INSS 395 paper revision

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Data Analytics in Healthcare
Name of Student
Institution Affiliation
The healthcare industry historically has generated large amounts of data, driven by record
keeping, compliance & regulatory requirements, and patient care. While most data is stored in
hard copy form, the current trend is toward rapid digitization of these large amounts of data.
Driven by mandatory requirements and the potential to improve the quality of healthcare
delivery meanwhile reducing the costs, these massive quantities of data (known as ‘big data’)
hold the promise of supporting a wide range of medical and healthcare functions, including
among others clinical decision support, disease surveillance, and population health management
[2-5]. Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At
this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes)
scale and, not long after, the yottabyte (1024 gigabytes). Kaiser Permanente, the California-based
health network, which has more than 9 million members, is believed to have between 26.5 and
44 petabytes of potentially rich data from EHRs, including images and annotations.
By definition, big data in healthcare refers to electronic health data sets so large and
complex that they are difficult (or impossible) to manage with traditional software and/ or
hardware; nor can they be easily managed with traditional or common data management tools
and methods . Big data in healthcare is overwhelming not only because of its volume but also
because of the diversity of data types and the speed at which it must be managed .The totality of
data related to patient healthcare and wellbeing make up “big data” in the healthcare industry. It
includes clinical data from CPOE and clinical decision support systems (physician’s written
notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other
administrative data); patient data in electronic patient records (EPRs); machine generated/sensor
data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called
tweets) , blogs ,
Big Data Analytics in Healthcare
Health data volume is expected to grow dramatically in the years ahead. In addition,
healthcare reimbursement models are changing; meaningful use and pay for performance are
emerging as critical new factors in today’s healthcare environment. Although profit is not and
should not be a primary motivator, it is vitally important for healthcare organizations to acquire
the available tools, infrastructure, and techniques to leverage big data effectively or else risk
losing potentially millions of dollars in revenue and profits. What exactly is big data? A report
delivered to the U.S. Congress in August 2012 defines big data as “large volumes of high
velocity, complex, and
variable data that require advanced techniques and technologies to
enable the capture, storage, distribution, management and analysis of the information”. Big data
encompasses such characteristics as variety, velocity and, with respect specifically to healthcare,
veracity. Existing analytical techniques can be applied to the vast amount of existing (but
currently unanalyzed) patient-related health and medical data to reach a deeper understanding of
outcomes, which then can be applied at the point of care. Ideally, individual and population data
would inform each physician and her patient during the decision-making process and help
determine the most appropriate treatment option for that particular patient.
Advantages to Healthcare
By digitizing, combining and effectively using big data, healthcare organizations ranging
from single-physician offices and multi-provider groups to large hospital networks and
accountable care organizations stand to realize significant benefits. Potential benefits include
detecting diseases at earlier stages when they can be treated more easily and effectively;
managing specific individual and population health and detecting health care fraud more quickly
and efficiently. Numerous questions can be addressed with big data analytics. Certain
developments or outcomes may be predicted and/or estimated based on vast amounts of
historical data, such as length of stay (LOS); patients who will choose elective surgery; patients
who likely will not benefit from surgery; complications; patients at risk for medical
complications; patients at risk for sepsis, MRSA, C. difficile, or other hospital-acquired illness;
illness/disease progression; patients at risk for advancement in disease states; causal factors of
illness/disease progression; and possible comorbid conditions (EMC Consulting). McKinsey
estimates that big data analytics can enable more than $300 billion in savings per year in U.S.
healthcare, two thirds of that through reductions of approximately 8% in national healthcare
expenditures. Clinical operations and R & D are two of the largest areas for potential savings
with $165 billion and $108 billion in waste respectively
Architectural Framework
The conceptual framework for a big data analytics project in healthcare is similar to that
of a traditional health informatics or analytics project. The key difference lies in how processing
is executed. In a regular health analytics project, the analysis can be performed with a business
intelligence tool installed on a stand-alone system, such as a desktop or laptop. Because big data
is by definition large, processing is broken down and executed across multiple nodes. The
concept of distributed processing has existed for decades. What is relatively new is its use in
analyzing very large data sets as healthcare providers start to tap into their large data repositories
to gain insight for making better-informed health-related decisions. Furthermore, open source
platforms such as Hadoop/MapReduce, available on the cloud, have encouraged the application
of big data analytics in healthcare. While the algorithms and models are similar, the user
interfaces of traditional analytics tools and those used for big data are entirely different;
traditional health analytics tools have become very user friendly and transparent. Big data
analytics tools, on the other hand, are extremely complex, programming intensive, and require
the application of a variety of skills.
For the purpose of big data analytics, this data has to be pooled. In the second component
the data is in a ‘raw’ state and needs to be processed or transformed, at which point several
options are available. A service oriented architectural approach combined with web services
(middleware) is one possibility [27]. The data stays raw and services are used to call, retrieve and
process the data. Another approach is data warehousing wherein data from various sources is
aggregated and made ready for processing, although the data is not available in real-time.
Figure 1 an applied conceptual architecture of big data analytics
While several different methodologies are being developed in this rapidly emerging
discipline, here we outline one that is practical and hands-on. There are main stages of the
methodology. In Step 1, the interdisciplinary big data analytics in healthcare team develops a
‘concept statement’. This is a first cut at establishing the need for such a project. The concept
statement is followed by a description of the project’s significance. The healthcare organization
will note that there are trade-offs in terms of alternative options, cost, scalability, etc. Once the
concept statement is approved, the team can proceed to Step 2, the proposal development stage.
Here, more details are filled in. Based on the concept statement, several questions are addressed:
What problem is being addressed? Why is it important and interesting to the healthcare provider?
What is the case for a ‘big data’ analytics approach? (Because the complexity and cost of big
data analytics are significantly higher compared to traditional analytics approaches, it is
important to justify their use). The project team also should provide background information on
the problem domain as well as prior projects and research done in this domain. Next, in Step 3,
the steps in the methodology are fleshed out and implemented. The concept statement is broken
down into a series of propositions. (Note these are not rigorous as they would be in the case of
statistical approaches. Rather, they are developed to help guide the big data analytics process).
Simultaneously, the independent and dependent variables or indicators are identified. The data
sources, as outlined in Figure 1, are also identified; the data is collected, described, and
transformed in preparation for analytics. A very important step at this point is platform/tool
evaluation and selection. There are several options available, as indicated previously, including
AWS Hadoop, Cloudera, and IBM BigInsights. The next step is to apply the various big data
analytics techniques to the data. This process differs from routine analytics only in that the
techniques are scaled up to large data sets. Through a series of iterations and what-if analyses,
insight is gained from the big data analytics. From the insight, informed decisions can be made.
In Step 4, the models and their findings are tested and validated and presented to stakeholders for
action. Implementation is a staged approach with feedback loops built in at each stage to
minimize risk of failure.
The next section describes several reported big data analytics applications in healthcare.
We draw on publicly available material from numerous sources, including vendor sites. In this
emerging discipline, there is little independent research to cite. These examples are from
secondary sources. Nevertheless, they are illustrative of the potential of big data analytics in
Premier, the U.S. healthcare alliance network, has more than 2,700 members, hospitals
and health systems, 90,000 non-acute facilities and 400,000 physicians and is reported to have
data on approximately one in four patients discharged from hospitals. Naturally, the network has
assembled a large database of clinical, financial, patient, and supply chain data, with which the
network has generated comprehensive, and comparable clinical outcome measures, resource
utilization reports and transaction level cost data. These outputs have informed decision-making
and improved the healthcare processes at approximately 330 hospitals, saving an estimated
29,000 lives and reducing healthcare spending by nearly $7 billion . North York General
Hospital, a 450-bed community teaching hospital in Toronto, Canada, reports using real-time
analytics to improve patient outcomes and gain greater insight into the operations of healthcare
delivery. North York is reported to have implemented a scalable real-time analytics application
to provide multiple perspectives, including clinical, administrative, and financial . Another
example, reported by IBM, is that of the large, unnamed healthcare provider that is analyzing
data in the electronic medical record (EMR) system with the goal of reducing costs and
improving patient care. (Data in the EMR include the unstructured data from physician notes,
pathology reports and other sources). Big data analytics is used to develop care protocols and
case pathways and to assist caregivers in performing customized queries
At minimum, a big data analytics platform in healthcare must support the key functions
necessary for processing the data. The criteria for platform evaluation may include availability,
continuity, ease of use, scalability, ability to manipulate at different levels of granularity, privacy
and security enablement, and quality assurance In addition, while most platforms currently
available are open source, the typical advantages and limitations of open source platforms apply.
To succeed, big data analytics in healthcare needs to be packaged so it is menu driven, userfriendly and transparent. Real-time big data analytics is a key requirement in healthcare. The lag
between data collection and processing has to be addressed. The dynamic availability of
numerous analytics algorithms, models and methods in a pull-down type of menu is also
necessary for large-scale adoption. The important managerial issues of ownership, governance
and standards have to be considered. And woven through these issues are those of continuous
data acquisition and data cleansing. Health care data is rarely standardized, often fragmented, or
generated in legacy IT systems with incompatible formats. This great challenge needs to be
addressed as well.
Big data analytics has the potential to transform the way healthcare providers use
sophisticated technologies to gain insight from their clinical and other data repositories and make
informed decisions. In the future we’ll see the rapid, widespread implementation and use of big
data analytics across the healthcare organization and the healthcare industry. To that end, the
several challenges highlighted above, must be addressed. As big data analytics becomes more
mainstream, issues such as guaranteeing privacy, safeguarding security, establishing standards
and governance, and continually improving the tools and technologies will garner attention. Big
data analytics and applications in healthcare are at a nascent stage of development, but rapid
advances in platforms and tools can accelerate their maturing process.
Work Cited
Raghupathi W: Data Mining in Health Care. In Healthcare Informatics: Improving Efficiency
and Productivity. Edited by Kudyba S. Taylor & Francis; 2010:211–223.
Burghard C: Big Data and Analytics Key to Accountable Care Success. ID Health Insights;
Bian J, Topaloglu U, Yu F, Yu F: Towards Large-scale Twitter Mining for Drugrelated Adverse
Events. Maui, Hawaii: SHB; 2012.
Raghupathi W, Raghupathi V: An Overview of Health Analytics. Working paper; 2013.
Zikopoulos PC, DeRoos D, Parasuraman K, Deutsch T, Corrigan D, Giles J: Harness the Power
of Big Data. McGraw-Hill: The IBM Big Data Platform; 2013.
Zikopoulos PC, Eaton C, DeRoos D, Deutsch T, Lapis G: Understanding Big Data – Analytics
for Enterprise Class Hadoop and Streaming Data.
McGraw-Hill: Aspen Institute; 2012. 32. Bollier D: The Promise and Peril of Big Data.
Washington, DC: The Aspen Institute; 2010.

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