynamic digital healthcare data breaches environment using fuzzy based computational technique.
In the healthcare business organization, Electronic Health Record (EHR) systems have changed paper-based systems, providing additional economical and better services to their clients. EHRs improve patient care, increase practice efficiency, improve disease diagnosis, foster patient cooperation, and provide constant access to patient health information [
Moreover, when these big databases are exposed by unauthorized individuals due to software vulnerabilities and security breaches, then the result is sensitive data is exposed as a result of data breaches. This sensitive big data is protected by loss and theft from sensitive data disclosure or attackers of in perspective of healthcare [
According to several practitioners, the overall number of people affected by healthcare data breaches was 249.09 million from 2005 to 2019. In the previous five years alone, 157.40 million people have been affected [
In the year 2020, there were 17 big data breaches reported from 17 countries [
Big data assets are at risk in the form of individuals and organizations, as demonstrated by the data presented above. The most concerning, the target of cybercriminals are healthcare businesses, and thus they are the most vulnerable. As a result, both individuals and businesses are concerned about big data privacy and confidentiality. Healthcare big data needs expanded privacy and security, as well as the ability to withstand data breaches. Our main goal in this research was to discuss healthcare big data breaches that had been reported or published by a variety of reputable and reliable sources and use the findings to enhance healthcare big data security. The healthcare big data breaches, led by elements, will be addressed in future research work targeted at improving healthcare big data security.
The remainder of this research is separated into the segments below. The applied methodology is defined in the second part. The third section contains information regarding the sources of big data. The fourth section contains the investigation of big data breaches, providing perceptions into big data breaches that are relevant to the healthcare industry. The next portion contains data analysis and related results through Fuzzy Analytic Hierarchy Process (fuzzy-AHP) based methodology. The ninth portion contains a discussion and a summary of the work’s findings, while the last section details the conclusion.
The main purpose was to analyse healthcare data breaches. The goal of this review was to study more about the causes and effects of big data breaches on individuals and businesses. In this study, the authors used the sources to analyze information about healthcare data breaches and also other sectors in more detail. These sources are: The Privacy Rights Clearinghouse (PRC), HIPAA journals, Human Services (hss.gov) USA, and the Office for Civil Rights (OCR) Department of Health, The Ponemon Institute reports on big data breach costs, and Verizon Big Data Breach Investing to do this (Verizon-DBIR).
In the following segment, we’ll go over various sources of information in detail. The format steps of big data analysis applied in this research are as follows:
The following sources were used to obtain relevant data for the current study project shown in
Sources | Descriptions |
---|---|
PRC big database | PRC is a non-profit organisation in the United States, formed in 1992 by Beth Givens. The organization’s chief motivation is to protect users’ information and advocacy services. Its main goal is to increase user awareness about the properties of technology on personal privacy and to establish guidelines for data management. It offers a comprehensive database of big data breaches. Various organisations have reported a total of 9016 big data breaches to the big database. According to the PRC big database, about 10 billion user records have been compromised since 2005. |
HIPAA journal | The HIPAA Act of 1996 resulted in the publication of a HIPAA journal. It’s a US-based publication that covers healthcare data breaches in depth, as well as HIPAA compliance and practical data breach prevention strategies. Since September 2009, it has been giving thorough information about data breaches in perspective of healthcare. |
OCR reports | Every year, bi-yearly, or tri-yearly, the US Department of Health and Human Services’ Office for Civil Rights publishes a “Report to Congress on Breaches of Unsecured Protected Health Information.” From 2009 to 2017, these big data breach reports [ |
Ponemon institute reports | The Ponemon Institute is a well-known research organization that emphases on subjects such as big data security, privacy, and information security, as well as legislation. It was founded in Michigan in 2002 by Dr. L. Ponemon. IBM supported the institute’s reports, which include a compilation of verifiable records of big data breach expenses [ |
Verizon-DBIR | Verizon Enterprises’ big data breach investigation reports are based on yearly assessments of big data breaches. Verizon published the first report of its kind in 2008. The papers detail big data breaches in both private and public organisations around the world. |
All of the big data sources are respectable and trustworthy, as well as globally recognised information sources that provide data breach reports. For this research project, we’ve also used the sources listed above to look at healthcare data breaches, their causes, and significances. These materials aided us in gaining a better sympathetic of the patterns of big data breaches.
A big data breach is defined as the unauthorised disclosure or use of information and is also well-defined by the US Department of Health and Human Services as “the unauthorised use or disclosure of confidential health information that compromises the privacy or security of that information under the privacy rules and poses a sufficient risk of financial, reputational, or other type of harm to the affected person” [
Data breaches can cause harm to individuals and organisations in a variety of procedures. Apart from the major financial losses that organizations face as a result of big data theft, such incidents also damage the organizations’ brand niche and brand value. There are two most common forms of data breaches, i.e, internal and external big data breaches. All cases of big data breach that are exposed through an organization’s internal sources are referred to as internal big data breach. Privilege abuse, unauthenticated disclosure/access, inappropriate disposal of redundant but sensitive big data, theft or loss, or accidental delivery of secret big data to the wrong address are all scenarios that could occur. A third-party company or source is responsible for external big data breaches. Among other hacking/IT problems, it could be a ransomware attack, malware attack, a spyware, phishing, or card fraud.
From 2020 to 2021*, the Privacy Rights Clearinghouse (PRC), a non-profit organisation in the United States, recorded 9208 big data breaches across several sectors. Intentional Disclosure and Unknown Approach (UNKN), such as sending Bi (DISC). The following types of enterprises have been impacted by these big data breaches:
MED; Educational Organizations are denoted by EDU; Businesses-Financial, Insurance Institutes and Organizations are denoted by BSF; Businesses-Other are denoted by BSO; Businesses-Retail, including Online Retail, are denoted by BSR; Government and Defense Institutes are denoted by GOV; and Non-Governmental Organizations are represented by NGO [
Each sector’s big data breach instances were likewise included in the PRC big database. Because no records were compromised in these attacks, the authors did not include these numbers in their reference to sector-wise picture of big data breaches. Subsequently extensive research on the PRC big database, the accumulated data is shown in
Sectors | Big data breaches in last 15 years (2005–2019) | Big data breaches in last 5 years (2015–2019) | Big data breaches in last 2 years (2020–2021) | |||
---|---|---|---|---|---|---|
Breaches | Percentages | Breaches | Percentages | Breaches | Percentages | |
EDU | 671 | 10.55 | 64 | 3.08 | 572 | 12.82 |
BSF | 410 | 6.45 | 194 | 9.36 | 1143 | 25.63 |
BSO | 426 | 6.70 | 113 | 5.45 | 132 | 02.96 |
MED | 3912 | 61.55 | 1587 | 76.59 | 993 | 22.26 |
GOV | 561 | 8.82 | 45 | 2.17 | 108 | 02.42 |
NGO | 75 | 1.18 | 7 | 0.33 | 77 | 01.72 |
BSR | 300 | 4.72 | 62 | 2.99 | 331 | 07.42 |
TOTAL | 6355 | 99.97 | 2071 | 99.97 | 4459 | 100.41 |
In three situations,
According to an assessment of the entire 17-year period, the healthcare (MED) sector had the largest number of big data breaches in three scenarios: (2005 to 2019), (2015 to 2019), and (2020–2021). There were 3908 breaches in the healthcare industry alone between 2020 and 2021, out of 6355 overall breaches. This accounts for 87.65 percent of the total. This is a huge cause for concern, and immediate action is required.
The authors categorized the big data on healthcare data breaches available in the HIPAA journal based on analysis results between 2010 and 2021*. To get results from these analyzed reports, the data was analyzed from a number of yearly, monthly, and other reports published by HIPAA magazine. It is not likely to deliver a link to every monthly & yearly HIPAA journal report that authors used to collect the data. We only mentioned valid sources of big data journals’ key references in the results. After collecting records of big data from the same sources, we found calculable differences in various reports, such as the number of big data breaches recorded in 2019 being 505 in one HIPAA report and 512 in the other. A new study cites the increase in health data breaches during the COVID-19 pandemic. According to the studies of healthcare data breaches in the first six months of 2021, the authors found that the healthcare industry saw a 51% increase in breaches/leakages compared to 2019. In such circumstances, we select to use the big data from the most current report, which is shown in
Year | No. of big data breaches | Exposed records (Millions) |
---|---|---|
2010 | 199 | 5.530 |
2011 | 200 | 13.150 |
2012 | 219 | 2.800 |
2013 | 280 | 6.950 |
2014 | 314 | 17.450 |
2015 | 270 | 113.270 |
2016 | 330 | 16.400 |
2017 | 360 | 5.100 |
2018 | 371 | 33.200 |
2019 | 512 | 41.200 |
2020 | 642 | 29.300 |
2021* | 674 | 3.350 |
Total | 4371 | 287.7 |
From 2010 to 2019, the united big data of various reports published by the Office for Civil Rights with the label “Report to Congress on Breaches of Unsecured Protected Health Information” was given in
Year | No. of big data breaches | Individuals affected (millions) |
---|---|---|
2010 | 207 | 5.400 |
2011 | 236 | 11.410 |
2012 | 222 | 3.270 |
2013 | 294 | 8.170 |
2014 | 277 | 21.340 |
2015 | 289 | 110.700 |
2016 | 334 | 14.570 |
2017 | 385 | 5.740 |
2018 | 302 | 12.200 |
2019 | 408 | 38.735 |
2020 | ------ | ------ |
2021* | ------ | ------ |
Total | 2954 | 231.535 |
Since ORC big data for the years 2020–2021 is not available, the united big data of HIPAA and ORC reports was compared from 2010 to 2019. According to a comparison of HIPAA and OCR healthcare data breach reports, we found a small difference in the number of data breaches which are reported each year.
According to HIPPA and ORC’s big data reports, there were 3055 data breaches and 255.05 total reports of these healthcare breaches from 2010 to 2019, with the uppermost number of big data breaches stated in 2019, and the maximum number of records exposed in 2015, as shown in
HIPAA big data breach reports have been thoroughly examined, and it has been discovered that the most common types of secured healthcare information leaks are illegal access (internal), hacking incidents, theft or loss, and inappropriate disposal of superfluous data. The approach to references [
All cyber-attacks that are employed to acquire unauthorised access to confidential data are classified as hacking incidents. The most common hacking techniques used to disclose protected health information are ransomware and malware [
This term refers to all forms of attacks that result in the disclosure of confidential health data using any internal source within a company. It could be unauthenticated access/disclosure, misuse of privileges, and so on.
This refers to any occurrence involving the theft or loss of a hard drive, laptop, or other portable device carrying secured data of healthcare sector, which results in the disclosure of secured health information. This could be the result of catastrophic device failure or loss.
Big data that isn’t needed but is sensitive and confidential should be carefully disposed of so that it can’t be recovered. Sensitive health information could be revealed if big data is handled incorrectly. Improper disposal attacks are defined as incidents that occur as a result of the inappropriate disposal of avoidable but confidential and sensitive data in healthcare perspective. The number of healthcare big data breaches that happened as a result of the above-mentioned disclosure types is shown in
Year | Hacking/IT incidents | Unauthorized access/disclosure incidents | Theft/Loss |
Improper disposal incidents |
---|---|---|---|---|
2010 | 8 | 10 | 148 | 10 |
2011 | 17 | 29 | 137 | 7 |
2012 | 18 | 28 | 148 | 8 |
2013 | 29 | 64 | 150 | 13 |
2014 | 39 | 87 | 149 | 12 |
2015 | 56 | 103 | 106 | 6 |
2016 | 115 | 130 | 79 | 7 |
2017 | 148 | 128 | 71 | 11 |
2018 | 168 | 140 | 54 | 10 |
2019 | 312 | 141 | 53 | 6 |
2020 | 429 | 143 | 54 | 16 |
2021* | --- | ----- | ----- | ---- |
Total | 1339 | 1003 | 1149 | 106 |
The number of disclosure types is shown in the Between 2010 and 2020, the above-mentioned disclosure categories were used to carry out a total of 3597 breaching events. Separate hacking/IT incidents accounted for 37.22 percent of the breaches. Internal unauthorised disclosures were responsible for 27.88 percent of the breaches. Theft/loss cases accounted for 31.94% of the cases. 2.94 percent of the incidents were caused by the inappropriate disposal of sensitive big data that was no longer needed. Over a ten-year period, the statistics reveal that theft/loss is the most common, followed by IT incidents/hacking and unauthorised internal disclosure, with only a few instances of inappropriate disposal. When we looked back over the last four years, we noticed a sharp growth in IT incidents/hacking. Out of the 1339 IT incidents/hacking recorded during a ten-year period (2010–2020), 1057 occurrences were reported in the last four years alone (2017–2020), accounting for 78.93% of the total, with 32.03 percent occurring in 2019.
According to our findings, other IT-related vulnerabilities/hacking have become a major worry for the healthcare big data industry in recent years. Internal disclosure and unauthorized access have risen in recent years as well, but not at the same rate as hacking incidents. Out of a total of 843 illegal internal disclosure instances, 542 were stated in the last four years. This represents 55.03 percent of the total, with 14.25 percent of the events taking place in 2019. When this percentage (16.84%) is compared to the same period last year (2019), it is apparent that hacking has climbed by 32.23 percent. The number of instances of improper internal disclosure has more than doubled. We also uncovered how the frequency and intensity of hacking attacks has increased, posing a severe threat to the healthcare business.
Theft/loss and improper disposal, on the other hand, have been reduced dramatically during the last four years. In the last four years, just 232 theft/loss incidents were reported, accounting for 23.86 percent of the total. Furthermore, in the last four years, just 43 instances of improper disposal have been documented, accounting for 40.56 percent of the total. According to their calculations, theft/loss and improper disposal have a minor negative impact on the healthcare industry.
While theft/loss and improper disposal incidents have reduced in frequency, IT incidents/hacking and unauthorised access occurrences have improved in frequency, as shown in the graph. In recent years, there has been a significant growth in the number of hacking and IT mishaps. The next subsection will go through where information has been leaked and where sensitive health information has been disclosed/breached.
Papers or electromechanical storage devices are used to store protected health information. This section describes the sites where protected health information is breached using various methods.
Year | Laptop | Desktop computer | Other PED | Paper/Films | Network server | EMR | Other | Total | |
---|---|---|---|---|---|---|---|---|---|
2021* | 19 | 0 | 2 | 1 | 3 | 42 | 6 | 2 | 75 |
2020 | 231 | 16 | 20 | 17 | 90 | 268 | 31 | 44 | 717 |
2019 | 214 | 24 | 34 | 15 | 61 | 132 | 39 | 52 | 571 |
2018 | 115 | 25 | 33 | 20 | 62 | 66 | 26 | 35 | 382 |
2017 | 92 | 20 | 38 | 18 | 64 | 82 | 34 | 39 | 385 |
2016 | 51 | 25 | 26 | 16 | 73 | 83 | 30 | 30 | 334 |
2015 | 32 | 34 | 29 | 19 | 70 | 52 | 27 | 26 | 289 |
2014 | 42 | 43 | 25 | 20 | 57 | 49 | 13 | 30 | 279 |
2013 | 26 | 70 | 40 | 23 | 60 | 31 | 16 | 32 | 298 |
2012 | 8 | 60 | 27 | 20 | 50 | 30 | 5 | 22 | 222 |
2011 | 3 | 48 | 32 | 30 | 65 | 21 | 5 | 32 | 236 |
2010 | 2 | 50 | 26 | 37 | 21 | 12 | 0 | 60 | 208 |
Total | 835 | 415 | 332 | 236 | 676 | 868 | 368 | 404 | 3996 |
The sites where secured health information has been hacked are listed in
With only 368 incidents, EMR are the least vulnerable. This accounts for barely 9.20 percent of the 3996 events reported during the same time period. Other Portable Electronic Devices (PED) come in second, accounting for 5.90 percent of the total. Desktop computers make up 8.25% of all computers. According to big data, attacks on network server locations and emails have increased significantly between 2017 and 2020. In the last four years, according to studies, the most prevalent causes of data breaches include obsolete security software, big database servers without passwords, and emails with weak or no passwords. Our research also found that the number of breaches of protected health information through paper, desktop computers, laptops, and films has decreased little over the last four years.
Our research confirms that cyber criminals are currently targeting sensitive healthcare data by employing various strategies, such as ransomware, malware, and phishing attacks [
It’s impossible to figure out just how much a data breach will cost. Many institutes have set characteristics and used various methodologies to assess the average cost of big data breaches. The Ponemon Institute collects both indirect and direct charges sustained by an organisation to calculate the average cost of a big data breach. This segment deliberates the financial effects of big data breaches, with a focus on healthcare data breaches. The Ponemon Institute’s big data breach cost reports, which were supported by IBM, were used to calculate the financial effects of big data breaches on individuals, organisations, and countries. From 2010 to 2019,
Year | Average cost of breach (Millions) | Average cost per record | Cost per record in healthcare |
---|---|---|---|
2010 | $7.24 | $214 | $294 |
2011 | $5.50 | $194 | $240 |
2012 | $3.20 | $136 | $233 |
2013 | $3.29 | $140 | $296 |
2014 | $3.50 | $145 | $359 |
2015 | $3.79 | $154 | $363 |
2016 | $4.00 | $158 | $355 |
2017 | $3.62 | $141 | $380 |
2018 | $3.86 | $148 | $408 |
2019 | $3.92 | $150 | $429 |
2020 | $3.86 | $146 | $7.13 |
2021* | $4.24 | $161 | $499 |
According to a big data breach cost study, the cost of a healthcare breached record has risen rapidly in comparison to the average cost of a breached record. The average cost of a record in 2010 was $214. That cost decreased by 10% in 2011. In comparison to the previous year, it decreased by 42.64 percent in 2012. After that, it continued to rise or fall year after year, with a 1.55 percent increase in 2019 over the previous year. Between 2010 and 2019, the average cost of healthcare enhanced by 45.91 percent, from $294 to $429. The cost of each breached healthcare record was $294 in 2010, and it continued to fall until 2012.
Between 2014 and 2015, it increased by 1.11 percent, 7.04 percent between 2016 and 2017, and 5.14 percent between 2018 and 2019. According to the Verizon DBIR research from 2018, financial gain fuelled 76 percent of big data breaches in 2018 [
AHP under the fuzzy environment has been used in this work for greater accuracy and efficiency. To determine the overall idealness and performance nature of the different sources of big data breaches in healthcare sectors, five above-mentioned sources of healthcare data breaches as attributes have been considered for this experiment. These attributes are symbolized as PRC Big Database (D1), HIPAA Journal (D2), OCR Reports (D3), Ponemon Institute Reports (D4), and Verizon-DBIR (D5) in the following tables. From 2010 to present year, the data analysis of these sources of big data breaches related to healthcare sectors and others are explained. With the help of [
D1 | D2 | D3 | D4 | D5 | |
---|---|---|---|---|---|
D1 | 1.00000, 1.00000, 1.000000 | 1.000000, 1.515007, 1.900331 | 0.480096, 0.630072, 1.000000 | 0.410052, 0.500743, 1.000000 | 0.220015, 0.200871, 0.415002 |
D2 | – | 1.00000, 1.00000, 1.000000 | 0.570043, 0.600657, 0.802200 | 0.300039, 0.393006, 0.500661 | 0.260079, 0.350021, 0.500176 |
D3 | – | – | 1.00000, 1.00000, 1.000000 | 1.000000, 1.310095, 1.550018 | 0.300009, 0.430052, 0.802007 |
D4 | – | – | – | 1.00000, 1.00000, 1.000000 | 0.530086, 0.910043, 1.583006 |
D5 | – | – | – | – | 1.00000, 1.00000, 1.000000 |
Defuzzification is used after the comparison matrix is built to generate a measurable value based on the derived TFN values. The defuzzification approach used in this study was developed from [
Datasets | D1 | D2 | D3 | D4 | D5 |
---|---|---|---|---|---|
D1 | 1.000000 | 1.490012 | 0.690010 | 0.640010 | 0.300027 |
D2 | 0.671136 | 1.000000 | 0.670070 | 0.410043 | 0.372004 |
D3 | 1.449250 | 1.492380 | 1.000000 | 1.290077 | 0.490035 |
D4 | 1.562480 | 2.438770 | 0.775148 | 1.000000 | 0.960036 |
D5 | 3.333030 | 2.688140 | 2.040670 | 1.041630 | 1.000000 |
C.R. = 0.0286547000 |
S. No. | Datasets | Weights | Percentages | Ranks |
---|---|---|---|---|
1 | D1 | 0.132225 | 13.22% | 4 |
2 | D2 | 0.107036 | 10.70% | 5 |
3 | D3 | 0.197540 | 19.75% | 3 |
4 | D4 | 0.229325 | 22.93% | 2 |
5 | D5 | 0.333874 | 33.38% | 1 |
The most important task for every researcher [
S. No. | Datasets | Fuzzy AHP | AHP | ||
---|---|---|---|---|---|
Weights | Percentages (%) | Weights | Percentages (%) | ||
1 | D1 | 0.132225 | 13.22 | 0.134575 | 13.45 |
2 | D2 | 0.107036 | 10.70 | 0.115454 | 11.54 |
3 | D3 | 0.197540 | 19.75 | 0.187740 | 18.77 |
4 | D4 | 0.229325 | 22.93 | 0.230655 | 23.06 |
5 | D5 | 0.333874 | 33.38 | 0.326584 | 32.65 |
Smart phones, cloud services, information systems, internet access, IOMT, and other web-connected smart gadgets have enabled the healthcare industry to transition from paper-based systems to electronic health record systems. In information and communication technologies, healthcare big data has grown more digitised, distributive, and mobile. Despite the numerous benefits of EHRs, the digital health data of patients is currently under threat. As revealed in our investigation, the big data breach patterns for infiltrating sensitive big data have also undergone a significant transformation. The healthcare industry is certainly a target for cyber thieves, as demonstrated in this research. Furthermore, in order to gain expertise and use it in our future studies, we analysed many big data breach reports created by various corporations and institutes. The study’s final findings are, as summarized by us: From 2005 to 2021, more than 12 billion records from several industries were exposed. MED, EDU, NGO, BSF, BSO, BSR, and GOV are the types of these sectors. In the healthcare industry alone, there have been 4371 documented big data breaches. From 2020 to 2021*, about 85 percent of health data was compromised, the highest rate in any industry. According to HIPAA and OCR statistics, hacking/IT incidents are the leading cause of healthcare data breaches. According to HIPAA data, 4371 healthcare data breaches harmed 287.7 million people between 2010 and 2021* and an average of 3,343,448 healthcare records were breached. IT incidents/hacking, internal disclosure/unauthorised access, loss/theft, and improper disposal are the types of assaults employed to disclose protected health data. Theft/loss and inappropriate disposal, on the other hand, have seen a decline in the graph during the last three to four years. From 2020 to 2021, the number of hacking/IT incidents climbed by 37.22 percent. However, from 2020 to 2021, illegal internal disclosure, theft/loss, and inappropriate disposal fell by 29.88 percent, 31.94%, and 22.22 percent, respectively. By concentrating on the characteristics of healthcare data breaches, security measures will be improved, evaluated, and prioritised. When compared to AHP, MCDM methods like as Fuzzy-AHP give more efficient findings, and hence develop as a suitable technique for estimating big data breaches in healthcare.
The healthcare business is the most expensive, with a cost of $4.24 million for a big data breach, but the average total cost of a big data breach in 2021 was only $4.24 million [
The authors believe that e-health data is extremely vulnerable because it is the most frequently targeted by attackers, based on their investigation of healthcare data breaches. According to a long-term investigation into big data breaches, healthcare records were exposed due to both internal and external threats such as hacking, theft/loss, unauthenticated internal disclosure, and inappropriate disposal of redundant yet sensitive data. According to the findings, both the quantity of big data breaches and their cost will enhance in the future. As a result, researchers, security experts, and the healthcare industry must prioritise and work on all preventive measures available. In addition, when creating a research study that attempts to provide perceptions of healthcare data breaches, there are a lot of other aspects to consider. Only the most important ones have been listed by the writers of this study. Identify and address the reasons for the majority of cyber-attack victims in the healthcare sector/organizations in recent years. The classification of IT incidents/hacking that result in healthcare data breaches, as well as the preventive steps that should be implemented to avoid them.
As research is a dynamic process, we cannot claim that our identified attribute collection is perfect, but it is a good starting point. Furthermore, the suggested assessment method, fuzzy-based AHP, is an effective but not optimum MCDM method. As a result, if possible, researchers can use additional approaches to improve their results. We shall concentrate our efforts in the future on the development of a theoretical framework.
We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP-2020/344), Taif University, Taif, Saudi Arabia.