Expert answer:Cognitive Computing in HealthCare
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COGNITIVE COMPUTING IN HEALTHCARE
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Cognitive Computing in Healthcare
Group Member’s Names: Edube Lenya
Cherryl Peel
Bunmi Adesanya
Cognitive Computing in Healthcare
10/5/17
COGNITIVE COMPUTING IN HEALTHCARE
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Executive Summary
Cognitive computing entails self-learning frameworks that use data mining approaches, natural
language, pattern recognition, in addition to, human sense processing, and system refinements
founded on real time attainment of patients and other data. This implies that the systems mimic
the same manner in which the human brain operates and continues to learn. In addition, cognitive
computing refers to a technology that utilizes Artificial Intelligence (AI) to develop predictions
based on provided data. Artificial intelligence means the technology that applies reasoning,
machine learning, natural language processing, and human interactions via vision and speech
interactions. Many organizations have adapted the use of cognitive computing in their systems.
IBM Watson Health and Michigan Health Information Network Shared Services (MiHIN) are the
most prominent users of cognitive computing in their healthcare operations. IBM Watson Health
has partnered with companies such as Johnson and Johnson, Medtronic, and Apple to attain various
other institutions and obtain innovation corporations. The MiHIN partners with various
stakeholders from the region such as Health Information Exchanges serving Michigan providers,
pharmacies, health payers, and the Governor’s Health Information Technology Commission to
boost the quality of health care provided. Cognitive systems are smart thanks to their ability to
assess patterns from data and are made better with every use. The objective is for the framework
is to be able to presume behavior and problems by providing suggestions and subsequent solutions
before a problem arises.
COGNITIVE COMPUTING IN HEALTHCARE
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Cognitive Computing in Healthcare
Introduction
This part presents a brief background lesson to comprehend the evolution of AI in
healthcare. Prior to Watson Health’s appearance in 2011, IBM started working with health
frameworks to improve oncology treatments and with medical institutions to enhance learning.
2015 marked the establishment of IBM Watson Health and the firm began partnering with Johnson
and Johnson, Medtronic, Apple and attaining various other innovation corporations (Kudva & Ye,
2017). As the partnership further developed, Apple’s Research and Health Kit adopted IBM
Watson Technology. Microsoft, in 2017 publicized the health care Next program founded on
collaborations to leverage cloud technology to produce insights regarding patient adherence and
healthcare and make faster samples in answering procedures for genome evaluation. This project
further analyzes IBM Watson Health and Michigan Health Information Network (MiHIN) Shared
Services as two healthcare organizations that utilize cognitive computing in their routine
operations.
Literature Review
The journal article, Towards Cognitive Machines: Multiscale Measures and Analysis,
shares how Life Sciences researchers are pressured to innovate faster as time progresses. Kinsner
noted how big data provided the promise of unraveling new insights and speeding breakthroughs
(2006). Although broad data is available, only a portion is being integrated, comprehended, and
assessed. The apparent challenge is in harnessing large volumes of data, assimilating the data from
COGNITIVE COMPUTING IN HEALTHCARE
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numerous of sources, and comprehending their sparse formats. Novel technologies in the form of
cognitive computing provide promise for tackling this challenge because cognitive solutions are
particularly designed to assimilate and evaluate big datasets (Kudva & Ye, 2017). Cognitive
solutions enable the comprehension of diverse forms of data, such as laboratory figures in a
structured database or the plain text attained from a scientific publication.
In 2012, Wang noted cognitive solutions are trained to comprehend industry-specific and
technical content. Thus, utilizing advanced reasoning, machine learning, and predictive modeling
approaches in advancing faster research. With use of a cognitive computing approach, Watson is
designed to support research in life sciences. This version of Watson incorporates medical
literature, genomics, patents, chemical data, and pharmacological information that researchers and
health professionals would utilize regularly in their career. In addition, Watson is equipped to
understand scientific terminology which enables it to make new connections in many pages of text
(Wang, 2012).
Watson has been used for numerous pilot studies in the sections of drug target
identification, as well as, drug repurposing. Findings by Wang and colleagues in 2010 suggest that
Watson can speed up identification of new drug candidates and novel drug targets by connecting
the possible big data. According to Wu, Wang and Lin (2007), cognitive innovations are used for
the development in computing that caricatures aspects of human thought procedures on a wider
scale. In such cases, this wider scale implies the capability to process the large volumes of data
and information present in the scientific sphere. Technology creators have recognized that human
reasoning, learning, and inference comprise one of the highly sophisticated thinking frameworks
in existence. Kinsner’s journal article observed that human cognition is characterized by two
precincts that involve scalability and prejudice. The cognitive frameworks described attempt to
COGNITIVE COMPUTING IN HEALTHCARE
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mimic factors of human thinking whilst adding the capability to address large amounts of data and
assess it without favoritism.
Intervention Method
IBM Watson Health and MiHIN represent two of the most famous organizations in terms of using
cognitive computing in their daily healthcare operations. MiHIN represents Michigan’s statewide
program offered to improve health care quality, effectiveness, and patient safety via the
transmission of electronic health information. In addition, this program reduces expenses for
patients, payers, and providers. MiHIN also operates as the state-designated firm for health
information transmission across Michigan and serves on the national scene through the eHealth
Exchange run by Heal the Way (Wang, 2012). Heal the Way is a non-profit, public-private
partnership that incorporates stakeholders from the region of Michigan, Health Information
Exchanges serving Michigan providers, pharmacies, health payers, and the Governor’s Health
Information Technology Commission. IBM Watson Health has also partnered with international
computing services to provide cognitive computing in healthcare (Kinsner, 2006). IBM Watson
Health and MiHIN were picked on the basis that they represent some of the widely recognized as
innovative healthcare organizations using artificial intelligence and cloud computing to provide
the best medical care across the world.
Outcomes
It has become routine for oncology advocates and leaders to advance cancer care in the
generation of healthcare transformation. Watson Health contributes by bringing an unparalleled
combination of materials and resources to assist overcome the hurdles faced in this medical
specialty. The firm’s genomics and oncology solutions help aid and surface relevant information,
COGNITIVE COMPUTING IN HEALTHCARE
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close disparate sources of data, and recognize treatments personalized to various patients.
Additionally, thanks to Watson, care teams across the globe execute a more patient-centered cancer
treatment by enhancing experiences for healthcare providers and patients on the same footing
(Wang, 2012). Watson Health based care provisions also enable innovations formulated to assist
drive value for healthcare providers and organizations. These providers and organizations are
fueled to give effective care, include consumers and patients, operate, manage, and influence
population health. They also are enabled to optimize business performance using the power of
data-propelled insights.
MiHIN offers an Active Care Relationship Service (ACRS) that provides the capability of
linking patients with prospective care team members. Care team members in this case imply
providers with active care relationships with the patient. MiHIN stipulates that providers form an
active care bond with patients when the former has visited the latter within the past two years and
plans to see them again. One of the most essential objectives of sharing patient data electronically
is assisting medical practitioners to develop complete, updated images of their patients utilizing
health information attained from numerous sources (Wu, Wang & Lin, 2007). The sources
typically encompass other specialists or doctors, clinics, hospitals, pharmacies, in addition to,
skilled nursing facilities among other healthcare environments where care is given.
Discussion
Both MiHIM and IBM Watson Health use cognitive computing in healthcare. Cognitive
computing is projected to play a growing role in clinical research. IBM Watson Health and MiHIN
understand and demonstrate that clinical trials are fundamental in clinical research and break
through discoveries. These healthcare institutions research using cognitive healthcare revealed the
efficacy as well effectiveness of novel drugs. One of IBM Watson’s recent reports noted that new
COGNITIVE COMPUTING IN HEALTHCARE
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treatments for unattained medical needs are the outcome of fruitful clinical trials. MiHIN has also
reported cognitive computing as the major optimizer of clinical research by deliberately dispensing
clinical trial data (Kinsner, 2006). There are countless ways IBM Watson and MiHIN both use
cognitive frame works in accelerating timelines, enhancing compliance, and improving data
analysis. A cognitive framework templates the time to design etiquettes, which are scientific papers
describing in detail a clinical research. By understanding necessary procedures, a cognitive
computer design templates processes to speed up the authoring of protocol papers. Both healthcare
firms have shown to believe that clinical trials generate huge amounts of structured and
unstructured data regarding the usefulness and effectiveness of a drug.
Conclusion
Cognitive computing can enhance patient research, as well as, business results. Through
interactive discussions between humans and machines, the correct choices can be recognized to
down to complex issues. An interactive debate permits both machines and humans to drill-down
on matters in a natural manner. The actual world applications through research has revealed that
cognitive computing represents a practical innovation that when used to big data produces results
of value and highest reputation. Cognitive computing has also shifted from concept to perception,
to lab, and practical uses. Future studies in this subject should focus on the following recommended
trajectories. One path needs to focus on improving foundational innovations, in the form of natural
language processing and machine learning systems. Researchers should enhance the aspect so that
cognitive frameworks can assume fluid conversations. Another path involves Cloud computing
that can be used to improve the ability to sense emotions and change communication designs
COGNITIVE COMPUTING IN HEALTHCARE
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reliant on emotional states. The last recommended path, would focus on sensor devices in
healthcare with use of Artificial Intelligence.
COGNITIVE COMPUTING IN HEALTHCARE
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References
Kinsner, W. (2006, July). Towards Cognitive Machines: Multiscale Measures and Analysis.
In Cognitive Informatics, 2006.ICCI 2006.5th IEEE International Conference on (Vol. 1,
pp. 8-14).IEEE.
Kudva, S., & Ye, X. (2017).Smart Cities, Big Data, and Sustainability Union. Big Data and
Cognitive Computing, 1(1), 4.
Wang, Y. (2012). Inference Algebra (IA): A denotational mathematics for cognitive computing
and machine reasoning (II). International Journal of Cognitive Informatics and Natural
Intelligence (IJCINI), 6(1), 21-47.
Wang, Y., Baciu, G., Yao, Y., Kinsner, W., Chan, K., Zhang, B., …& Miao, D. (2010).
Perspectives on cognitive informatics and cognitive computing. International Journal of
Cognitive Informatics and Natural Intelligence (IJCINI), 4(1), 1-29.
Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the
healthcare industry: A structural equation model. International journal of medical
informatics, 76(1), 66-77.
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