The Power of the Mind in Well-Being and Healing

Opinion

I have always believed from my teens that positive belief can help overrule the frailties of our body. For example, the placebo effect (placebo meaning “I will heal”) has been well known for a long time. There is a a considerable history of how its effect has confused the statistical interpretation of clinical trials to determine the efficacy of new drugs. A person seemingly taking a drug with known side effects can not only be helped by the drug but can also experience those side effects such as nausea, headaches, and perhaps changes in blood pressure, even with a dummy drug. The more they are informed about the side effects, the more likely they are to report them. These negative effects are referred to as the nocebo effect (“I will harm”) by (Robson [1]). What has become more apparent is how powerful this effect can be. For example, even if a person knows they are taking a dummy pill there can be a positive effect. These positive and negative effects are not just imagination, they are real and can be measured physiologically. There is evidence that a good expectation can have a statistically significant effect on the rate of healing. We see then that it all depends on what we expect, described by Robson as the “expectation effect”. He gives a great deal of evidence to support his case, covering diverse topics such as what I regard as three fundamentals, exercise, diet, and sleep, and he also adds stress, willpower, intellectual capacity, and aging (e.g., living longer).

If we expect something to happen, our body takes action to help fulfil that expectation. It sets up the appropriate body chemistry. For example, we know that endorphins (oxytocin and endogenous opioids) are released by the hypothalamus and pituitary gland in response to pain or stress (and with tears), and this group of peptide hormones both relieves pain and creates a general feeling of well-being. They work by binding to the opioid receptors in your brain to block the perception of pain. The brain also produces neurotransmitters like dopamine and andreline that can assist us, though they caan be a problem with addiction. Inflammation is the immune system’s response to harmful stimuli, a defense mechanism that is vital to health. In response to tissue injury, the body initiates a chemical signaling cascade that stimulates responses aimed at healing affected tissues. Such signals activate leukocyte chemotaxis from the general circulation to sites of damage. These activated leukocytes produce cytokines that induce inflammatory responses. However, if our body does this due to our expectations, inflammation can be a problem. Our body is an amazing machine, and even calling it a machine is doing disservice to its amazing capacity for adaption. When under extreme stress, adrenalin is produced, and we read of a young lady who lifts a car to release a loved person, or someone leaping a high fence when being chased by a bull! Also, there are people who carry out great feats of endurance to survive, e.g., Victor Frankl, a famous Jewish psychiatrist, who survived a prison camp because of his expectations and self-belief. Our mind shapes our life for better or for worse with beliefs becoming self-fulfilling prophecies. Negative expectations can have a huge effect on our bodies. For example, a person who believes in a curse put on them or has undergone bone pointing, can lead them to shut down and physically deteriorate to prepare the body to cope with negative consequences, and ultimately even death.

A high proportion of people have sleep problems (Seber [2]). In terms of our next day’s feelings and performance, we sleep as well as we think we did. Being anxious about falling asleep can chase away sleep! Positive and negative self-fulfilling prophecies can also determine memory capacity, concentration and fatigue during hard mental tasks, and creativity in problem solving. As well as distress profoundly affecting us, there is also a positive stress, promoted by Hans Selye as “eustress”. It is our attitude to stress, and whether we view it as positive or negative will determine how it affects us. To take a very simple example of expectation, a husband is asked by his wife to wipe down the shower box after a every shower. He can either be annoyed about it or he can regard it as useful form of exercise. Just being positive isn’t always enough, as it can simply lead to denial and worse difficulties. No, it is an appropriate attitude to life’s difficulties that can make a difference. It is specific beliefs rather than a general optimism or pessimism. It is not self-deception. As a counsellor/psychotherapist of 18 years practice I sometimes use cognitive-behavioral therapy (Seber [2]) from my toolbox, using ABC, where a an activating event can lead to emotional and behavioral consequences C. By dealing with the underlying belief system B, the behavior can be changed, e.g., “she does say some hurtful things sometimes, but I know she loves me [3].” Let us rethink our negative expectations!

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The Right to Life in the Legal Order of the Republic of Slovenia

Life as a Social Good

On the issues of life and dying, and the question of who’s a life is, I am unable to place the professional competence, persuasiveness, and credibility within myself, to critically test the weight and reach of legal opinions, theories, positions, or judgements in a concrete problem field, and to understand and persuasively justify why some positions are more likely to be followed than others. Merely summarising the views of legal theorists, judgements, and legal theories to suit my own beliefs, and ideological views, with serious argumentation, has nothing to do with my legal knowledge about living and dying. The 2017 litigation before the European Court of Human Rights (ECtHR) concerns Mr Mortier’s complaint against Belgium [1] for an alleged breach of the State’s obligation to protect the right to life by failing to prevent a doctor from ending the life of a patient suffering from chronic depression, without the knowledge and consent of her son and daughter. In this court case, the complainant’s mother, diagnosed with chronic depression, was euthanised by a doctor who failed to inform the complainant and his sister.

The Administrative Board, which is responsible for verifying the procedure and compliance with the conditions laid down by the Belgian Euthanasia Act, did not find any irregularities, even though the decision was not made public. The complainant claimed that the State had failed in its duty to protect his mother’s life. He argued that the safeguards provided for by law were illusory, due to a failure to follow due process. He also thought that the investigation carried out was not sufficiently effective. He questioned the impartiality of the Administrative Board, since the doctor in question was one of the members of the Board and, a few weeks before her death, the complainant’s mother had donated €2,500 to the medical association of which the doctor was president. The complainant also asserted that the facts of the case violated his mental integrity and interfered with his family life. He pleaded a violation of Articles 2 and 8 of the Convention on Human Rights. As I was studying the court case and looking for a timeless concept of humanity and its value, a few questions came to my mind.

Which behaviour is the most human? Helping a person to die because they asked for it, for whatever reason? Would it not be more humane to say: ‘You matter because you are you, and you matter for the rest of your life. We will do everything we can, not only to help you die peacefully but also to help you live until you die.’ As in the case of Vincent Lambert [2] from France in 2019, these are two opposing concepts of humanity and human rights: on the one hand, a humanist concept that values and protects the intrinsic dignity of each person, and on the other hand, an individualist notion that does not believe in human nature, but only in individual will. The case of Mortier v. Belgium raises many questions to which I am still seeking answers. Legal theory and case law also distinguish between the capacity to act [3] and the capacity to judge [4]. According to legal theory, the capacity to judge is the actual capacity to understand the meaning of one’s own decisions and actions, the capacity to understand the meaning of a statement of one’s own will and the legal consequences that such a statement of business will entail.

The discrepancy between the two concepts arises when a person with the capacity to act loses the actual capacity to judge, for example, due to dementia, alcohol consumption, medication, or stress. Discretion is the essence of the capacity to act, but the will of a person who is incapable of discernment is null and void, so there is no valid declaration of will, and such a person does not understand the meaning and consequences of the declaration because they were incapable of discernment at the time. The concept of discernment is a legal concept, not a medical one. The court can only conclude on the person’s capacity on the basis of a determination of all the circumstances relating to the person’s mental state. Of course, the legislature has undoubtedly taken into account the advances of medical science in the matter of discernment. The psychiatric expert has a special role in the process of establishing capacity, but only in the sense that their expertise enables the court to ascertain the necessary circumstances of the mental state and then, on the basis of that evidence, to determine whether or not the person was capable of exercising judgement at the time of the declaration of intent, or whether or not they were capable of acting at that time.

Human dignity is the highest ethical value and the benchmark and limit for state power. The constitutional legal order is therefore built on values that fundamentally belong to the individual: the free human being. The Charter of Fundamental Rights of the European Union [5] makes it clear that human dignity is inviolable and must be respected and protected. Article 17 of the Slovenian Constitution provides for the same. Everyone has the right to life and personal integrity. Everyone has the right to respect for their physical and mental integrity. In the fields of medicine and biology, free consent must be respected, after prior information of the person concerned in accordance with the procedures laid down by law. In our country, this is laid down in Article 26 of the Patients’ Rights Act [6]. For many years now, there has been much talk of a legal framework that, at least in theory, would be so safe and secure that it would not allow abuses in the area of life and dying. The law always exists within social developments. Modern medicine is often a reflection of the society that pushes with all its might, believing that at the end of life, there is still something more that can be done for the patient, perhaps with more aggressive medical treatment, which does not improve the quality of life or prolong it.

The pressure on medicine and on the dying is exerted through legal means, through lawsuits by relatives who cannot cope with their relative’s dying and who claim that the doctors did not do everything they could, even if the action is medically unreasonable and is also privately rejected by the patients but who often bow to the pressure of the relatives. This happens despite patients’ rights to relieve their suffering and pain and despite their right to choose how to live their life with a terminal illness. The rule of law and its protection of patients, even on less controversial issues such as accompanying the dying, cannot protect them from abuse. It is necessary to demystify death, which has been erased from everyday conversation, to allow dying and to let people die when their time comes, because it is a matter of respecting the limits of one’s own dying.

Right to Life

The human right to life is not a legal right, but a natural right of the highest value, without which society cannot exist. The right to life is a foregone right. If the law were to establish a norm which, in principle, denied the right to life, it could not be a legal norm, since it would contradict the foundations of law. The human right to life is an essential and constitutive element of the protection of human dignity. Human life is a necessary precondition for the protection of dignity as a supreme constitutional value and idea. The inviolability of human life does not permit a restrictive or negative valuation of the life of the individual, a valuation which would define a human being as less useful or less worthy than others, or as unworthy of life altogether, on account of their physical or mental condition. The Constitution of Slovenia [7] commands an equal valuation of the life of all individuals and opposes the notion of a human as an object. Human beings are subject to rights and fundamental freedoms. The requirement of a categorically positive valuation of human life is clear from the provision of Article 17 of the Constitution.

The right to life is an inherent right of individuals and is closely linked to them. It can neither be transferred nor waived. The right to life cannot be the basis for the right to death, which is its substantive opposite. The inviolability of life does not confer on the individual a dispositive right over their own life, which would, for example, oblige the State to assist an individual in committing suicide. The Universal Declaration of Human Rights [8] guarantees the right to life in Article 3, which states that everyone has the right to life, liberty, and personal safety. The International Covenant on Civil and Political Rights [9] regulates the right to life in Article 6, which states that everyone has the inherent right to life. This right must be protected by law. No one shall be arbitrarily deprived of life. It is clear from international practice, the provisions of national constitutions and various international instruments that the right to life constitutes a general principle of international law [10].

Conclusion

The proposed Slovenian law on assisted voluntary end-of-life, which has been under public consultation since October last year, places the responsibility for assisting in the execution of a person on the chosen attending physician, which is contrary to the physician’s mission of preserving life. In this context, I justifiably wonder what place life occupies in Slovenian society and how society protects human dignity as its individual and collective value. How much is life worth in Slovenian society? As I have already written, the realisation of life and its inviolability and sanctity are the central foundations of any human society. The case law of the ECtHR has taken the view that the right to life does not include the right to die. The question arises as to who can decide on assistance to end life voluntarily. Should requests be dealt with only by expert teams composed of doctors, philosophers, lawyers, and other professionals, or should such requests be dealt with by the courts? It is unacceptable for the decision to be in the hands of just one individual a doctor.

The value of life cannot be evaluated by the law. The medical profession can agree on the evaluation and determine from what point onwards it is unethical and contrary to medical science and the profession to artificially maintain someone in a state that does not constitute life. The Patient Rights Act contains a legal standard, that is, the principle of the greatest medical benefit for the patient. This standard means that hopeless treatment should not be continued, taking into account all the circumstances of the particular case and avoiding abuse of the law. The Patient Rights Act also gives patients the right to refuse medical care, for example to be put on a ventilator, and the doctor can take this into account. Such a waiver is not analogous to euthanasia. I have reservations about the substance for a simple, mundane reason – I have always seen law as an imperfect way of regulating human life because, as a lawyer, I must also be aware of the limitations of my profession. The law is rooted in humanity, which is also rooted in a profound awareness and acknowledgement that I can be wrong.

The famous saying errare humanum est, by which we justify our human and professional mistakes, encapsulates the primary characteristic of human existence. Legislation and regulation in the sphere of the end of our lives must have a safeguard that also allows for a return to the original state, for example, the reversal of a decision made because of one or another mistake. From a biological and psychological aspect, the process of natural dying is not just an event in the sense of someone flicking a switch to end a life. I have no medical or psychological knowledge myself, and I can only add that the process of dying is considerably more complex than it seems at first sight and that we are far from having the answers to the questions of the extent to which we can legally intervene in what is happening. It is a question to which we will all one day find the answer. That is why legal interventions in the final phase of our lives need deliberate and careful thought. Once death occurs, there is, unfortunately, no undoing it, no going back to the way things were.

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Therapeutic Perspectives of Brivaracetam Against Epilepsy

Perspective

Seizure is the fourth leading neuro disease affect about 85 million people worldwide. The symptoms of seizures are due to an aberrant synchronized activation of excitatory neurons, characterizes this disorder [1]. A pulse of voltage termed as a paroxysmal depolarisation shift happens when neurons fire simultaneously. During this time, the neurons’ resistance to firing decreases, resulting in numerous nerve impulses which produce abnormal high electronic impulses in brain [2]. Brivaracetam, it is propyl counterpart of levetiracetam, an anticonvulsant and racetams compound, was approved as an add-on medication by FDA in February 2017. It is approved for the treatment of POS in adolescents and adults and old age people [3] (Figure 1).

Pharmacology, Toxicology and Safety

Brivaracetam examined in vitro activity in rat hippocampus slice after spread with a high potassium-low calcium solution values ranging from 1–10 μM. Brivaracetam at promiscuity dosage reduced the spontaneous bursts, but LEV don does not react against these drug-resistant marker of epileptiform activity [4]. Brivaracetam has been widely researched in in vivo epilepsy and convulsion model. The corneally ignited mouse is a partial epilepsy model. Brivaracetam at doses several orders of magnitude lower than those required LEV for prevent animal to secondary generalised motor seizure (ED50 value 1.2 versus 7.3 mg/kg, i.p.). Brivaracetam suppressed both severity of motor seizure and then liberation length more profoundly than LEV in another model of focal epilepsy, the subcortical structures of rat. Brivaracetam’s action was also studied in models with generalised seizures. Brivaracetam effectively protected mice genetically predisposed to audiogenic seizures from chronic convulsions (ED50 value 2.4 versus. 30 mg/kg, i.p.) [5]. Brivaracetam suppressed spike-wave discharges more completely in compare to LEV in a model without epilepsy, the genetic non-epilepsy rat from Strasbourg (GAERS).

Figure 1.

Chronic pre-treatment before to corneal stimulation with LEV or 10 times lower dose of brivaracetam two times daily (1.7- 54 mg/kg i.p. versus 0.21-6.8 mg/kg i.p.) suppressed kindling development in the same corneal kindling model. Most significantly, discontinuing therapy with sustained corneal stimulation led in a more dramatic and long-lasting suppression of the kindling process than LEV. Brivaracetam’s action versus partially drug-resistant selfsustaining status epilepticus (SSSE) in rats was tested to determine its anticonvulsant characteristics in an acute seizure paradigm [6]. The model demonstrated the stimulating excitatory pathway may result in reverberating limbic circuits in which seizures are self-sustaining, causing brain injury. Once started, this process is resistant to common anticonvulsants i.e., diazepam and phenytoin. Explicit path stimulation generated SSSE in adult male rats.

At 20 and 300 mg/kg, the aggregate duration of active seizures was reduced to 11% and 0.8 percent of controls, respectively [7]. Brivaracetam’s oral acute toxicity demonstrated to minimal in rat, mice, and dog, with short time CNS effect typically arising at dose of 100 mg/kg or above in a dose-dependent manner. Under continuing medication, these effects subsided after a few days. There have been no severe cardiovascular, respiratory, or gastrointestinal problems noted (UCB, data on file). Based on clinical symptoms, the maximal nonlethal oral single dose in rats was over 1000 mg/kg, and in male and female rat a no-effect limit at 500 mg/kg was determined. Dogs, rats, and monkeys were tested for chronic toxicity [8].

Pharmacokinetics

Brivaracetam bioavailability is quick and nearly complete after oral dosing. At a dose range of 10-600 mg, drug exhibits linear pharmacokinetics. At supratherapeutic doses, brivaracetam metabolic clearance increases in a time-dependent manner; a constant stage is attained within one week of treatment repeated. Plasma protein binding is modest (20%), with a volume of distribution near to total body water (0.6 L/kg). Brivaracetam’s terminal half-life of elimination is about eight hours and does not change with administered dose [9]. The Brivaracetam absorption profile was examined using pharmaco-scintigraphy (UCB, data on file). Brivaracetam uniformly absorbed in Gastrointestinal system and demonstrated by comparative AUC (completely bioavailable in stomach) values of 97, 98, and 101 % in the different part of stomach and intestines [10].

Future Directions

With the increase in new AEDs since 1994, a new AED must either demonstrate significantly improved safety and performance or address a market demand. Claiming a far greater safety profile is a risky endeavour, as safety problems are often identified after the drug has been provided to tens of hundreds of patients. Certain novel medicines are definitely more effective in some people than others in terms of efficacy, although the efficacy profiles among most new drugs appear to be comparable [11]. For these reasons, the Brivaracetam development programme focused on unmet needs. Infantile spasms are a prime example of an unsatisfied demand, as there is presently no FDA-approved therapy for this illness. Although epilepsy affects both men and women equally, it is believed over one million American women of reproductive age suffer from it [12]. Many women’s health problems are exacerbated by epilepsy, particularly those of reproductive age.

Exacerbations of seizures have been connected to a decline in endogenous progesterone levels during the perimenstrual phase, and research suggests that exogenous progesterone therapy can lower seizure frequency [13]. Brivaracetam may be especially beneficial against catamenial seizures since it is a neuroactive synthetic equivalent of allopregnanolone, a naturally occurring progesterone metabolite [14]. Brivaracetam’s minimal teratogenicity makes it an excellent therapy option for women hoping to have children. Brivaracetam’s safety, tolerability, pharmacokinetics, and anticonvulsant efficacy as just a contribute therapy in women with catamenial epilepsy who are uncontrollable on their current AED regimen are being studied [15].

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Machine Learning Application to Combat Superbugs in Hospitals: A Primer to Infection Prevention Practitioners

Introduction

Healthcare-associated infections (HAIs) which defined as infections arising and developing during hospital stay or during the process of medical care in healthcare facilities. It is also defined as infections which are not present or incubating when the patient is hospitalized and are acquired after 48 hours of hospital stay [1]. HAIs represent the most serious threat to patient safety, and it also represent global public health concern [2]. HAIs have a significant clinical as well as financial impact due to prolonged hospitalization, increased mortality, and morbidity, increased antimicrobial resistance and increased direct costs for medical services [2]. Antimicrobial resistance is on the rise, raising worries about the impact on individuals with multidrug resistance bacteria [3]. As a result, significant efforts have been made to investigate the clinical outcomes of patients infected with such pathogens, which have shown higher mortality and treatment failure rates than those infected with susceptible isolates [3]. The rise of resistant hospital pathogens has posed a difficulty to providing high-quality in-patient treatment. The overuse of antibiotics in hospitals is largely to blame for this problem [4]. Resistant bacterial infections have a negative impact on the treatment outcomes, cost, disease spread, and sickness duration, offering a severe challenge to future chemotherapies [4].

The systematic collection of data on the occurrence of HAIs, analysis and transformation of the data into valuable information, and dissemination of this knowledge with those who may take action to avoid HAIs are all part of HAIs surviellance systems [5]. The first criteria in an infection preventionist’s minimum standard of practice are surviellance and epidemiology [5]. Already 40 years ago, many studies proved that there is 32% reduction in HAIs rates in hospitals with active surveillance programs compared with those without such programs [6]. The first goal of any surveillance system is to determine infection rates, infection sites, common pathogens, and antibiotic use, as appropriate empiric therapy is recognized to be the most crucial component in patient’s outcome. As a result, it is crucial to identify the microorganisms that cause infections as well as their antimicrobial resistance pattern to find the optimal antimicrobial treatment [6]. Surviellance in its conventional way, in which every patient’s file is reviewed for the presence of HAIs, is time consuming and labor intensive [5]. To improve the efficiency and strength of infection prevention and surveillance systems, information technology, data science and artificial intelligence have been recently applied. We need tools that help prediction, early diagnosis, surveillance, and treatment of HAIs to prevent human efforts of disease containment from being overwhelmed.

Definition of Artificial Intelligence and Machine Learning

Artificial intelligence (AI), which is defined as computer algorithms with cognitive-like characteristics such as learning capabilities, is already having an impact on our lives in a variety of ways [7]. In radiology, dermatology and pathology, AI- assisted image analysis has already established a significant position. In genomics, another data-intensive science, AI aids in the prediction of phenotypes from genotypes [7]. Also, AI has been applied in infectious disease management specially to aid the detection and prevention of diseases [7]. The application of AI in healthcare began with the creation of expert systems based on rules extracted from interviews with medical specialists and experts, which were then translated and programmed [8]. The first expert system in medicine was developed in 1976 aiming at suggesting antimicrobial treatment for severe bacterial infections [8]. Machine learning (ML) considered a subset of AI, demonstrates the experiential “learning” associated with human intelligence, while also having the ability to learn and improve its analysis via the use of computing algorithms [9]. These algorithms recognize patterns and effectively “learn” in order to teach the computer to make autonomous suggestions or decisions using vast volumes of data inputs and outputs. The machine can take and input and anticipate a result with enough repetitions and modifications to the algorithm [9]. The algorithm’s accuracy is then judged by comparing the output to a collection of known outcomes, which is then iteratively changed to perfect the capacity to anticipate future results [9]. The predictive capabilities of machine learning are rapidly being employed in the realm of healthcare. ML models have been presented and evaluated as potential answers to a range of challenges involving diagnostic errors, treatment errors, workflow inefficiencies and obstacles to value-based care as a convergence between health and data science [9].

Machine Learning Methods

ML is divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning. The term “supervised learning” refers to algorithms that use labeled data as a training dataset. Labeled data are datasets in which the outcome of interest has already been determined; for example, to train an algorithm for sepsis prediction, we utilize a dataset in which patients are already classified as having sepsis or not. The algorithm will then select the best model to predict the desired outcome [8]. Unsupervised learning is the utilization of data without a specified or predefined outcome of interest. Algorithm are left to detect patterns and extract hidden structure from data on their own, with no expert labeling. Unsupervised learning is mostly used in medicine for clustering with the goal of discovering groups in data, such as related groups of patients based on clinical data [8]. Through trial and error, reinforcement learning algorithms uncover activities that provide the greatest rewards. In this category, the algorithm is set up to consider survival or a shorter hospital stay as a reward. The approach employs a training dataset to run several tests in order to generate the model with the highest reward [8].

Ml In Infection Prevention and Control

AI and ML offer huge potential in infection prevention and control (IPC) [10]. Its applications in IPC have enormous promise for implementing WHO core components. [10]. AI and ML have potential benefits in the three main areas highlighted by the WHO: 1- HAIs surviellance, 2- Improved laboratory diagnosis to facilitate IPC interventions, 3- Hand hygiene practice [10]. In HAIs surveillance, ML application have been used to monitor trends, identify clusters and outbreaks in a timely manner. It is also used in outbreak simulation to mitigate interventions. Also, ML is a very helpful tool in predicting the risk of nosocomial infections as nosocomial Clostridium difficile infection [10]. While more research is needed to validate these findings, this method has the potential to change HAIs surveillance and IPC [10]. ML data mining tools as well could use the clinical microbiology laboratory results to detect and predict clusters or outbreaks of multidrug-resistant pathogens in healthcare settings [10]. AI and ML enhanced laboratory microscopy could speed up infection diagnosis and aid AMR prevention initiatives by facilitating targeted antibiotic management and IPC intervention [10]. Studies showed that gram stain interpretation with AI-assisted tools could lower cost and time with good accuracy [10]. Wearable technology using ML applications provide benefits for healthcare environment in general and IPC in specific in the form of supporting healthcare staff IPC education, audit and behavior change.

ML In Prediction and Early Detection of Hais

AI and ML are being used by researchers in public health surveillance to predict disease outbreaks and evaluate surveillance tools [11]. Identifying patients at increased risk of HAIs in ICUs is a serious public health concern. ML could improve patient risk classification and lead to more specific infection prevention and control study. ML models could be made for surveillance of blood stream infections (BSI), CD infections (CDI), urinary tract infections (UTI), pneumonia and surgical site infections (SSI). Van der Werff et al [12] developed a fully automated surviellance algorithm for hospital acquired UTI using electronic health record (EHR) data. This study concluded that a fully automated surveillance algorithm based on artificial intelligence and machine learning to detect UTI symptoms from EHR had acceptable performance HA-UTI compared to manual record review. Taylor er al [13] showed that machine learning algorithms accurately diagnosed positive urine culture results and accurately predict UTIs in emergency department. Mancini et al [14] built a predictive model using a cloud platform (DSaaS), online and user-friendly platform, to predict multi-drug resistant (MDR) UTI in hospitals. DSaas can help physicians to built easy prediction models that could help them to treat hospitalized patients.Their model is based on supervised ML regression and classification algorithms. They developed this model to assist in the antimicrobial stewardship program implemented in their hospital [13]. Nemati et al [15] developed an Artificial Intelligence Sepsis Expert (AISE) algorithm for early prediction of sepsis. Using data available in the ICU in real time, AISE can accurately predict the onset of sepsis in an ICU patient 4 to 12 hours prior to clinical recognition [14]. Many studies showed that ML based clinical decision support (CDS) tools embedded within electronic medical record improve early detection and therapy in patients with early blood stream infections and can predict septic shock [14,16].

Conclusion

Many studies suggest that machine learning algorithms outperforms conventional statistical approaches in term of predictive performance, implying that the machine learning approaches could be used to identify and predict patients at higher risk of HAIs at hospital admission, giving clinicians enough time to potentially prevent HAIs and mitigate their severity by targeting specific infection prevention and control interventions at high-risk groups in order to improve quality of care.

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Doctor-Patient-Artificial Intelligence Relations in Smart Healthcare

Introduction

Smart Healthcare leverages the latest mobile and digital advances in E-Health and mHealth, driving the development of smart and connected medical devices. The approach to medicine is also changing with smart trackers and other similar devices, doctors have much more opportunities to constantly monitor patient indicators outside of medical institutions and, accordingly, prevent diseases. In recent years, the term “Smart Medicine” has emerged and is becoming more widely used. By “Smart medicine” we mean intelligent healthcare, which uses the latest mobile and digital achievements in the field of eHealth and mHealth, which encourages the development of smart and connected medical devices that ensure constant monitoring of patient indicators outside of medical institutions and, accordingly, the prevention of diseases. In some cases, this type of monitoring can recognize or predict critical health condition of patients and it can warn health institutions if immediate first aid is needed. The Smart medicine will allow the doctor to quickly communicate with the patient, conduct a remote course of treatment. Through special sensors and chips installed on the human body, the doctor, regardless of the location, will be able to get acquainted with important information about the patient’s health status. For example, the doctor will be able to track body temperature, pulse, respiration rate, blood sugar, and blood pressure.

Together with the concept of “Smart Medicine”, the concept of “Smart Hospital” began to be actively applied. Smart Hospital can be defined as interactive intelligent digital environment that represents the meta-system to manage clinical pathways based on on-line monitoring of vital functions in combination with the operational personnel access and patient’s information (including virtual councils) with the wide use of mobile applications and robotics. Current development of technologies paves a way to the situations when most medical institutions will implement computer science and other contemporary ways of provision of medical support in their activities. This is not only a way to provide better healthcare, but also to minimize costs to uniform digital solutions. Doctor-Patient relations have already changed due the developments brought by Smart medicine. Further developments come to place with the active processes that bring Artificial Intelligence (AI) technologies into the medical field. It should be noted here that AI is not one technology, but rather a collection of them; and thus, specific processes and tasks that they support can vary widely [1]. The numerous uses of AI are already changing and will change even more the relationship between the patient and the doctor. AI systems can be beneficial for doctors and patients, as they can provide them with new tools and opportunities. However, many ethical and legal challenges need to be resolved, such as obtaining informed consent to the use of AI, security, reliability and reliability of data sets, transparency of algorithms, algorithmic fairness and impartiality, data ownership, data confidentiality. We will look at these issues in our paper.

Artificial Intelligence and how it Can Be Useful for Doctors and Patients

Today, artificial intelligence is widely used in various fields of medicine, appropriate systems are developed by many scientists and companies. The usage of AI in medicine has a number of potential benefits to both doctors and patients. Patients can benefit from the application of AI systems from the point they get access to medical services. Today, in the emergency medical centers of hospitals, the order of admission of patients depends on how urgently the patient needs help. New technologies, e.g., special apps with AI system in it, can analyse patient’s symptoms in order to determine the degree of urgency. AI can accelerate the diagnosis process and medical research; thus, patients can have benefits from AI during patient’s care. And even the patients are not in the medical institutions, they can anyway get personalized information and advice, for example, in case AI systems are used in chat-bots and provide answers to patient’s request. Thus, even when the patients are at home, they can use digital tools and modern telemedicine to get necessary advice, answers and prescriptions.

On the other hand, it helps doctors enhance their abilities in provision of healthcare services and solve a variety of problems: • Assistance in making personalised diagnoses and prescribing treatment. For example, AI systems can analyse data from Electronic Health Record (EHR) systems, biosensors, watches, smartphones, conversational interfaces and other instrumentation, software can tailor recommendations by comparing patient data to other effective treatment pathways for similar cohorts [2].

• Real-time data analysis of critically ill patients. This analysis may predict the risks of developing medical complications or conditions such as sepsis and ARDS, or use of clinical and physiological data to aid in the monitoring of patients undergoing ventilatory support [3].

• Assessing the likelihood of complications of diseases. Using AI and machine learning technologies, medical researchers can identify the relationship between the patient’s diseases, the conditions in which he lives, and his habits. Even the state of the environment can help to establish which patients in a given region are at the highest risk. It is also possible to find the most vulnerable regions or segments of the population, which can help to give them recommendations in advance, before serious medical care is required [4].

• Remote first aid. It may include real-time analysis, so as to prevention or notification of possible negative tendencies in their health conditions.

Application of AI can be valuable also in the process of overcoming patient’s non-compliance and absent of proper involvement into the process. As some studies show, less than 25 % of the patient are highly engaged in the healthcare process [5]. This means that patients do not take prescriptions, do not follow instructions from doctors and do not comply with the requirements of doctors. This can significantly influence the quality of the medical service. AI systems are viewed as a way to change patient’s behaviour, as they can analyse and address patient’s needs, by alerting patients at proper times, by providing them with targeted information and content to provoke actions and compliance with recommendations [6].

The application of AI system is Smart medicine is a reality which needs to be assessed from various points of view. However, there’s a very important question which arises when we discuss the creation of any E-health system in the context of Smart medicine: who is the true owner of medical data of a concrete patient? Who is owner of Electronic Health Record of a patient? Who can dispose of them (patient, doctor, clinic, insurance company, employer, or computing service) and to what extent? These are the questions that are important not only from theoretical point of view; they need to be addressed and solved in practice, and necessary legislative amendment might be necessary to regulate in detail all the relations of the parties concerned so as to ensure security and personal health data protection. There are two main types of formation and storage of data about patient health in Electronic Health Record:

- Hospital-oriented system when EHR is owned by a hospital (polyclinic).

- Patient-oriented system when a patient is the owner of his EHR. It is he who decides what to store, where to store and to whom to give access to his EHR.

We considered the first case in our work [7]. In this paper, we pay more attention to the patient-oriented system. Thus, we will discuss the issues that are connected with the said approach, e.g., right of patient to obtain information about his health, to give informed consent to the application of AI, diagnosis and liability.

Patient and his Communication with AI and Doctor in Smart Healthcare

The patient oriented EHR system allows a patient to generate, administer and manage medical data from one central location using online technologies, which makes resource storage, retrieval and sharing extremely efficient. Each patient has absolute control over their medical records and can share medical data with a set of consumers, such as medical report providers, family members and friends. Although it is simple to provide access to EHR to anyone and everyone, there seems to be a number of security and privacy issues. The main cause of concern is whether patients have absolute control over their EHRs [8]. The ideal EHR includes personal medical information from various sources and provides complete and accurate personal medical information via the Internet or portable media, while maintaining security and confidentiality [9]. Cloud servers’ merit specific attention. Many EHR system are transferred to data storage on cloud servers as a result of the advent of cloud computing, which allows for more flexible use of resources and lower operating costs. However, when placing EHR data in the cloud, patients will face privacy issues. External cloud storage systems are often vulnerable to various attacks. It is extremely important to have precise “data access control” that works with untrusted servers to ensure that users (patients) manage their own EHRs. Thus, before storing data in cloud, it is advisable to encrypt it [10]. The EHR owner must choose how to encrypt the data and who has access to it. Only users who have been provided with the decryption key can access the EHR, while the rest of the clients must remain confidential. In addition, the patient should always be able not only to log in, but also to get authorization permission when he believes that it is really required. However, with such extensibility of the EHR system, patient-centered privacy is often at risk. Thus, it may be difficult to ensure proper access to medical information while maintaining flexibility and responsiveness in the encryption process [11].

Secure sharing of personal health records in the cloud is an area of specific concern, as patients are sometimes allowed to upload encrypted EHRs to the cloud, giving users access to certain parts of the EHR [12]. The owners grant each user in a group of users of a later type of access to the EHR to a certain extent, depending on the role of the user. Another important requirement of “patientoriented” EHR is that each patient has a specification about who has access to their personal EHR information. The EHR may be banned for some users [13].

Privacy and Security Concerns in Smart Healthcare

Medical information is a private area, which is even considered intimate by many people, so patient confidentiality is the most important issue. Thus, it is not only highly desirable, but strictly important to ensure that there are appropriate security measures in place, as digital data can be easily transferred anywhere in the world, as this is the mechanism of functioning of global networks. Storage and transmission of medical results, medical analysis and tests requires specific attention. Doctors need to ensure that they will not disclose private and sensitive information about the patient to any third party, as it is very simple when new devices are used, and there are many perpetrators that try to obtain this information [14]. Security is one of the most serious problems for artificial intelligence in healthcare. To realize the potential of AI, developers need to make sure of several key things [15,16]:

a) Reliability and reliability of data sets: the data sets used must be reliable and valid, because the better the training labeled data, the better the AI will work.

b) Data sharing: the need for huge amounts of data for analysis requires extensive data sharing.

c) Ensuring the transparency of algorithms: in the interests of the safety and trust of clinicians and patients, it is necessary to ensure some degree of transparency of algorithms, although in the real world there are problems related to the protection of investments and intellectual property, as well as cybersecurity.

Many AI systems that operate in healthcare rely on the existence of the big amounts of sensitive data, which sometimes conflicts with the data protection legislation. Of course, the data can be depersonalized, especially for the reasons of scientific research and big data analysis, which is often performed by AI. By performing depersonalization of data (anonymization), we will get both confidentiality and data integrity. This data will be useful for introducing innovations and strengthening cooperation between suppliers and partners, which will also benefit smart city medicine, including through the exchange of knowledge between doctors from around the world.

Informed Consent for AI Application in Smart Healthcare

A right to seek, receive and impart information and ideas through any media and regardless of frontiers is one the major human rights enshrined in Article 19 of the Universal Declaration of Human Rights. The right to access one’s personal information is not only part of respect for basic human dignity, but it is also central to effective personal decision-making; for example, access to medical records, for example, can help individuals make decisions about treatment, financial planning and so on [17]. Does the patient need or has the rights to give access to the information about the application of AI in diagnosing his or her condition? This is a question to be answered.

Issues related to obtaining informed consent for the use of AI: • Under what circumstances should the doctor notify the patient that AI is generally used for diagnosis, diagnosis and choice of treatment method?

• Under what circumstances should the principles of informed consent be applied in the field of AI?

It is also needed to consider the limits of the provision of information on AI to public. AI might take important decisions as to one’s health. Many algorithms rely on very complex and difficult to deconvolute mathematics, sometimes called the “black box”. In the medical area there are situation where it can be extremely important to know the reasons for decisions because they can affect not only patient’s health, but his life in general. But can a patient ask to have the algorithm disclosed? On one hand, a patient who is diagnosed with a severe condition using AI system, or received a specific prescription (presumable, on the basis on big data analysis by AI) might be interested in knowing the reasons and algorithm that formed the basis for the decision.

On the other hand, the developer of the appropriate AI system might also have ground to object to such disclosure, so as to protect his investments and effort, as he might be afraid that in such a way his competitors will discover his know-how and violate other IP rights, including reverse engineering of the software. Let us look at one example from another sphere. In a Wisconsin v. Loomis case (USA), a criminal defendant challenged a state trial court’s use of a (non-machine learning) risk assessment algorithm (developed by a private company) to determine his sentence. He argued that his due process rights were violated, as the company refused to disclose how the risk score there determined, claiming that information was a “trade secret”, and due to the “proprietary nature” of the algorithm he could not assess the information that was used for sentencing. The Wisconsin Supreme Court rejected the defendant’s arguments, stating that the company had the right to protect its proprietary information; and it release sufficient information that satisfied due process requirements [18].

However, sometimes knowing of the algorithm is important, as it can be a way to overcome a so called “algorithmic bias”, which is cases by the decisions of AI which are based on factors that should not be in fact relevant to the case. For example, training data can be biased because they are based on discriminatory human decisions. Such situation occurred at a medical school in the UK in the 1980s, where a computer program was introduced to sort the applications. The training data were the admission files from earlier years, when selection of the applications was done by persons. And it turned out that computer program discriminated against women and against people with immigrant background [19]. Another possible algorithmic bias can be caused by the under-representation of poor people in a data set; poor people are less likely to have smartphones and other smart devices, and lower possibilities of access to paid medicine services in general, thus they might not be fully taken into account in the medical studies. Attentions should also be given to the ways of overcoming the so-called “automation bias”. Automation bias is a tendency to believe computers without additional consideration of the results; as human decision-makers tend to follow computer advice, either because they try to minimize their responsibility, or because they do not have enough time, context or skills to make an adequate decision in the individual case [20]. Thus, it is important to properly train doctors and other medical workers so as to ensure that they do not trust the AI algorithms blindly and take due care so as to ensure the accuracy of the results, taking into consideration other possible options.

Diagnosis-Making Using AI Systems and Liability

The most AI systems are used to help doctor to make a diagnosis. Most of diagnosis in medicine are made based on analysis of medical images. The use of AI in the analysis of medical images is under continuous evolution. There are already very good results that are shown by AI systems in detection of skin cancer: in 2017, the case was reported were researchers have trained a neural network (a dataset of 129450 clinical images consisting of 2032 different diseases was used); and the neural network achieved performance on par with all tested experts, demonstrating that AI was capable of classifying skin cancer at a level of accuracy comparable to that of dermatologists [21]. As some reports show, the impact of AI is especially relevant in neuroscience (neurosurgery, neurology). This area is based on the combination of AI-mediated technologies with advances in photonics (merging of applied optics and electronics) and engineering, together with other clinical disciplines (pharmacology, psychology) and related sciences (biology and genetics, biochemistry) [22]. AI systems are able to analyse complex data, moreover, they need data to learn and to operate properly. The quality of data affects the quality of the outcome. One part of the problem is the time and expenses that are needed to collect and insert this data into appropriate AI system in healthcare [23]. It is especially problematic in situations when patient’s data are stored in different institutions in a random way and incompatible formats, thus requiring additional resources for their collection and standardization.

As we discuss diagnosis, we can briefly note one more issue. AI systems can also create a new method of remuneration of medical workers. At the moment, doctors are encouraged to have many visits from patients, and take multiple tests (which are often viewed as unnecessary and burdensome), and thus basically their work is assessed on the volume of treatment, which is then reflected in the remuneration. AI systems might be able to assess the value of the treatment for the patient, that is, whether the treatment was successful or not, and how the strategy which was proposed by the doctor influenced patient’s condition in general. It is expected that a value-based remuneration will provide additional incentive for doctors to improve their skills and knowledge [24]. Another question: can a doctor rely entirely on AI? One of the common mechanisms of application of machine learning (which is a form of AI) is healthcare is precision medicine, that is, predicting what treatment protocols are likely to success on a patient based on various patient attributes and the treatment context; this requires a training dataset for which the outcome variable (e.g. onset of disease) is known (so called “supervised learning” [25]. However, the cognitive systems have problems with the quality and volume of medical information. The data accumulated in patients’ medical records may be incomplete, contain errors, inaccuracies, and nonstandard terms. There are not enough records of the patient’s life, habits, and behaviour. Effective mechanisms for collecting this information do not yet exist. In addition, many of the AI algorithms are considered as black box in which the decision-making process is hidden in network layers. This can be problematic especially in situations that are not present in data set used to train AI algorithms, which will likely result in inaccurate AI decisions.

Application of AI in healthcare thus causes concerns when we think about possible liability issues. We agree with the research that say that while it may be fairly easy to identify a wrongful act or effect resulting from the use of an AI system, it will often be less straightforward to identify the blameworthy actors [26]. Specifically, criminal liability generally requires showing knowledge or intention of the relevant actors, and it is clear that AI systems have no such mental state [27]. In case AI itself bears no legal liability, who is going to be liable? Which criteria do we need to use to choose the guilty one? Can be a doctor that relied on AI assistance? Or a technical worker that inserted, maybe unintentionally, wrong data, which lead to the wrong results? Or shall we put blame on the software developer, who did not think about possible options and did not teach AI system properly? This all-causes concerns and leads to the certain degree of unpredictability. However, it clear that legal solutions to these issues need to be further discussed. One the possible ways which is proposed by some scholars it to rethink the liability principles, so as maybe to split liability between manufacturers of AI systems (they can be held liable for their product causing harm under the genera product liability regime), physicians and patients [28]. There are already some developments in the legal sphere in considering liability for the actions of robots; e.g. under German tort law the following principles apply:

- There can be no fault-based liability if the malfunction of the robots is not foreseeable to the person using it;

- Liability is excluded if the patient has consented to the use of the medical robot; however, the patient is to be informed beforehead about all the circumstances and risks of the medical intervention, and also about all the alternative treatment measures which are equal at the achieving of the same treatment goal [29].

Of course, as robots “do not make independent movements and do not make their own decisions, but are completely controlled by the operator”, current law does not have any gaps in liability in this respect [30]. AI systems are of course different due to their nature. However, the approaches described above might be taken into consideration in developing legal rules on liability for the decisions and actions of AI systems. One of the main questions is: “What data processing can be considered an interpretation that has a real risk of harm to the patient’s health?” It is proposed to consider the processing of clinical data about a patient as such an interpretation, as a result of which new, clinically significant information missing from the initial data is produced (formed), which is necessary and used when making a clinical decision and/or performing a medical intervention [31]. It should be noted here that there’s no doubt that mistakes are inevitable. As some studies show, even not, according to data collected from several EU nations, medical errors and healthcare related adverse events occur in eight to twelve percent of hospitalizations; preventing such mistakes could help to prevent more than 3.2 million days of hospitalization each year within the EU [32,33]. Thus, application of AI by doctors needs to be done in such a way that minimizes the possibilities of mistake.

Having said that, it is important to note that legal regulations should also aim at taking due account of situations when AI technologies are used for evil purposes. For example, there’s a risk that medical worker would like to introduce changes into human genome or make other illegal activities. Thus, it is necessary to introduce appropriate safeguards, so as to ensure that patients are not at risk when there’s application of AI technologies by doctors. This may be achieved by introduction of specific forms of notification of authorities and control mechanism. And of course, it is necessary to raise patient’s awareness so that they know the signs that can show that there can be danger in dealing with the specific medical institution.

Conclusion

In this article, our intention was to consider AI use in Smart Healthcare from two different aspects: information technologies and legislation. There’s no doubt that information technologies are actively developing and will make medicine much more effective and will help people to have healthier life. However, at the moment legislative framework has not yet been fully adapted to the new reality where medical professionals increasingly rely on AI systems. In our mind, any further improvements to the legal framework of Smart Healthcare, need to be based on the study and development of the following aspects of the legislation:

- Defining responsibility rules for medical doctor, AI and patient in making diagnosis and choosing the treatment.

- Increasing the role of the patients in Hospital Information Systems, as a condition for the development of personalized medicine, with the possibility of limiting access to their Electronic Health Records.

- The right to information about their health and free access to information affecting the freedoms, rights, duties, interests of the patient, including the use of mobile applications.

- The use of web-services, remote interaction between the doctor and the patient through a variety of means: social networks, smartphone, tablet, etc.

- The protection of personal data and legally defined secrets.

- So, the triad: medical achievements, information technology and advanced legislation will change the medicine of the future.

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