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Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs.

Despite the need for a well-trained emergency medicine (EM) workforce in India, there are still many shortcomings. The COVID-19 pandemic has brought to the limelight the critical importance of digital health data, tools, technologies, and services in monitoring and combating adverse health effects of the pandemic. On the other hand, relevant health care stakeholders have often been underprepared to embrace digital health solutions wholeheartedly and rise to the pandemic challenge. The Global Strategy on Digital Health 2020-2025 clearly states how relevant digital health interventions (DHI) would enhance the access and improve the health outcomes, if used judiciously. Currently, India's G20 Presidency health priorities are focussed on health emergencies prevention, preparedness, and response, by incorporating digital health at its core. Keeping these policy directives in mind, health professional educators should actively incorporate DHI as an integral part of health professional education for the domain of EM. To attain an optimal growth of EM as an academic discipline in India, the curriculum has to be made up to date, incorporating the latest DHIs. The PG curriculum committees of both the National Medical Commission and the National Board of Examination must form a consensus for implementing such curriculum throughout India. The modern-day emergency room (ER) or casualty may soon have AI-enabled robots doing a lot of repetitive and precise activities to save lives. Healthcare professionals working in the ER have to be aware and efficient in handling these robots and other clinical decision support systems well., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Journal of Family Medicine and Primary Care.)

Background: Countries, including India, were quick to adopt telemedicine for delivering primary care in response to the widespread disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. This expeditious adoption was critical and the challenges faced during this exigency could guide the design and delivery of future telemedicine applications toward strengthening primary healthcare services., Methods: To identify the challenges in delivering primary care via telemedicine technology in the Indian context, a scoping review was conducted. Drawing from the systems approach in healthcare delivery, the review findings are summarized at four levels, patient, provider, healthcare organizations, and policy., Results: The initial search yielded 247 articles and 13 met our inclusion criteria. This review highlighted that telemedicine facilitated the continuity of care during COVID-19 but not without challenges. Low levels of education and computer literacy along with the language barriers posed the predominant challenges at the patient level. Providers had concerns related to digital literacy, clinical process flows, legal liabilities, and unethical behavior of the patients. The policy-level challenges include data privacy and security, reimbursement models, unethical behavior by the patient, or provider, and regulating prescriptions of psychotropic drugs. A lack of an integrated telehealthcare model covering diagnostics, prescriptions, and medication supply mirrored the existing fragmentation of care delivery., Conclusion: Telemedicine has the potential to improve primary healthcare delivery even beyond COVID-19. Currently, telemedicine applications in India are only facilitating a remote consultation wherein an integrated person-centered care is lacking. There is a need to acknowledge and factor in the inter-connectedness of health system elements for ensuring an effective and efficient healthcare delivery via telemedicine., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Journal of Family Medicine and Primary Care.)

Background: Although well recognized for its scientific value, data sharing from clinical trials remains limited. Steps toward harmonization and standardization are increasing in various pockets of the global scientific community. This issue has gained salience during the COVID-19 pandemic. Even for agencies willing to share data, data exclusivity practices complicate matters; strict regulations by funders affect this even further. Finally, many low- and middle-income countries (LMICs) have weaker institutional mechanisms. This complex of factors hampers research and rapid response during public health emergencies. This drew our attention to the need for a review of the regulatory landscape governing clinical trial data sharing., Objective: This review seeks to identify regulatory frameworks and policies that govern clinical trial data sharing and explore key elements of data-sharing mechanisms as outlined in existing regulatory documents. Following from, and based on, this empirical analysis of gaps in existing policy frameworks, we aimed to suggest focal areas for policy interventions on a systematic basis to facilitate clinical trial data sharing., Methods: We followed the JBI scoping review approach. Our review covered electronic databases and relevant gray literature through a targeted web search. We included records (all publication types, except for conference abstracts) available in English that describe clinical trial data-sharing policies, guidelines, or standard operating procedures. Data extraction was performed independently by 2 authors, and findings were summarized using a narrative synthesis approach., Results: We identified 4 articles and 13 policy documents; none originated from LMICs. Most (11/17, 65%) of the clinical trial agencies mandated a data-sharing agreement; 47% (8/17) of these policies required informed consent by trial participants; and 71% (12/17) outlined requirements for a data-sharing proposal review committee. Data-sharing policies have, a priori, milestone-based timelines when clinical trial data can be shared. We classify clinical trial agencies as following either controlled- or open-access data-sharing models. Incentives to promote data sharing and distinctions between mandated requirements and supportive requirements for informed consent during the data-sharing process remain gray areas, needing explication. To augment participant privacy and confidentiality, a neutral institutional mechanism to oversee dissemination of information from the appropriate data sets and more policy interventions led by LMICs to facilitate data sharing are strongly recommended., Conclusions: Our review outlines the immediate need for developing a pragmatic data-sharing mechanism that aims to improve research and innovations as well as facilitate cross-border collaborations. Although a one-policy-fits-all approach would not account for regional and subnational legislation, we suggest that a focus on key elements of data-sharing mechanisms can be used to inform the development of flexible yet comprehensive data-sharing policies so that institutional mechanisms rather than disparate efforts guide data generation, which is the foundation of all scientific endeavor., (©Nachiket Gudi, Prashanthi Kamath, Trishnika Chakraborty, Anil G Jacob, Shradha S Parsekar, Suptendra Nath Sarbadhikari, Oommen John. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.05.2022.)

This viewpoint summarizes the discussion that occurred during the "Translating Policy to Practice in Telehealth-Lessons from Global Implementation Experiences" panel that was held virtually at Telemedicon2020, December 18-20, 2020. This panel brought together policy and implementation experts from some countries of South Asia, Kuwait, and the European Union to share their experiences in the development and implementation of telehealth standards and of the scale up of telehealth interventions within health systems. Several common themes arose from the discussion, including the significant role of people; encouragement by respective government policymakers; addressing concerns, particularly related to privacy, confidentiality, and security; and capacity building of human resources. These are discussed in turn, along with the future directions identified by the panelists, which emphasized the need for active encouragement toward the adoption and diffusion of digital health in general and of telehealth in particular. All stakeholders, ranging from governmental policymakers to common citizens, need to come together to build trusting partnerships to realize the advantages offered by telehealth., (©Oommen John, Suptendra Nath Sarbadhikari, Thanga Prabhu, Ashvini Goel, Alexander Thomas, Sunil Shroff, Fazilah Allaudin, Chaminda Weerabaddana, Dari Alhuwail, Udaya Koirala, Jayalal Johnrose, Patricia Codyre, Andy Bleaden, Shubnum Singh, Shuchin Bajaj. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 08.02.2022.)

India's journey in the digital health arena and its contribution to the landmark resolution on digital health by the World Health Organization has been recognized globally. India has demonstrated its commitment to leverage digital health as a health system strengthening intervention, as outlined in the National Digital Health Blueprint based on which, the National Digital Health Mission (NDHM) is currently being piloted by the National health authority. Further, the Sandbox environment of NDHM is actively encouraging all the ecosystem partners to familiarize with the evolving national digital health infrastructure. The strategy to enhance the India's progress in implementation and scale-up of digital health interventions are drawing attention to workforce capacity building, harnessing health data to facilitate research and development, evidence-informed development of policies, sustaining efficiency and quality of system through appropriate monitoring, and periodic evaluation informed by frameworks specific to digital health or those adapted to evaluate health informatics applications., Competing Interests: None

Strengthening of the health system is a safety imperative, especially in a crisis as caused by the ongoing COVID-19 pandemic. While there is a need for enhancing the number and skill sets of the public health professionals, especially the frontline workers, it will be prudent to use the digital health technologies, including artificial intelligence, in enhancing the capacity of the healthcare professional education and delivery. However, it has to be ensured that an ethical and safe approach is adopted to develop and use digital health technology and, ethically appropriate training is imparted, to enhance the capacity of the human resources for health, leading to an overall health system strengthening., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors.)

Digital health interventions are globally playing a significant role to combat coronavirus disease 2019 (COVID-19), which is an infectious disease caused by Severe Acute Respiratory Syndrome coronavirus 2. Here, we present a very brief overview of the multifaceted digital interventions, globally, and in India, for maintaining health and health-care delivery, in the context of the Covid-19 pandemic., Competing Interests: None

In February 2018, the Government of India announced a massive public health insurance scheme extending coverage to 500 million citizens, in effect making it the world's largest insurance program. To meet this target, the government will rely on technology to effectively scale services, monitor quality, and ensure accountability. While India has seen great strides in informational technology development and outsourcing, cellular phone penetration, cloud computing, and financial technology, the digital health ecosystem is in its nascent stages and has been waiting for a catalyst to seed the system. This National Health Protection Scheme is expected to provide just this impetus for widespread adoption. However, health data in India are mostly not digitized. In the few instances that they are, the data are not standardized, not interoperable, and not readily accessible to clinicians, researchers, or policymakers. While such barriers to easy health information exchange are hardly unique to India, the greenfield nature of India's digital health infrastructure presents an excellent opportunity to avoid the pitfalls of complex, restrictive, digital health systems that have evolved elsewhere. We propose here a federated, patient-centric, application programming interface (API)-enabled health information ecosystem that leverages India's near-universal mobile phone penetration, universal availability of unique ID systems, and evolving privacy and data protection laws. It builds on global best practices and promotes the adoption of human-centered design principles, data minimization, and open standard APIs. The recommendations are the result of 18 months of deliberations with multiple stakeholders in India and the United States, including from academia, industry, and government., (©Satchit Balsari, Alexander Fortenko, Joaquín A. Blaya, Adrian Gropper, Malavika Jayaram, Rahul Matthan, Ram Sahasranam, Mark Shankar, Suptendra N Sarbadhikari, Barbara E Bierer, Kenneth D Mandl, Sanjay Mehendale, Tarun Khanna. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.07.2018.)

Competing Interests: There are no conflicts of interest.

Human body energy storage operates as a stock-and-flow system with inflow (food intake) and outflow (energy expenditure). In spite of the ubiquity of stock-and-flow structures, evidence suggests that human beings fail to understand stock accumulation and rates of change, a difficulty called the stock-flow failure. This study examines the influence of health care training and cultural background in overcoming stock-flow failure. A standardized protocol assessed lay people's and health care professionals' ability to apply stock-and-flow reasoning to infer the dynamics of weight gain/loss during the holiday season (621 subjects from seven countries). Our results indicate that both types of subjects exhibited systematic errors indicative of use of erroneous heuristics. Stock-flow failure was found across cultures and was not improved by professional health training. The problem of stock-flow failure as a transcultural global issue with education and policy implications is discussed.

The National Board of Examinations is a body formed to enhance the standards of post graduate examinations in modern medicine in India. Unfortunately, the outdated mode of examinations and the arbitrarily set high cut-off marks for passing the examinations defeat the very purpose of its formation and functioning.

Background: Medical Informatics in India is still in its infancy. Although the Indian Association for Medical Informatics (IAMI) was founded in 1993, proposed by major healthcare delivery institutions, the absence of independent career options in medical informatics in India has resulted either in the exodus of the needed faculty members for supporting education in the field. However, this situation may have been changing in the past few years, but a large gap exists which needs to be filled up quickly. The purpose of this report is to provide an assessment of the present situation of research and training in medical informatics in India, and its implications for future development of the field., Objectives: To assess the current situation regarding the opportunities for research and education in Medical Informatics in India and related issues like availability of career options., Methods: A survey questionnaire was sent by postal mail to well-known Indian institutions engaged in medical informatics training and research. In addition, key stakeholders working towards imparting education and awareness on the principles and practice of medical informatics were contacted to provide information about training and research in medical informatics in India. This was a purposive sampling based on prior knowledge. The responses were thematically analyzed., Results: A total of six courses were identified in the survey. These were administered through face to face (F2F), e-learning and other modes of distance learning. In general, most of the students are graduates in medicine (allopathic, homeopathic, ayurvedic), allied sciences (nursing, physiotherapy) and medical administrators or graduates in engineering or library and information sciences. Most of them are also working, thus, the majority of the courses are for part-timers and act as on-job value addition. Most of the courses however do not directly train for jobs. Therefore, as most of the participants are already working somewhere, the question of placement due to the course may not be measurable directly. Since most of the students from India are already employed, by attending this course they gain further insights into health informatics that they want to pursue as a career.

Background: Regular, "moderate", physical exercise is an established non-pharmacological form of treatment for depressive disorders. Brain lateralization has a significant role in the progress of depression. External stimuli such as various stressors or exercise influence the higher functions of the brain (cognition and affect). These effects often do not follow a linear course. Therefore, nonlinear dynamics seem best suited for modeling many of the phenomena, and putative global pathways in the brain, attributable to such external influences., Hypothesis: The general hypothesis presented here considers only the nonlinear aspects of the effects produced by "moderate" exercise and "chronic" stressors, but does not preclude the possibility of linear responses. In reality, both linear and nonlinear mechanisms may be involved in the final outcomes. The well-known neurotransmitters serotonin (5-HT), dopamine (D) and norepinephrine (NE) all have various receptor subtypes. The article hypothesizes that 'Stress' increases the activity/concentration of some particular subtypes of receptors (designated nts) for each of the known (and unknown) neurotransmitters in the right anterior (RA) and left posterior (LP) regions (cortical and subcortical) of the brain, and has the converse effects on a different set of receptor subtypes (designated nth). In contrast, 'Exercise' increases nth activity/concentration and/or reduces nts activity/concentration in the LA and RP areas of the brain. These effects may be initiated by the activation of Brain Derived Neurotrophic Factor (BDNF) (among others) in exercise and its suppression in stress., Conclusion: On the basis of this hypothesis, a better understanding of brain neurodynamics might be achieved by considering the oscillations caused by single neurotransmitters acting on their different receptor subtypes, and the temporal pattern of recruitment of these subtypes. Further, appropriately designed and planned experiments will not only corroborate such theoretical models, but also shed more light on the underlying brain dynamics.

In India, the healthcare delivery systems are based on manual record keeping despite a good telecommunication infrastructure. Unfortunately, Indian policy makers are yet to realize the importance of medical informatics (including tele-health, which comprises e-Health and Telemedicine) in delivering healthcare. In the medical curriculum also, nowhere is this treated as a subject or even as a tool for learning. The final aim of most of the medical and paramedical students should be to become good users, and if possible, also experts for advancing medical knowledge base through medical informatics. In view of the fast changing world of medical informatics, it is essential to formulate a flexible syllabus rather than a rigid one for incorporating into the regular curriculum of medical and paramedical education. Only after that one may expect all members of the healthcare delivery systems to adopt and apply medical informatics optimally as a routine tool for their services.

Medical Informatics is the science and art of processing medical information. In this age of "Information Explosion" choosing the useful one is rather difficult, and there lies the scope of electronic database management. However, still many outstanding personnel related to the healthcare sector take pride in being "computer illiterate". The onus of the best use lies on the end-user health care providers only. Another term tele-health encompasses all the e-health and telemedicine services. Computer aided or assisted learning (CAL) is a computer based tutorial method that uses the computer to pose questions, provide remedial information and chart a student through a course. Now the emphasis in medical education, is on problem based learning (PBL) and there CAL could be of utmost help if used judiciously. Basic Medical Education and Research lays the foundation for advancing and applying proper healthcare delivery systems. There is no doubt that deep knowledge of anatomy is mandatory for successful surgery. Also, comprehensive knowledge of physiology is essential for grasping the principles of pathology and pharmacology adequately, to avoid incorrect and inadequate practice of medicine. Similarly, medical informatics is not just a subject to be learnt and forgotten after the first professional MBBS examination. The final aim of every student should not only be to become a good user but also an expert for advancing medical knowledge base through medical informatics. In view of the fast changing world of medical informatics, it is of utmost necessity to formulate a flexible syllabus rather than a rigid one.

Electroencephalograms (EEGs) reflect the electrical activity of the brain. Even when they are analyzed from healthy individuals, they manifest chaos in the nervous system. EEGs are likely to be produced by a nonlinear system, since a nonlinear system with at least 3 degrees of freedom (or state variables) may exhibit chaotic behavior. Furthermore, such systems can have multiple stable states governed by "chaotic" ("strange") attractors. A key feature of chaotic systems is the presence of an infinite number of unstable periodic fixed points, which are found in spontaneously active neuronal networks (e.g., epilepsy). The brain has chemicals called neurotransmitters that convey the information through the 10(16) synapses residing there. However, each of these neurotransmitters acts through various receptors and their numerous subtypes, thereby exhibiting complex interactions. Albeit in epilepsy the role of chaos and EEG findings are well proven, in another condition, i.e., depression, the role of chaos is slowly gaining ground. The multifarious roles of exercise, neurotransmitters and (cerebral) hemispheric lateralization, in the case of depression, are also being established. The common point of reference could be nonlinear dynamics. The purpose of this review is to study those nonlinear/chaotic interactions and point towards new theoretical models incorporating the oscillation caused by the same neurotransmitter acting on its different receptor subtypes. This may lead to a better understanding of brain neurodynamics in health and disease.

A method for EEG compression is proposed, using Iterative Function System (IFS) and Genetic Algorithms (GAs) with elitist model, keeping the quality sufficiently good for clinical purposes. Compression using IFS is usually called fractal compression. The self transformability property of the EEG signals is assumed and is exploited in the fractal compression technique. To ascertain the self transformability of the EEG signal, some isometric transformations have been applied. The technique described here utilizes Genetic Algorithm that decreases the search space for finding the self similarities in the given signal. This article presents theory and implementation of the proposed method. The fidelity of the reconstructed signal obtained by the present compression algorithm has been assessed both qualitatively and quantitatively. The compression ratios, for the EEG signals in various states, are found to be comparable to the other available techniques for EEG compression. In our method at least 85% data reduction has been achieved.

Manual differentiation of electroencephalography (EEG) paper recordings in cases of depression is not very helpful. So, a Multilayer Perceptron (MLP) has been used to differentiate the EEG power density spectra (qEEG) in the wakeful state from animals (control, exercised and depressed). The qEEG ranging from 1 to 30 Hz, at 1 Hz increments (30 input features) and also a slow, medium and fast activity (represented by three ranges of frequencies at the input) were used. After training with depressed and control qEEG only, the MLP has been found to distinguish successfully between the normal and the depressed rats in more than 80% of the cases, identifying, in the process, most of the exercised groups' EEG as normal. The reduction in the dimension of input features from 30 individual frequencies to 3 frequency bands has produced similar results. The rules generated for making such distinctions have been found to be similar to the clinical views.

The EEG from frontal cortex, EMG and EOG were recorded from rats exposed to only exercise (Treadmill), only stress, exercise + stress and neither (control). In comparison with the control group, the percent of Delta activity in the awake was significantly increased in the depressed group and significantly decreased in the exercised groups, while for Beta-2, the reverse occurred; Theta increased and Beta-2 decreased in the NREM sleep state of the depressed group and the opposite happened for the exercised groups. Delta and Alpha-2 activity significantly increased in the depressed group, and they were significantly decreased in the exercised groups whereas the Beta-2 activity showed contrary changes in the REM sleep state. These findings indicate that exercise has the opposite effect from what stress has on qEEG and concomitant physical exercise reduces the effects of stress. Behavioral tests were done by Open Field (OF) and High Plus Maze (HPM). Slow EEG activity (Delta, Theta, Alpha) was significantly positively correlated with immobilization in the OF and defecation in both OF and HPM and negatively with the food intake, transfer latency in HPM; rearing, grooming and total ambulation in OF Whereas, fast activity (Beta-2) was significantly negatively correlated with immobilization in OF and defecation in OF and HPM, while positively with ambulation in the central squares of OF and time spent at the central cross and number of times arms crossed in the HPM.

The use of an artificial neural network (ANN) system to differentiate the EEG power density spectra in depressed from normal rats was tried. The beneficial effects of chronic physical exercise in reducing the effects of stress and therefore depression was also to be tested in animals by the same method. In this study, rats were divided into 4 groups, subjected to (i) chronic stress (D group); (ii) chronic exercise by treadmill running (EO group); (iii) exercise with stress (ES group) and (iv) handling (C group). The prefrontal cortical EEG, EMG and EOG were recorded simultaneously on paper and the digitized EEG signals were also stored in the hard-disk of a PC-AT through an ADC. After filtering the digitized signals, the EEG power spectra were calculated by an FFT routine. Three successive 4 s artefact-free epochs were averaged. The REM and NREM sleep periods as well as the awake period signals were analyzed separately. The FFT values from each of the 3 states, in the 4 groups of animals were tested by an ANN with 30 first layer neurons and a 2nd layer of a majority-vote-taker. The ANN could distinguish the depressed from the normal rats' EEG very well in REM (99%) sleep, NREM (95%) sleep and awake (81%) states. In most of the cases it identified the exercised rats' EEG as normal.