Assessing Healthcare Capacity in India

Rajagopalan, Shruti; Choutagunta, Abishek

doi:10.2139/ssrn.3570651

Cited by 11 publications

(14 citation statements)

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“…Even within states, there is a lot of variation between urban and rural healthcare capacity, partly driven by the fact that much of private‐sector healthcare is in the larger urban areas. The private healthcare sector in India is four times bigger in overall capacity than the public sector, and it has 55% of the total hospital bed capacity, 90% of doctors, and 80% of ventilators (Rajagopalan & Choutagunta, 2020).…”

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

“…Starting with hospital beds, which became a major constraint during the pandemic, Rajagopalan and Choutagunta (2020) estimate about 131 beds per 100,000 persons in India, with high variation across states in India, in Figure 4. In fact, government hospitals routinely report close to 100% use of the critical care units (Krishnan, 2020; Thacker, 2020).…”

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

“…Rajagopalan and Choutagunta (2020) estimate that India has an overall capacity of about 1.75 million hospital beds across India. And using other surveys suggesting Intensive Care Unit (ICU) beds at about 5% of the total number of beds in India, Rajagopalan and Choutagunta (2020) estimate total ICU capacity at 87,979 beds (p. 11). But ICUs are concentrated in larger metropolitan areas and typically operate at full or close to full capacity because of high costs, and only a fraction of the existing ICU beds would have been available for COVID‐19 patients.…”

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

“…As India experienced the beginning of the pandemic, it was clear that India's healthcare capacity, which usually operates at full capacity even in non‐pandemic times, would be insufficient to cope with the pandemic (Rajagopalan & Choutagunta, 2020). To flatten the curve, therefore, on March 24, 2020, the Indian Prime Minister Narendra Modi imposed a sweeping, nationwide lockdown.…”

Section: Introductionmentioning

confidence: 99%

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Battling COVID‐19 with dysfunctional federalism: Lessons from India

Choutagunta

Manish

Rajagopalan

2021

Southern Economic Journal

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The Indian federation is highly centripetal, and historically, this has left states without the requisite legislative and fiscal authority to take independent action and initiate policies of significance. Consequently, India's response to the global COVID‐19 pandemic was to impose a very severe countrywide lockdown using the mandate of the Union (federal) government. This centralized one‐size‐fits‐all diktat was imposed despite high variations across states in resources, healthcare capacity, and incidence of COVID‐19 cases. We argue that India's dysfunctional federalism is the reason for the centralized lockdown, preventing state and local governments from tailoring a policy response to suit local needs. Using mobility data, we demonstrate the high variation in curtailing mobility in different states through the centralized lockdown. We find that India's centralized lockdown was at best a partial success in a handful of states, while imposing enormous economic costs even in areas where few were affected by the pandemic.

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Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

Section: Variation In Healthcare Capacity Across Indiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Battling COVID‐19 with dysfunctional federalism: Lessons from India

Choutagunta

Manish

Rajagopalan

2021

Southern Economic Journal

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COVID‐19 critical success factors in Indian healthcare industry—A DEMATEL approach

Gedam

Raut

Inamdar

et al. 2021

Multi Criteria Decision Anal

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The prosed study aims to provide COVID‐19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision‐Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID‐19 are identified and prioritized using the DEMATEL method. The findings indicate that the high‐quality personal protective equipment (PPEs; LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision‐makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post‐COVID‐19 circumstance. The policy/decision‐makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision‐makers swiftly build up the HCI to cope with the future pandemic.

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A Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic

Bora

Nirali

Chaudhari

et al. 2021

Lecture Notes in Electrical Engineering

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Medicine plays important role for patient's treatment and disease prevention in case of pandemic situations. A healthcare institutions such as pharmacies or hospitals must ensure the availability of medicines as well as other resources such as masks, beds, PPE kits, etc. Hospitals as a health service provider have to face several challenges. One of the challenges is unavailability of Medicines during crucial periods/outbreaks, also due to lack of centralized systems for inventory management results in poor treatment to patients in hospitals. Predictive analysis of the amount of medicine usage and resources can help in hospital asset management. But forecasting medicine usage based on only inventory data will not provide good results when an outbreak happens. As outbreaks cannot be time-series forecasted, it is not feasible to forecast medicine usage only using time-series forecasting models. We propose a forecasting model combination of time forecasting model and epidemiology SIR model to find different trends and seasonality during outbreaks.

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Assessing Healthcare Capacity in India

Cited by 11 publications

References 1 publication

Battling COVID‐19 with dysfunctional federalism: Lessons from India

Battling COVID‐19 with dysfunctional federalism: Lessons from India

COVID‐19 critical success factors in Indian healthcare industry—A DEMATEL approach

A Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic

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