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Medical Doctor Joel Arun Sursas Discusses The Integration of Chatbots Into Remote Healthcare

Originally published on roboearth.org

While chatbots have existed for some time supporting online business, they are also applicable in healthcare. The COVID-19 pandemic has sparked widespread use of telemedicine programs. As healthcare professionals continue to rely on these programs, they are beginning to explore the clinical, ethical, and legal aspects of chatbots. Here, Dr. Joel Arun Sursas discusses the potential impact, both positive and negative, of the implementation of chatbots in healthcare.

What can chatbots do in remote healthcare?

Chatbots assist healthcare professionals in a variety of different ways. They can triage patients and guide them to the appropriate area or healthcare provider to get the help they need. They are also considered a more accurate and more reliable alternative to self-help when patients seek medical advice on websites such as WebMD to find out what is causing their symptoms [1]. With these benefits in mind, chatbots hold real promise as a useful piece of technology when applied to the medical industry.

Chatbots can also help patients who may be unsure about whether they should seek care from a healthcare professional or not. An individual can connect with a chatbot regarding their concern rather than visiting a healthcare professional, therefore, saving money. Additionally, medical professionals can focus more of their time on the pressing issues that have already been filtered by chatbot technology.

Positive impacts of using chatbots in remote healthcare

Chatbots can help healthcare professionals by taking care of tasks like data collection. After the chatbot analyzes the patient data, they can suggest the next step, typically an online consultation with a doctor. This technology will also serve to match patients with the correct doctor, as specific symptoms will have already been screened in advance. From there, doctors can come into a consultation more prepared with an idea of what could be affecting the patient.

Chatbots can also offer instant help in the event of an emergency, help with medication management, and provide solutions for medical issues that are less complex. Chatbots can quickly extract valuable information from data sources like doctor’s notes, clinical trial reports, and patient health records. With this information, they can see a patient’s medical condition, medication dosage, strength, frequency, and apply them to their diagnosis and treatment.

Chatbots can also help speed up the process during a doctor visit, consultation, or emergency room services. Chatbots can address user questions via audio, video, or chat, and they can also help them navigate different healthcare services. This makes chatbots a great tool to pave the way to a quick, precise, and consistent diagnosis, treatment, and recovery.

Potential negative impacts of using chatbots in remote healthcare

Perhaps the most significant risk of using chatbots in remote healthcare is user privacy. Some users might be nervous or reluctant about sharing their personal information and symptoms with a bot. Chatbots are also highly susceptible to cybercrime, thus putting patient data at risk [2]. To minimize the risk, providers should make sure that the bots they are using are secure, and they should be supervised for any breaches.

Additionally, some patients may not trust the bots’ advice or may ignore it completely, which could be detrimental to their health. It’s also possible that misunderstandings can arise between the patient and chatbot regarding diagnosis, treatment, or instructions. These confounding factors should get more fluid as chatbot technology improves, and more data becomes available regarding how best to program chatbots to interact with patients.

The misunderstandings between a chatbot and patient can be detrimental to a patient’s health if no human intervention is ever inserted to clear up the confusion and answer any questions that the chatbot cannot answer. One single mistake by a chatbot can lead to a catastrophic error. A great deal of research and thought should be put into all chatbot programming before making them available to the public.

To summarize, chatbots can considerably assist healthcare professionals by collecting basic patient data and analyzing the information about patients’ conditions and symptoms to suggest the next steps. But, they also have their challenges, mostly surrounding privacy concerns. As remote medicine continues to evolve and chatbots become more common, they will support even more tasks, and their security measures will continue to enhance.

About Joel Arun Sursas:

Joel Arun Sursas holds a Bachelor’s Degree in Medicine and Bachelor’s Degree in Surgery from the National University of Singapore and is continuing his education to obtain a Certificate in Safety, Quality, Informatics and Leadership from the Harvard Medical School, and Masters in Applied Health Science Informatics from the Johns Hopkins University (both expected in 2020). His technical skills include SPSS, RevMan, and Python. Dr. Joel Arun Sursas’ most recent engagement is with a medical device start-up company Biorithm where he serves as Head of Clinical Affairs, working to take fetal surveillance out of the hospital and into the home, revolutionizing the obstetric practice globally.

References

“Chatbots in Healthcare: Benefits, Risks, and Use Cases: Codete Blog.” Codete Blog – We Share Knowledge for IT Professionals, 18 Aug. 2020, codete.com/blog/chatbots-in-healthcare/.

“Chatbots in Digital Healthcare: Future Applications, Pros & Cons.” Dr. Hempel Digital Health Network, 8 Jan. 2018, www.dr-hempel-network.com/digital-health-technolgy/chatbots-in-digital-health-care/.

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Dr. Joel Arun Sursas Shares How AI Can Be Used to Help Hospitals Securely Share Data

Originally published on agreensign.com

Health systems and hospitals are often resistant to sharing patient data due to strict legal and privacy protocols. Therefore, it can be difficult for multiple medical providers to obtain an accurate health history for a patient. Artificial intelligence could potentially help bridge this gap using a combination of learning algorithms and centralized servers that store data. AI has the potential to store data from multiple sources securely, therefore maintaining patient privacy and increasing providers’ access to accurate information. Here, Dr. Joel Arun Sursas shares how AI can be used to help hospitals securely share data.

Using AI to Identify Potential Issues

Hospitals can use AI to spot behavioral patterns within the hospital’s infrastructure and applications to flag any abnormal usage [1]. By detecting anomalous or suspicious activity and spotting potential intruders before accessing data, AI prevents patient information from being accessed by hackers and keeps it secure.

Using AI to Process Patients’ Data

AI also makes it easier for doctors and other healthcare professionals to sort through patient data [2]. This is because AI is so good at combing through and identifying patterns even when the numbers are expansive.

With the assistance of AI, doctors can have an easier time getting all of a patient’s medical records even when they come from multiple sources. Instead of doctors having to spend their valuable time sorting through patient data, they can focus on what matters most: their patients’ health and treatment.

The AI will do the searching, and the doctor will do the diagnosing and treating with access to complete medical history. AI will never come in contact with the patient, but rather prepare the doctor to treat the patient more effectively. Since AI sorts through the documents, there are fewer human eyes at play and fewer people accessing the patient data, therefore, keeping it secure.

Using AI to process patient data will also help tremendously to prevent physician burnout. On top of diagnosing and treating patients, many physicians are expected to complete manual tasks such as documenting, taking notes and entering them into the system, entering orders, billing and coding, managing their inboxes. These tasks add up to the point that some physicians spend hours upon hours at their computers instead of treating patients. When all of the functions are done by AI, it frees up the physician to focus on their patients and takes away the menial tasks that can lead to burnout.

Additionally, sharing patient data securely with AI may even help to improve the health of entire communities. AI can provide state and human services departments with critical insights to data that can then be used to improve a population’s health by reducing substance abuse, unemployment, and even homelessness [3].

Using AI to Personalize Treatments

Securely sharing data has the potential to connect hospitals, clinics, government agencies, and community organizations to provide a more accurate and complete picture of a person and their individualized healthcare needs. Analyzing broad data from different sources allows AI to develop potential solutions that would be otherwise impossible to a human. While there are many misconceptions surrounding AI, computerized intelligence is a vital tool for healthcare professionals.

To advance and continue meeting patient needs in an exceedingly digital world, using AI to share and process patient data is crucial. Humans can no longer manage the astronomical amount of data in our healthcare systems, and artificial intelligence is here to help.

With the implementation of AI into our hospitals, we can build a safer and more efficient form of communication to exchange vital medical data. AI has the potential to eliminate many of the errors that human beings naturally make, thus streamlining the passing of data between departments and guaranteeing secure storage of data in compliance with legal and privacy protocols.

About Joel Arun Sursas:

Joel Arun Sursas holds a Bachelor’s Degree in Medicine and Bachelor’s Degree in Surgery from the National University of Singapore and is continuing his education to obtain a Certificate in Safety, Quality, Informatics and Leadership from the Harvard Medical School, and Masters in Applied Health Science Informatics from the Johns Hopkins University (both expected in 2020). His technical skills include SPSS, RevMan, and Python. Dr. Joel Arun Sursas‘ most recent engagement is with a medical device start-up company Biorithm where he serves as Head of Clinical Affairs, working to take fetal surveillance out of the hospital and into the home, revolutionizing the obstetric practice globally.

References

Cohen, Jessica Kim. “3 Ways Hospitals Can Use AI to Boost Cybersecurity .” Becker’s Hospital Review, www.beckershospitalreview.com/healthcare-information-technology/3-ways-hospitals-can-use-ai-to-boost-cybersecurity.html.

Published on May 5, 2020. “AI in Healthcare: How It’s Changing the Industry.” HIMSS, 14 Aug. 2020, www.himss.org/resources/ai-healthcare-how-its-changing-industry.

EHRIntelligence. “How Can Artificial Intelligence (AI) Improve Clinician EHR Use?” EHRIntelligence, 2 Dec. 2019, ehrintelligence.com/news/how-can-artificial-intelligence-ai-improve-clinician-ehr-use.

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Dr. Joel Arun Sursas Discusses the Application of Virtual Reality-Based Training in Surgical Education

Originally published on infotechinc.net

Medical education programs are rapidly integrating simulation-based training. Virtual reality simulators generate datasets that can later be analyzed with machine learning for training and educational purposes. Here, Dr. Joel Arun Sursas outlines the growing role of integrated artificial intelligence and virtual reality simulation in surgical training.

Artificial intelligence (AI) and machine learning have become vital to effective training in many fields, especially medical and surgical. The massive databases used by these systems assist in understanding a wide array of skills and expertise. [2, 3]

The rapid growth in recent years of medical technology and data management capacities has dramatically increased the feasibility of advanced surgical procedures and the overall safety of technology-assisted procedures. AI is becoming more vital in teaching surgical techniques and assessing performance, especially concerning new processes and technology. Usually, surgeons have limited training with new devices provided short-term by manufacturers. [1] As a result, surgeons have often gone a while since before performing that skill on a patient. As a result of this “on-the-job training”, skill development and patient outcomes can suffer. Virtual reality-based training allows surgeons to train when they are able before performing an actual procedure on a patient. [2, 1]

A recent study conducted by UCLA’s David Geffen School of Medicine found that virtual reality training leads to a performance rating on specific procedures more than twice that of traditional surgical training. [4] AI and VR are more important in training as new procedures become more technical. Surgeons are expected to complete certain numbers of cases to become competent in new procedures. As complexity with new procedures continues to grow, more surgical residents are finding that they are not prepared to handle certain procedures on their own. [5] VR training allows these individuals to become more comfortable and competent with a procedure before working directly on a patient. Although the expense of full-scale simulators can be prohibitive and usually does not employ machine learning and adaptation, modern VR is driven by software and often only involves a headset and hand controllers.

A recent study with state-of-the-art algorithms indicates that sufficiently powerful machine learning tools can differentiate between training participants based on their skill level and experience. Based on careful examination of the performance of experienced surgeons, residents, and medical students, training systems can direct learning exercises based on individual skill, allowing for remedial work for those who need it and more advanced exercises when appropriate. [4]

Because of the greatly enhanced training capacities and the unlimited opportunity for individualized, risk-free surgical training, medical education is sure to become more rooted in the world of virtual reality in the coming years.

About Dr. Joel Arun Sursas

Dr. Joel Arun Sursas is a team leader and facilitator with a proven track record and a niche skill-set developed over the past seven years in his capacity as an established Medical Doctor and Health Informatician. He is most passionate about Medical Informatics, working to bridge the gap between doctors and engineers to improve patient care. His interest in the field emerged when he began working as a Project Officer for PACES — the Patient Care Enhancement System for Singapore Armed Forces (SAF). Dr. Sursas has been instrumental in designing and implementing the largest Electronic Medical Record (EMR) system in Singapore, spanning 53 medical centers, as well as developing a data analytics platform to trend epidemiological data.

References

Harvard Business Review, Research: How Virtual Reality Can Help Train Surgeons, October 16, 2019, Retrieved from https://hbr.org/2019/10/research-how-virtual-reality-can-help-train-surgeons

Khor WS, Baker B, Amin K, Chan A, Patel K, Wong J., Augmented and virtual reality in surgery-the digital surgical environment: applications, limitations and legal pitfalls, Ann Transl Med. 2016;4(23):454, Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5220044/

PLOS Journal, The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine, February 27, 2020, Retrieved from https://doi.org/10.1371/journal.pone.0229596

JAMA, Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation, August 2, 2019, Retrieved from https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2740782

Stat News, Virtual and augmented reality can save lives by improving surgeons’ training, August 16, 2019, Retrieved from https://www.statnews.com/2019/08/16/virtual-reality-improve-surgeon-training/

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Dr. Joel Arun Sursas Discusses Advances in the Development of Software as Medical Devices (SaMD)

Originally published on techicy.com

Computer technology and software capabilities are increasing exponentially in every industry in the modern world. Healthcare is at the cutting edge of software development, and Software as Medical Devices (SaMD) is one of the most important parts of current advances in medical care. The U.S. Food and Drug Administration (FDA) cites the International Medical Device Regulators Forum in defining SaMD as “software intended to be used for one or more medical purposes that perform these purposes with being part of a hardware medical device.” In other words, SaMD are programs used to perform medical services using data and analysis rather than physical devices or testing. In this article, Dr. Joel Arun Sursas discusses some current SaMD developments.

SaMD Examples

SaMD software operates independently of medical hardware to provide medical analysis or a medical function. For example, a program that takes patient data and assesses it to determine proper medication types and dosages is a SaMD. Analytical programs that use artificial intelligence to analyze MRI images to diagnose cardiovascular conditions are SaMD. SaMDs generally include programs that use inputs for analysis of medical conditions and treatment recommendations.

Software that operates a medical device is not SaMD. For example, software that controls medical pumps, motors, or record-keeping systems support hardware systems and are not medical devices standing alone.

Why Classification of Software as SaMD by the FDA Is Important

Software developers that produce SaMD face a regulatory framework vastly different from ordinary software programs. The FDA requires clinical evaluations for SaMD applications and has specific regulations for quality management and control during development. FDA premarket approval requires studies made up of appropriate clinical data. Before approving a SaMD, the FDA will analyze the software’s efficacy and safety. SaMD developers are held to quality standards similar to drug and hardware device developers.

Intellectual Property Issues for SaMD Developers

As SaMD are treated for regulatory purposes like hardware medical devices or drugs, developers must be aware of the particular intellectual property issues involved. Unlike ordinary software, which is treated like other scientific or literary creations for intellectual property law, SaMD is generally treated like a drug or other medical device. Different levels of protection are available through utility patents, design patents, and private contractual protections.

About Dr. Joel Arun Sursas

Dr. Joel Arun Sursas is a Medical Doctor and Health Informatician who designs and implements the latest technological developments to solve administrative problems in healthcare. His primary focus is on developing technological advances between doctors and engineers to improve patient outcomes through improved monitoring while protecting patient privacy. Dr. Sursas’s interest in the field of Medical Informatics emerged when he began working as a Project Officer for PACES — the Patient Care Enhancement System for Singapore Armed Forces (SAF). At the SAF, he worked with multiple doctors and engineers on the largest Electronic Medical Record (EMR) system in Singapore. He also developed a data analytics platform to assess epidemiological data throughout the system. Dr. Sursas is currently serving as Head of Clinical Affairs for Biorithm, a medical device start-up company working to move fetal surveillance from hospitals into home settings, revolutionizing the obstetric practice globally.

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Joel Arun Sursas is a team leader and facilitator with a proven track record and a niche skill-set developed over the past seven years in his capacity as an established Medical Doctor and Health Informatician. He is most passionate about Medical Informatics, working to bridge the gap between doctors and engineers to improve patient care. His interest in the field emerged when he began working as a Project Officer for PACES — the Patient Care Enhancement System for Singapore Armed Forces (SAF). At the SAF, his extensive collaboration with other doctors and engineers resulted in designing and implementing the largest Electronic Medical Record (EMR) system in Singapore, spanning 53 medical centers, as well as developing a data analytics platform to trend epidemiological data. Joel Arun Sursas holds a Bachelor's Degree in Medicine and Bachelor's Degree in Surgery from the National University of Singapore, and is continuing his education to obtain Certificate in Safety, Quality, Informatics and Leadership from the Harvard Medical School, and Masters in Applied Health Science Informatics from the Johns Hopkins University (both expected in 2020). His technical skills include SPSS, RevMan, and Python. Dr. Arun’s most recent engagement is with a medical device start-up company Biorithm where he serves as Head of Clinical Affairs, working to take fetal surveillance out of the hospital and into the home, revolutionizing the obstetric practice globally.