Digital health for patients with chronic pain during the COVID-19 pandemic

Innovative Applications of Telemedicine and Other Digital Health Solutions in Pain Management: A Literature Review

Since the COVID-19 pandemic, healthcare systems are facing extraordinary challenges. Our approaches to medicine have changed and created a whole new generation of people who have chronic pain. Various medical services were postponed. The pandemic significantly impacted the bio-psychosocial model of pain and the management of chronic pain. These new challenges affected millions of patients worldwide, with more burden on patients with chronic pain. Telemedicine and digital health rather than traditional office visits have become essential tools for communications, resulting in an unmatched surge in telehealth adoption. This new approach facilitated the remote treatment and follow-up of patients who have difficulty to access the healthcare services, particularly patients with chronic pain and those who were receiving regular controlled medications. An extensive computer search was conducted, during the period (from January 2014 to March 2024), and included literature from PubMed, Scopus, MEDLINE, and Google scholar. According to preset inclusion and exclusion criteria, a total of 38 articles have been included in this review article. This literature review focuses on the innovation of telemedicine and digital health in pain management, especially in the context of the challenges posed by the COVID-19 pandemic. The manuscript provides a comprehensive overview of telemedicine and digital communications, their evolution, and their significance in healthcare. It also emphasizes the benefits, challenges, limitations, and the ethical concerns of telemedicine in pain management after the COVID-19 pandemic. Furthermore, the document explores the different modes of the telecommunications and discusses the future directions of the digital health technology.

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review

PURPOSE OF REVIEW

As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety.

RECENT FINDINGS

We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.

Chronic pain experience through COVID-19: a comparison of reports prior and during the early stages of the pandemic

ABSTRACT

The impacts of COVID-19 and imposed restrictions on individuals with chronic noncancer pain continue to emerge, varying across countries. More recent research (including with longitudinal designs) suggests that the pandemic may not have such a disproportionate effect on chronic noncancer pain and its management as first thought. This longitudinal study, with assessments before the pandemic (2019) and early during the pandemic (May-July 2020), examined changes in validated measures of pain severity, pain interference, prescription opioid misuse, and mental health symptoms. Patients (N = 236) self-reported significant improvements in pain severity, pain interference, pain self-efficacy, pain catastrophizing, prescription opioid misuse, depression, and anxiety symptoms over time. Approximately 30% and 33% of patients achieved minimally important reductions (10% change) in pain severity and pain interference, respectively. In follow-up exploratory analyses, prepandemic sociodemographic and psychological factors predictive of 10% improved (vs 10% worse) pain severity and interference were investigated in logistic regressions. Reduction in pain interference was predicted by current employment, older age, and higher pain self-efficacy. There were no significant predictors of reduction in pain severity. The impact of COVID-19 on patients' pain experience and mental health was negligible in the early stages of the pandemic, and findings suggest improvements through the period. Targeted interventions that promote the protective factor of pain self-efficacy and build resilience may buffer patients' future response to the pandemic because it evolves as a part of our new normal. Targeted social determinants of health interventions that direct resources toward maintaining employment could also be important.

Digital health in pain assessment, diagnosis, and management: Overview and perspectives

Managing pain is essential for social, psychological, physical, and economic reasons. It is also a human right with a growing incidence of untreated and under-treated pain globally. Barriers to diagnosing, assessing, treating, and managing pain are complicated, subjective, and driven by patient, healthcare provider, payer, policy, and regulatory challenges. In addition, conventional treatment methods pose their own challenges including the subjectivity of assessment, lack of therapeutic innovation over the last decade, opioid use disorder and financial access to treatment. Digital health innovations hold much promise in providing complementary solutions to traditional medical interventions and may reduce cost and speed up recovery or adaptation. There is a growing evidence base for the use of digital health in pain assessment, diagnosis, and management. The challenge is not only to develop new technologies and solutions, but to do this within a framework that supports health equity, scalability, socio-cultural consideration, and evidence-based science. The extensive limits to physical personal interaction during the Covid-19 pandemic 2020/21 has proven the possible role of digital health in the field of pain medicine. This paper provides an overview of the use of digital health in pain management and argues for the use of a systemic framework in evaluating the efficacy of digital health solutions.

Chronic pain and COVID-19 hospitalisation and mortality: a UK Biobank cohort study

ABSTRACT

The risk of COVID-19 in those with chronic pain is unknown. We investigated whether self-reported chronic pain was associated with COVID-19 hospitalisation or mortality. UK Biobank recruited 502,624 participants aged 37 to 73 years between 2006 and 2010. Baseline exposure data, including chronic pain (>3 months, in at least 1 of 7 prespecified body sites) and chronic widespread pain (>3 months, all over body), were linked to COVID-19 hospitalisations or mortality. Univariable or multivariable Poisson regression analyses were performed on the association between chronic pain and COVID-19 hospitalisation and Cox regression analyses of the associations with COVID-19 mortality. Multivariable analyses adjusted incrementally for sociodemographic confounders, then lifestyle risk factors, and finally long-term condition count. Of 441,403 UK Biobank participants with complete data, 3180 (0.7%) were hospitalised for COVID-19 and 1040 (0.2%) died from COVID-19. Chronic pain was associated with hospital admission for COVID-19 even after adjustment for all covariates (incidence rate ratio 1.16; 95% confidence interval [CI] 1.08-1.24; P < 0.001), as was chronic widespread pain (incidence rate ratio 1.33; 95% CI 1.06-1.66; P = 0.012). There was clear evidence of a dose-response relationship with number of pain sites (fully adjusted global P -value < 0.001). After adjustment for all covariates, there was no association between chronic pain (HR 1.01; 95% CI 0.89-1.15; P = 0.834) but attenuated association with chronic widespread pain (HR 1.50, 95% CI 1.04-2.16, P -value = 0.032) and COVID-19 mortality. Chronic pain is associated with higher risk of hospitalisation for COVID-19, but the association with mortality is unclear. Future research is required to investigate these findings further and determine whether pain is associated with long COVID.

Effectiveness, cost-utility and physiological underpinnings of the FIBROWALK multicomponent therapy in online and outdoor format in individuals with fibromyalgia: Study protocol of a randomized, controlled trial (On&Out study)

Introduction: The On&amp;Out study is aimed at assessing the effectiveness, cost-utility and physiological underpinnings of the FIBROWALK multicomponent intervention conducted in two different settings: online (FIBRO-On) or outdoors (FIBRO-Out). Both interventions have proved to be efficacious in the short-term but there is no study assessing their comparative effectiveness nor their long-term effects. For the first time, this study will also evaluate the cost-utility (6-month time-horizon) and the effects on immune-inflammatory biomarkers and Brain-Derived Neurotrophic Factor (BDNF) levels of both interventions. The objectives of this 6-month, randomized, controlled trial (RCT) are 1) to examine the effectiveness and cost-utility of adding FIBRO-On or FIBRO-Out to Treatment-As-Usual (TAU) for individuals with fibromyalgia (FM); 2) to identify pre–post differences in blood biomarker levels in the three study arms and 3) to analyze the role of process variables as mediators of 6-month follow-up clinical outcomes.

Methods and analysis: Participants will be 225 individuals with FM recruited at Vall d’Hebron University Hospital (Barcelona, Spain), randomly allocated to one of the three study arms: TAU vs. TAU + FIBRO-On vs. TAU + FIBRO-Out. A comprehensive assessment to collect functional impairment, pain, fatigue, depressive and anxiety symptoms, perceived stress, central sensitization, physical function, sleep quality, perceived cognitive dysfunction, kinesiophobia, pain catastrophizing, psychological inflexibility in pain and pain knowledge will be conducted pre-intervention, at 6 weeks, post-intervention (12 weeks), and at 6-month follow-up. Changes in immune-inflammatory biomarkers [i.e., IL-6, CXCL8, IL-17A, IL-4, IL-10, and high-sensitivity C-reactive protein (hs-CRP)] and Brain-Derived Neurotrophic Factor will be evaluated in 40 participants in each treatment arm (total n = 120) at pre- and post-treatment. Quality of life and direct and indirect costs will be evaluated at baseline and at 6-month follow-up. Linear mixed-effects regression models using restricted maximum likelihood, mediational models and a full economic evaluation applying bootstrapping techniques, acceptability curves and sensitivity analyses will be computed.

Ethics and dissemination: This study has been approved by the Ethics Committee of the Vall d’Hebron Institute of Research. The results will be actively disseminated through peer-reviewed journals, conference presentations, social media and various community engagement activities. Trial registration number NCT05377567 (clinicaltrials.gov).

African American Women's Willingness to Participate in Online Health Communities During COVID-19

Introduction: The use of online health communities (OHCs) for health information, disease self-management, and social support increased during the COVID-19 pandemic. However, there are limited data on the willingness of African American women (AAW) to participate in OHCs. Materials and Methods: A sample of 985 AAW completed an online survey. Multinomial logistic regression examined associations between three age groups (18-29, 30-50, and 51+ years) and 10 motivators and 10 barriers to participating in OHCs. Women 51+ years were the referent group. Results: Women 51+ years were more likely to have been diagnosed with obesity and hypertension than the other groups (p < 0.01), but less likely to be diagnosed with a mental health condition than the other groups (p < 0.01). The top 2 motivators were to learn about a disease/condition (70%) and to prevent a disease/condition (64%). There were no significant differences with these variables. However, compared with women 18-29 years of age, women 51+ years were more likely to be motivated to manage an illness (p < 0.001), and more likely than the other groups to be motivated to support others (p = 0.011). The top 2 barriers were being too busy (53%) and privacy concerns (45%). Compared with women in the two other groups, women 51+ years were more concerned about privacy (p < 0.001). Discussion: AAW expressed a willingness to participate in OHCs. Willingness to participate in OHCs will vary by the topic and disease/condition and the age group. Conclusions: Opportunities exist to recruit AAW in age-specific OHCs.

The impact of COVID-19 on patients with chronic pain seeking care at a tertiary pain clinic

Empirical data on the health impacts of the COVID-19 pandemic remain scarce, especially among patients with chronic pain. We conducted a cross-sectional study matched by season to examine patient-reported health symptoms among patients with chronic pain pre- and post-COVID-19 pandemic onset. Survey responses were analyzed from 7535 patients during their initial visit at a tertiary pain clinic between April 2017-October 2020. Surveys included measures of pain and pain-related physical, emotional, and social function. The post-COVID-19 onset cohort included 1798 initial evaluations, and the control pre-COVID-19 cohort included 5737 initial evaluations. Patients were majority female, White/Caucasian, and middle-aged. The results indicated that pain ratings remained unchanged among patients after the pandemic onset. However, pain catastrophizing scores were elevated when COVID-19 cases peaked in July 2020. Pain interference, physical function, sleep impairment, and emotional support were improved in the post-COVID-19 cohort. Depression, anxiety, anger, and social isolation remained unchanged. Our findings provide evidence of encouraging resilience among patients seeking treatment for pain conditions in the face of the COVID-19 pandemic. However, our findings that pain catastrophizing increased when COVID-19 cases peaked in July 2020 suggests that future monitoring and consideration of the impacts of the pandemic on patients' pain is warranted.

Pain Intensity, Depression, and Anxiety Levels Among Patients With Chronic Pain During COVID-19 Pandemic

Limited data are available for real-world impact of the COVID-19 pandemic on chronic pain patients. This study aimed to evaluate pain intensity, depression, and anxiety status in chronic pain patients during the COVID-19 pandemic. A total of 110 patients with chronic pain participated on a voluntary basis in this questionnaire survey. The questionnaire form elicited information on sociodemographic characteristics and prepandemic and pandemic data on analgesic need, access to medication, visual analog scale (VAS) pain, Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) scores. The COVID-19 pandemic resulted in increased levels of depression (74.5%), anxiety (66.4%), increase in analgesic need (60%), and limited access to analgesic drugs (40.0%). In conclusion, our findings revealed significant increase in VAS pain intensity, BDI, and BAI scores during the COVID-19 pandemic compared with prepandemic period among chronic pain patients, particularly for patients with increased need of analgesics during pandemic.

Impact of coronavirus disease 2019 on patients with chronic pain: multicenter study in Korea

Background

The coronavirus disease 2019 (COVID-19) pandemic has caused significant changes. This study aimed to investigate the impact of COVID-19 on patients with chronic pain.

Methods

Patients with chronic pain from 23 university hospitals in South Korea participated in this study. The anonymous survey questionnaire consisted of 25 questions regarding the following demographic data, diagnosis, hospital visit frequency, exercise duration, time outside, sleep duration, weight change, nervousness and anxiety, depression, interest or pleasure, fatigue, daily life difficulties, and self-harm thoughts. Depression severity was evaluated using the Patient Health Questionnaire-9 (PHQ-9). Logistic regression analysis was used to investigate the relationship between increased pain and patient factors.

Results

A total of 914 patients completed the survey, 35.9% of whom had decreased their number of visits to the hospital, mostly due to COVID-19. The pain level of 200 patients has worsened since the COVID-19 outbreak, which was more prominent in complex regional pain syndrome (CRPS). Noticeable post-COVID-19 changes such as exercise duration, time spent outside, sleep patterns, mood, and weight affected patients with chronic pain. Depression severity was more significant in patients with CRPS. The total PHQ-9 average score of patients with CRPS was 15.5, corresponding to major depressive orders. The patients’ decreased exercise duration, decreased sleep duration, and increased depression were significantly associated with increased pain.

Conclusions

COVID-19 has caused several changes in patients with chronic pain. During the pandemic, decreased exercise and sleep duration and increased depression were associated with patients’ increasing pain.

The impact and applications of social media platforms for public health responses before and during COVID-19

Background

 Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions.

Objective

This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature.

Methods

 We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health.

Results

 The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy.

Conclusions

 Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.

Smokers' Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial

Background

Information and misinformation on the internet about e-cigarette harms may increase smokers’ misperceptions of e-cigarettes. There is limited research on smokers’ engagement with information and misinformation about e-cigarettes on social media.

Objective

This study assessed smokers’ likelihood to engage with—defined as replying, retweeting, liking, and sharing—tweets that contain information and misinformation and uncertainty about the harms of e-cigarettes.

Methods

We conducted a web-based randomized controlled trial among 2400 UK and US adult smokers who did not vape in the past 30 days. Participants were randomly assigned to view four tweets in one of four conditions: (1) e-cigarettes are as harmful or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) uncertainty about e-cigarette harms, or (4) control (physical activity). The outcome measure was participants’ likelihood of engaging with tweets, which comprised the sum of whether they would reply, retweet, like, and share each tweet. We fitted Poisson regression models to predict the likelihood of engagement with tweets among 974 Twitter users and 1287 non-Twitter social media users, adjusting for covariates and stratified by UK and US participants.

Results

Among Twitter users, participants were more likely to engage with tweets in condition 1 (e-cigarettes are as harmful or more harmful than smoking) than in condition 2 (e-cigarettes are completely harmless). Among other social media users, participants were more likely to likely to engage with tweets in condition 1 than in conditions 2 and 3 (e-cigarettes are completely harmless and uncertainty about e-cigarette harms).

Conclusions

Tweets stating information and misinformation that e-cigarettes were as harmful or more harmful than smoking regular cigarettes may receive higher engagement than tweets indicating e-cigarettes were completely harmless.

Trial Registration

International Standard Randomized Controlled Trial Number (ISRCTN) 16082420; https://doi.org/10.1186/ISRCTN16082420

Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study

BACKGROUND

Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before.

OBJECTIVE

The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing.

METHODS

All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass.

RESULTS

A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added.

CONCLUSIONS

This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ.

Understanding the Relationship Between Official and Social Information About Infectious Disease: Experimental Analysis

BACKGROUND

Communicating official public health information about infectious diseases is complicated by the fact that individuals receive much of their information from their social contacts, either via interpersonal interaction or social media, which can be prone to bias and misconception.

OBJECTIVE

This study aims to evaluate the effect of public health campaigns and the effect of socially communicated health information on learning about diseases simultaneously. Although extant literature addresses the effect of one source of information (official or social) or the other, it has not addressed the simultaneous interaction of official information (OI) and social information (SI) in an experimental setting.

METHODS

We used a series of experiments that exposed participants to both OI and structured SI about the symptoms and spread of hepatitis C over a series of 10 rounds of computer-based interactions. Participants were randomly assigned to receive a high, low, or control intensity of OI and to receive accurate or inaccurate SI about the disease.

RESULTS

A total of 195 participants consented to participate in the study. Of these respondents, 186 had complete responses across all ten experimental rounds, which corresponds to a 4.6% (9/195) nonresponse rate. The OI high intensity treatment increases learning over the control condition for all symptom and contagion questions when individuals have lower levels of baseline knowledge (all P values ≤.04). The accurate SI condition increased learning across experimental rounds over the inaccurate condition (all P values ≤.01). We find limited evidence of an interaction between official and SI about infectious diseases.

CONCLUSIONS

This project demonstrates that exposure to official public health information increases individuals' knowledge of the spread and symptoms of a disease. Socially shared information also facilitates the learning of accurate and inaccurate information, though to a lesser extent than exposure to OI. Although the effect of OI persists, preliminary results suggest that it can be degraded by persistent contradictory SI over time.

The Impact of the First Wave of the COVID-19 Pandemic on Patients' Perceptions of Chronic Pain

Purpose

On 10 March 2020, Greece entered an increasingly restrictive 42-day lockdown, in order to contain the first wave of the COVID-19 pandemic. All scheduled appointments and activities of the pain clinics around the country were postponed indefinitely. The aim of this prospective study was to assess the perceived impact of the first wave of the pandemic on pain, quality of life, and access to treatment, during the first austere lockdown in Greece.

Patients and Methods

In this cross-sectional study, 101 patients suffering from chronic pain completed a structured questionnaire. Levels of depression, anxiety, stress, personal wellbeing, optimism and personality traits were also evaluated, using the Depression, Anxiety and Stress Scale (DASS42), the Ten Item Personality Index (TIPI), the Life Orientation Test-Revised (GrLOT-R) and the Personal Wellbeing Index (PWI).

Results

Despite the dramatic decrease in health care visitations before, during and after the imposed lockdown, most patients did not feel that access to pain physicians and medication was significantly affected. Higher levels of stress, anxiety, depression, neuroticism, openness to experience and general satisfaction with life seemed to be important determinant factors in how patients experienced their level, intensity and duration of pain, quality of life and response to medication.

Conclusion

The effects of the lockdown had a more severe impact on patients than the pandemic itself. For most, the level of their pain was not affected by the pandemic and was affected only slightly by the lockdown. Quality of life, however, was affected formost participants. Both the necessity and the complications of introducing the use of telemedicine to Greek chronic pain patients became evident during the study.

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.

Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology

The COVID-19 pandemic has resulted in massive disruptions within health care, both directly as a result of the infectious disease outbreak, and indirectly because of public health measures to mitigate against transmission. This disruption has caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care. Therefore, the traditional face-to-face patient-physician care model has had to be re-examined in many countries, with digital technology and new models of care being rapidly deployed to meet the various challenges of the pandemic. This Viewpoint highlights new models in ophthalmology that have adapted to incorporate digital health solutions such as telehealth, artificial intelligence decision support for triaging and clinical care, and home monitoring. These models can be operationalised for different clinical applications based on the technology, clinical need, demand from patients, and manpower availability, ranging from out-of-hospital models including the hub-and-spoke pre-hospital model, to front-line models such as the inflow funnel model and monitoring models such as the so-called lighthouse model for provider-led monitoring. Lessons learnt from operationalising these models for ophthalmology in the context of COVID-19 are discussed, along with their relevance for other specialty domains.

Artificial Intelligence and technology in COVID Era: A narrative review

Application of artificial intelligence (AI) in the medical field during the coronavirus disease 2019 (COVID-19) era is being explored further due to its beneficial aspects such as self-reported data analysis, X-ray interpretation, computed tomography (CT) image recognition, and patient management. This narrative review article included published articles from MEDLINE/PubMed, Google Scholar and National Informatics Center egov mobile apps. The database was searched for "Artificial intelligence" and "COVID-19" and "respiratory care unit" written in the English language during a period of one year 2019-2020. The relevance of AI for patients is in hands of people with digital health tools, Aarogya setu app and Smartphone technology. AI shows about 95% accuracy in detecting COVID-19-specific chest findings. Robots with AI are being used for patient assessment and drug delivery to patients to avoid the spread of infection. The pandemic outbreak has replaced the classroom method of teaching with the online execution of teaching practices and simulators. AI algorithms have been used to develop major organ tissue characterization and intelligent pain management techniques for patients. The Blue-dot AI-based algorithm helps in providing early warning signs. The AI model automatically identifies a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection sound pressure, and light level detection. There is now no looking back as AI and machine learning are to stay in the field of training, teaching, patient care, and research in the future.