Articles36 AI MAGAZINE I t might be thought that artificial intelligence techniques or other types of computational methods are irrelevant in countries with few technological resources. As just one example of the possibilities, however, take road traffic in cities. The chaotic and spectacular road congestion that is characteristic of developing-world cities is a microcosm of opportunities for applying AI methods. The problems are mainly caused by inadequate infrastructure (for example, road layouts that have not changed significantly despite decades of economic growth, unsealed or pothole-strewn roads), and a lack of resources to monitor or control traffic (for example, scarce and possibly corrupt traffic police, rolling blackouts affecting traffic lights). Computational solutions might come in the form of ways to cheaply gather realtime data, to advise individuals or emergency vehicles on optimal routes, to dynamically redeploy a limited number of

Background We developed a composite index–hospital preparedness index (HOSPI)–to gauge preparedness of hospitals in India to deal with COVID-19 pandemic. Methods We developed and validated a comprehensive survey questionnaire containing 63 questions, out of which 16 critical items were identified and classified under 5 domains: staff preparedness, effects of COVID-19, protective gears, infrastructure, and future planning. Hospitals empaneled under Ayushman Bharat Yojana (ABY) were invited to the survey. The responses were analyzed using weighted negative log likelihood scores for the options. The preparedness of hospitals was ranked after averaging the scores state-wise and district-wise in select states. HOSPI scores for states were classified using K-means clustering. Findings Out of 20,202 hospitals empaneled in ABY included in the study, a total of 954 hospitals responded to the questionnaire by July 2020. Domains 1, 2, and 4 contributed the most to the index. The overall preparedness was identified as the best in Goa, and 12 states/ UTs had scores above the national average score. Among the states which experienced high COVID-19 cases during the first pandemic wave, we identified a cluster of states with high HOSPI scores indicating better preparedness (Maharashtra, Tamil Nadu, Karnataka, Uttar Pradesh and Andhra Pradesh), and a cluster with low HOSPI scores indicating poor preparedness (Chhattisgarh, Delhi, Uttarakhand). Interpretation Using this index, it is possible to identify areas for targeted improvement of hospital and staff preparedness to deal with the COVID-19 crisis.