Given the insufficient public investment in the health sector in India, optimal utilisation of resources in the sector is crucial. This column analyses the efficiency of secondary government hospitals in West Bengal. It finds a huge slack in the use of resources such as doctors. With better monitoring, hospitals will be able to serve more patients with existing resources.
n recent years, there has been a lively debate on insufficient public investment in healthcare across developing countries. India, a supposedly upcoming economic superpower, traditionally under-invests in this extremely crucial social sector, with share of Gross Domestic Product (GDP) in publicly-financed healthcare never exceeding 2.5% in the post-independence period. In addition to the pressing need for increasing this share, optimal utilisation of these scarce resources in developing countries has been emphasised, to ensure best possible output from available infrastructure and manpower (Anton 2013, Masiye 2007).
In this context, we undertook a study of the technical efficiency1 of 78 secondary government hospitals in the state of West Bengal in India in 2010 (Bandyopadhyay, Dutta and Ghose 2012, 2014)2 3. We examine whether these hospitals optimally utilise inputs for healthcare to provide out-patient and in-patient services to the general public. We focus on misallocation and under-utilisation of scarce resources in this extremely crucial sector.
While examining this issue, we need to keep in mind the key characteristics of West Bengal, the eastern Indian state bordering Bangladesh. Due to tremendous influx of migrants from erstwhile East Pakistan (now Bangladesh), which has continued till date, the state has the second highest density of population in India (Census 2011). 55% of the state’s population belongs to socially backward classes or minority religious groups, far higher than the national average of 45% (Census 2001).According to the 60th round (2004) of the National Sample Survey (NSS), over 27% of the population still lives below the poverty line, and about 82% of the population seeks treatment in government-run hospitals as in-patients (as against the national average of 40%). This picture highlights the need for a strong, public-funded healthcare system in the state.
Data and methodology
We used data from a survey of secondary hospitals that we conducted in 2010-11 in all districts of West Bengal4. We use the Data Envelopment Analysis (DEA) - a dominant methodology used to measure the technical efficiency of many economic and social sectors, especially non-profit. Essentially, this involves construction of a ‘best-practice’ frontier based on the outputs produced per unit of inputs by the best performers. In the context of our study, the hospitals that lie on this frontier are considered to be efficient ‘peers’. This frontier creates a standard for comparison as it describes the most efficient performance conditions within a group of hospitals. For each hospital, efficiency in the production of hospital services is measured relative to this best-practice frontier. Hospitals operating below the frontier are considered relatively inefficient because their output falls short of what could have been produced given the inputs used. The best-practice hospitals have an efficiency score of one, while the rest of the hospitals lying below the frontier have scores less than one.
We look at eight inputs and two outputs. The inputs are hospital beds, infrastructure, equipment, doctors, administrative staff, nursing staff, medical support staff, Group D Staff (related to labour such as ward boys, sweepers etc.), and capital (expenditure less salary, per day in real terms). The two outputs considered are out-patient visits per day and in-patients admissions (net of referred out and death), which are quality adjusted. In case of the former, we adjust for quality by multiplying out-patients per day with the Index for Doctor’s Involvement (IDI) in the hospital. IDI captures the extent to which doctors shirk their responsibilities by attending less working hours than the norm. Multiplying the total number of out-patients with the ideal value of IDI represents the number of patients that can be attended to in a proper manner if the doctors completes the required hours in the hospital’s Out-Patient Department (OPD). In case of in-patients, the average Perception Index (PI) of the In-Patient Department (IPD) that captures the perception of patients with regard to the hospital services, cleanliness, diet, security etc., is multiplied with the actual numbers of admitted patients.
Apart from inputs, environmental factors such as share of emergency admission, nurse-non nurse ratio etc., also influence technical efficiency of hospitals and we take these into account as well in our analysis.
We assume that all hospitals have access to the same technology, and inability of converting inputs to outputs efficiently is not due to the lack of access to technology. However, often, technology across hospitals does vary based on geographical location. Following Bhandari and Ray (2012), we also undertake a group analysis of different regions in West Bengal, assuming that hospitals in different regions do not have access to similar technology of production. For example, the relatively backward western region of the state might be deprived of certain technology, which pushes hospitals below the best-practice frontier. Therefore, disaggregated group analysis based on region is called for. For this purpose, we divide the state into four regions (slightly altering the National Sample Survey Organisation (NSSO) classification of regions): Northern Bengal, Ganga Belt, Western Rarh and the region adjoining Kolkata. The average efficiency of hospitals in a particular region is called Group Efficiency of that region, and the average efficiency of those hospitals in relation to all hospitals is called Grand Efficiency of those hospitals. The ratio between these two is called the Technology Closeness Ratio (TCR). TCR essentially captures how hospitals in a particular geographical region are different from all hospitals in terms of technical efficiency.
Measuring technical efficiency
Out of the 78 hospitals, only 26 are found to be relatively efficient with a score of one. The average efficiency of all hospitals is 0.73, suggesting that on average the hospitals could produce at least 37% more output with same amount of input, if they become as efficient as the hospitals on the best-practice frontier. The highest slack in input utilisation is observed for doctors and Group D staff, with share of under-utilisation being 25.5%and 25.8% respectively. Given that West Bengal is known to have a large under-supply of doctors, particularly specialists, in relation to the demand (Government of India 2010), it is surprising that almost one fourth of the available doctors are not utilised to their full capacity.
Further, we find that hospitals located in areas adjacent to the city of Kolkata have the highest levels of average grand efficiency as well as group efficiency, and relatively high TCR (0.88). On the other hand, the backward Western region has a high value of average group efficiency (0.94), but a low value of average grand efficiency, indicating that in this region almost all hospitals show similar, poor performance.
Looking at environmental factors that determine which hospitals are on the best-practice frontier, we find that as the percentage of emergency admission in total admission increases, the hospital becomes less likely to be on the efficiency frontier. This implies that secondary hospitals in West Bengal do not have proper infrastructure to tackle emergency cases, and this may be the reason for the concentration of patients in tertiary hospitals in the state. In all these secondary hospitals, emergency admissions are taken not because of trust but because there is hardly any other option available. As the hospitals are not equipped to handle these cases, a majority of the patients are either referred out and/ or die, resulting in a drop of the second output (people receiving in-patient care). Also, increase in the ratio between doctors and non-doctor manpower in the hospital lowers the efficiency score, and that of nurses to non-nurse manpower improves it. This means that support staff needs to be increased along with an increase in doctors in order to perform efficiently.
The key policy implications from the study are as follows:
- A large slack in input utilisation points towards the need for tighter monitoring by hospital authorities so that hospitals can cater to more patients.
- The health department of the state government should not only increase manpower availability at the hospital level, but also ensure that the ratios of different categories of hospital staff are at appropriate levels. Mere allocation of doctors appears to be counterproductive; more nurses are also required as they are the primary care-givers.
- The regional analysis suggests the following:
- Average-enhancing policy: One option is to aim to improve the average efficiency across the hospitals. A relatively easy way to achieve this is to target inefficient hospitals within regions that are performing better.
- Divergence-reducing policy: The other option is to aim to improve efficiency of inefficient hospitals that are in low-performing groups. This will decrease gaps in service quality among different types of groups.
In other words, the state government of West Bengal can either prioritise the improvement in efficiency of hospitals adjoining the city of Kolkata, (a better performing region) or they can focus on inefficient hospitals from backward regions. The latter will be more challenging but more essential and important from an equity perspective.
In conclusion, we see that the cause of concern with regard to hospitals in developing countries is not just a resource crunch, but also huge gaps in planning and implementation by government authorities as well as managerial incapability at the hospital level. Although availability of inputs falls short of international norms set by the World Health Organization (WHO), the existence of huge slack in input utilisation across hospitals shows that managerial and monitoring intervention can improve technical efficiency in a big way.
The column is based on a project led by Arijita Dutta and financed by Department of Health & Family Welfare, Government of West Bengal.
- Technical efficiency refers to the effectiveness with which inputs are used to produce outputs.
- Secondary hospitals are at a higher tier than Community Health Centres (CHC) (first point of contact with health professionals and general medical practitioners) and at a lower tier than tertiary-level hospitals (advanced investigation/ speciality treatment). Secondary hospitals include district (zila) hospitals, sub-division (tehsil) hospitals and a special group of state general hospitals.
- In West Bengal, secondary public hospitals have 39.22% of total beds in public hospitals, provide 51% of public hospital admissions, and perform 47% of deliveries and 61% of surgeries (Government of West Bengal 2010).
- All secondary hospitals in West Bengal were covered except in Gorkha Hill Council and a sub-division in Bankura.
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- Census of India (2011), ‘Census Report’, Government of India.
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- Dutta A, S Bandyopadhyay and A Ghose (2012), Efficiency of Government Hospitals in West Bengal. Report submitted to Department of Health and Family Welfare, Government of West Bengal.
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- Government of India (2010), ‘Rural Health Statistics in India’, Ministry of Health and Family Welfare.
- Government of West Bengal (2004), ‘The Health Sector Strategy 2004-13’, Department of Health and Family Welfare.
- Masiye, F (2007), ‘Investigating Health System Performance: An Application of Data Envelopment Analysis to Zambian Hospitals’, BMC Health Services Research, 7, 1-11.
- National Sample Survey Organization (2004), ‘Morbidity Health Care and the Conditions of the Aged’, 60th round, 25th Schedule, Ministry of Statistics and Programme Implementation, Government of India.
- Simar, L and P Wilson (2007), “Estimation and inference in two-stage, semi-parametric models of production processes”, Journal of Econometrics, 136: 31-64.