Tag: TSI Tamil Nadu

For many years it has been the dream of Digital Health enthusiasts to use cutting-edge technology to provide need-based, cost-effective, appropriate, technology-enabled remote healthcare 24/7 to anyone, anytime, anywhere.

As early as 2015, I was personally involved in setting up a 24/7 Tele emergency service in the Himalayas .

In the last 9 years, several patients diagnosed remotely were transferred by surface or helicopter to the nearest tertiary hospital. Though almost 3 miles above sea level with temperatures of -25C these patients were still on earth!!!

What does one do when the patient is 230 miles above the earth and remote diagnosis confirms that surgical intervention or ICU management is required immediately? To start building a prototype Space Ambulance (hoping that it will never ever be used!) requires extraordinary involvement, passion, and dedication besides knowledge and skills.

But then, what else would you expect from an astronaut who has spent 49 days in space in four shuttle missions, three spacewalks, the first human to perform an in-flight repair to the Space Shuttle’s exterior, and also the first to take a “ SELFIE” in space.

It was indeed a privilege to spend 1 hour, one to one (on Saturday a holiday) with Dr Stephen Robinson former astronaut and presently Head, Dept of Aerospace and Mechanical Engineering, University of California, Davis.

Humility personified, with the enthusiasm of a Seenager (like me, he is an elderly teenager!), Prof Robinson gave me a glimpse of what his 17 Ph.D students are doing under his tutelage.

Though there will be no traffic jams, designing a well-equipped Ambulance that will precisely dock with a spacecraft traveling at 28,000 km per hour, 400 km above the earth, presupposes mastery of scores of multi-disciplinary sciences.

As Dr Stephen pointed out funding for such a project would really take off (pun intended) only when there is a use case!! Today the whole world is only talking about RoI to justify investment. While there could be spinoffs benefitting earthlings, many would question funding to build a Space Ambulance.

For the individual astronaut or space tourist critically ill RoI’s should not matter. Dr Stephen indicated that it would take 2.5 hours for the space ambulance to reach the astronaut (sometimes this is the time taken on Earth also!!).

For gizmos, jeeks, and space enthusiasts preliminary details of the proposed prototype are given below along with the patient module.

This article has been previously published on https://www.digitalhealthnews.com/ Please click here to view the Latest daily healthcare news & updates-Digital Health News.

The history of artificial intelligence (AI) in healthcare is a story of innovation, gradual development, and challenges.

It began in the 1960s, when AI’s early focus was on diagnostic support through expert systems such as MYCIN. Over time, the scope of AI has expanded to include areas like predictive modeling, clinical decision support, drug discovery, and administrative tasks, deeply influencing modern healthcare.

Despite these advancements, challenges such as data privacy, ethical concerns, and algorithmic bias persist.

This summary explores the evolution, key milestones, and the future of AI in healthcare.

Early Developments

AI in healthcare took its initial steps during the 1960s with the development of problem-solving programs, specifically expert systems. One of the earliest notable applications was ‘Dendral’, designed for organic chemistry, which paved the way for MYCIN. MYCIN was developed in the 1970s to assist clinicians in diagnosing infectious diseases by recommending antibiotics based on the patient’s symptoms. Alongside MYCIN, systems like INTERNIST-1 and CASNET aimed to help clinicians by providing diagnostic insights, though none of these early systems saw widespread clinical adoption. This lack of adoption was primarily due to the limitations in computing power and the complexity of translating medical knowledge into programmable rules.

Growth of AI in the 1980s and 1990s

The 1980s and 1990s were transformative years for AI in healthcare due to the rise of microcomputers and enhanced network connectivity. During this period, researchers focused on developing AI systems that could operate with incomplete data, thus mirroring the uncertain conditions healthcare professionals often face. Techniques like Fuzzy set theory, Bayesian networks, and artificial neural networks became prevalent. These advancements allowed AI to improve its decision-making processes, particularly in medical fields where data could be ambiguous or incomplete.

AI made its mark in diagnostic pathology through digital pathology tools, enabling the scanning and analysis of microscopy slides to diagnose diseases. This innovation led to AI’s use in diagnosing conditions such as breast cancer, gastric cancer, and hepatitis B. These AI systems not only improved diagnostic accuracy but also reduced the workload for pathologists by automating large-scale data analyses.

Predictive Modeling and Clinical Decision Support

AI in healthcare has seen significant growth in predictive modeling. Algorithms can analyze patient data to predict disease risks and possible outcomes. These predictions are often based on electronic health records (EHRs), which contain a wealth of patient data. For instance, a rule-based AI system could follow decision trees, linking specific symptoms and observations to potential diagnoses.

AI’s ability to use large datasets for predictions has contributed to more personalized medicine, where treatment strategies are tailored to individual patients based on their history and genetic data.

AI has also been instrumental in clinical decision-making. Its role has expanded beyond basic diagnostic aid to include support in emergency departments, where AI algorithms can prioritize cases and reduce wait times by processing data faster than humans.

The 2000s: EHRs and Telehealth Integration

The 2000s marked a critical turning point for AI in healthcare, particularly with the growing adoption of electronic health records (EHRs). The widespread use of EHRs facilitated the integration of AI systems into clinical workflows, providing a vast pool of data for analysis. AI algorithms began using EHRs to predict treatment outcomes, detect disease patterns, and even recommend care plans tailored to patient needs.

AI also began to enhance telehealth services, making healthcare more accessible, especially in remote areas. Through telemedicine, AI-supported platforms allowed real-time monitoring of patient health, creating opportunities for remote diagnosis and consultation. This integration was especially useful during the COVID-19 pandemic, as the need for virtual healthcare delivery grew.

Ethical Challenges and Regulatory Concerns

Despite the promising advances, AI in healthcare faces significant ethical challenges. A key concern is the lack of transparency in AI decision-making processes, often referred to as the black box problem. Clinicians may hesitate to trust AI recommendations if they cannot understand how the AI arrived at its conclusions. Moreover, algorithmic bias poses another challenge. AI systems are only as unbiased as the data they are trained on, and datasets lacking diversity can lead to biased outcomes that disproportionately affect underrepresented populations.

Data privacy is another critical issue. With the vast amounts of patient data needed to train AI systems, safeguarding this information is paramount. Stricter data protection regulations, such as GDPR in Europe and HIPAA in the U.S., are necessary to ensure patient confidentiality. Regulatory frameworks must also evolve to address the liability concerns that come with AI-driven clinical decisions.

Recent Developments and Future Directions

Recent years have seen exponential growth in the application of AI technologies in healthcare. ‘Machine learning’ (ML) and ‘deep learning’ (DL) models, such as convolutional neural networks (CNNs), have significantly improved diagnostic accuracy in areas such as ‘medical imaging’. AI now assists radiologists in detecting anomalies in X-rays and MRIs, speeding up diagnoses and reducing human error.

AI has also made breakthroughs in ‘drug discovery’. Algorithms can rapidly analyze molecular structures and predict how new drugs might interact with biological systems. This capability has the potential to shorten the drug development cycle and lower research costs. AI-driven drug development was notably accelerated during the COVID-19 pandemic, when researchers used predictive models to identify possible treatments.

In the field of ‘robotic surgery’, AI has enabled more precise, minimally invasive procedures. Robots guided by AI can perform delicate surgeries with a level of precision that surpasses human capabilities. Additionally, AI-powered prosthetics and rehabilitation technologies have improved patient outcomes in post-operative recovery and physical therapy, enhancing mobility and independence.

Future Trends and Implications

Looking ahead, AI is expected to play an increasingly central role in healthcare delivery. The integration of ‘precision medicine’—which uses patient-specific data to tailor treatments—will become more widespread as AI systems become better at analyzing complex genetic, lifestyle, and environmental factors. In ‘telehealth’, AI-driven tools like Chabot’s and personalized health information will continue to improve patient-provider communication and enable more effective monitoring of chronic conditions.

However, to realize AI’s full potential in healthcare, ‘collaborative ecosystems’ between healthcare providers, AI developers, and regulatory bodies are essential. Interdisciplinary partnerships will be needed to address both technological and ethical challenges, fostering a smoother integration of AI into daily healthcare practice.

Conclusion

The history of AI in healthcare is one of continuous progress, characterized by groundbreaking innovations and critical challenges. From its early beginnings with diagnostic tools like MYCIN to its current applications in telemedicine, robotic surgery, and drug discovery, AI has transformed healthcare practices. However, as AI becomes more embedded in healthcare, ethical concerns, data privacy issues, and the need for regulatory oversight will remain crucial. As the technology evolves, striking a balance between innovation and patient safety will be key to ensuring that AI improves both medical practices and patient outcomes.

As part of the AAROGYAM 2024 conference, I was invited as a guest speaker to present on the topic “Creating Health Screening Ecosystem for Community Research.” The session took place on the 9th of August 2024, from 12:30 pm to 01:00 pm.

Key Points Covered:

• The importance of establishing a robust health screening ecosystem for community research.
• Strategies for integrating health screening into community settings.
• The role of healthcare professionals in advancing community health initiatives through research.
• Case studies and examples of successful community health screening programs.

Outcome:

The session was well-received by the attendees, including healthcare professionals and academic researchers. The discussion sparked interest in developing similar initiatives within their respective communities, aligning with ICanCaRe’s mission to promote health and well-being through research and education.

Follow-Up:

• Potential collaboration opportunities were discussed with attendees, which may lead to further involvement with ICanCaRe’s initiatives.

Next Steps:

• Explore potential partnerships with interested attendees to expand ICanCaRe’s outreach and impact in community health screening.

The session was an incredible opportunity to share insights on how we can build stronger health screening programs within our communities, driving impactful research and improving public health outcomes.

The use of technology-assisted mental health services by both practitioners and clients is increasing significantly so the telepsychology clinicians need to get ready to give competent care in this constantly changing service delivery environment. The “Guidelines for the Practice of Telepsychology” were published by the American Psychological Association in 2013 and, comparable guidelines for therapeutic online work have been published by the British Association for Counselling and Psychotherapy (BACP) in the UK. The aforementioned advancements highlight the growing fascination with virtual therapy among the counselling and psychotherapy fields. They have cleared the path for “telepsychology,” commonly referred to as online therapy, e-therapy, or e-counselling abroad, to gain official recognition.

The human interaction that is essential in a counsellor’s office is merely not feasible through computers or phones (Maples & Han, 2008). Therefore, in a therapeutic relationship, a lack of physical presence can impede the formation of a therapeutic alliance between the therapist and the patient. Misunderstandings can occur more frequently when there are no visual or auditory cues present, such as body language, tone of voice, or facial expressions. This suggests that boundaries are quite important in telepsychology. Counsellors and clients mostly communicate face-to-face through body language, posture, eye contact, and other nonverbal cues. Reading nonverbal cues is undoubtedly a crucial part of counselling. Since these nonverbal cues are invisible when receiving counselling online, their absence may compromise the effectiveness of the session.

Psychologists today are challenged as ethical decision makers by the ever-expanding development and use of digital technology in their own lives and in the lives of their clients. Boundaries are generally helpful from a clinical standpoint because they help clarify the nature of the therapeutic relationship and assist both the client and the clinician in controlling their conduct in a way that optimizes benefits and reduces risks (Borys, 1994). In addition, boundaries can support various other therapeutic goals such as creating a trustworthy working alliance, teaching the client assertiveness techniques, increasing the client’s sense of value (by honoring their autonomy and rights), and maintaining the integrity of the relationship (Borys, 1994; Smith & Fitzpatrick, 1995). Appropriate boundaries shield physicians from damage in addition to protecting the client.

BOUNDARY RELATED ISSUES
Time-related considerations and issues
Time boundary problems are not unusual in traditional in-person therapies; they usually manifest as early or late session arrivals, session extensions, meeting at strange or inappropriate hours, excessive contact outside of sessions, and booking specific clients for the last appointment of the day (Zur, 2007). These problems can definitely arise in telepsychology contexts as well, and they might even be more common because of the medium’s flexibility, the idea that it is convenient for both sides, and the possibility of a less controlled and regulated work environment.

Aspects and problems connected with settings
The adaptability of telepsychology and the capacity to work at any time and from any location offer one difficulty. Because of this flexibility, therapeutic interactions between clients and clinicians may occur in public places. While social interactions in public places might seem harmless at first, they can have serious repercussions. In addition to putting the client’s confidentiality at danger (for example, by using an insecure public internet connection or inadvertently letting others access the therapeutic discussions), the clinician can face legal and professional repercussions for their actions. Apart from jeopardizing the client’s anonymity, this could potentially undermine the therapeutic gains since the surrounding stimuli could operate as distractions and hinder the client’s ability to fully comprehend the therapeutic material.

Considerations and issues specific to the therapist
Boundary difficulties can also develop in other contexts that are unique to the therapist, such as when the therapist faces challenges to their own self-imposed limits, when they are concerned with language and phrase choices, or when they are concerned with safeguarding their work. Regarding obstacles to one’s own personal limits, specialists may find themselves working at hours that they would not ordinarily work due to the flexibility that telepsychology services allow.

RECOMMENDATIONS

• Be mindful of the session times and observe professional hours.

• Make sure that feedback is given on time and consistently, and their is control over communication.

• Establish a Confidential, Uniform, Expert, and Culturally Appropriate Environment.

• Protect Non-Clients’ Privacy and Avoid Inadvertent Self-Disclosures.

• Make Sure the Technologies Used for Telecommunication Project Professionalism.

• Establish Proper Self-Boundaries.

• Protect the Therapist’s Work Privacy.

• Speak professionally and take into account alternate interpretations.

• Assure Competence in Telepsychology Practice

Telemedicine Practice Guidelines, published by the Indian government lately, legitimize teleconsultations. However, there is not much discussion of psychotherapy methods in these guidelines. These evolving psychotherapy delivery technologies will raise an increasing number of moral and legal questions. Concerns regarding the safety of therapists are also raised by the paucity of research documenting instances of harassment experienced by them in virtual environments. Few psychologists are equipped to handle such difficult circumstances since the ethical concerns raised when clients threaten, stalk, or harass their psychotherapists online have typically received little attention in the literature. Thus, both the patient and the therapist will benefit from greater research in this field.

References

Borys, D. S. (1994). Maintaining therapeutic boundaries: The motive is therapeutic effectiveness, not defensive practice. Ethics & Behavior, 4(3), 267-273.

Drum, Katherine B. and Littleton, Heather L. (2014). Therapeutic boundaries in telepsychology: Unique issues and best practice recommendations. Professional Psychology Research and Practice, 45(3); 309-315.

Hudson-Allez, G. (2006). The stalking of psychotherapists by current or former clients: Beware of the insecurely attached!. Psychodynamic Practice, 12(3), 249-260.

Kotera, Y., Kaluzeviciute, G., Lloyd, C., Edwards, A. M., & Ozaki, A. (2021). Qualitative investigation into therapists’ experiences of online therapy: Implications for working clients. International journal of environmental research and public health, 18(19), 10295.

Lokai, A. D., Liqiaonan, C., Terry, K., Eubanks, C. F., & Muran, C. (2021). Transitioning to virtual space: Teletherapy in the time of COVID-19. Psychotherapy Bulletin, 56(2), 29-34.

Maples, M. F., & Han, S. (2008). Cybercounselling in the United States and South Korea: Implications for counselling college students of the millennial generation and the networked generation. Journal of Counselling & Development, 86(2), 178-183.

Mateescu, V. M. (2021). Distance and boundaries issues in the transition from face-to-face talking therapy to online therapy in the time of Covid-19. Studia Universitatis Babes-Bolyai-Sociologia, 66(2), 113-123.

Mondal, I., Anand, N., Sharma, M. K., Kohli, T., Thakur, P. C., Kande, J. S., … & Ajith, S. J.

(2020). Telephonic psychotherapy in India: A reminder of challenges in times of COVID-19. Asian journal of psychiatry, 53, 102432.

Smith, D., & Fitzpatrick, M. (1995). Patient-therapist boundary issues: An integrative review of theory and research. Professional psychology: research and practice, 26(5), 499. Stoll, J., Müller, J. A., & Trachsel, M. (2020). Ethical issues in online psychotherapy: A narrative review. Frontiers in psychiatry, 10, 498439.

Wu, K. S., & Sonne, J. L. (2021). Therapist boundary crossings in the digital age: Psychologists’ practice frequencies and perceptions of ethicality. Professional Psychology: Research and Practice, 52(5), 419.