Dr. Patchava said, "different healthcare and banking, rather, it is a more emotional industry. Healthcare in artificial intelligence means using non-human systems, such as a computer, to perform tasks such as decision-making, rather than contact with human needs. in medical terms, although artificial intelligence can solve some problems, such as in the supply chain, enhance intelligence is more applicable in clinical medicine. "
As for data, Patchava explains why a distributed ledger can facilitate data across systems, networks and services, mobility and exciting solutions.
Patchava said, "In the banking sector, it is very concerned about the high-tech low-touch, which is acceptable, because people want to do. They seek a convenient, fast and effective access as possible in the time they want consumers to experience solution score their call. they almost do not want to relate to people, because it is time-consuming, often interfere with, rather than facilitator. in the field of health care, which is actually a turning point. different consumer mentality, the patients are seeking sympathy and consolation. in fact, it is these two factors that trigger the feeling, has been shown to help patients. healthcare consumer (or patient) of psychology, physiology and pathology has been very complicated So far, the human interaction gives them a huge advantage. "
However, health systems around the world face a number of challenges, it is a generally accepted truth. An aging population, the growth of non-communicable diseases of obesity, diabetes, cardiovascular disease, as well as consumer expectations continue to rise, which gave health care providers tremendous burden. Traditionally, large-scale, widespread adoption of new technologies within the health care system and for medical professionals is a huge obstacle. However, if properly applied, emerging technologies can help eliminate some of the stress, medical care from passive to active, from treatment to prevention. However, these techniques to be successful, the key is that they must be embedded into the workflow, improving productivity without sacrificing humanity.
She said, "to the use of artificial intelligence in the medical field, for example, we do not want to completely replace the artificial intelligence of doctors and medical staff; we need to find cooperative partnership is needed is a combination of technology and contacts to provide the best results for doctors and patients . "
She Medic Bleep, for example, health care "WhatsApp" is a real-time security service, is to replace the old outdated pagers created, while maintaining sensitivity to security and privacy regulations. In the hands of doctors, nurses, hospital managers, Medic Bleep can achieve effective communication processes, events and information. The most recent time motion analysis results show that average savings of 21 minutes per shift nurses, doctors save an average of 48 minutes per class. And considering the week, month and year of the cases, the time saved is very significant. In addition, this technology can promote improved working priority, easier collaboration, and reduce errors and adverse events by providing auditable records. So who refused to provide safer, more effective treatments?
She said, "If Medic Bleep technology will be extended to span multiple network emergency medical teams, not just the hospital, and even extends to the social medical field, so you can view real-time medical professional and patient care, they need comprehensive care, do not worry about the results will be transferred from doctors to social workers body.
We know that the British health care system have been pushing for changes in hospital pager agenda, while Medic Bleep provides a powerful solution that can achieve real-time communication and data flow in overall health, and may one day even be social and emergency care services . "
AI challenge in the medical field and the reasons for adopting enhanced intelligence
Patchava said, "Not long ago, a panacea for IBM's Watson artificial intelligence engine known as the health care, but I think Watson technology failed to achieve its potential, and failed to impress, mainly because they do not understand they tried to solve the problem of hard to explain the complexity of health care exist. there are many stakeholders, the point of contact, the field of disease, treatment options, etc. in addition, if the thought of each medical pain point, it may be present in the individual level, population level and / or system level, this is a very complex network. "
Patchava said, "at least in my lifetime, application of artificial intelligence in clinical care is not yet mature. Although there are several areas that can be deployed machine learning process to improve the system, but how to get the output from the input agnostic and clinical over-reliance on the decision support system is still a moral issue unresolved, thereby slowing replace human work in the healthcare field. For example, if I do a breast X-rays, although 10 there may be six or seven times to confirm, artificial smart may be able to accurately detect breast cancer, if I scan results are 10 times out of three or four times is considered 'not clear', I sure hope my doctor diagnose human, I can consult them, and let I'm sure their diagnosis latest progress in artificial intelligence algorithms narrowed the gap between computer and human experts in terms of detection of breast cancer, but we differ on standard computers. Although artificial intelligence systems to meet the Turing test (exhibit human capacity to the doctor or quite indistinguishable intelligent behavior), but patients (consumers) will accept this? I Letter, if artificial intelligence to replace human beings in the health care field, this alternative must prove its accuracy is improved, not just equality. "
Application of Artificial Intelligence in healthcare How valuable?
She says, "computer will never fully understand human. Algorithm and code can not really replicate the behavior of human consciousness and subconscious, attitudes, ideas, expectations, however, a machine learning significant development of genomics to study a set of genes, the combination of an organism. use of artificial intelligence faster, cheaper, more accurate and more efficient for DNA sequencing and analysis, may have a significant impact on the way we provide health care through genomics, we can better understand the patient behavior. for example, they are susceptible to the diseases subject to, and use it to make decisions about their care. earlier we talked about the transition from treatment to prevention, genomics constitutes the missing part. pharmacogenomics can help we know how to make individual reactions to certain drugs, thus promoting personalized treatment. in turn, it can also help prepare for the future. for example, if someone has not been suffering from diabetes, but a high risk of diabetes, we can they provide a higher level of targeted interventions To reduce the risk and / or prevention of catastrophic events, can reduce overall costs and improve results. "
She said, "I think that genomics will drive the future of medicine, we can provide customized personalized service, but I also think that genomics will never be the complete answer. I still want to be a doctor or health care professional at least to explain the diagnosis, it is expected to provide suggestions for me, and to develop treatment / action plan, taking into account my concerns and preferences. as a doctor, we must remember that the patient is the person sitting in front of you, not just algorithms or code. "
Patchava noted that "the potential of various sectors of automation technology very different. In the financial services industry and use our data capture is far superior to the individual, as well as in terms of numbers recently. By doing so, they can be based on income, for example, obtained to identify patterns, and deployment of predictive analytics to predict consumer behavior. some backward in health care, there are still a number of different and disparate data sets in the health care system. to be effective, we need to bring together not only in the individual and population levels now there are data sets, but we also need to get a deeper and more comprehensive chart health status of people, not just the disease. this is a new wearable technology equipment and connection equipment, can help fill some gaps in place for healthcare professionals and systems 'disease care' into 'medical care'. "
Enter the DLT for health care
In most cases, if people hear the word block chain, may immediately think of Bitcoin, some people may even think of dark network, money laundering, fraud, gambling and so on. Therefore, many people away from the block chain technology, and distributed books regardless of the underlying technology (DLT) potential.
She said, "We have discussed for more data to help make decisions. However, the major challenges of global health care and health system faced liquidity data. Barriers, regulatory, policy, procedures will prevent the flow of data within the system.
Now if someone patient to find a general practitioner doctor, then what happens? The general practitioner may not know the patient hospitalized last week, and did not receive its most recent test results. The patient must then call the clinic to find the test results. If you are lucky, you can send the results via e-mail, are more likely to be faxed to the general practitioner. Now imagine, if patients have more complex needs, he needs to assume social care at the hospital, but did not find the patient or caregiver to bring many difficulties and inconveniences, or worse, is eventually returned to the hospital for treatment. People can almost see the immediate data illiquidity healthcare professional decisions, serious impact on patient outcomes, the system costs incurred. "
She said, "Electronic health records were never designed to manage multi-agency lifelong medical records. At present, patient health data will be distributed to various organizations, pharmacies, fitness centers, hospitals, clinics, because their health journey from one institution data warehouse evolved into another. we need for health care professionals, technology companies, data scientists, managers solve the problem is, how do we make the data stream perpetuate? how do we make the communication system to each other, between them accurate transmission interpretable data without reduction of the budget would have been a huge increase in costs? this is where the DLT may have potential.
For the current system, even if we can capture data can be stored in different electronic health records, but there is no communication between the two layers. No traffic, no one can fully understand the situation of the patient.
Thus, while our aim is to use data from a wide and diverse sources, including pharmacogenomics, a wearable device, Alexa, Google Home, in order to promote better health care decisions and results. We still need to break us how to effectively allow the right people access to this data at the right time.
Unless we are able to access the whole data set, all data in one place, otherwise our ability to analyze and draw meaningful insights are limited. How accurate training computer (machine learning) in the case do not provide a complete picture? "
DLT in the Stone Age
Although organizations such as MedicalChain technology companies are deploying distributed books to partially solve interoperability issues, provide long-term access to patient health data immutable trust and distributed security, but Patchava believe that, as currently exists, as the Internet technology is still in the stage; when logged onto the Internet using a modem connected to a telephone line, dial the local call rate to the Internet via the telephone, which is very slow and frustrating.
Eventually, as iterative and evolutionary, she will be seen as something DLT one kind exist in all areas of infrastructure, such as cars. People might say, 'I have a new car', it might say 'I have a BMW car,' but they will not say, 'I have a BMW car, and then say that the engine type, power and operating agencies. '
There are still some challenges to overcome. First, we need to block the chain DLT be conscious of decoupling, in order to initiate dialogue on DLT first. In a prospective health systems, early adopters is full of interest. However, we can not replace decades of legacy infrastructure to the existence of the hospital, and this process might cause serious interference hospital operations.
Therefore, the challenge is to determine where and how the use of DLT in the legacy infrastructure in order to improve the efficiency of existing processes. We have recognized DLT first can be regarded as 'plug and play' technology. One kind is an existing system to recognize and to replace equipment as part of existing systems in the case without impairing the integrity of the existing system, function or reliability.
She cited the example of Dovetail laboratory. She said, "Dovetail laboratory and surgical Permits together to create a shared decision-making platform, paperless, personalized consent form can be used for elective surgery based on technology licensed books distributed. This is to create a fully agree to personal preference, associated with different risks and benefits of treatment, make decisions, take action, and patient-reported experiences and results database. over time, this may not only consent, but with existing medical record interoperability, and contribute to machine learning, to ensure that every decision in this patient are correct.
By identifying minor problems and to solve this problem, they have done well proven, if we find the right use case for the DLT, you do not have to replace the hospital infrastructure, but can be run in conjunction with other aspects of hospital infrastructure. Use cases and by smaller incremental improvements, we can promote the adoption. Only through these deployments, we can test, understand, and iterative improvement techniques. "
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