Managing Digital Systems

Table of Contents

Introduction and background.

Discuss business contexts, issues and key objectives of the report.

Evaluation.

Conclusion.

References.

Introduction and Background of Artificial Intelligence-Enabled Healthcare

The COVID-19 crisis shows that digital health technology has the potential to address our biggest general wellbeing challenges. The White House Science and Technology Agency has approached innovation organizations to find a way to enable mainstream researchers to respond to high-need logical inquiries concerning various serious diseases. The Centers for Disease Control and Prevention likewise perceives that innovation and observation frameworks can assume a fundamental function in supporting response of the general wellbeing. Below are a few examples of how technology can play a necessary role in further reducing curves, limiting the spread of the virus, and helping to treat infected people. Maybe the positive effect of this innovation will additionally quicken the selection and significance of computerized development in medical services. Be that as it may, such developments should be offset with the continuous requirement for protection.

Digital health technology helps manage epidemics by providing early signals of potential infections. As widely published, many public health authorities limit the patient's symptoms and the qualifications of medical personnel for treatment. Also, if an infected person can perform a diagnostic test, there is a time delay between the onset of physical symptoms and the receipt of results. A company called Kinsey Health provides smart thermometers for recording heat at home using thermometers connected to the Internet. Users of smart thermometers can instantly report fever and symptoms. Thermometers may not confirm a person's coronavirus, but heat spikes captured by a thermometer are the first sign of a possible infection. Data from the Kinser Thermometer helps health authorities plan resource allocations The instant reporting feature allows Kings to distribute heat and share information through an online interactive map and display signs to individuals via postcodes. Clusters of heat spikes may indicate to health authorities where medical resources need to be allocated and where measures need to be taken to prevent viral infections (Alhashmi, et al. 2019).

Discuss Business Contexts, Issues and Key Objectives of The Report

Hospital visitor and patient observation

Artificial intelligence has been applied to clinics in the United States and abroad to screen clinical experts and screen contaminated patients. Emergency clinics with admittance to computerized wellbeing innovation can all the more viably screen and oversee Covid pandemics In Florida, for instance, Tampa General Hospital Care.I, Inc. It utilizes created artificial intelligence to screen medical clinic guests with a camera-fabricated face scanner that breaks down facial highlights and warmth outputs to decide whether the guest has a fever. Thus, specialists at Amherst University in Massachusetts are building up an artificial intelligence gadget called FluSense, which is intended to dissect hack hearing to survey the likely spread of viral respiratory disease. Emergency clinics have introduced such gadgets to help decrease the spread of the infection.

Distant observation, another type of artificial intelligence innovation, can be applied by clinical offices to secure staff and screen patients with care. In Israel, for instance, Sheva Medical Center keeps on checking patients in distant clinic quarters and underground parking areas. Created by Arilens Limited, Shaber's sensor innovation is put on the patient's sleeping pad and dissects the patient's pulse, respiratory rate and body developments. Clinic staff would then be able to screen the patient distantly and be ready when their wellbeing decays. This innovation not just diminishes presentation to the infection to clinical experts, yet in addition benefits patients by improving the nature of medical care.

Tools and materials 3D printing

It is recognized that the crisis of 3D printing has the potential to improve, which we have confirmed with various serious diseases. The Chinese use a 3D princehouse to isolate infected patients. Facebook has a group called OSCMS that specializes in the design, verification and collection of open source emergency care supplies. The use of 3D printing to create advice and material on ventilator quality has been created by technologists and shared by medical professionals around the world through tools such as Google Docs and WhatsApp. There are different stories of short-term use ventilators using 3D printing technology (Kaur and Mann., 2017).

Evaluation of Artificial Intelligence-Enabled Healthcare

Reflect on the usefulness and limitations of the digital transformation analysis as well as any management issues identified.

Artificial intelligence (AI) and related technologies are becoming more prevalent in business and society, and healthcare has begun to be applied. These technologies have the potential to change not only many aspects of patient care, but also management processes between suppliers, suppliers and pharmaceutical companies.

There are already many research studies that suggest AI may or may not be better than humans in important healthcare activities. Today, algorithms already surpass radiologists in malignant tumor detection and guide researchers on how to collaborate on costly clinical trials. However, for a variety of reasons, the broad processing of domains may have taken place many years before AI. These and other primary-based systems have shown accurate diagnostic and therapeutic potential, but have not been adopted in clinical practice. These were no better than human diagnoses and were poorly integrated with clinician workflow and medical recording systems.

There are various features that have emerged as AI game changers in the healthcare industry. The following is an example of today's use.

Radiology-AI solutions are being developed to automate image analysis and diagnosis. It helps radiologists highlight areas of interest in scanning, increasing efficiency and reducing human error. There are also opportunities for a fully automated solution (automatically reading and interpreting scans without human supervision). This allows for instant interpretation in case of poor services or after a few hours. Advanced demonstrations of tumor detection with MRI and CT show progress towards new opportunities for cancer prevention. Meanwhile, U.S. distributors have already received FDA approval to analyze and interpret cardiac MRI images on platforms operated by AIA.

Drug Discovery-AI solutions have been developed to identify new potential treatments from an extensive database of existing drug drugs and can be redesigned to deal with deadly threats such as the Ebola virus. In response to the threat of serious illness you can speed up the process of bringing new drugs to market and improve your drug efficiency and success rates.

Patient Risk-Identification By analysing a large amount of patient historical patient data, AI solutions can provide real-time assistance to physicians in identifying patients at risk. Highlight patients who are at risk of falling into the current focus and are likely to return to the hospital within 30 days of discharge. Hospitals involved in redemption operate multiple institutions and treatment systems partially and are developing solutions based on information from patients ’electronic health records through pressure from donors to reduce hospitalization costs. Other recent studies have shown the ability to predict the risk of cardiovascular disease based on stable images of the patient's retina (Yu, et al. 2018).

Primary Care / Trace-Multiple organizations work directly with patients to resolve trajectories and provide counseling through voice or chat-based conversations. It provides quick and scalable access to first questions and treatment issues. This avoids unnecessary trips to the GP and reduces the growing demand for primary health care providers. Other conditions provide a basic direction that is not available to residents of accessible or poorly serviced areas. Even after the concept is clear, these solutions still need sufficient personal relevance to prove patient safety and efficacy.

Limitation of Digital Transformation in Health Care Sector

There is one more risk around privacy. The need for large datasets has led developers to collect such data from many patients. Some patients may sue on the basis of data sharing between large medical systems and AI developers; for fear that this collection may compromise confidentiality. AI may be involved in privacy in other ways. Although the AI was able to predict the patient's personal information, the algorithm did not receive that information. (In fact, this is often the goal of healthcare AI.) For example, an AI system can detect a person's Parkinson's disease based on the vibrations of a computer mouse without disclosing that information to anyone. It could be. This can be considered a privacy violation, especially if third parties such as banks and life insurance companies have access to the hierarchy of the AI system.

Superstition and discrimination. Healthcare AI carries the risk of bias and inequality. AI systems can learn from trained data and include bias from this. For example, if the data available in AI is collected primarily at academic medical centers, the resulting AI system will learn less about overcrowded patients who do not visit academic medical centers frequently. Used as a copy of notes, but AI's functionality may be compromised (Reddy, et al. 2019).

Discussion and Analysis

In times of a grave medical crisis, medical practitioners and researchers across the globe have researched on well developed devices that are powered by Artificial Intelligence which can analyze and detect medical conditions in real time.

This FluSense is one of it's kind revolutionary device that was envisioned for practical application to detect flu, cough or cold specially for use in the time of Corona. It was devised to use in larger public spaces like hospitals and healthcare centres. These are the cutting edge technological weapons that are used as gold standards in medical observation. Mild to acute symptoms of medical cases like seasonal flu to Covid-19 can be uncovered and informed in the public domain for better treatment and care (Panch, et al. 2019).

These data pools in sourced information about the timing, place and potent cause for the disease. It also assists in organizing healthcare campaigns that would help spearheading social rallies and operations to specifically weed out diseases at large. They help the public at large by informing them first hand about the causative agents, their research models and the immediate cure. Vaccine and medical supplies, potential remedies and restrictions etc are announced through these interactive gadgets. Accurate detection of flu and eventual treatment proceedings are carried out via such ubiquitous computing technologies.

The FluSense device consists of a cost effective microphone jack and an array of operative engines. It scans and images data with the help of thermal power and a raspberry Pi beta version of the software and other assembled components to intrinsically identify information and other data’s like images and speech formats.

This mobile surveillance gadget works on a pre structured algorithm that is programmed on the device to later use as detection alarm for diseases. The main motive behind developing the device was to build predictive models to reckon with. This aids in reversing the curve of ill health and potential viral infections.

The FluSense devices were lab tested into accuracy by placing them in a series of rectangular box almost about the size of two dictionaries in four healthcare waiting rooms at the health service clinics of the University of Massachusetts.

A member of forecasting teams compriaing of valuable health practitioners that are a multidisciplinary association of flue forecasters. A system of autonomous, discreet network of chained devices that tracks the number of folks coughing or sneezing in public and estimating the numbers for future stat analysis later in the healthcare facilities. Patients that report slight symptomatic or asymptomatic cases of illnesses during whimsical seasonal changes are allocated safe quarantined spaces centrally. Treatment is done by house staffs and physicians at the hospital setting. However, no one symptom is the same marker for all the fits as it may change from one area of infection to the other. Sickness indicators might change from one part of the metropolis to the other as a person might be well protected than the other exposed to similar environmental conditions. What FluSense does is tap the potential of disease fighting capacity by expanding its ability to survey viral respiratory outbreaks and seasonal flu. Pandemics, epidemics and SARS that hit across the entire range of the globe follow a natural ebb and meandering pattern that helps us better understand the severity of diseases and their targeted interventions through vaccination (Sun and Medaglia, 2019).

December 2018 to July 2019 saw a huge shift in the platform basics after analyzing over 350,000 images in thermal print and over 20 million audio sample files from the public domain. This way FluSense, build itself up as a credible brand in delivering it’s promise of discovering daily intangible or perceptible illness rates. Multiple value added worthy information about contemporary disease eradicating techniques can be congregated and prediction efforts put to use, the information from which can be later utilized as a completely new and renewed data source to predict the trends for epidemiology. Epidemiologists across the globe can detect the outbreak and infection patterns to comprehend the viral potency and transmission. It is devised on the principle of machine learning where audio analysis of people coughing in tandem in public spaces can be converted into simple comprehensible data for later use. It is rather imperative to feed the information on the device storage chips that would be pertinent to treat virulence factors later in the day. These health sensors are well researched and developed in a lab based cough model. The neural signals are adopted on thermal plates by classifying people bound into boxes representing people with the help of intense neural network classifier (He, et al. 2019).

Conclusion on Artificial Intelligence-Enabled Healthcare

The healthcare sector is majorly influenced by the usefulness of this app. This application assists in detecting responsive public health concerns across healthcare centres. This system helps monitor trends of illness and their spreading patterns. Thereby surveilling public spaces for seasonal outbreaks. They assist the medical team in forecasting the imminence of a public heath concern thereby determining restrictions and precautionary measures with optimal accuracy. This helps in allocating designated medical supplies.

References for Artificial Intelligence-Enabled Healthcare

Alhashmi, S.F., Salloum, S.A. and Abdallah, S., 2019, October. Critical success factors for implementing artificial intelligence (AI) projects in Dubai government United Arab Emirates (UAE) health sector: Applying the extended technology acceptance model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 393-405). Springer, Cham.

He, J., Baxter, S.L., Xu, J., Xu, J., Zhou, X. and Zhang, K., 2019. The practical implementation of artificial intelligence technologies in medicine. Nature medicine25(1), pp.30-36.

Kaur, J. and Mann, K.S., 2017, December. AI based healthcare platform for real time, predictive and prescriptive analytics using reactive programming. In Journal of Physics: Conference Series (Vol. 933, No. 1, p. 012010). IOP Publishing.

Panch, T., Mattie, H. and Celi, L.A., 2019. The “inconvenient truth” about AI in healthcare. Npj Digital Medicine2(1), pp.1-3.

Reddy, S., Fox, J. and Purohit, M.P., 2019. Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine112(1), pp.22-28.

Sun, T.Q. and Medaglia, R., 2019. Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly36(2), pp.368-383.

Yu, K.H., Beam, A.L. and Kohane, I.S., 2018. Artificial intelligence in healthcare. Nature biomedical engineering2(10), pp.719-731.

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