Business Intelligence Assignment: Big Data in Healthcare Industry
Question
Task
In this business intelligence assignment, you are required to choose one of the following industries: Healthcare, Insurance, Retailing, Marketing, Finance, Human resources, Manufacturing, Telecommunications, or Travel. This assessment consists of two parts as follows:
Part A - You are required to prepare a report on how Big Data could create opportunities and help value creation process for your chosen industry.
Part B - You need to identify at least one dataset relevant to the industry and describe what opportunities it could create by using this dataset.
In Part A, you will describe what new business insights you could gain from Big Data, how Big Data could help you to optimise your business, how you could leverage Big Data to create new revenue opportunities for your industry, and how you could use Big Data to transform your industry to introduce new services into new markets. Moreover, you will need to elaborate how you can leverage four big data business drivers- structured, unstructured, low latency data and predictive analytics to create value for your industry. You are also required to use Porter’s Value Chain Analysis model and Porter’s Five Forces Analysis model to identify how the four big data business drivers could impact your business initiatives.
In Part B, among several open source and real-life datasets, you will identify at least one dataset that is relevant to the industry you had chosen. The dataset can be a collection of structured, unstructured or semi-structured data. Using this dataset, you will first discuss how you chose this dataset among other datasets. Then, you will identify and present the metadata of the dataset. Using the chosen dataset, you will need to describe the opportunities it could create for the chosen industry.
The length of the report should be around 2500 words. You are required to do extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities and value creation process. You need to provide in-text referencing of chosen articles.
Your target audience is executive business people who have extensive business experience but limited ICT knowledge. They would like to be informed as to how new Big Data technologies might be beneficial to their business. Please note that a standard report structure, including an executive summary, must be adhered to.
The main body of the report should include (but not limited to) the following topics:
1. Big Data Opportunities
2. Value Creation using Big Data
3. Porter’s Value Chain Analysis
4. Porter’s Five Forces Analysis
Answer
Executive Summary
The present business intelligence assignment sheds light on the concept of Big Data that derives its identity from three key properties – volume, velocity, and variety. These properties made it achieve what conventional systems haven’t been able to e.g. ability to mine huge data sets without the need to transform data into a human-readable format, all this at a great speed which enables quick decision making through insights. Big Data’s application in the healthcare industry can be considered particularly important because of the magnitude of impact it can create.
Wearable technology data can be used to customize user experience to cater to an individual’s health needs and understand consumer behaviour. Huge data sets can be mined for new drug discoveries and better designed clinical trials. The said technology has already resulted in a drastic reduction in the cost of sequencing and can be used to understand the human genome further.
The resulting economic impact can be summarized to be the result of four business drivers – Big Data’s ability to manage both structured (e.g. transactional data like patient records, prescriptions, and payments) and unstructured data (e.g. signals from monitoring devices, genomes, literature texts), the low-latency between data availability and visualization and creation of high accuracy predictive models e.g. cancer probability, high utilization of medical insurance, etc.
Porter’s Value Chain analysis for a healthcare provider reveals how a truly technology integrated company can create a sustainable competitive advantage. The primary and secondary activities of the value chain i.e. from patient monitoring for prevention of disease, to diagnosis, treatment, recovery and post-recovery services, Big Data can prove beneficial in improving provider’s quality and speed of service as well as patient experience.
Further, an evaluation of the industry on Porter’s Five Forces, reveals how the threat of new entrants and the bargaining power of suppliers, buyers can be reduced by incorporating Big Data technology into the value chain. Providers who are successful in this can create a differentiated service and inch above the competition.
An example of predictive analytics for diagnosis has been explored by using dataset containing liver patients test results, to determine if the patient has liver disease or not.
PART A
Introduction
Big Data technology has found its application in almost every industry, and has opened up myriad possibilities. This can be attributed to the most emphasized three properties of Big Data (Laney, 2001) - ability to handle large volumes of data, with a velocity which can’t be achieved through conventional systems, and the ability to process a variety of data not limited to the traditional row and column databases.
Healthcare has been chosen for this report as this industry influences every individual in aspects related to healthy living, longevity and quality of life. Technology disruption in this sector can be expected to have maximum economic impact and can lead to numerous new business opportunities. Big Data has the potential to reduce healthcare expenditure in the U.S by at least $300 Bn, if used effectively (The big-data revolution in US health care: Accelerating value and innovation, 2013).
Further, there is an immense scope of creating meaningful value in the healthcare sector by leveraging Big Data. For example, understanding and drawing insights from complex data like the human genome, for advancement in medical research (O’Driscoll, Daugelaite and Sleator, 2013), helping healthcare providers decide the drug regime that works best for those suffering from terminal diseases by analysing patient history, or enabling healthcare technology companies to process data collected from wearables to detect health anomalies and implications (Mezghani et al., 2015).
Big Data Opportunities
Healthcare is an umbrella industry for organizations that work in medical research and development, pharmaceuticals, healthcare technology, providers, payers and insurance. This section elaborates various opportunities in some of these sectors made possible by Big Data -
Wearable technology
Wearables in the market track health and fitness of individuals by sensing heart rate, sleep and exercise patterns, and also store input from the user on their disease history, physical parameters and habits e.g. diet, water intake etc. This data can be used by businesses to offer services related to personal healthcare management. Pooled data from millions of users can be processed to predict clinical conditions for the individual (Wu, Li, Cheng and Lin, 2016)
Big Data analytics can be used to gain insights into consumer behaviour based on data gathered from wearables, this can be used to personalize the experience offered to the customer. In general, analytics can reveal emerging market trends which can be used to launch new products & services quickly or differentiate existing products to better cater to the company’s customer segment
Pharmaceuticals and Healthcare providers
Drug discovery is a complex process, the primary objective is to develop a new treatment for a disease, where the existing approach has been unsatisfactory. Big Data can be used by pharmaceutical companies to understand the cause of the ailment and identify bio-targets for treatment. Using Big Data, a fair prediction can be made of how the said drug will interact with the body, and can be used to design efficient clinical trials (Brown et al., 2018)
Pharmaceutical companies and healthcare providers can also collaborate to use the patient history for insights into optimum therapeutic pathways. E.g. Oncology patient data can be used to predict an individual’s response to the assigned drug regime. Such optimizations can help pharmaceutical companies save insurance expenses, and healthcare providers maximize their resources
Medical Research
Sequencing technology has advanced in recent times, to an extent that expenditure on analyzing the human genome has shrunk manifold and is now affordable at an individual level. Personal genome is about 100 GB of data, and exploring it with the help of Big Data can help businesses discover genes and metabolic paths. This can be used to design new medical products (O’Driscoll, Daugelaite and Sleator, 2013)
Value creation using Big Data
The real impact of Big Data can be understood better if we deep dive into the key drivers of value creation (Four Big Data Value Drivers for Organizations | EMC, 2014). First driver, the ability of big data to process every bit of transactional data available within the organization. Typically, data is stored by organizations in an aggregate format which leads to loss of information. Second is its ability to handle unstructured data e.g. social media, sensors, audio/video, text from blogs, comments etc. The third driver is the quick insight capability of Big Data, often termed as low-latency, which makes real-time decision making possible. And the fourth driver is predictive analytics made possible by mining and learning from huge data sets. Predictive analytics is an outcome of Big Data, but is executed by machine learning and artificial intelligence. Thus, industries need to build additional capabilities in these fields to derive most benefits.
Structured data
Examples of structured data generated in the healthcare industry are - pharmacy prescriptions, healthcare insurance/payer data. Analysing it using the Big Data holds several opportunities like -
Sales & Marketing functions can use this data to identify performance of their products vs. competitors, and tweak their business model as required. They can identify market trends e.g. which physicians are not prescribing their drug in which regions, identify related reasons by combining other market parameters e.g. competitor products, payer offers, season, sales manager performance, physician affinity etc.
Organizations can also use this data to analyse their product portfolio, and discover new market opportunities. E.g. go-to-market strategies can be formulated based on prescription data to identify promising region, physicians and customer segments to target
Unstructured data
A majority of data generatedby healthcare industry is unstructured i.e. it is in form of images, sensor reading, literature texts, and it could be even more complex e.g. the human genome.
Patient monitoring systems generate data in form of images and bio-signals. Physicians or healthcare providers can use processed data to provide remote personalized care to those who wish to opt for it e.g. chronic patients, elderly, patients suffering from a terminal disease, patients who underwent surgery recently. This can become a unique selling point for the provider. Such a platform can be integrated with the core systems of the provider for round the clock assistance to patients and manage key performance indicators e.g. inpatient services, wait times etc.
Low Latency of data
One of the biggest advantages of Big Data technology over traditional systems is low-latency. Limited computational resources can be used concurrently through Big Data analytics, to enable accurate and quick decision making.
In typical systems, data is aggregated and insights are generated on historical data, decision-makers are then presented with a snapshot of the past. Their consequential strategic moves are based on questions like – ‘how we performed in the last quarter’, ‘which products lost market share and why’ etc. Big Data makes real-time insights possible
Real-time data from Internet of Things (IoT) devices e.g. blood pressure and heart rate monitoring systems, blood sugar level checking device, along with individual’s medical history can be used for self-monitoring and providing advanced healthcare services like drug recommendation for patient’s condition. It can be used to administer remote treatment as well(Chen et al., 2017)
Predictive Analytics
Predictive Analytics has become more and more reliable because of Big Data technology -
Business risk can be minimized by healthcare insurance providers, similar to the use of predictive analytics by credit card companies in scoring their customers to identify high potential or high-risk clients (David, Smith-McLallen and Ukert, 2019). The corresponding score in this context can be healthcare utilization by the individual and interventions can be planned by the firm accordingly, e.g. a high score individual has possible care gaps which can be minimized to reduce risk to the insurance provider. This action will have a direct impact on the healthcare expenditure of government
Several cases, patient survival depends on early detection of an illness e.g. cancer. Quality of life can get impacted in several other cases e.g. Alzheimer’s. Predictive analytics can be used in such scenarios to determine if a patient is likely to develop cancer, by analysing factors related to the person’s genes, family history, available test results etc. (Alharthi, 2018).
Porter’s Value Chain Analysis
The economic impact of Big Data in healthcare industrycan be enhanced by integratingthe four business drivers within the value chain. As part of value creation and Big Data opportunities, the focus so far has primarily been on the core functions of the industry. However, with the help of Porter’s Value Chain analysis a more holistic view can be obtained for a healthcare provider (Martinelly, Riane and Guinet, 2009)-
Primary activities
Prevention of disease through monitoring or consultatio
Healthcare providers can increase their customer base by offering preventive tools that can help individuals self-monitor and manage own health. This can be enabled through IoT devices, wearable technology and smart applications. Medical history of the patients can be scanned to uncover potential ailments that the patient might suffer and take preventive measures
Diagnosis of condition
Big Data analytics can be used for quick and accurate diagnosis of medical conditions. Patient history available with the healthcare provider can be used to get diagnosis right at the first time and enhance patient experience
Treatment and recovery
Understanding of optimum treatment pathway through Big Data technology can ensure effective treatment and quick patient recovery. Patient behaviour can be fed to machine learning algorithms to improve accuracy of future predictions and help design better treatment methods
Post-recovery services
Remote support for patients can be provided to ensure speedy recovery, through the use of health technology products, which can be linked to the healthcare provider’s system. Real-time monitoring is possible to ensure adherence to therapy and suggest lifestyle changes
Secondary activities
Marketing & Sales
Market trends and sales performance can be analysed on a real-time basis through Big Data tools, by the decision-makers for quick strategic moves, leading to a direct impact on provider’s cost and revenue streams
Research & Development
Literature texts can be analysed real-time to support the primary activities of provider e.g. diagnosis and treatment. Results from diagnostic and therapeutic centres e.g. blood test reports, vital sign monitors etc. along with prescriptions can be used to determine body’s response to treatment
Procurement & support functions
Equip Administration, procurement functions with predictive tools/dashboards to monitor and plan inventory of equipment, stock of medicines etc. This will help rationalize the provider's store management. Further, payment records can be analysed to prevent fraud/leakages in the system
Porter’s Five Forces Analysis
As a result of incorporating Big Data into value chain as described in the previous section, a business can create new sustainable competitive advantages.
A sustainable competitive advantage is a resource, capability or a combination of these two, which can’t be replicated easily, or developed in a short period of time. The head start provided by this asset is huge enough to help the firm maximize business over a long period of time, if aided by continuous innovation and improvement.
Porter’s Five Forces Analysis helps conclude a similar point for players in the healthcare industry using the business drivers of Big Data –
Bargaining power of suppliers
The primary suppliers for healthcare providers are pharmaceutical companies that provide drugs and medical equipment, which care essential for functioning of the provider. However, these companies don’t have visibility of patient records, payer history and physician behaviour unless they collaborate with the provider. Thus, Big Data analytics by the healthcare provider can help reduce supplier bargaining power, by use of the structured and unstructured data processing capability
Threat of new entrants
Threat of new entrants in the healthcare sector can be considered low, as huge investment and regulatory compliance are required to become a provider. Additional capabilities of Big Data if available with a provider can further lower the threat
Bargaining power of buyers
Patients are the buyers, and they have low bargaining power as they are individuals with limited knowledge of the specialized services extended by a healthcare provider. However, with the advent of IoT and wearable technologies, patients have become self-aware and are capable of managing their health to some extent. If a provider can leverage predictive analytics and low-latency features of Big Data to enhance patient experience it can further reduce buyer power and increase its patient base
Threat of substitutes
Substitute to a healthcare provider is alternative medicine which is not covered by payers. The cost of any unconventional care provider is thus very high and poses low threat
Competitive rivalry
Healthcare industry can be considered as highly competitive due to presence of multiple providers, and low threat of substitutes, new entrants. Big Data integration into the value chain for real-time insights on patients, predictive diagnosis, enhanced patient experience by exploiting structured/unstructured data etc. will help some providers inch higher on quality and service parameters
Conclusion
Players in the healthcare industry can be forward-looking by integrating Big Data technology into their core activities. This can facilitate improvement of their existing offerings on quality and service parameters, and also enable business expansion into uncharted markets e.g. new business initiatives in health technology or patient care segments. With the help of Big Data technology healthcare can be provided remotely, thus, extending reach into new geographical locations. Advancements in medical research have already impacted the quality of life and will continue to do so. Early adopters in the industry have already utilized the technology to create valuable business assets in form of patents. Big Data’s truly pervasive nature is only beginning to be realized.
PART B
Dataset identification
For dataset selection, the four business drivers of Big Data in the healthcare industry have been considered while going through the literature texts for similar exercise by various researchers. The basic search criteria used is –
Big Data as the primary topic
Study explores application of Big Data in the healthcare industry
Study addresses benefit of Big Data to create value
As a result, patient diagnostic records data has been identified as suitable for the purpose of this report. With the help of UCI ML open database, Breast Cancer Wisconsin (Diagnostic) Data Set has been picked up, and using Big Data and machine learning algorithms it can be predicted if the cancer is benign or malignant.
Metadata of chosen dataset
Source: UCI ML Repository - http://archive.ics.uci.edu/ml/index.php
Dataset: http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)
Snapshot:
Business opportunities through the chosen dataset
A predictive (binomial classification) analysis on the above data can be performed by segregating the data into test and training observations. This will help create a predictive model to determine if a new patient record containing diagnostic test results show a benign or malignant mass. Healthcare providers can use this to prevent breast cancer in patients by identifying at-risk populations.
Patients that come in for routine check-up can be examined using this approach, which is non-invasive, and accurate, it will save patient from emotional stress of cancer diagnosis at a later stage. Effectiveness of cancer treatment and survival rate of the patient largely depends on the time of discovery. With the help of Big Data quality and effectiveness of the service i.e. treatment can be improved. The patient can be guided to live a healthier lifestyle. The healthcare provider emerges as a patient-centric organization.
Healthcare providers can leverage Big Data to optimize their resources and get oncology diagnosis right the first-time.
References
[1] Laney, D 2001,‘3D Data Management: Controlling Data Volume, Velocity, and Variety’,Gartner File No. 949,viewed 22 April 2020,
[2] McKinsey & Company2013,‘The Big-Data Revolution In US Health Care: Accelerating Value And Innovation’, viewed 22 April 2020,
[3] O’Driscoll, A, Daugelaite, J &Sleator, R 2013,‘‘Big Data’, Hadoop And Cloud Computing In Genomics’,Journal of Biomedical Informatics, vol. 46, no. 5, pp.774-781.
[4] Mezghani, E, Exposito, E, Drira, K, Da Silveira, M &Pruski, C 2015,‘A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare’,Journal of Medical Systems, vol. 39, no. 12.
[5] Four Big Data Value Drivers For Organizations | EMC, viewed 22 April 2020,
[6] Wu, J, Li, H, Cheng, S & Lin, Z, 2016,‘The promising future of healthcare services: When big data analytics meets wearable technology’, business intelligence assignment Information & Management, vol. 53, no. 8, pp.1020-1033.
[7] Brown, N, Cambruzzi, J, Cox, P, Davies, M, Dunbar, J, Plumbley, D, Sellwood, M, Sim, A, Williams-Jones, B, Zwierzyna, M and Sheppard, D 2018, ‘Big Data in Drug Discovery’,Progress in Medicinal Chemistry, pp.277-356.
[8] Chen, M, Yang, J, Hao, Y, Mao, S and Hwang, K 2017,‘A 5G Cognitive System for Healthcare’, Big Data and Cognitive Computing, vol. 1, no. 1, p.2.
[9] David, G, Smith-McLallen, A and Ukert, B 2019,‘The effect of predictive analytics-driven interventions on healthcare utilization’, Journal of Health Economics, vol. 64, pp.68-79.
[10] Martinelly C.D, Riane F, Guinet A 2009,‘A Porter-SCOR modelling approach for the hospital supply chain’, International Journal of Logistics Systems and Management, vol. 5,no. 3, pp. 436-456.
[11] Wanga, Y, Kungb L, Wangc W, Cegielski C.G 2018,‘An integrated big data analytics-enabled transformation model: Application to health care’,Information & Management, vol. 55, pp.64-79.
