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Файл:Intelligent data analysis in medicine. Study aid
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CHAPTER 7. IMPLEMENTATION OF INTELLIGENT SYSTEMS
INTO MEDICAL PRACTICE
Digital health and AI in healthcare provide an opportunity to transform the lives
of communities around the world. Today, users are demanding more than ever before,
and healthcare is expected to keep pace with the era of innovation while transitioning
to new patient care paradigms. As awareness of AI in healthcare grows, so does the
public expectation: they expect AI to be used to improve everyday experiences.
New advances in medicine and concepts that were once the contents of futuristic
science fiction are slowly becoming a reality. Gene therapy, 3D printing of human
organs, liquid biopsies, robotic surgeries and voice-controlled personal assistants are
now realities that are becoming more sophisticated over time.
Advances in technology impact not only the practice of medicine, but also public
perceptions and attitudes toward health, lifestyle, and what it means to be healthy.
Healthcare must be innovated wisely to attract more patients to this type of technology.
Digital health technologies are developing at a rapid pace. The impact of data science,
AI, machine learning and connected health technologies is enormous and requires an
open mind and a willingness to engage in an ever-evolving environment with as much
knowledge as possible.
Healthcare itself is moving from volume-based value to patient-centered value.
There is a shift in medicine in which pharmacology is not the first and only line of
treatment, and more emphasis is placed on the role of lifestyle as medicine for
preventative and therapeutic purposes.
Traditional payment systems in healthcare are based on rewarding providers
based on the number of patients referred and treated. Volume-based care focuses on
economies of scale. Providers receive a discrete amount of money for all services
provided to patients over a set period of time. Value for money, patient’s experience
and quality of care are subsequently secondary considerations in the evaluation.
Providers are incentivized by the number of patients and the cost of overall care
provided to patients. Lack of health care resources and costs, disgruntled doctors, and
motivated clinicians have unknowingly contributed to the “see as many patients as
possible” approach, which has led to the treatment of diseases from a “drug first,
research later” standpoint. Hospitals and clinicians are incentivized to see as many
patients as possible, perform as many tests as possible, and approach the disease from
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a drug-first perspective. As a result, volume-based care models are typically modeled
on financial metrics, such as optimizing profits or minimizing costs per patient, rather
than health outcomes.
Healthcare is currently in the process of shifting toward delivering patientcentered care, which is synonymous with value over volume. A value metric that
should also be considered is patient experience or patient-centricity. Patient
centeredness is an important, but not necessarily dominant, indicator of value-based
care quality. Patient-centered care takes a multifaceted approach that revolves around
the patient, his or her goals, and broader boundaries in decisions and assessments.
The focus on efficiency ensures a scalable and efficient approach, allowing
providers to reduce healthcare costs and improve clinical outcomes for patients.
Preventing disease by quitting smoking, changing diet, changing lifestyle, activity,
sleep, and identifying genetic risk factors reduces the burden on health care resources.
Healthcare providers are interested in monitoring their patients' health more closely.
Startups and digital technology companies are developing digital health tools that
disrupt the traditional doctor-patient relationship and enable providers to become third
parties involved in sustainable health.
The goal of value-based care is to standardize healthcare processes through best
practices and democratize access and quality of care. Analyzing data and historical
evidence can determine which methods work and which don't. Keeping people healthy
reduces the cost of providing health care and optimizes the use of resources. For
example, in the treatment of some chronic diseases, value-based care uses a collaborative
and interdisciplinary approach to prevent complications associated with them.
Patients interact with a medical team that is aware of the patient's progress and
health status. The health care team may include a nurse, dietitian, behavioral health
educator, and other specialists who help the patient improve his or her condition. The
team will set patient-centered goals and assist the patient in the following:
– maintain control of blood glucose levels;
– provide health coaching and assistance in maintaining habits;
– use the latest evidence base to provide nutritional advice;
– work with the psychological aspects of diseases.
Incentives are being transformed. Take for example a hospital that, instead of
paying based on how many patients it can admit, is compensated based on the opposite.
The hospital is paid based on how many patients are in good health and how many beds
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are available. The focus shifts from the moment of hospitalization to predicting the
likelihood of future risks and anticipating them through preventive solutions.
In addition to face-to-face interactions, ongoing patient support can be provided
digitally, such as through health coaching, providing an exercise program, or assistance
with mental health issues using apps, wearable technology, or telemedicine. AI and
predictive analytics enable healthcare providers to optimize the delivery of these
services, with a focus on disease prevention and treatment. The simultaneous collection
of behavioral, demographic, health and engagement data provides the opportunity for
machine learning and the development of new AI systems to quickly improve and learn
from user behavior and outcomes.
Managing healthcare operations and subsequently assessing healthcare quality
is complex. To evaluate a value-based care model, healthcare organizations must
collect and analyze data, objectively measuring effectiveness through quality of care,
patient health outcomes, and cost-effectiveness. Providers can report and model
preventative care metrics such as hospital readmission rates, error rates, disease
progression, population health improvements, and engagement strategies. The quality
of medical care is determined by several indicators.
Patients’ experience and satisfaction ratings are usually the first evaluation
indicators. Metrics such as time spent per patient, patient engagement, medication
savings, and medication adherence are examples of quality that is associated with
productivity.
Many AI applications in healthcare are located in hospitals. Hospital
management platforms provide a return on investment by keeping organizations
prepared and equipped. Systems that predict peak patient flow times, readmission
times, and use real-time data to cope with real-world demand are considered long-term
systems of value to healthcare providers. Applications related to improving clinical
care show enormous potential. Long-term studies are needed to assess the impact on
disease treatment and public health. Evidence-based medicine is based on decisionmaking, formed with the latest and most reliable scientific evidence. It is an approach
to solve a clinical problem by combining the best research and clinical evidence with
real-world clinical experience and patient values. In the case of healthcare, these are
considered randomized controlled trials with increasingly real-world evidence. The
datafication of society and the Internet of Things make a significant contribution to the
evidence base.
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Interestingly, the traditional evidence base for pharmacology has been shown to
be biased, leading to questions of validity. Randomized controlled trials are considered
the most reliable form of evidence for assessing the effectiveness of treatments.
Evidence shows that the reliability of randomized trials can be influenced by many
factors, including methodological quality, quality of reporting and source of funding.
Pharmaceutical companies fund most clinical drug trials and face conflicts of interest.
Evidence breaks down traditional hierarchies and evidence-based approaches. Data
about the human experience from mobile phones, social media, digital communities,
health apps, nutrition tracking, wearables, and the Internet of Things have enabled
patients to become the evidence base and influence the scientific community's
understanding of healthcare. Patients, for example, can compare the results of
personalized medicine.
About 10 % of the risk of the disease is due to genetics. Each person has a unique
version of the human genome. Groups of specific patients may share common genomic
characteristics and therefore disease risk. For example, research shows that people of
South Asian descent have a higher prevalence of type 2 diabetes compared to
Caucasian British people in the UK.
Personalized medicine, also known as stratified or precision medicine, is an
approach that divides patients into groups and makes informed clinical decisions about
treatments and interventions based on expected patient responses. Personalized
medicine approaches to disease treatment are tailored to the patient. Patient’s health is
managed at an individual level to achieve the highest possible optimal health status.
For example, our genetic variations determine how our body will react to a particular
drug. One drug may not meet all requirements. Two people taking the same dose of the
same drug may react differently. With personalized medicine, everyone can choose the
right combination of drugs and their dosage.
This approach to medicine is not new, and medical professionals have been using
it since the time of Hippocrates. However, hyper-personalization, achieved by
leveraging the patient's genome and reducing its cost, as well as medical data from
medical records, wearable technology and the Internet of Things has renewed public
interest. Previously, it has never been possible to predict the risk of a disease or the
human body’s response to a specific medicine.
The combination of new technologies will usher in an era of personalized care
and innovation in healthcare. Predictive tools can be used to assess health risks and
develop personalized health care plans to reduce risks, prevent disease, manage
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disease, and treat diseases exactly when they occur. As healthcare becomes more
personalized for patients in both treatment and service delivery, it is critical to expand
access to ensure the participation of all groups in society.
Diagnostic tests, such as blood tests, are usually used to determine appropriate
treatment based on the patient's physiological tests. The choice of optimal treatments
will be increasingly personalized depending on the patient's genome. Healthcare
professionals will be able to diagnose current diseases, predict the risks of future
diseases, and instantly determine the predicted response to treatment based on subtle
signs. Genetic testing has begun to have an impact on personalized medicine; DNA test
results can only be imported into services that can personalize treatment.
Currently, it takes several weeks to receive DNA test results. The era of instant
genetic test results will allow patients to make informed choices about treatments,
services, products, medications and results. Raising serious ethical questions, the
decision to perform a genetic test and the consequences of its results deserve careful
preparation and consideration.
This allows healthcare providers to fully engage in personalized, value-based
care where quality of patient care is paramount. For patients, this can pinpoint genetic
mutations that predispose a person to a disease. The physical and mental consequences
of such knowledge can be profound and controversial among the medical establishment
and the general public. The natural division among all people on specific issues is to
be either for or against a topic of discussion. Progress does not come without moral
consequences, and legislation must be able to withstand the speed with which problems
first arise. People are conflicted because they have a lot of information about
themselves.
Vision of the future
Over the next decade, fetal DNA profiling will be carried out in the womb,
creating a direct profile of an individual's health and disease risk and enabling the
development of health and lifestyle treatment plans starting in the earliest years.
Genetic profiling will also allow potential problems to be identified more quickly and
potential genetic defects or unfavorable characteristics to be modified or eliminated.
Predisposition to the disease will be assessed and healthy lifestyle plans will be
developed for patients to follow on a daily basis. Patients will be constantly monitored.
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Aggregation of patient data will facilitate understanding of baseline health status and
allow algorithmic visualization of indicators of healthy people. Data monitoring and
predictive analytics will instantly alert healthcare workers and patients in case of
deviation from the norm, informing users about poor health and possible diseases. It
will be possible to download a clinically proven app that can detect, diagnose and treat
many diseases before visiting a clinic.
Sensors will become much less invasive — chips under the skin and smart tattoos
that maintain constant communication. Visiting a doctor's practice will be very
different. Your digital personal assistant may be able to suggest that you see a doctor
based on the fact that your voice sounds bad, or at least different.
The treatments will also include 3D printed treatments and digital interventions
delivered through mobile apps. Digital interventions measured by engagement and
health outcomes will be hyper-personalized based on demographics, behavior, health,
goals and preferences. Invisible wireless sensors will enable instant notification of
changes, while algorithms and artificial intelligence models will be used to diagnose
diseases and treat patients. Early diagnosis of health problems will be more common
with ongoing monitoring before they become more severe. Care will be provided
through a hybrid of digital and in-person interactions.
Virtual and augmented immersive experiences will enhance and support
behavior change. Drones will deliver your medications to wherever you are if your
autonomous car doesn't get you there first. You will be immediately alerted when data
sources report an adverse effect of your medication, and you will be immediately
offered the most appropriate, tailored alternative.
Various integrated medical disciplines will be involved in patient care to ensure
a holistic and humane approach to healthcare. Innovations will free up doctors, nurses
and other healthcare workers to do more humane work. The AI robot will be used to
perform more physical tasks such as moving patients, creating a sterile environment,
performing blood tests, radiological examinations, etc. Patient problems will be
resolved in real time. For example, if a patient is exhibiting symptoms of atrial
fibrillation, the doctor can record the patient's heartbeat on his tablet and upload it to
the system, which will confirm or refute the doctor's concerns. If abnormalities are
detected, the video is instantly sent to a cardiologist, who can make a diagnosis and
begin an individualized treatment plan for the patient. The meetings and follow-up will
take hours or days, not weeks or months. A connected care network means multiple
experts can review a patient's concerns and provide a second opinion at the same time.
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Precision medicine is another new approach that deals with modifying
treatments to account for individual variability. This measure adds three additional data
sets to existing traditional patient health records: patient environmental exposure,
lifestyle, and genomic data. This information can help clinicians determine which
approaches, treatments, and prevention methods will be effective for specific patients.
Medical devices
Wearable devices and the Internet of Things are the key to new technologies
in medicine. The data captured by these devices' sensors is playing an increasingly
important role in healthcare, contributing to the development of patient-centric
healthcare systems. Many factors are accelerating medical decision-making related to
wearable devices, especially their use in clinical trials and academic research to
monitor patients' health and lifestyle factors. For example, during a study, you can
record vital health indicators using Android Watch, Apple Watch, or other devices.
Participants use the apps to record their lifestyle habits, nutrition, activity and
medication adherence, as well as track medication’s side effects.
Wearable devices and patient data are beginning to be used by insurance
companies to promote healthy lifestyles. Insurance companies have historically
targeted such insurance products at digitally savvy consumers, offering the latest
gadgets to improve health. However, incentive insurance products will become
commonplace among the wider population and have been proven particularly effective
in tackling noncommunicable diseases.
Providing connected digital health offers opportunities to monitor, treat and cure
diseases, expand the risk portfolio and extend the life expectancy of populations with
reduced morbidity. As sensors become faster, smaller and more efficient, patient health
record profiles will eventually include detailed sleep analysis, continuous blood
glucose monitoring data, heart rate, blood pressure and estimated calories burned. The
smartwatch will combine a variety of diagnostic tools capable of monitoring blood
pressure, heart rate variability, blood glucose levels, ketones and more. Medical
sensors will become embedded, biodegradable and always connected, playing a critical
role in tasks such as patient care. It is worth noting that evidence suggests that fitness
trackers have a limited lifespan and do not help patients lose weight.
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Questions for self-control
1. What are the expectations of modern society from the introduction of artificial
intelligence technologies in medicine?
2. What are the most notable achievements of artificial intelligence in the field
of medicine today?
3. What are the benefits of making the healthcare system centered on the patient
and his needs?
4. What contribution does the datafication of society make to the development
of society?
5. What is personalized medicine? What modern technologies can be used in
personalized medicine?
6. How do medical devices contribute to the development of medical artificial
intelligence systems?
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CHAPTER 8. EXAMPLES OF USE
OF INTELLIGENT SYSTEMS IN MEDICINE
Patient data
Patient’s information can be stored simultaneously in dozens of clinics and
medical records. Without any doubt, this complicates the collection of anamnesis and
diagnosis. Interpretation of tests, and medical images may also not be accurate enough
due to the large volume of data. Even if the doctor has all the necessary information at
hand, he cannot always interpret it correctly and notice every detail. The lives of
patients may depend on this.
Thus, the MedClueRx service analyzes symptoms and not only diagnoses the
disease, but also selects the safest and most effective drugs depending on the patient’s
characteristics. Google's Deepmind Health system analyzes symptoms and offers
several diagnoses. The search results are based on millions of pages of scientific
information, covering even the most obscure diseases.
Diagnostics
One of the main purposes of AI systems is to recognize a disease at the earliest
possible stage. For example, Zebra Medical Vision and Arterys services help
diagnosticians focus on communicating with patients and eliminate the need to peer
into the smallest details of lung scans and cardiac ultrasounds.
These types of AI programs can be used not only by doctors, but also by patients.
The 23andMe service analyzes genetic information and tells the user about his
ancestors. The startup Sophia Genetics uses genetic data to identify susceptibility to
certain diseases. This is how patients adjust their lifestyle, and doctors choose the most
likely diagnoses.
Drug creation
Vaccine development and subsequent clinical trials are long and expensive
processes. AI can reduce the time to develop new drugs by several times by analyzing
the molecular structures of existing drugs and suggesting new ones according to given
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requirements. For example, in 2019, Insilico Medicine created several drug options for
the treatment of muscle fibrosis in this way. For this task, the algorithms needed 21
days, after which the scientists selected the most suitable drug options and conducted
a test on laboratory animals in 25 days. Thus, it took 46 days to select a suitable drug.
However, the traditional drug development process takes about 8 years and costs
pharmaceutical companies several million dollars. New technologies give hope that
with their help we will be able to quickly obtain cures for diseases that currently cannot
be treated: multiple sclerosis, Alzheimer's disease, etc.
Process automation
There was an imbalance and shortage of senior and mid-level medical personnel
throughout the world even before the coronavirus outbreak. According to the World
Health Organization, in order for people around the world to have access to health
services by 2030, low-income countries need 18 million more health workers. In the
future, the situation will most likely not stabilize due to population growth, aging
society and changes in the clinical picture of diseases. These factors will only increase
the demand for highly qualified healthcare workers and complicate access to medical
care. Therefore, innovative technologies must contain AI and a knowledge base in the
subject area. This way, they will free doctors from routine everyday tasks: entering
information into a medical record, detailed analysis of a large array of data from
medical history, etc. This allows providers to focus their time and effort on addressing
critical diagnostic questions and treatment decisions. Modern AI technologies can help
the healthcare system increase the satisfaction of patients and medical staff, reduce the
cost of medical services and improve the quality of medical care.
Online consultations
Conducting remote consultations increases access to qualitative medical care,
especially in sparsely populated areas where it is needed most. In addition, online
consultations provide an opportunity to reduce healthcare costs and obtain a second
opinion on research results to clarify the diagnosis and treatment plan. AI makes
telemedicine much more convenient. It is used for remote diagnostics, collecting
medical indicators and working with patient information.
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