Abstract
The COVID-19 pandemic had posed significant challenges for public health systems worldwide. Countries had been experimenting with innovative solutions to address the crisis. Artificial Intelligence (AI) has emerged as a promising technology in this regard, with its potential to improve disease detection, contact tracing, and vaccine distribution. The Government of India had administered more than 1,000 million vaccines in a short period since the outbreak of COVID-19, that is, in March 2020. The government harnessed the potential of AI for providing services to citizens during this uncertain time. This article aims to explore the potential of AI in creating public value in the context of COVID-19 management in India. The analysis is based on primary and secondary sources such as research articles, reports published by government and non-government organisations, online databases, and discussion papers. The findings of the study suggest that AI played a critical role in enhancing the effectiveness of COVID-19 management in India. The application of AI has led to significant improvements in disease surveillance, diagnosis, and treatment, thereby creating public value. The study highlights that the assimilation of AI-enabled services increases the citizens’ participation and overall satisfaction and enhances public trust in the government initiatives. The study recommends for a supportive policy environment which fosters innovation and collaboration between stakeholders in leveraging AI. The study also highlights the need for a robust regulatory framework which ensures data privacy and security.
Introduction
The COVID-19 pandemic adversely impacted every aspect of human life, causing the loss of lives and livelihoods. The governments across the globe adopted public policy tools (Capanoa et al., 2020), such as incorporating special-purpose rules and regulations, permissions, guidelines and safe processes (Sokolowski, 2020). They also adopted measures like social distancing to contain the spread of the virus (Silva et al., 2020). During the pandemic, the government was expected to reduce delays and non-compliance to accelerate public sector-driven initiatives. However, the traditional method of identifying patterns to various patient health parameters is time-consuming. Human presumptions and involvement may affect the quality of the diagnosis. Integrating digital technology with public policies and healthcare systems helps the government craft and implement strategies to fight the pandemic (Whitelaw et al., 2020). The situation compelled the policymakers to take a trans-disciplinary perspective incorporating governance, technology, public participation, and risk communication in managing the pandemic. Thus, the government or organisations adopted technologies like Artificial Intelligence (AI) to intelligently deliver services and avoid possible physical contact (Chatterjee et al., 2020). Instead of using the traditional paper-driven methods, the application of AI seems to have promoted quick policy response with accurate data. AI has enabled societies to move forward in the health crisis (The Times of India, 2020); communities seemed to have become resilient to the pandemic when the government adopted AI and data science to effectively mitigate the emerging adverse health situation (Gupta, 2020).
Public value refers to the positive outcomes or benefits that are created for citizens and society through the actions, policies, and programmes of government. It is a key principle in public administration and emphasises the importance of government serving the needs and interests of its citizens. The present study aims to explore the potential of Artificial Intelligence (AI) in creating public value in the context of COVID-19 management in India. The study’s significant finding is that the application of AI creates value to public service delivery in both health and non-health-related emergencies in India. AI technology plays a vital role in identifying unvaccinated citizens by public authorities. AI created trust amongcitizens to get vaccinated by providing personalised communication, optimising vaccine distribution, developing predictive models, and detecting fraud. AI-enabled authorities to provide real-time updates, monitor hotspots, contact tracing, enabling telemedicine, and optimising vaccine distribution. By improving communication and taking proactive measures, AI has helped build trust between authorities and the public, increasing citizens participation and satisfaction. Thus, the study highlights that the AI-enabled services during COVID-19 pandemic increases citizens’ participation and satisfaction, resulting in public-value-generation. The findings of this study may be helpful for organisations and policymakers in developing a digital-governance model for targeted service delivery. The integration of AI with public policy has the potential to create reliable, sustainable and future-oriented digital governance.
This article is divided into six sections. The second section deals with the research methods followed in conducting the research. The third section provides a theoretical framework of public value theory. The fourth section discusses the integration of AI with public policy, and the fourth section analyses the application of AI in COVID-19 management to create public value. The last section provides policy suggestions for adopting AI in pandemic management.
Method and Material
The study uses qualitative approach and exploratory research design to explore the AI-enabled digital governance practices adopted during the COVID-19 pandemic. Secondary data were collected from various sources, including articles, government reports, policy documents, discussion papers, newspaper articles, and online databases such as Google Scholar. The data sources were selected based on their relevance and reliability in providing an overview of the Indian experience in applying AI to COVID-19 management.
Theoretical Framework of Public Value
The theoretical framework of public value was developed by Mark Moore in the 1990s as a way to bridge the gap between public management and public policy. According to Moore, public value is created when public organisations and institutions use their resources to address social problems and meet the needs and interests of citizens. The creation of public value requires public organisations to focus on outcomes rather than outputs, to engage with stakeholders, and to be accountable for their actions.
Moore’s framework identifies three elements that are essential for creating public value:
Value creation: Public organisations must create value that is meaningful to citizens and stakeholders. This involves identifying social problems, setting goals, and developing strategies to achieve those goals. Mission fulfilment: Public organisations must fulfil their mission and mandate to provide public services and meet public needs. This involves ensuring that resources are used efficiently and effectively to achieve the desired outcomes. Resource management: Public organisations must manage their resources in a way that is consistent with public values and priorities. This involves being accountable for the use of public resources, engaging with stakeholders, and being responsive to changing needs and circumstances (Fisher & Grant, 2012).
The concept of ‘public value’ is applied in evaluating the performance of public administration by assessing the effectiveness and efficiency of public services (Moore, 1995). The Public Value Theory (PVT) advocates shifting the focus of public sector management within the organisational boundaries to society. It is more concerned with delivering public services satisfying the customers’ needs rather than focusing merely on producing public services. The sources for public value include delivering high-quality services, achieving outcomes through improvement in health, environment quality and reduction of poverty, and authenticity and trust in public institutions (Kearns, 2004). These dimensions are captured in Figure 1.
The PVT redefines public organisations to function autonomously to improve services’ effectiveness to achieve maximum satisfaction for citizens (Bryson et al., 2014). For this purpose, public organisations need to operationalise and strategise public services by assimilating technology in public services (Dahl & Soss, 2014). Advanced technology can help public organisations to be more efficient and effective in achieving their goals, and ultimately create more public value. For example, AI can be used to improve public services, make better policy decisions, and enhance transparency and accountability. Digital technologies are being employed in governance to achieve organisational values such as transparency, accountability, efficiency, and democratic values such as equality, openness, and fairness (Bonina & Cordella, 2008). However, the government must create infrastructure to promote public value creation, including ease of online access, data distribution, and mobile applications (Dahl & Soss, 2014; Panagiotopoulos et al., 2019).
Application of AI in COVID-19 Management andCreation of Public Value
AI is a collection of advanced general-purpose digital technologies that effectively enable machines to perform highly complex tasks (Hall & Pesenti, 2017). According to NITI Aayog (2018), ‘AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem-solving and decision making’. The government organisations in both developed and developing nations-have employed AI in regular operational services to deliver services for citizens in a seamless manner (Smith & Heath, 2014). The countries like the United States (US), China, the United Kingdom (UK), Canada, and Singapore are exploring the potential of AI to improve governance and service delivery through their country-specific initiatives (Butcher & Beridze, 2019). The integration of AI in public policy intervention was exponentially increased during the outbreak of the COVID-19 pandemic. AI is seen as a trustworthy alternative technology to ease the working of government and routine work of citizens during a very critical time. Governments had limited the usage of AI to manage welfare programmes, healthcare, and law enforcement activities before the pandemic.
The application of AI in COVID-19 management created public value by improving the overall response to the pandemic and addressing the challenges faced by healthcare systems and citizens. Specifically, AI contributed to the creation of public value through improved decision making, increased efficiency and enhanced citizens’, engagement. AI facilitated citizens engagement and increased access to information, helping citizens to make informed decisions about their health. This increased transparency and trust in public health systems. AI played a crucial role in the development of new treatments and vaccines. By analysing large volumes of data and identifying potential drug candidates, AI helped to accelerate the development process and save lives. The adoption of AI enables public organisations to enhance operational procedures’ fusion, which ultimately improves the overall strategy of public policies (Alonso et al., 2016). Any organisation planning to adopt AI technology requires considering multistage processes, including pre-adoption (i.e., initiation), the decision for adoption, post-adoption (Hameed et al., 2012). The data analysis capacity of AI technology assists quick and accurate decision making. It creates public value in public service delivery with more reliability, effectiveness and transparency (Cordella & Bonina, 2012).
This article examines the public value generated by various AI-driven initiatives implemented in India to manage the COVID-19 pandemic. Here the application of AI is analysed concerning the sources related to the creation of public value as mentioned in the section ‘Application of AI in COVID-19 Management and Creation of Public Value’. Table 1 also shows the different attributes associated with them. The sources related to creation of public value include:
Attributes Associated with Sources of Public Value Creation.
Delivery of quality public services,
Achievement of outcomes, and
Development of trust in public institutions (Kearns, 2004).
Delivery of Quality Public Services
The pandemic highlighted that the government must deliver quality services in dynamic and complex circumstances. The variety and scale of public services and citizens’ diverse needs and demands created an opportunity to provide public value through the implementation of AI-enabled services. AI benefits government and healthcare professionals in effective screening, identification, isolation, forecasting, contact tracing, treatment, public-management, drug-development, and financial aid.
The following paragraphs discuss the role of AI in screening, detection, contact tracing, and prediction and how it creates value to the provision of health service in times of pandemic.
See Figure 2 for application of AI in COVID-19 management
Application of AI in Management of COVID-19 Pandemic.
Screening
AI was utilised in screening patients to prevent the spread and manage the COVID-19 pandemic. AI algorithms are used to analyse patient data, such as symptoms and travel history, to determine the likelihood of an individual having COVID-19. AI-powered thermal cameras and sensors are also used to screen individuals for fever, a common symptom of COVID-19, in public areas such as airports and hospitals. AI-based chatbots and virtual assistants are used to provide real-time guidance to individuals on COVID-19 symptoms, testing, and prevention measures, thereby reducing the burden on healthcare systems and minimising the risk of transmission. The government of Kerala (GoK) used AI-powered thermal and optical imaging cameras for fever screening. It started an open-source project, that is, Corona Safe Network, to understand the fluctuations in case numbers, enabling the government to prepare mitigation plan and defence mechanism for similar incidences in the future (Thomas, 2020). Besides, the government used autonomous robots and machines (Karmi-Bots developed by Asimov Robotics) in various cities to distribute masks and sanitisers and the individuals’ thermal imaging on public places’ entrances (Sarkar, 2020). In Maharashtra, an AI-based voice tool was used to remotely detect infections in an individual. This innovative tool detects the distortion in voice due to the viral infection, which is otherwise tricky through human ears (Bajpai & Wadhwa, 2020). The AI-based rapid screening of patients helped healthcare professionals prioritise testing and treatment, as well as isolate patients who might be infected to prevent further spread of the virus. Figure 3 highlights the differences between AI and non-AI based approaches in COVID-19 management.
Detection
After the screening of patients AI was used in disease detection. AI-powered tools are employed to analyse large amounts of data, including medical records and genomic data, to identify patterns and insights that may help identify potential COVID-19 cases and predict the spread of the disease. AI algorithms are also used to analyse medical imaging scans, such as chest X-rays and CT scans, to detect COVID-19 symptoms in patients. Governments have applied AI-based technologies to quickly analyse medical images for automated scanning and disease detection accuracy in less time. In the diagnosis the AI technology is generally applied with the integration of medical imaging technologies such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans (Vaishya et al., 2020).AI-based deep learning algorithms integrate the CT and MRI scan images and show the possibility of more precision in detecting the COVID-19 infected patients. It is an inexpensive system for diagnosis and management and promotes rapid decision-making. The automated disease detection with high accuracy helped healthcare professionals diagnose and treat COVID-19 cases more effectively, reducing the risk of transmission and preventing the spread of the virus.
Contact Tracing
A non-pharmaceutical strategy to stop the spread of viruses is contact tracking. The method of fast tracing digital contacts was extensively used through mobile applications like Bluetooth, GPS, social networks, contact information, network-based APIs, mobile tracking data, card transaction data, and system physical address. Comparatively speaking, digital contact tracing is faster than conventional non-digital contact tracing. The gathered information helps in more accurate and insightful analyses. The AI-enabled mobile applications complement the other initiatives, like the ‘geofencing’ applications for tracking the people under the quarantined and other manual and technological measures. The government of Karnataka has been using the ‘Corona Watch’ application to trace the contact details and places visited by COVID-19 positive persons in the State, including foreign travel history. This application enables the patients to follow the self-quarantine rules by using their geo-tagged photos (Bansal et al., 2020; Johnson, 2020).
Prediction
AI was used in prediction efforts during the management of the COVID-19 pandemic. Machine-learning algorithms are being developed to predict the spread of the virus and the likelihood of future outbreaks. These algorithms analyse various data sources, such as infection rates, mobility patterns, and weather conditions, to make accurate predictions about the trajectory of the pandemic. AI is also being used in the development of predictive models to identify high-risk populations and areas where the virus is likely to spread. This helped in proactive measures such as targeted testing, contact tracing, and isolation to prevent the spread of the disease. Moreover, AI-powered tools are being developed to predict the need for medical resources such as hospital beds, ventilators, and personal protective equipment. This helped in proactive measures to ensure that resources were allocated efficiently and effectively to prevent shortages. These benefits of AI make it an unavoidable part of the healthcare system for predictive, preventive and reliable healthcare services in developing is highly populated countries like India.
Telemedicine, Emergency Travelling and Shopping
AI was used in various ways to facilitate telemedicine, emergency travelling, and shopping during the pandemic time. In telemedicine, AI-powered chatbots and virtual assistants were used to offer remote consultations and medical advice to patients, reducing the need for in-person visits and minimising the risk of transmission. AI algorithms were used to analyse medical images and help healthcare professionals make accurate diagnoses and treatment decisions. In emergency travelling, AI-powered tools were developed to predict the risk of infection and provide guidance on safe travel routes and transportation options. In shopping, AI-powered tools were developed to facilitate contactless transactions and reduce the need for physical contact. For example, AI-powered chatbots and virtual assistants helped customers place orders and make payments online, reducing the need for in-person interactions. Moreover, AI-powered robots were being used for tasks such as disinfecting public spaces, delivering essential supplies, and assisting with emergency response efforts. These robots can perform tasks that would otherwise require human contact, reducing the risk of transmission.
In Kerala, shops app was launched for ordering essential goods and Blue Telemed, a telemedicine app for medical consultation. Doctors issued e-pass to the patients for the emergency hospital visit through the Blue Telemed app. Kerala police provided e-transportation passes enabling citizens for emergency trips without any personal visit to police offices. In this process citizens only needed to enter their travel details for police verification and approval (Sarkar, 2020).
Similarly, Maharashtra utilised the MahaKavach mobile application to manage COVID patients, updating citizens regarding quarantine status, testing dates and efficient disciplinary actions on violation of pandemic rules (Bajpai et al., 2020). In Maharashtra and Andhra Pradesh, the use of AI-powered drones by the police forces helped them to monitor sensitive areas and check the violation of regulatory norms. This enabled government authorities to make decisions and deploy resources concisely. Hyderabad traffic police employed the AI-based automated number plate recognition system to check the speed of vehicles and the location of their residence. This helped the administration to monitor the possible violations of any rules during lockdown (Choudhury, 2019). Table 2 shows the future road map to combat the COVID-19 pandemic using AI, which includes short term and long-term plans for managing the COVID-19 pandemic.
Future Roadmap to Combat COVID-19 Pandemic Using AI.
Having discussed the role of AI in delivering public services in COVID-19 management, let us move towards achieving outcomes through the adoption of AI.
Achievement of Outcomes
The use of AI-enabled initiatives during COVID-19 management achieved better outcomes by improving public health through data analysis and resource allocation, enabling faster and accurate diagnosis with AI-powered tools, and increasing efficiency in operations by streamlining administrative tasks. These benefits led to better outcomes for patients, reducing the number of cases and fatalities, improving resource allocation, and enabling public institutions to focus on providing care and services. Following are the cases which explain the achievement of outcomes through AI.
The Case of the Dharavi Model of ‘Chasing the Virus’
WHO has recognised the Dharavi Model of tackling COVID-19 infection spread (Shaikh, 2020), including citizen-participation, micro-mapping, robust surveillance, public-private partnership, extensive tracing, testing, isolating, and treating infected patients. It was a very challenging task to combat COVID-19 in Dharavi due to the reasons mentioned below:
Dharavi is Asia’s largest slum with a very high population density, that is, 225,000 people/km2 (600 times the national average population density) (Kaur, 2020). There is a high dependency on community toilets in Dharavi as nearly 80% of the population regularly uses 450 community toilets. Food is supplied from outside as there is no food production in rural agricultural areas. There is compact habitation with small rooms accommodating 8–10 members of a family. The slum is congested due to narrow roads and houses where the ground floor is used for residence and other floors used for small-scale enterprises (Shaikh, 2020). The Government of Maharashtra was able to contain the spread in this densely populated slum through an innovative public health policy response popularly known as ‘chasing the virus’.
The local municipal administration used AI proactively in their response strategies, including screening, surveillance and four Ts (tracing, tracking, testing and treating). Figure 4 highlights the method used in Dharavi for combating the COVID-19 pandemic. The successful implementation of containment strategies largely depends on the correct information delivered in the right way to the local citizens. The local administration applied a data-driven risk communication approach through AI-based tools like mobile applications and digital platforms to reach each individual for ensuring proper compliance to preventive steps and preparedness. The government engaged the community at every stage of the containment strategy and promoted the humanistic approach in the tackling crisis (Golechha, 2020).
AI was utilised in several ways to help contain the spread of the virus in Dharavi. One key strategy was to use AI algorithms to analyse large amounts of data from various sources, such as social media, news reports, and government databases, to identify high-risk areas and potential outbreak hotspots. This helped public health officials to quickly identify and respond to new cases and clusters of infections, and to implement targeted interventions to prevent further spread. In addition, AI-powered chatbots and virtual assistants were used to provide residents with real-time information on COVID-19 symptoms, prevention measures, and testing facilities. This helped to increase awareness and reduce confusion about the virus, while also reducing the burden on healthcare workers. AI was also used to optimise contact tracing efforts in Dharavi. Contact tracing is a critical strategy for controlling the spread of the virus, but it can be time-consuming and resource-intensive. AI algorithms were used to streamline contact tracing efforts by identifying potential contacts more quickly and accurately, reducing the risk of further transmission.
After the successful containment of COVID-19 in Dharavi, the Philippines government also adopted the Dharavi model for combating COVID-19 in the densely populated slums of the Philippines (Hindustan Times News, 2021). Figure 5 shows the COVID-19 containment model of Dharavi.
AI in Handling the Workload
AI has played a significant role in managing the workload of health professionals during the pandemic. AI-powered medical-imaging tools, such as X-ray and CT scan analysis, have been used to detect and diagnose COVID-19 cases. This has helped reduce the workload on radiologists and other healthcare professionals involved in the diagnosis of the disease. AI-powered chatbots and virtual assistants have been used to provide real-time support and guidance to patients. These tools can answer questions, offer advice on symptom management, and direct patients to appropriate healthcare services. AI-based Electronic Medical Record Systems have been used to streamline patient care and reduce administrative workload on healthcare professionals. These systems can automatically flag patients with COVID-19 symptoms, prioritise patient care based on severity, and generate real-time alerts for critical cases.
The training was imparted to the medical students and doctors through AI-based simulation. Such mass training helped mitigate the challenge of skilled human resources to tackle the pandemic (Agbehadji et al., 2020; Vaishya et al., 2020).
AI for Improving Governance
The Government of India launched the National Digital Health Mission to provide universal health care to citizens with more effective service delivery in public health. Thus, technology is enabling the government to resolve the crisis in front of society this efficiently. AI can make healthcare services more accessible and improve efficiency through a quick preliminary diagnosis of patients (Suri, 2021). AI-enhanced governance during the management of the COVID-19 pandemic in several ways which included:
Data Analysis and Insights: AI helped governments collect, analyse, and interpret data on the spread of COVID-19 virus. This included identifying trends, patterns, and risk factors, which were used to inform policy decisions and resource allocation. Predictive Modelling: AI-powered predictive models helped governments forecast the spread of COVID-19 virus and anticipate future healthcare needs. This helped policymakers develop more effective response plans and allocate resources more efficiently. Contact-Tracing: AI-based contact tracing tools helped governments track the spread of COVID-19 and identify potential clusters of infection. This enabled policymakers to take targeted actions to contain the virus and prevent further spread. Public Communications: AI-powered chatbots and virtual assistants provided real-time support and guidance to the public during pandemic. This included answering questions, providing advice on symptom management, and directing patients to appropriate healthcare services. Resource Allocation: AI algorithms helped governments allocate resources more efficiently during the pandemic. This included forecasting the demand for medical equipment, optimising the distribution of personal protective equipments, and predicting the need for ventilators and other medical equipments. Risk Assessment and Decision Support: AI-powered risk assessmentand decision-support tools helped governments evaluate the impact of different policy decisions and identify the best course of action during theCOVID-19 pandemic.
AI-based tools helped authorities for early detection of the outbreak of any disease through bio-surveillance. In bio-surveillance, the data are usually collected by scanning social media, news reports, and other online sources to further analyse localised disease outbreaks before the actual pandemics. This approach helps the public health system in early planning for prevention and effective management (Bansal et al., 2020). There is need to increase the collaboration between public and private stakeholders for better implementation of AI-driven initiatives. See Figure 6 for advantages of using AI for managing the COVID-19 pandemic.
Development of Trust in Public Institutions
The use of AI-enabled initiatives during COVID management has led to the development of trust in public institutions by facilitating rapid and accurate decision-making, improving access to services, enhancing safety measures, providingpredictive analytics, and promoting transparency, all of which demonstrate public institutions’ commitment to manage the pandemic effectively and efficiently. Following are the case of Aarogya Setu App, drug repurposing, vaccine development and management, and the Co-WIN platform for vaccination, which highlighted the development of trust in public institutions through AI-enabled services.
The Case of Aarogya Setu App
The Aarogya Setu app was widely used in India as a prevention strategy (Sriram & Mohanasundaram, 2020). The app uses the innovative ‘Digital Phenotyping’ (DP) to collect the data through surveys. Passive data like voice, text, location and screen use related data from the Smartphones are collected to produce observable characteristics or health-related parameters. This app harnesses the simple Bluetooth technology to notify the COVID-19 positive patient in the mobile phone user’s locality. It has been used to contact the individuals encountered with the positive patient within 30 days. This app is a cost-effective tool for the risk assessment and stratification of the patient (Bansal et al., 2020).
The Government of India has introduced two more important innovative initiatives to strengthen and increase the coverage of Aarogya Setu apps, such as ‘Aarogya Setu Wristbands’ and ‘Aarogya Setu Interactive Voice Response System (IVRS)’. Wristbands are used to monitor patients’ movement in hospitals or in-home quarantine. This initiative is also helpful in remotely monitoring their temperature and symptoms. On the other hand, IVRS covers people who do not have Smart Phones but have a landline and a basic mobile phone. It is a toll-free service with 1921 to get citizens’ health details as input and link information with Aarogya Setu’s database through AI. It further alerts to notify through Short Message Services based on the risk assessment. This service is available in eleven regional languages to increase citizens’ inclusion in the Aarogya Setu ecosystem (Bajpai et al., 2020). The Government of Tamil Nadu launched an Aarogya Setu IVRS system for people who cannot download the Aarogya Setu app due to the lack of Smartphones (Raval, 2020).
The Aarogya Setu app has been a significant initiative in India’s response to the COVID-19 pandemic, providing real-time information and enabling effective tracking and containment efforts. However, concerns around data privacy and the mandatory download requirement remain an issue, and the government needs to address these concerns to maintain public trust in the app.
Drug Repurposing, Vaccine Development and Management
AI has the potential to improve drug discovery, planning, design treatment, and monitoring the improvements in concerned patients. Traditional drug development and drug repositioning are two critical strategies for treatment. AI-enabled tools are used to identify pills to treat COVID-19 by predicting an old drug’s reusability (Mohanty et al., 2020). The computational power and learning-prediction model used in AI technology have enhanced drug repurposing speed and accuracy, which helps quickly identify accurate drugs for COVID-19 treatment. With the help of AI, toxic molecules can be weeded out automatically without the actual laboratory test. Such helpful applications of AI reduced the cost and enhanced the speed of drug development (Hitachi, 2021).
The COVID-19 vaccination programme faces challenges related to manufacturing, distribution and administration. These aspects involve order management, and patient-scheduling through efficient slot allotment, feedback mechanism and quick addressing queries for better service delivery. AI enables the authorities to complete these repetitive human tasks at scale in a short period, gaining citizens’ trust in the vaccination programme. Public trust and participation are essential for successfully implementing any government initiative for the citizens, and vaccination is crucial for bringing normalcy to the country (Sicular, 2021).
Co-WIN Platform for Vaccination
The Co-WIN (COVID Vaccine Intelligence Network) platform is a digital platform developed by the Government of India to manage the vaccination drive against COVID-19 in India. The platform is designed to streamline the entire vaccination process, from registration and appointment scheduling to vaccine administration and tracking of adverse events following immunisation (Ministry of Health and Family Welfare, Government of India, 2021). The platform allows eligible individuals to register for the vaccine through a mobile application or website, and provides real-time information on the availability of vaccine doses in different locations. Once registered, individuals can book appointments for vaccination at their nearest vaccination centre. The platform also provides an electronic certificate of vaccination, which can be downloaded and used as proof of vaccination. It also enables healthcare workers to monitor vaccine supplies and stocks, track the number of people vaccinated, and monitor adverse events following immunisation. The Co-WIN platform has played a crucial role in the vaccination drive in India, helping to streamline the entire process and ensure that vaccines are distributed equitably across the country. It has also helped to ensure that individuals receive accurate and timely information about the vaccine, and has facilitated effective monitoring of the vaccination drive. The AI-driven CoWIN platform has achieved two critical objectives. One is vaccination to a large population in a hassle-free, transparent, efficient, and seamless manner. Second, it enhanced the ease of getting a vaccination for citizens at their conveniencewithout any bureaucratic interference. See Figure 7 for services provided by the Co-WIN platform.
Conclusion
In conclusion, the application of AI in COVID-19 management in India has demonstrated significant potential for creating public value. This research paper has explored the various ways in which AI-enabled initiatives, such as the Co-WIN platform and the Aarogya Setu app, have contributed to improving public health outcomes, efficient resource allocation, faster and accurate diagnosis, and increased operational efficiency. The analysis of the application of AI focuses on its potential to create public value through the delivery of quality public services, the achievement of outcomes, and the development of trust in public institutions. The study sheds light on the policy implications that can be derived from India’s use of AI in managing COVID-19. These include the significance of collaboration between public and private stakeholders, the requirement for a robust regulatory framework to ensure data privacy and security, and the necessity of effective communication and community engagement.
The findings of this research paper demonstrate the potential for AI to play a crucial role in managing public health emergencies such as COVID-19, and highlight the importance of policy frameworks that support the development and application of AI in ways that create public value. This research paper provides important insights for policymakers and stakeholders in India and around the world, seeking to leverage AI in their own health emergency management efforts.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.