Unstructured Data Management in Healthcare Industry

This report discusses the challenges and best practices for managing unstructured data in the healthcare industry. It explains how AI and machine learning can help healthcare professionals access, store, share, and maintain patient data. The report also proposes a question related to unstructured data in healthcare and suggests software and technologies that can help answer it.

Unstructured Data
Management

TABLE OF CONTENT
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Explaining what sort of unstructured data could be used by an AI or Machine Learning
algorithm in the area you chose:..................................................................................................3
Discuss best practice and options for:..........................................................................................4
Proposing a question that could be asked in relation to your unstructured data and what
software might help you to run AI and answer the question:......................................................6
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................1

INTRODUCTION
Healthcare industry is one of the most important sector of every economy, this not only
contribute in the betterment of health structure but this empowers economic development and
health facility as well. Healthcare industry is advancing digitalization in their process and
management, many areas of healthcare include smart management system allowing healthcare
authority to manage patient and their health with AI and powered base technology (Lee and
Yoon, 2021). Inclusion of technological advancement allow healthcare professional to manage
operation and surgery, CT scan is one of the best example which help doctor to construct image
using AI and smart technology . Both healthcare and technological advancement will promote
life quality, collaboration of these industry boost betterment of people and country.
Healthcare industry is rapidly moving towards technological depended on management,
many innovations in this industry help authority to reduce death rate and increase quality of
treatment. Inclusion of image reconstruction is proof that healthcare industry will reduce chance
of operation failure, image reconstruction allow healthcare professional to reconstruction image
of patient critical part. For example; if healthcare professional is focusing on brain surgery then
they will construct image of brain and main part of brain where operation meant to be done.
Clear image will allow healthcare professional to improve their quality of operation because they
will know exactly where to operate and how to reduce chances of operation failure.
MAIN BODY
Explaining what sort of unstructured data could be used by an AI or Machine Learning algorithm
in the area you chose:
Healthcare industry manage large number of data which is constructed according to the
requirement but number of unstructured data is also available which is challenging arranging and
analyse these data. Healthcare industry usually manage data with the help of computer and
storing system, however, use of AI is not general in the health care industry because many
healthcare professional and authority consider human efforts in arranging medical data (Erdmier,
Hatcher and Lee, 2016). There are certain kind of medical data which become unstructured when
not properly managed by the authority, although these data contain each and every information
related with patient and their health status.
Many healthcare professional after considering AI and Machine learning technological
advancement consider untouched and unstructured data to improve their treatment and quality of

care. It is very clear that data is available in 100% form which means healthcare professional can
access patient's every information included in system and paperwork (Strang and Sun, 2020).
However, from an estimation, only 20% of data is available in the constructed manner with
healthcare professional including information about patient's age, health status, ICD & CT scan
information and other records, remaining 80% of data is unstructured which include critical
information about patient and their health, these may include information about their internal
health status, area to operate, complexity of blood pressure and even chances of surviving the
treatment.
The management of unstructured data is only possible with the help of AI (Artificial
Intelligence) and Machine learning althorgirm, these smart technology analyse the data and
information and construct them for healthcare professional for better understanding of case (Paul
and et.al., 2018). Image reconstruction is one of the best example where unstructured data is
turned into useful information for the management of patient and their critical treatment case. CT
scan including image reconstruction allow healthcare professional to get more detailed insight of
patient's internal health. CT with image reconstruction provide area highlighted in different
colour helping professional to understand area they need to operate.
Discuss best practice and options for:
Accessing/collecting: Accessing data in the healthcare industry is one of the most challenging
task in which healthcare professional need to access both systematic data available in
computerized data management system or non computerized data available in the form of notes
and audio, video and records (Kumar, Boehm and Yang, 2017). Unstructured data accessing is
challenging where healthcare professional need to analyse practitioner notes, words of patient
and care taker, records from medical, records and notes written by patient themselves and many
other source. Accessing these informations is tough but these data provide most beneficial
information to healthcare professional, with the help of AI and Machine learning, unstructured
data can be arranged and easily accessible.
Storing: Storing of data is another challenging task for user, before technological advancement
storing was done on paper and other written documentation. However, digitalization allow user
to store data and information online and access them any time anywhere. In healthcare industry,
data is stored on private storing platform of healthcare authority which may include AI based
cloud storing platform. Unstructured data in healthcare is stored with cloud computing and

storing software, information related to patient include image reconstruction and health status is
stored in these platform which is only accessible by authorized person (Li and et.al., 2017).
There are certain best practice or digital option for storing medical data, these are: Euris Health Cloud: This is one of the best data management system allowing healthcare
professional to store both constructed and unstructured data in online digital platform
accessible anytime.
 Australian Digital Health Agency: This is another platform allowing healthcare
professional to manage data related to health and status of patient, this agency help
authority to structure their unstructured data.
Sharing: Sharing is one of the most important process in the healthcare industry where
healthcare professional need to share important and confidential information with each other. In
healthcare industry, every data is confidential which need to be shared with the safest option
available. Sharing both structured and unstructured data is critical, healthcare professional often
use AI based smart technology to share information between two parties. Many healthcare
authority have private mailing system allowing healthcare professional to share information with
each other. However, sharing unstructured data is challenging because healthcare professional
know the consequences of losing unstructured data. There are certain option available which
allow user to share confidential information in the safest manner, these are:
 Dropbox: This is one of the most powerful data management platform allowing user to
share information with each other in the safest manner, with proper authorization, data is
shared with one user to another.
Documenting: Documenting data and information is significantly important in every industry,
documentation allow user to collect data in structured manner which means there will be not
confusion between organized data. Documentation is necessary in healthcare industry where
healthcare professional need to carefully arrange data and information in proper supported
document. However, there are two type of documenting, paper work documenting and digital
documenting (Bloom, Sadun and Van Reenen, 2016). With the help and effective use of AI,
healthcare professional consider online documenting allowing them to arrange structured and
unstructured data and medical records, AI and technologically advance software help healthcare
professional to manage data related to patient. There are two type of documenting in healthcare
industry, these are:

 Paperwork records: Healthcare industry often store data and records in paper and notes
format which means data is recorded in written format. This data is often called
unstructured data which include information in notes.
 Electronic Medical Records (EMR): This is online documenting platform allowing
healthcare professional to manage data and information in documentation. EMR have
featured of managing data, these informations is available in document format accessible
by healthcare professional and authority.
Maintenance of the data: Maintenance of data is one of the most important element in every
industry, data is maintained for further investigation and enquiry conducted by user. In
healthcare industry, maintenance of data is most important as this allows healthcare professional
to ensure safety of patient's privacy and security of confidential information. Maintaining data is
challenging because healthcare authority may need advance level of technology and software and
use of these technologies become more challenging. There are two types of data management
and maintenance software in healthcare industry, these are:
 Data Management Software (DMS): This is commonly used data management system in
the world allowing user to maintain data and optimize information for further use,
healthcare professional consider this software to manage both structured and unstructured
data.
Proposing a question that could be asked in relation to your unstructured data and what software
might help you to run AI and answer the question:
Q. What kind of unstructured data available in healthcare industry? And what are key
technologies and software to manage?
Ans. In healthcare industry, data is available in two forms, structured and unstructured.
Structured data contain 20% data of patient and their personal information including age, health
issue, health related information and current treatment protocol. However, unstructured data
contain 80% data of patient including health notes provided by practitioner and care taker,
personal notes of patient, major records CT scan report and other records allowing healthcare
professional to manage (Chen, Lv and Song, 2019). AI and Machine learning based technology
and data management system is the best software and technology allowing professional to
manage patient and their data in the process of healthcare.

CONCLUSION
This report has discussed data management and management of unstructured data in the
healthcare industry including image reconstruction application. Later this report has discussed
healthcare industry and image reconstruction as application allowing professional to manage
healthcare process. At last this report will discuss the best practice and option available for the
management of data in the process.

REFERENCES
Books and journals
Bloom, N., Sadun, R. and Van Reenen, J., 2016. Management as a Technology? (No. w22327).
National Bureau of Economic Research.
Chen, J., Lv, Z. and Song, H., 2019. Design of personnel big data management system based on
blockchain. Future generation computer systems, 101, pp.1122-1129.
Erdmier, C., Hatcher, J. and Lee, M., 2016. Wearable device implications in the healthcare
industry. Journal of medical engineering & technology, 40(4), pp.141-148.
Kumar, A., Boehm, M. and Yang, J., 2017. Data management in machine learning: Challenges,
techniques, and systems. In Proceedings of the 2017 ACM International Conference on
Management of Data (pp. 1717-1722).
Lee, D. and Yoon, S.N., 2021. Application of artificial intelligence-based technologies in the
healthcare industry: Opportunities and challenges. International Journal of
Environmental Research and Public Health, 18(1), p.271.
Li and et.al., 2017. Crowdsourced data management: Overview and challenges. In Proceedings
of the 2017 ACM International Conference on Management of Data (pp. 1711-1716).
Paul and et.al., 2018. Artificial intelligence in the healthcare industry. The Centre for Internet
and Society, India.
Strang, K.D. and Sun, Z., 2020. Hidden big data analytics issues in the healthcare
industry. Health informatics journal, 26(2), pp.981-998.
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