Business Analytics Assignment: A Literature Review on BA in Healthcare
Question
Task: Perform a literature review on a known topic in business analytics and prepare a detailed report on business analytics assignment. It can be any topic on tools, methodologies or applications.
Answer
Abstract: Business Analytics has contributed in advancing the processes and the decision-making in various sectors. Healthcare has been continuously evolving and application of the latest technologies in healthcare has been always given the highest priority. This business analytics assignment presents a literature review analysis on the “application of business analytics in healthcare.” This paper introduces the topic, discusses the aspects of different literature with critiques, findings from the analysis, and the conclusion. The analysis will help in understanding the severity of the influence of Business Analytics in healthcare
I. Introduction
Business analytics is the field that is completely based on data driven pragmatic changes in any given business environment. Business analytics include statistical application in the field of analysis which sharply focuses on imparting distinct actionable recommendations. The business analysts use various analytical tools suitable for the particular industry and derive insights from the collected data [1]. The collected data aids in the process of analysis to draw concrete references and conclusions about the industry in question to achieve organisational goals and objectives. Business analytics efficiently combines the aspects of computer science, management and business [2]. The business part requires high standard understanding on the business in hand also keeping in mind the limitations that exists in the field. The analytical part requires critically studying data to understand any underlying pattern to derive solutions and recommendations out of it. Business analytics simply bridges the gap between technology and management in an effective way by producing accurate results and enhanced results. Business intelligence uses data to gather inform on the business. The intelligence gathered is put through definite tools to inform any prominent changes of a business. For this purpose, predictive models and tools are utilised to impart insights for the proposed change outcomes. Business analytics enables data driven decision making process that carries the strength to improve efficiency and increased profits while helping the company in the process to take informed business decisions [2]. The predictive analytical tools allow the organisations to plan their future endeavours in an efficient way backed by data.
II. Literature Review
Business analytics have completely revolutionised the decision making process of the managers and organisations [3]. The data collected give insights on various consumer behaviour and market conditions which help in analysing the overall situation to increase the effectiveness of the business. Business analytics tools are hugely used in each and every sector of business to improve performance and the overall revenue of the company. The applications of business analytics are extremely vats and there are multiple ways to implement the technology in various aspects of the market. The benefits are huge and clear as business analytics provide data driven informed decisions which provides the business with a competitive edge over the others in the same industry [3]. The advent of business analytics has created ample new jobs in the analysis sector and there are in extremely high demand. It is an iterative exploration guided by methods of organisational data. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to using data to gain insights that inform business decisions. Specific kinds of this field include descriptive analysis, predictive analysis and lastly prescriptive analysis. Descriptive analysis tracks the valued KPIs for better comprehension of the current market and business situation. Predictive analytics assess data trends to understand the likelihood of the future and what can be the possible outcomes [3]. Prescriptive analytics uses past data and performances in order to generate solutions and recommendations on handling of a similar situation in recent future.
The initial analytics is simply done on a set of data of small sample size to understand trends and patterns. The tools include complex data mining process and other predictive modelling applications [4]. The analysis and decision making process include studying patterns to identify relationships and the process is iterated until the objective of the business is met. Deployment of models involves the scoring of recoded data mostly from a database and the scores are use to optimise the applications to provide real time decision for different business processes [4]. Business analytics has lead to the automation of decision making process in response to any unforeseen circumstances or to provide support other real time responses. Business analyst uses different models to provide critical insights and consultations to their business partners.
The model is selected on the basis of the industry or the sector it is being applied to. The model needs validation in the initial steps, once it clears the validation step, it is deployed on the system of the organisation to perform critical analysis on incoming data packets and stream [5]. The model requires constant monitoring for accuracy of performance. Models include activity diagram, process low diagrams, mind maps and road maps. Popular business analytical tools are R which is a robust as well as a versatile tool and can easily handle large chunks of data, SAS, Apache Spark, Pig & Hive and Apache Storm. Apache spark focuses on analytics of unstructured data or large quantity of data. Storm is one of the major big data tool for efficiently handling data in one continuous stream. Storm is absolutely ideal for analysis of real time data or data stream processing. Commercial business analytics tools include SAS, Excel, Tableau, Qlikview and Splunk.
III. Business Analytics In Healthcare System
The healthcare and service system is at cross roads and analytics plays a pivotal role for such a sector. Healthcare system constantly captures real time information; the database of a healthcare system is ever expanding with multiple patient data and critical information about them [6]. The future of healthcare is driven by digitalisation and data analysis. The healthcare industry is experiencing a global paradigm shift from volume based model of business to a value based business model. The transformation has been brought forward due to the excessive demand of an effective system to meet customer satisfaction and service. Business analytics promises to provide a better future for the health sector industry. Business analytics in the healthcare system contains multiple challenges namely the lack of data standards, lack of highly skilled analysts and barriers to the collection of high-quality data [7]. There are also unmentioned managerial issues including deployment of data consistency in the improvement of the sector. To survive any market competition, having a competitive advantage over old rivals and being strong for the new entrants in the market or the new approaches are constantly increasing complication; here data analytics comes to the rescue. Business analytic tools will ease the complex data torrent and in addition help the healthcare service system to cater to the growing demands.
Healthcare analytics involve data collection and the analysis of the collected set of data in the mentioned industry to gain potential insights and improve the decision making system [8]. Medical cost, patient behavior, clinical data and pharmaceuticals, business analytics in healthcare or healthcare analytics can be of tremendous use on micro as well as macro levels for effective streamlining and optimization of healthcare operations, reduction of overall cost and improvement of patient care. Health care data is a complex industry in itself. Electronic health records (EHR) for the monitoring of real time patient stats and vital signs, such data are acquired from multiple sources which have to be in compliance with set regulations of the government [9]. The process is extremely delicate and complex which in turn needs additional connectivity and security solution that can only be provided by embedded analytics.
The embedded analytics can produce improvement of core functionalities and reporting solution. There is notable improvement in the performance of data delivery. Analytics helps in reduction of patient time by leveraging as well as measuring staffing and scheduling procedures. There is a complete improvement in patient quality of care and satisfaction by smartly streamlining the various tedious processes related to processing insurance, appointments and referral production. The most significant positive aspect of healthcare analytics is reducing readmission rates and this is done by leveraging health data of the given population against each data of the patient to accurately predict patient risk.
Healthcare can be complicated and extremely expensive for emergency service patients. Digitized records of patient data help in making identification process easy and efficient for the users of the system. Predictive analytics can help in isolating patient who is at risk at crisis situation and suffering from health conditions that is chronic in nature. Business analytics provides corrective plans to reduce emergency visits. Monitoring such patients provides critical data for future reference and also for the purpose of offering customized solutions to such patients. This will not be possible without the help sufficient real time data which will be used to analyze probable solutions in healthcare [10]. Business analytics will reduce probable human error and prevent health concerns. Business analytics can be leveraged for the analysis of patient data and medication prescriptions for the purpose of data corroboration and alerting users of the same. Moreover healthcare analytics and software helps in smoothing out operations and provides improved consumer service and satisfaction.
VI. Conclusion
Business analytics in the healthcare system contains multiple challenges namely the lack of data standards, lack of highly skilled analysts and barriers to the collection of high-quality data. There are also unmentioned managerial issues including deployment of data consistency in the improvement of the sector. The applications of business analytics are extremely vast and there are multiple ways to implement the technology in various aspects of the healthcare market. The benefits are huge and clear as business analytics provide data driven informed decisions which provides the business with a competitive edge over the others in the same industry.
VII. References
[1] Holsapple, Clyde, Anita Lee-Post, and Ram Pakath. "A unified foundation for business analytics." Decision Support Systems 64 (2014): 130-141.
[2] Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey. "Business intelligence and analytics: From big data to big impact." MIS quarterly (2012): 1165-1188.
[3] Wixom, Barbara H., Bruce Yen, and Michael Relich. "Maximizing Value from Business Analytics." MIS Quarterly Executive 12, no. 2 (2013).
[4] Laursen, Gert HN, and Jesper Thorlund. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons, 2016.
[5] Delen, Dursun. Real-world data mining: applied business analytics and decision making. FT Press, 2014.
[6] Ward, Michael J., Keith A. Marsolo, and Craig M. Froehle. "Applications of business analytics in healthcare." Business horizons 57, no. 5 (2014): 571-582.
[7] Wills, Mary J. "Decisions through data: Analytics in healthcare." Journal of Healthcare Management 59, no. 4 (2014): 254-262.
[8] Mehta, Nishita, and Anil Pandit. "Concurrence of big data analytics and healthcare: A systematic review." International journal of medical informatics 114 (2018): 57-65.
[9] Wang, Yichuan, LeeAnn Kung, and Terry Anthony Byrd. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations." Technological Forecasting and Social Change 126 (2018): 3-13.
[10] Acito, Frank, and Vijay Khatri. "Business analytics: Why now and what next?." (2014): 565-570.
