Big Data Technologies, Applications, and Problems in Healthcare

This report investigates the applications of big data in the healthcare sector, focusing on technologies such as Electronic Health Records (EHRs), real-time alerting systems, predictive analytics, Hadoop, NoSQL databases, data lakes, and in-memory databases. It explores how these technologies are utilized to improve patient care, streamline workflows, and enable data-driven decision-making. The report also identifies key challenges in the healthcare industry, including data capture, cleaning, and security, and proposes solutions to address these issues. The analysis covers the benefits of big data in areas like improving the delivery of care, and also addresses the complexities associated with integrating and managing large datasets within healthcare organizations. The report emphasizes the importance of data governance and the use of automated tools to enhance data quality and security. The overall focus is on the transformative potential of big data to revolutionize healthcare practices.

BIG DATA THEORY AND PRACTICE
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Contents
PART A...........................................................................................................................................3
1. Investigation of Big data applications in healthcare sector.....................................................3
2. Big data applications used by healthcare industry-.................................................................3
2.1 Electronic health Records (EHRs).........................................................................................3
2.3 Real time Alerting..................................................................................................................4
2.4 Predictive Analytics in healthcare.........................................................................................4
3. Discuss the different big data technologies.............................................................................5
3.1 Hadoop...................................................................................................................................5
3.2 NoSQL...................................................................................................................................6
3.4 Data Lakes.............................................................................................................................6
3.5 In-memory database...............................................................................................................7
4. Determine the problem in healthcare sector and type of data should be used.........................7
5. Solution Adopted to address data Governance issues-..........................................................10
REFERENCES..............................................................................................................................12
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PART A
1. Investigation of Big data applications in healthcare sector.
Nowadays, Big data is buzz word in current scenario, heard everywhere, especially in the
healthcare industry. Usually, there are large amount of data or information used by healthcare
sector, which was stored or collected as hard copy. Various type of data has a capability to
support wide range of health care and medical functions (Agrawal, 2020). Big data is become
consider as digitalization, which are completely related with the patient health care and well-
being makes up big data.
Over the past decade, big data is becoming one of the advance technology so as applicable
in the healthcare industry. For example- Electronic health records (EHR) have been widely used
or adopted in different hospitals or clinics worldwide.
2. Big data applications used by healthcare industry
2.1 Electronic health Records (EHRs)
It is one of the most widely spread application of big data in term of medicine. Every
patient has its own digital record which including demographics, allergies, medical history and
laboratory tests etc. these are the best way to the record all essential information. In most of
cases, it should have exchange or share the record via secure information system. This is
available for providers from private as well as public (Canales, 2020). EHRs can provide as
trigger warning, reminders when a patient should get new lab test. This will help for tracked the
prescriptions.
On the other hand, Electronic health Record automates access to data or information,
which has potential to easily streamline the clinician’s workflow. The ability of HER technology
which help to handle the better support and also related to activities indirectly or directly through
various interfaces. Sometimes, it should include evidence based decision in order to maintain the
quality management. As a result, it will be generating accurate result or outcome.
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2.3 Real time Alerting
It is another big data application in healthcare sector, which means that become crucial
functionality. In different hospitals, clinical decision support (CDS) software has used to analyse
large amount or medical data or information. Afterwards, it provide the better health practitioner
with advice as they make a better prescriptive regarding decision-making.
By using CDS analytical software, it will be easily accessed the information from large
data set of health. It allows the doctors to compare data in a social-economic context. This will
help for modifying any kind of every delivery strategies accordingly (Chen, 2020). For example-
whenever, doctor will check the patient’s blood pressure in which it will increase, generate
alarmingly. System will automatic way to send an alert in real time to doctors, who will take
important action to reach accurate right decision.
2.4 Predictive Analytics in healthcare
It has been recognised that predictive analytics as one of the biggest business intelligence
trends but it is potential applications reach at right position. For example- medical staff members
have been used the EHRs to collect or store large amount of information. In order to create
individual database of patient and then predictive way to analyse the information. This will help
for improving the delivery of care.
The predictive modelling is useful in healthcare that can easily observed during
emergency care, intensive care and surgical procedures. Usually, it come from the patient who is
directly related to quick reaction and helping in acute decision. If a complex situation takes place
which needs to be used predictive analysis in healthcare (Delanoy, 2020). This will help for
generating accurate result or outcome. At some point, patient face possible threat related to their
wellbeing and also consideration of development in context of infections treatment. So that
predictive modelling applied in healthcare in order to react as possible, also helping to find out
the accurate solution.
The primary goal of healthcare online enterprise intelligence is helping the medical
professionals to make data driven decision within few seconds, improve the better patient’s
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treatment. In particularly, it is useful in case of patient with handle large amount of medical data
histories.
3. Discuss the different big data technologies
3.1 Hadoop
It is based on the open-source software framework for storing or collecting huge amount of
data. It is also supported for running applications on clusters of commodity hardware, which
provide the massive storage of any kind of data or information, enormous processing power and
ability to handle virtually limitless tasks.
For example- in healthcare industry, Hadoop as big data technology used to gain direct as
well as indirect benefits. This type of modern technology will support for increasingly the
inexpensive server that are mostly in cloud. Therefore, different industries have been achieved
the significant cost saving (Finkelstein, 2020). Additionally, it has more ability of Hadoop to
collect or gather massive data, insight derived from crunching relevant information. As a result,
important decision has made by enterprise in real world. Hadoop technology will support for
providing the better consumer segment, fix error in processes, and optimise floor operations.
Therefore, it became easier to predictive analytics etc.
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3.2 NoSQL
It is kind of big data approach in which design database that can accommodate a wide
range of data models, including key values, columnar and other graphical formats. NoSQL which
stands for “not only SQL” and consider as alternative traditional relational database in which
stored information in the form of different tables. Sometimes, data schema is usually designed
before Database is built. So as it become useful for performing the different tasks with help of
large data sets, distributed within similar units.
The NoSQL term can be applied in the healthcare industry while gathering or organising
the large amount of data. Therefore, it become easier to predict the relational database
management system. This is commonly refer as database built and its purpose to cluster in cloud
and web applications. NoSQL is consider as one of the advance technology in big data while
maintain the entire performance and scalability outweighed the need for immediate manner.
3.4 Data Lakes
It is kind of big data technology which means that consolidated repository for storage of
large amount of data or information at certain levels (Jain, 2020). Data or information can be
stored or collected in both structural as well as unstructured manner. whenever, data can be
saved at the time of accumulation as without being transformed into structured data. Sometimes,
it enable them to perform various kind of data analysis by using dashboards & data visualisation
into big data transformation as real time processing.
In most of cases, health care industry is used the Data lakes as big data technology which
help to response as better business growth opportunities. Therefore, enterprises are engaging
with different clients, sustainability productivity, active device maintenance & familiar with
decision making (Shi, 2020). This is the best way to increase overall business growth and
development in marketplace. On the other hand, enterprises that use data lakes which means that
able to defeat their peers, new type of analytics can be conducted in proper manner by using
machine learning. These are helping to consider as important source of log files, fetch data from
different social media, click-stream and even IoT devices. Whenever, data can be saved in the
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particular format but without transformation, it will be executing different analytical tasks and
then represents information in visualisation manner.
3.5 In-memory database
This type of big data technology is also known as IMDB which stored huge amount or data
within computer RAM, controlled by in-memory database management system. In prior, there is
some conventional database which are automatically stored on the disk drives.
If an individual person should consider, conventional disk based database are properly
configured with a proper attention towards block machines at certain level. Data or information
should be used for both written as read purpose. In-memory database big data technology will
refer to another part, it feels that handle all essential blocks to read on particular disk
(Upadhyay, 2020). It is consider as one of non-issue with involvement of in-memory database
where establish a interlinked or connection of database. Moreover, it is also supporting to
monitor or track overall performance of system through direct indicators.
4. Determine the problem in healthcare sector and type of data should be used
Big data analytics is usually turning out to be one of biggest challenges undertaking in
recent memory for healthcare industry. Service provider who are becoming aware about the large
amount of data or information, grips with putting data their electronic health records (EHS). In
this way, health care organisation are successfully adopting big data technologies and integrate
with data driven insight into their operational as well as clinical processes. Medial professional
will check the actual health condition of patient, increase visibility in context of performance,
higher consumer satisfaction.
But at some point, healthcare industry has face a typically challenges, when booting up as
big data analytics.
Challenge-1
Capture- All data or information come from different sources, but unfortunately different heath
care service provider, which are not always gather information and does not have impeccable
data governance habits (Schiuma, 2018). Whenever, capturing the data that is complete, clean,
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formatted and accurate for use in various system. For Example- in context of ophthalmology
clinic, EHR data has been matched with patient report data in just 23% of records. If in case,
patient has been identified 3-4 eye related symptoms, but HER data did not agree at all. This type
situation has been occurred due to poor HER usability, convoluted workflows and also
incomplete understanding towards big data techniques. Therefore, it is very important to capture
accurate information so that it can easily eliminate quality issues.
Solution-1
The best solution in data capture challenge to improve a particular routine by prioritizing
valuable information type for their particular projects. It is an essential aspect to enlist the data
governance, integrity expertise of health related information management staff members.
Therefore, it will be developing a proper clinical documentation improvement program in order
to provide better guidance of clinicians about how entire that particular data or information is
useful for analytics purpose.
Challenge-2
Cleaning- in health care industry, different health service provider are intimately familiar
with significance of cleanliness in operating room, but it may not be quite aware about cleansing
of specific data or information. Unnecessary data can easily integrated with big data analytics
project, especially when gathering large amount of data from multiple source. It must have
needed to record clinical as well as operational element in slightly different formats.
Data cleaning is one of the most challenge for health industry especially in big data
analytics. In some situation, healthcare provider are unable to ensure that datasets are correct,
consistent, relevant and not corrupted. This type of challenging situation occur because service
provider will do data cleaning through manually. Therefore, it would be generated errors,
inaccuracy etc.
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Solution-2
The most appropriate solution for removing the data cleaning related challenge which
needs to be used automated scrubbing tool or platform. it provide the better facilities or service
because it works with a proper logic rules to compare, contrast and correct large data sets.
Automated scrubbing tool is likely to become important to use in sophisticated as well as
precise as machine learning technique. It continue to improve the advancement of rapid
performance and also helps to reduce the time, ensure high level of accuracy and integrity in
healthcare data warehouses.
Challenge- 3
Security- it is one of the most important priority for healthcare organisations, especially
in which handle rapid fire series of data breaches, hacking and other kind of ransomware.
Nowadays, it is one of the most challenge for healthcare industry because increases the phishing
attacks to the sensitive information of health care data ware house. It is subjected to nearly as
infinite array of vulnerabilities.
Security related challenge is directly affecting the entire business performance and
efficiency. It has a chance to misuse the information or detailed of patient’s. Most of patient has
done their payment through online platform. Healthcare companies are stored the sensitive
information of client. If any kind of phishing attacks on healthcare data ware house, which is
directly affecting on the patient’s.
Solution-3
The best solution for maintaining the security related challenge in healthcare industry. In
this way, it would require to use HIPAA security rules which include a list of technical
safeguards (Arunachalam, 2018.). These are storing or collecting health care information so that
safe-guards will support to follow some security procedures by using anti-virus software, setting
up firewalls, encrypted sensitive data.
But even the most tightly secured data centre which has been taken down by fallibility of
staff members, who tend to give prioritize convenience over software or hardware updates.
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Additionally, healthcare industry must have frequently remind their professionals in regards of
data security protocols, consistently review who has access high value information or data assets.
Thus, it prevent through malicious parties, who are causing any kind of damage.
Challenge-4
Querying- The common challenge face by healthcare industry such as data Siloes and
interoperability problem or issue. Usually, this type of challenge has been examined at the time
of data processing. Health care professionals must have typically overcome challenge of data
siloes and interoperability at the time of query processing. Sometimes, this problem will directly
affect on the health care data processes which means that minimise level of performance,
accuracy and management.
Solution-4
The solution is to use robust metadata and strong stewardship protocols which make it
easier for healthcare organization to handle the complex query in data processing. The ability of
query data which means that generate or create a strong reporting and analytics (Upadhyay and
Kumar, 2020). Healthcare industry has been appropriate protocols that help to store large amount
or data. It may possible to generate as complete portraits of healthcare industry status, individual
patient’s health.
5. Solution Adopted to address data Governance issues
As more healthcare industry look to accelerate their growth and development.
Sometimes, it engage with potential clients, big data has taken as important adopted solution in
the governance issues such as data management, IT oversight, risk oversight to real time, poor
performance in healthcare operational tasks. Therefore, healthcare service provider is also
focused on the leverage of data or information in better way to manage the complex operations in
proper manner. Nowadays, it has been rapidly increasing the new trend, early adopter is very
informal in which they can start or execute (Gepp, 2018). As big data technologies has become
consider as mainstream, organisation have looked for different ways to measure the significant
impact of investment.
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By using analytics, healthcare organisations are using the sensors in which determine the
actual maintenance or replacement as per needed, predictive the maintenance emerges as one of
important aspect of ROI. However, it will be decreased since consumers found the ROI which
takes a longer than expected. Initially, healthcare industry is mainly focused on the investment
and always follows a proper IOT ecosystem. Sometimes, it is the biggest challenges slowing IoT
adoption which often because of cost or price exceeding the expected return to data.
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REFERENCES
Book and Journals
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recommendations for general practice. Heredity. 124(4). pp.525-534.
Canales, C., Lee, C. and Cannesson, M., 2020. Science Without Conscience Is but the Ruin of
the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative
Medicine. Anesthesia and analgesia. 130(5). p.1234.
Chen, P.T., Lin, C.L. and Wu, W.N., 2020. Big data management in healthcare: Adoption
challenges and implications. International Journal of Information Management. 53.
p.102078.
Delanoy, N. and Kasztelnik, K., 2020. Business Open Big Data Analytics to Support Innovative
Leadership and Management Decision in Canada. Business Ethics and Leadership. 4(2).
pp.56-74.
Finkelstein, J., Zhang, F. and Cappelli, D., 2020. Using big data to promote precision oral health
in the context of a learning healthcare system. Journal of public health dentistry. 80.
pp.S43-S58.
Jain, S. and Kashya, S.K., 2020. Big Data Management: Theory & Practice. Journal of Software
Engineering Tools & Technology Trends. 7(2). pp.13-22.
Shi, M., Jiang, R. and Shang, J., 2020. A privacy protection method for health care big data
management based on risk access control. Health care management science, 23(3),
pp.427-442.
Upadhyay, P. and Kumar, A., 2020. The intermediating role of organizational culture and
internal analytical knowledge between the capability of big data analytics and a firm’s
performance. International Journal of Information Management. 52. p.102100.
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Schiuma, G. and Carlucci, D., 2018. Big Data in the Arts and Humanities: Theory and Practice.
CRC Press.
Burns, R., Dalton, C.M. and Thatcher, J.E., 2018. Critical data, critical technology in theory and
practice.
Gepp, A., Linnenluecke, M.K., O’Neill, T.J. and Smith, T., 2018. Big data techniques in auditing
research and practice: Current trends and future opportunities. Journal of Accounting
Literature. 40. pp.102-115.
Arunachalam, D., Kumar, N. and Kawalek, J.P., 2018. Understanding big data analytics
capabilities in supply chain management: Unravelling the issues, challenges and
implications for practice. Transportation Research Part E: Logistics and Transportation
Review. 114. pp.416-436.
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