Bioequivalence & Bioavailability - Juniper Publishers
Abstract
We encourage the growth of data analytics and other
computer methods including artificial intelligence and machine learning
in the growth of procedure to diagnose and treat those inflicted with
disease or indications of the spread of infectious diseases. With the
rapid advances in machine intelligence, we have seen the development of
the application of machine learning in business forecasting, analyzing
treatment data and the results of analytic and diagnostic tests.
Keywords:
Quality control and improvement; Diagnostic testing; Data analytics;
Artificial intelligence; Machine learning; Autoregressive-integrated
moving average (ARIMA); Multivariate methods
Abbreviations:
AI: Artificial Intelligence; ML: Machine Learning; HTA: Health
Technology Assessment; TQM: Total Quality Movement; ASQC: Automated
Statistical Quality Control; ASPC: Automated Statistical Process
Control; EWMA: Exponentially Weighted Moving Average; SPC: Statistical
Process Control; SQC: Statistical Quality Control; MQC: Most Popular
Multivariate; MEWMA: Multivariate Exponential Moving Average Method;
ARIMA: Autoregressive-Integrated Moving Average
Introduction
Modern methods of management enter the field of
healthcare, diagnostics and bioequivalence in a variety of ways.
Everywhere one looks from the production of medical and diagnostic
equipment the use of such equipment in medical offices, hospital and
other health care providers we observe the automation of procedures and
the production of medicines which are similar to each other. We refer to
this as automation, but it is the advances in computer technologies
that drove this mechanization of seemingly simple but technological
advanced tasks to streamline production and development methodologies.
The growth of these technologies in the future will accelerated by
breakthroughs in artificial intelligence (AI) and machine learning (ML)
which will continue the mechanization of tasks improve the quality of
output. To incorporate AI into heath care procedures is not simple but
it includes the methodology of statistical/ mathematical science as it
applies the data-driven methodologies. In this study, we focus on one
such plan that involves the analytics associated with a volume of
diagnostic tests to produce plans to generate treatments.
Recently, Allen, Sudlow & Downey [1], in a large
data prospective study of resources for the investigation of the
genetic, environmental and lifestyle determinants of a many diseases of
mid-life and older patients. The employed the notion that data analytics
can yield great results and alter the methods by which health care
solutions are determined. In addition, Abelson, Giacomini, Lehoux &
Gauvin [2], indicated that health care
coverage decisions utilize health technology assessment (HTA) for
crucial information to provide for diagnostics and health
strategies. This indicates that health care policy and technology
combined to improve the health of human populations and bring changes to
those populations whose quality of care are equal to those who can
afford the expenses associated with the better health programs. This is
especially true to those populations who do not have the ability to
acquire the best reproductive health programs. Jarrett [3], expanded the
applications of using data analytics in managing health and medicine
using new multivariate methods to suggest quality care solutions.
In another opinion article, Marcus and Davis [4],
advance some new notions concerning the development of data analytics
via AI and the new development of computer technology. Recent programs
such as “Google Duplex” suggest that machine learning is on its way to
solving ordinary problems in life and produce the hypothesis that
machine will take over many tasks done by humans and lead to great
strides in producing strategies now common to only humans. The great
applications of this program is the notion that machines can learn, but
in health care policy improvement through technology it is extremely far
from aiding health practitioners in prescribing patient care
strategies. Machine learning and AI must turn it focus on solving the
difficult problems in patient care. In, addition, machine learning
should also employ strategies utilize in other field that do lend
themselves to the usefulness of computer technology.
Quality Movement in Diagnostics
Improvements in diagnostic care whether in hospitals,
treatment and diagnostic centers and other health care units are a
central function of quality health care. In many places, they are
the principal methods by which patients can secure care. Planned
Parenthood is one such example where patients can receive care
and treatment in an affordable and often convenient manner. A
client enters the clinic to possibly have diagnosed a severe set
of symptoms for which scientific tests are given to determine
a condition and the therapeutic plan to produce a treatment to
successfully reduce the problem and achieve positive results.
Earlier in industrial applications, this process was called “total
quality movement (TQM)” which is a plan to achieve successful
outcomes to the patient’s health problem. In the future we, expect
AI and TQM to spread everywhere and become a central focus
of machine outcomes. This is similar to the development of the
laser industry and its applications in medical care. Examine the
current research in automobiles and the relative changes made
by the driverless vehicle. The purpose is to have cleaner exhausts
from motor vehicle and greater safety. Humanity is not there
as of now but encouragement by governments through proper
regulations and other programs changed the motor vehicle
industry greatly. Similarly, motor vehicle parts may change this
product immensely in the future. Data analytics and ML are both
components of the new frontier in the motor vehicle and motor
vehicle parts industries as well as the health care industry.
To consider the depth of management science, data
analytics, AI and machine learning topics in health care include
the following manuscripts by Jarrett [5]; Jarrett & Pan [6], In
addition, others including Patel et al. [7], Machado and Costa
[8], Khoo and Quah [9], and more recently, Acampora et al. [10],
added specific illustrations of new computer-based methods.
Technology firms such as Google, Amazon, Microsoft, and Apple
in recent years made huge investments in AI to deliver tailored
search results and build items called personal virtual assistants.
The technology is seeping down to hospital care and other
forms of diagnostic and treatment methodology in health care
in general. With reforms in health care, health care reform law
will enable physicians and other health care personnel to be
assisted in choosing medicines and treatments for patients in
both an efficient and timely manner. For example, a physician
will be able to choose the best medicine to counter the effect
of a patient’s severe diagnosis quickly. With the huge number
of medications available much of a physician’s decision making
will be automated thanks in part to the push for computer
systems to prescribe the best treatment available. No longer will
a physician need to observe volumes of data bases to find the
optimal treatment. The computer will perform the search and
inform health care personnel to act quickly and optimally. Health
policy makers must encourage the greater development of these
methods.
Today, data collection by health statisticians include volumes
of patient demographics, clinical data and billing data that
are available in an electronic format for analysis by intelligent
software. For these difficult tasks AI software can analyze quickly
to perform the tasks of recommending medicines, treatment
protocols and general advice to assist physicians in attacking
the problems associated with difficult diagnoses. For example,
applications of AI have been utilized in intensive care for nearly
a generation; Hanson & Marshall [11], and Liu & Salinas [12].
Digital devices and home tests are allowing a more thorough
patient examination from remote places, which addresses some
of the previous setbacks of telemedicine. Remote diagnostic
tools such as Tyto, Scanadu and Med Wand are expanding the
perception of telemedicine. Heartbeat and respiration rate can
now be checked remotely. The same is true for blood pressure,
blood glucose, body temperature, and oxygen levels. A device
may contain a high-definition camera that can look down throats
and ear canals. Cameras can also provide high-resolution images
of skin to examine lesions, suspicious skin changes and other
dermatological issues. Urine-testing kits may also be employed
in the home or specific diagnostic centers to provide information
to medical personnel to suggest a treatment without the patient
being at the same physical location as the medical personnel.
At this point, we should consider automated statistical
quality control or (ASQC) or automated statistical process control
(ASPC) as it applies in the quality movement. These terms are no
longer new in diagnosis and treatment. however, they are based
on previous applications in industry, in banking and everywhere
one seeks assistance in the analysis of data where the timing of
decisions is very important. The quality movement is the field
that ensures that management maintains a set of standards
set and continually improves the process to achieve successful
goals. Instead of final, end-of-service inspection (whether the
patient is found healthy or not after the treatment ends). The
quality movement according to Lee & Wang [13], and Weihs
& Jessenberger [14], provides guidelines for this. Otherwise,
instead of end-of-service inspection and decision-making TQM
emphasizes prevention, integrated source inspection, process
control and continuous improvement [15-17]. The mitigating
of risks of type I and type II errors are the prime purpose of
these methods. In addition, AI will provide software, services,
and analytics solutions to the ambulatory care market. Also,
Health care information technology and services companies
that deliver the foundational capabilities to organizations will
aid the promotion of healthy communities. Technology provides
a customizable platform that empowers physician success,
enriches the patient care experience and lowers the cost of health
care and, in turn, health insurance. Stated simply, AI statistical
quality control monitors the incidence characterized by the
results of multiple tests on a similar fluid per period of a short
interval over a lengthy period (10 - 20 weeks). The monitoring
requires an intelligent system analyzing items (control charts,
for example) and seeking whether there are common causes of
variation or special causes of variation. In industrial applications,
these were called Shewhart charts. Later, others suggested
additional methods including the use of exponentially weighted
moving average (EWMA) control charts [17].
The great rise of health information systems enables AI and
machine learning in the very early stages of its development
to match one’s own intelligence. Computers certainly cannot physicians, however, machine learning software and computer
technology contain the capability of processing vast amounts
of data and identifying patterns that humans cannot. Machine
learning solves the complex algorithms that analyze this data
and is a useful tool to take full advantage of electronic medical
records, transforming them from mere e-filing cabinets into fullfledged
physician analysts’ who can deliver clinically relevant,
high-quality data in real time to allow doctors to use the
technology in prescribing treatment.
AISPC and AISQC
SPC (statistical process control) and SQC (statistical quality
control) environments usually assume a steady process behavior
where the influence of dynamic behavior does not exist or is
ignored. The focus of control there is only one variable (i.e.,
medical test) over a lengthy interval of time. SPC controls for the
changes in either the measure of location or dispersion or both.
These procedures as practiced in each phase may disturb the flow
of the service production process and operations. We not that in
recent years the use of SPC to address processes characterized
by more than one test or treatment emerged. First, we review the
basic univariate procedures to improve the process of SPC and
allow machine learning to enter the process.
Shewhart control charts were the central foundation of
univariate (one variable) SPC has a major flaw. The process
considers only one piece of data, the last data point, and does
not carry the memory of the previous data collected. Often,
a small change in the mean of a random variable is not likely
to be detected quickly. Griggs & Spiegelhalter [18], EWMA
control charts improved upon the detection process of small
process shifts. Rapid detection of relatively small changes in
the characteristic of interest and ease of computations through
recursive equations are some of the important properties of the
EWMA control chart that makes the process attractive and easy
to use the intelligent software to detect changes.
The EWMA chart is used extensively in time series modeling
where the data contains a gradual drift [19], EWMA provides for
identifying gradual shifts in medical tests by predicting where
the observation will be in the next period of time. Hence, the
EWMA process improves decision support in future time periods
and is therefore dynamic [20]. The EWMA statistic is useful for
monitoring the results of lengthy periods of tests having short
intervals when the actual tests are performed. Furthermore, the
method gives less and less weight to data as they become more
remote in time. Montgomery [21], contains the development of
models for finding control limits in this univariate process, but
appears to be another example of where intelligent software
applies.
Univariate Models and Its Obsolescence
Alwan [22], found that the great majority of SPC applications
studied results in control charts with misplaced control
limits and essentially false signals to the care providers. The
misplacement results from auto correlated process observation.
The auto correlated time series observations violate an
assumption associated with Shewhart control charts [14].
Autocorrelation of process observations is common in many
applications. For example, cast steel [22], wastewater treatment
plants [23], chemical processes [24], and many other processes
in the health care industry, especially diagnostic care and similar
applications. In addition, Alwan and Roberts [25], suggested
using an autoregressive integrated moving average (ARIMA)
charts for decision analysis. Continuous intelligent software can
be of particular aide to identification of the appropriate methods
for decision analysis if one follows the works of Atienza, Tang
and Ang [26], Box, Jenkins and Reinsel [27], West, Dellana and
Jarrett [28], who employed ARIMA modeling with Intervention;
and, in addition, Jarrett [29,30], summarized many of these
method in SPC. All these models are in the process of being
computerized to develop intelligent systems that will enable
computers intelligently point to optimal patient treatments and
diagnoses. The notion of physicians having patient-centered
diagnostic programs using AI will be of immense aid.
Multivariate Quality Controls (MQC) and ML
Multivariate methods utilize additional analyses due to
having two or more variables that are the results of several
diagnostic procedures to determine specific plan of care
(treatment). The use of univariate analysis can lead to incorrect
interpretation of data due to the co-integration of the tests
performed. The most popular multivariate (MQC) methods are
those based on the Hoteling T2 distribution [15,28,31], and
multivariate exponential moving average method (MEWMA).
Other MQC methods include those developed by Kalagonda and
Kulkarni [32,33]; Jarrett and Pan [34-37]; Vanhatalo and Kulachi
[38], and Billen et al. [39]. All the above MQC modelers produced
results that achieve superiority to SQC analysis because of one or
more of the following factors:
a. The control region of variables is represented by an
ellipse rather than parallel lines.
b. The Intelligent software is programed to maintain a
specific probability of a type I error in the analysis.
c. The determination of whether the process is out of
control is a single control limit (ARL).
d. Correcting T2 based MQC analysis where
autocorrelation is present.
e. Use of MEWMA, when time series methods have unique
schemes.
As a result, the above methodology indicate that
intelligent
software cannot ignore the various possibilities to lead to nonoptimal
decisions. However, proper machine learning methods
will adjust to new research and patient assisted analytical
software will be of great use to find diagnoses that enable one
to use AI to solve difficulties with patient care. A recent study by
Makridakis [40], indicated the possibilities of machine learning
in prediction which give evidence that data analytics can produce the
best results many situations. Hence, medical diagnostic tests
may then be couple with newer programs in machine learning
[41-42].
Summary and Conclusion
The purpose of this review and study is encourage
development in a very important and growing industry called AI
as it applies in the technology of health care. AI based platforms
for digital transformation will play an increasing role in patient
diagnoses health programs. The growth will occur in treatment
and emergency care centers as well as intensive care units.
Intelligent software is being developed which will suggest to
physicians and other health care workers the meaning of studying
data bases of information data analytics. In turn, intelligent
software will prescribe and set protocols for treatments of
difficult prognoses and intensive care. Intelligent programs are
AI-based platform for digital transformation. They are modular
and an interconnected mixture of flexible digital technologies
that span from robotic automation to ML. The programs learn
over time and produce new ways to arrive at results. The study
indicates that new ways to get results and in timely fashion.
The blending of intelligent software and comprehensive data
analytics will eventually move health care analysts from the
task of interpreting results to have protocols produced for
them. Intelligent software will blend seamlessly with a decision
maker’s operations insights and produce a unique domain
expertise to create better analytical conclusions in the real world.
By examining quality operations, we observe how AI shares the
burdens of care and assists health care personnel in achieving
their goals. As stated earlier, AI in health care incorporates AI
into many heath care procedures that are not simple but includes
the methodology of statistical/ mathematical science as it applies
the data driven methodologies. The notions of bioequivalence
will become clairvoyant as one becomes more knowledgeable in
modern healthcare and diagnostic innovations.
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