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Data Acquisition and Management assignment focusing on unstructured data within the healthcare industry


Task: How to utilize Data Acquisition and Management assignment research methods to investigate unstructured data within the healthcare industry


Executive Summary
It is observed on this Data Acquisition and Management assignment that the Healthcare industry provides us with various facilities and services like diagnosis, drugs, treatment, medical equipment, and many more. Those data and information which does not follow any specific data model are referred to as unstructured data and are harder to process throw convention processing methods. Unstructured data and the healthcare industry co-exist in nature and it is inevitable. The purpose of this Data Acquisition and Management assignment is to understand unstructured data in healthcare industry. Another aim of this report is to know the implementation of AI to process unstructured data in healthcare industry. This report includes the background of the healthcare industry and the presence of unstructured data in this industry. This report also draws attention to practices and handling of data and also the challenges due to the presence of unstructured data. This Data Acquisition and Management assignment concludes that the implementation of AI for processing unstructured data can be a revolution in the healthcare industry.

As per research done during this Data Acquisition and Management assignment, the Healthcare industry includes many interlinked companies and provides many services like medicine, remedies, nursing, treatment of disease, medical equipment, etc. Unstructured data are those data which does not follow any specific data model which makes them difficult to process. The aim of this report is to analyze unstructured data and its relation to the healthcare industry. Another objective of this report is to find out the usage of AI/Machine Learning algorithms in the healthcare industry. This report throws light on the background of the healthcare industry and unstructured data present in the healthcare industry. This report highlights unstructured data and AI/Machine Learning algorithms. This report includes practices for accessing/collecting, storing, sharing, documenting, and maintaining via five Vs of big data. This draws attention to the challenge that arises due to the presence of unstructured data in this industry.

The healthcare industry
The healthcare industry consists of many companies and organization that facilitates various medical equipment, clinical services, drug manufacturing along with health relative services for instance medical insurance (Kim & Han 2020,p.7-15). Medical assistance, nursing, and treatment are offered in case of injury or disease by this industry. This Data Acquisition and Management assignment also identifies that this industry also provides remedial and prevention facilities to patients.

Data Acquisition and Management assignment for Healthcare Industry
For the proper working of the services and facilities offered by this industry, there must be effective coordination among the various individuals which imparts a great role in this healthcare industry. These individuals include doctors, nurses, Pharmaceuticals, medical equipment manufacturers, insurance companies (medical), and government agencies (Wernhart, Gahbauer & Haluza 2019, p. e0213067). For simplicity it can be summarized in segments as follows:

Healthcare providers
Healthcare providers are the actual service providers offered inthe healthcare industry. They also provide teaching and training for medical staff. They can be further categorized as follows:

Hospitals or medical and research Centers.
Nursing homes
Ambulatory services

Life Sciences
Pharmaceutical firms lie in this segment of the Healthcare industry. The goal of this industry is to make diagnostic drugs and also to produce various chemicals that are required in the premises of the healthcare industry (Laurenza et al., 2018). Researches and new vaccines are part of these industries.

Healthcare Financiers
Healthcare financiers can be government as well as private. These financiers provide various schemes and policies for health insurance. These bodies also provide healthcare services on charities basis. The task of charity financiers is to raise fund collection through insurance policies and contributions of employees.

Data Acquisition and Management assignment: Unstructured Data and AI/Machine Learning
Unstructured data are that information which does not obeya simple data model and makes it difficult to understand it by the computer algorithm so it does not fit in the mainstream database. Unstructured data in the healthcare industry are equally important as structured data and hence they need to be preserved regardless of being complex in nature(Alhashmi, Salloum & Mhamdi 2019, p.27-42).

According to research, it is found that unstructured data holds 80% of the total data in healthcare information.
Form of Unstructured data in the medical industry

Medical reports consist of images that are produced by different image-generatingdevices. For example, endoscopes, x-rays, ultrasound, etc.
Biosignal data is generated by wearable monitoring devices kept in care units.
Verbally generated audio data during the treatment of Psychic patients are important in further treatment.

Benefits of Processing Unstructured Data using Data Acquisition and Management assignment techniques
Disease Diagnostics

Interpretation and managing of unstructured data using analytical tools help for better examination of disease and its severity. It also enables them to forecast upcoming variations in the patient’s condition (Adnan et al., 2020,p.301-323).

Choosing a method for treatment
Unstructured data may include important information regarding the disease of the patient. Hence, it helps to determine the best possible way the treatment of disease.

Medical Care
ThisData Acquisition and Management assignment also observed that processing more and more medical data can uncover new patterns which can lead to the improvement of medical care quality.

Management of Unstructured Data
Five Vs of big data

Big data refers to the area of data science that deals with large sets of data in the modern days. Tradition methods of processing used for these kinds of data are not efficient in storing and analyzing. To understand and handle unstructured data, we need to understand the five Vs of Big data.

Figure: 5 V’s of Big Data
(Source: Balar &Chaabita 2019)

The volume of big data refers to the amount of data being generated. As per investigationsdone during this Data Acquisition and Management assignment, the volume depends on the data size and more accurate when larger. Nowadays in the medical industry, unstructured data are generated from various medical procedures.It became a challenge for the medical industry to store and analyze unstructured data using traditional methods(Senthilkumar et al., 2018, p.57-69). Medical industries have to introduce AI/Machine learning to store and analyze such a large amount of data in real-time.

Although data are being generated in a huge amount not all the data is worth keeping. Despite collecting every single data generated it is better to sort out the unstructured data which will be needed for further treatment or record purposes. Analyzing the unstructured data collected so far is crucial to find out its worth in the further treatment and procedure.

Velocity refers to the speed of production, processing, and storage of data. Data are being generated at a tremendous rate in the process of diagnosis and treatment of numerous patients via different medical equipment. This data must be recorded inreal-time so it can be utilized whenever required(Ristevski &Chen 2018).In the medical industry, time is the most precious asset for the patients as well as for the medical staff also.

Although velocity and volume are directly added to the Big Data variety also plays important roles important role in it. According to research done on this Data Acquisition and Management assignment, the human genome took 10 years to analyze using conventional processes but now the result can be achieved in one week using Big Data. Data are produced in different categories but they can be sorted according to their variety.

Veracity and Validity refer to the quality and correctness of the gathered data. Since Big data is huge and gathered from different sources so probably all the data collected so far is not accurate or correct. So here comes the validity of the data. In the Medical industry validity of data has great importance as it decides the further procedure of the treatment.

Data collection & Access

In the healthcare industry data collection is the primary task for the healthcare provider to provide treatment or operation. Nowadays, the data collecting process through medical equipment became an integral part of the healthcare industry(Rasmi et al., 2020, p.48-60). Some tools are listed for data collection in the healthcare industry are as follows:

Remote monitoring devices
Electronic health records (EHR)
Customer relationship management (CRM)

Data Storage
AI/Machine learning and technology helpthe medical industry to manage the exploding data generated every day. The Healthcare industry frequently improvesits data storage for better accumulation of data. The Healthcare industry uses three tools to store data in accordance with the five Vs of Big data are as follows:

Onsite data storage.
Hybrid cloud data storage.
Public cloud data storage.

Data Sharing
In the healthcare industry, sharing data improved health institutions, and patients’ medical care and also helps in removing global pandemic threats. It also gives authorization to families and patients to access the medical reports which make them aware of the situation and make correct decisions (Mehta & Pandit 2018,p.57-65). Although in the healthcare industry, there are so many advantages collaboration of different healthcare is very complex.

Data Acquisition and Management assignment: Data Documentation
In the last three decades, the medical industry evolved significantly which ultimately give rise to the need for documentation. Practices of data documentation ensure the correctness of medical records. Implementation of electronic health records is quite effective but it also gives some obstacles to overcome.

Data Maintenance
Data maintenance in the healthcare industry refers to the managing of the data of the patient throughout its medical journey. This journey of the patient includes diagnosis, treatment, research, payments, etc. this Data Acquisition and Management assignment observes that data is crucial towards enhancing the security as well as standard of patient care.

Challenge of Unstructured Data in the Healthcare Industry
After understanding unstructured data in the healthcare industry, one of the biggest questions arises what are the challenges that occurdue to the presence of unstructured data in the healthcare industry What software might help to run AI
While working with EMR, interpretation, and analysis of unstructured data in the healthcare industry is the most challenging work. As unstructured data includes various valuable information so its interpretation is equally important (Fan et al., 2020,p.567-592). This unstructured data is further processed in a language in which computer software can analyze the data. IBM Watson Health software can run AI in the healthcare facilities.

Unstructured data in the healthcare industry are present in a huge amount and are inevitable. Most of the useful information about the patient is present in the form of unstructured data. So processing of these data is crucial and fundamental part of this industry. Interpretation of these data has too much complexity. Hence, introducing AI/Machine learning algorithms can save a lot of time forthe healthcare industry.Interpretation of these data makes faster processes and improvement in the patient’s medical care. There is a possibility of finding out hidden information in the bulk of unstructured data which cannot be possibly done by people. From the Data Acquisition and Management assignment, we can conclude that introduction of AI or Machine learning algorithms can overcome the challenges faced due to the presence of unstructured data in the healthcare industry.

Adnan, K, Akbar, R, Khor, SW & Ali, ABA 2020,Role and challenges of unstructured big data in healthcare. Data Management, Analytics and Innovation.
Alhashmi, SF, Salloum, SA & Mhamdi, C 2019. Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. Int. J. Inf. Technol. Lang. Stud, Data Acquisition and Management assignment, 3(3). BALAR, K & CHAABITA, R 2019,Big Data in economic analysis: Advantages and challenges. International Journal of Social Science and Economic Research, 4(07).
Fan, W, Liu, J, Zhu, S & Pardalos, PM 2020,Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1). Kim, KB & Han, KH 2020, A study of the digital healthcare industry in the fourth industrial revolution. Journal of Convergence for Information Technology, 10(3).
Laurenza, E, Quintano, M, Schiavone, F & Vrontis, D 2018,The effect of digital technologies adoption in healthcare industry: a case based analysis. Business process management journal.Data Acquisition and Management assignment Mehta, N & Pandit, A 2018, Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114.
Rasmi, M, Alazzam, MB, Alsmadi, MK, Almarashdeh, IA, Alkhasawneh, RA& Alsmadi, S 2020, Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case study). International Journal of Healthcare Management, 13(sup1).
Ristevski, B & Chen, M 2018,Big data analytics in medicine and healthcare. Journal of integrative bioinformatics, 15(3).
Senthilkumar, SA, Rai, BK, Meshram, AA, Gunasekaran, A. & Chandrakumarmangalam, S, 2018,Big data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business, Data Acquisition and Management assignment4(2).
Wernhart, A, Gahbauer, S &Haluza, D 2019,eHealth and telemedicine: Practices and beliefs among healthcare professionals and medical students at a medical university. PloS one, Data Acquisition and Management assignment14(2).


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