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Incorporating Cognitive Artificial Intelligence Systems and Real-Time Data Analytics in Clinical Care Delivery.

1. Introduction

The likely improvement of health care may transform noticeably as entrepreneurs provide ways out that alter how clinicians prevent, diagnose, and remedy health conditions, employing artificial intelligence. (Garbuio and Lin, 2019) Artificial intelligence may ameliorate the care path of chronic disease patients, recommend precision treatment for complex illnesses, cut down medical errors, and enhance subjects' admission into clinical trials. (Douglas Miller and Brown, 2018) If artificial intelligence is to immediately impact and optimize clinical care provision, consequently a comparable evidence criterion is required to validate better-quality outcomes and an absence of unexpected effects. (Maddox, Rumsfeld, and Payne, 2019)

2. Conceptual Framework and Literature Review

The use of large-scale artificial intelligence tools in medicine will bring about more adequate health care provision, possibly with relevant expense savings by transferring preventative treatment from unsuitable to pertinent patient subgroups. (Dilsizian and Siegel, 2018) Artificial intelligence can diagnose diseases employing an elaborate algorithm based on large volumes of biomarkers, rendering outcomes from huge numbers of patients, gathered published clinical research from scholarly databases, and a great quantity of clinicians' transcriptions from electronic health records. (Krittanawong, 2018) Artificial intelligence requires precise statistics so as to produce an irrefutable diagnosis, but triage by artificial intelligence can be swifter, more fact-based, and more sensitive, being derived from far more parameters than are achievable at this stage. (Liu, Keane, and Denniston, 2018)

3. Methodology and Empirical Analysis

Building our argument by drawing on data collected from CBInsights, GMInsights, Massachusetts Medical Society, McKinsey & Co., and NEJM Catalyst, we performed analyses and made estimates regarding value to date from artificial intelligence adoption (by business function), U.S. healthcare artificial intelligence market size (by application), most significant barriers organizations face in adopting artificial intelligence, how digitized organizations source capabilities and talent needed for artificial intelligence work, diagnostics as a major driver of healthcare artificial intelligence equity deals, and top benefits of using technology for patient engagement.

4. Results and Discussion

Cognitive artificial intelligence systems become proficient by gathering and assessing reactions at all layers of the system: feasible knowledge, treatments, and devices for patients and their doctors economize time, build up cost-effectiveness, optimize clinical decision-making, encourage users, and may enhance health outcomes in addition to patient and clinician satisfaction. (Dankwa-Mullan et al., 2018) Wearable devices having adequate sensors are useful in monitoring patient symptoms permanently and supplying valuable observations on disease evolution, clinical feedback, or complications. Instantaneous data analytics and artificial intelligence are assisting the doctors, healthcare systems, and legislators in adjusting the resources and ameliorating patient outcomes. (Kataria and Ravindran, 2018) (Tables 1-7)

5. Conclusions and Implications

Clinicians should embrace decision-support devices advanced by artificial intelligence. (Pearce et al., 2019) Machine learning approaches are being gradually used in physical healthcare. (Tiffin and Paton, 2018) There are adjusting criteria for analytic decision software that is put into action as a component of a medical device, while lacking for decision support software that networks by a direct route with a doctor who subsequently proceeds. (Shortliffe and Sepulveda, 2018) Artificial intelligence deals with how computers utilize data and resemble human thought processes, increasing cognitive capacity, and collaborating with decision support systems considerably, thus shaping the likely advancement of health care. (Noorbakhsh-Sabet et al., 2019) Computationally practical and statistically intricate artificial intelligence systems are capitalizing on previously inconceivable amounts of data formulate diagnostic and predictive judgments. (London, 2018) Incorporating artificial intelligence into clinical workflow may relevantly boost the cognitive load confronting clinical teams and generate higher stress, diminished cost-effectiveness, and unsatisfactory clinical care. (Maddox, Rumsfeld, and Payne, 2019) The mix of artificial intelligence with instantaneous, remote patient monitoring allows users to maintain more supervision over their care, consequently enabling them to systematically keep track of their specific health conditions. (Kang et al., 2018)

Acknowledgements

This paper was supported by Grant GE-1227682 from the Health Economics Research Unit, Glasgow.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

REFERENCES

Dankwa-Mullan, I., M. Rivo, M. Sepulveda, Y. Park, J. Snowdon, and K. Rhee (2018). "Transforming Diabetes Care through Artificial Intelligence: The Future Is Here," Population Health Management. doi:10.1089/pop.2018.0129

Dilsizian, M. E., and E. L. Siegel (2018). "Machine Meets Biology: A Primer on Artificial Intelligence in Cardiology and Cardiac Imaging," Current Cardiology Reports 20: 139.

Douglas Miller, D., and E. W. Brown (2018). "Artificial Intelligence in Medical Practice: The Question to the Answer?," The American Journal of Medicine 131(2): 129-133.

Garbuio, M., and N. Lin (2019). "Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models," California Management Review 61(2): 59-83.

Kang, M., E. Park, B. H. Cho, and K.-S. Lee (2018). "Recent Patient Health Monitoring Platforms Incorporating Internet of Things-enabled Smart Devices," International Neurourology Journal 22(2): S76-S82.

Kataria, S., and V. Ravindran (2018). "Digital Health: A New Dimension in Rheumatology Patient Care," Rheumatology International 38(11): 1949-1957.

Krittanawong, C. (2018). "The Rise of Artificial Intelligence and the Uncertain Future for Physicians," European Journal of Internal Medicine 48: e13-e14.

Liu, X., P. A. Keane, and A. K. Denniston (2018). "Time to Regenerate: The Doctor in the Age of Artificial Intelligence," Journal of the Royal Society of Medicine 111(4): 113-116.

London, A. J. (2018). "Groundhog Day for Medical Artificial Intelligence," Hastings Center Report 48(3): 1.

Maddox, T. M., J. S. Rumsfeld, and P. R. O. Payne (2019). "Questions for Artificial Intelligence in Health Care," JAMA 321(1): 31-32.

Noorbakhsh-Sabet, N., R. Zand, Y. Zhang, and V. Abedi (2019). "Artificial Intelligence Transforms the Future of Health Care," The American Journal of Medicine. doi:10. 1016/j.amjmed.2019.01.017

Pearce, C., A. McLeod, N. Rinehart, R. Whyte, E. Deveny, and M. Shearer (2019). "Artificial Intelligence and the Clinical World: A View from the Front Line," The Medical Journal of Australia 210(S6): S38-S40.

Shortliffe, E. H., and M. J. Sepulveda (2018). "Clinical Decision Support in the Era of Artificial Intelligence," JAMA 320(21): 2199-2200.

Tiffin, P., and L. Paton (2018). "Rise of the Machines? Machine Learning Approaches and Mental Health: Opportunities and Challenges," The British Journal of Psychiatry 213(3): 509-510.

Gerald Duft

duft@aa-er.org

The Cognitive Labor Institute,

New York City, USA

(corresponding author)

Anna Siekelova

anna.siekelova@fpedas.uniza.sk

Department of Economics,

Faculty of Operation and Economics of Transport and Communications,

University of Zilina, Zilina, Slovak Republic

Juraj Kolencik

juraj.kolencik@fpedas.uniza.sk

Department of Economics,

Faculty of Operation and Economics of Transport and Communications,

University of Zilina, Zilina, Slovak Republic

How to cite: Duft, Gerald, Anna Siekelova, and Juraj Kolencik (2019). "Incorporating Cognitive Artificial Intelligence Systems and Real-Time Data Analytics in Clinical Care Delivery," American Journal of Medical Research 6(1): 61-66. doi:10.22381/AJMR61201910

Received 4 December 2018 * Received in revised form 13 March 2019

Accepted 15 March 2019 * Available online 5 April 2019
Table 1 Big pharma's interest boosts artificial intelligence drug
discovery deals

2015 Q1   3
2015 Q2   2
2015 Q3   4
2015 Q4   7
2016 Q1   4
2016 Q2   2
2016 Q3   3
2016 Q4   3
2017 Q1   4
2017 Q2   2
2017 Q3  10
2017 Q4   4
2018 Q1   9
2018 Q2  11
2018 Q3  12
2018 Q4  13

Sources: CBInsights; our 2018 estimates.

Table 2 Diagnostics as a major driver of healthcare artificial
intelligence equity deals

Q1 2016   6
Q2 2016   4
Q3 2016   2
Q4 2016   1
Q1 2017  14
Q2 2017   4
Q3 2017  10
Q4 2017  16
Q1 2018  16
Q2 2018  18
Q3 2018  19
Q4 2018  21
Q1 2019  24
Q2 2019  26

Sources: CBInsights; our estimates.

Table 3 How digitized organizations source capabilities and talent
needed for artificial intelligence work (% of respondents)

Building artificial intelligence capabilities          57
in-house

Partnering with businesses or others (e.g., academic   34
institutions, foundations) to find talent

Buying and/or licensing capabilities from large        41
technology companies

Retraining and/or upskilling internal talent           39

Buying capabilities from artificial                    30
intelligence-focused start-ups

Crowd-sourcing artificial intelligence capabilities    11
(e.g., contest- or challenge-based platforms)

Buying capabilities from professional-services or      22
systems-integrator firms

Acquiring other companies                              12
Hiring external talent                        36

Sources: McKinsey & Co.; our survey among 1,100 individuals conducted
November 2018.

Table 4 Top benefits of using technology for patient engagement (%,
multiple responses)

Support patients in efforts to be healthy              71

Provide input to suppliers on how patients are doing   62
when not in clinic

Create ecosystem that allows for better predictive     54
analytics around patient health and more timely
intervention

Augment current capabilities of bricks-and-mortar      44
health system

Provide extra motivation to patients since they know   26
clinician will observe data

Replace case management and other                      21
personnel-intensive ways of monitoring patient
behavior

Create mechanism that allows people to make            14
high-risk behavior more difficult

Sources: NEJM Catalyst; Massachusetts Medical Society; our survey
among 1,100 individuals conducted November 2018.

Table 5 Most significant barriers organizations face in adopting
artificial intelligence (% of respondents)

Lack of clear strategy for artificial intelligence    27

Lack of talent with appropriate skill sets for        31
artificial intelligence work

Functional silos constrain end-to-end artificial      26
intelligence solutions

Lack of leaders' ownership of and commitment to       24
artificial intelligence

Lack of technological infrastructure to support       17
artificial intelligence

Lack of available (i.e., collected) data              21

Uncertain or low expectations for return on           18
artificial intelligence investments

Underresourcing for artificial intelligence in line   17
organization

Limited usefulness of data                            17

Personal judgment overrides artificial                14
intelligence-based decision making

Limited relevance of insights from artificial         15
intelligence

Lack of changes to frontline processes after          16
artificial intelligence's adoption

Sources: McKinsey & Co.; our survey among 1,100 individuals conducted
November 2018.

Table 6 Value to date from artificial intelligence adoption, by
business function (% of respondents)

                                    Moderate value  Significant value

Manufacturing                       22              61
Risk                                27              54
Supply-chain management             25              51
Product and/or service development  34              46
Strategy and corporate finance      33              36
Service operations                  37              34
Marketing and sales                 40              38
Human resources                     38              31

Sources: McKinsey & Co.; our survey among 1,100 individuals conducted
November 2018.

Table 7 U.S. healthcare artificial intelligence market size, by
application (2025, $ million)

Medical imagining and diagnosis  1640
Drug discovery                   1820
Therapy planning                  815
Hospital workflow                 612
Wearables                         420
Virtual assistants                840
Others                            210

Sources: GMInsights; our estimates.


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Author:Duft, Gerald; Siekelova, Anna; Kolencik, Juraj
Publication:American Journal of Medical Research
Article Type:Report
Date:Apr 1, 2019
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