March 2007

A GIS Based Referral Planning System

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The referral mechanism has been introduced and made functional in most of the districts but are providing mixed results in terms of compliance and non-compliance to the defined hierarchical pattern of referral. The characteristic  of referred cases reveal various lacunae

Referral system is one of the important aspects of efficient public healthcare delivery mechanism. It defines processes for effective use of multi-tier system of health centres and hospitals for treatment of patients, according to the severity of illness. Complicated cases beyond the scope of treatment in a particular facility is stabilized first with appropriate medical care and then promptly referred and transferred to a technically equipped higher-tier hospital by following a definite referral chain.  Such a system has been introduced in most of the states of India within various administrative units.

The basic concept of a referral system is to emanate from lowest level and to end in the tertiary care facility. The referral system planning involves two decisions, which are: (i) deciding on the referral protocol, (ii) deciding on the referral chain. While referral protocols contain administrative guidelines, facility, equipment and service norms, referral chains, at present, are designed based on primarily two criteria. They are distance and availability of required service facility at the nearest point, following the hierarchical pattern of healthcare delivery.

The referral mechanism has been introduced and made functional in most of the districts but are providing mixed results in terms of compliance and non-compliance to the defined hierarchical pattern of referral. The characteristic  of referred cases reveal various lacunae. Patients often do not reach the appropriate health facilities, patients most often do not reach health facilities in time, and patients often do not follow the referral advice. Moreover, patients bypass lower level health facilities, unnecessarily overcrowding the higher  level hospitals.

The outcome of this system is mixed in terms of compliance and non-compliance to the defined hierarchical pattern of referral, due to a number of factors ranging from awareness, spatial and supportive logistics and socio-economic conditions of the beneficiaries. This is particularly relevant in areas with a difficult terrain, where spatial factors and socio-economic conditions play a decisive role in complying with the suggested referral chains, to the pre-defined hierarchical health units. Thus planning for an adequate health system with an efficient referral mechanism, requires a combination of facility and spatial analysis to derive an optimal service delivery system and GIS could serve as a useful mechanism for decision support planning, considering the incorporation of spatial and non-spatial data in a single reference frame.

The use of GIS in health has been attempted by different agencies in India. Danida-assisted National Leprosy Eradication Programme is one of the foremost in introducing GIS in health, in the country [1]. Apart from DANLEP, many development agencies [3,4,5] and government institutions are exploring health GIS in India. Malaria Research Centre, New Delhi [2], Vector Control Research Center, Pondicherry, UNICEF, WHO for leprosy, TB, Malaria and Pulse Polio programmes, HIV/AIDS programmes in TN, Orissa and MP are few of the recommended studies.

However, all these studies aim at developing health / disease maps to aid in facility and preventive planning. An interesting work has been carried out by LN Balaji [4] of NATMO Kolkata, using GIS to study the influence of  locational attributes on health conditions and also to determine the nature of disease diffusion across geographic regions. Some research has been attempted on creating health database, and using it as a support for health facility planning. The study by Mili Ghosh. Shantanu Lal and Dr. MS Nathawat of BIT Meshra[6] is on these lines and it provides a facility upgradation plan. So far no attempt has however been made for referral system design in India, as institutionalization of the referral mechanism is a relatively new management concept in public healthcare delivery system. A somewhat similar study in identifying referral regions based on the service population and catchment area features has been attempted by Dartmouth Atlas of Health Care in the United States.

There is no spatial component in the state health referral system. However, in the arduous terrains like Sundarbans, West Bengal, the spatial component has a major role to play while deciding for a referred health center. The major factors, in addition to distance and disease type/condition, are type of road, availability of river route, seasonal dependency, time of the day (day or night for river route), available conveyance type, etc.

Here we show how GIS can be used as a useful tool for decision support planning, considering the incorporation of spatial and non-spatial data in a single reference frame. Sunderban region of India, located in the state of West Bengal, has been chosen as a case study area. The present state of health referral system is devoid of specific spatial considerations, except for crude nearness estimate between the source and the destination health centers. While this absence of detailed spatial considerations may be acceptable for urban, semi-urban or even mainland rural areas that enjoy a good connectivity by rail & road; it is a cause of grave concern for arduous terrains like the Sunderbans where free movement between a source point to a destination health center ften gets heavily impaired due to spatial limitations.

We have proposed a network optimization model, based on several spatial as well as non-spatial factors, to minimize an integrated cost function. An optimization model, incorporating spatial and non-spatial data, has been proposed for designing an effective referral system model, specific to arduous terrains. The model has been developed considering the geographical spread and terrain characteristics, natural and climatic conditions, seasonal deviation, land use, infrastrutural and service facilities, connectivity and communication network, etc. and to identify the natural and physical conditions and factors limiting mobility.

The optimization model analyzes several routes from one health center to another center, using road, river or a combination of road and river, depending on several factors like disease condition, severity of disease, season, time, socio-economic condition, etc. Whenever there are changes in health center availability, new roads and river routes, the spatial database could easily be updated and new routes will be derived.

Insight into the Optimization Model

The study thus attempts to formulate a health-system-aware and terrain-sensitive referral strategy that would take the spatially dominant factors into due consideration. The overall strategy is formulated as an optimization problem where, initially, every health center is considered an equally potential referral candidate for any patient, originating from any village in the region and having any possible complaint or condition. A set of feasibility constraints is overlaid on the whole set to prune out a smaller subset that qualifies as one of the viable referral points. Finally, a composite cost function that computes the economic, temporal, qualitative and other variant costs, makes a choice of the 'ideal' referral point that minimizes the cost and therefore, maximizes benefits. In order keep the option open for subjective judgments that may not be captured in the model (due to lack of data and / or timely update),
we generate two or more ranked referral-point candidates and allow for a final human selection.

The Cost Function

The objective of the referral system design activity is to create a networked optimization model based on several relevant spatial as well as non-spatial factors that would minimize the cost functions, which hinder the effective use of the referral chain. Amongst various parameters, the cost function would attempt to optimize the following:

Commutation Cost

This model describes the distance between any two points, the different modes of transport used and the total cost to reach the destination. For Sundarbans, in order to reach a particular point from a given point, one has to go by the land or by the river or both. Thus the distance information is broken up as land distance and river distance respectively. The sum of the above two distances gives the total distance to travel. Likewise the total cost to travel is the sum of the costs to travel on land and the costs to travel by river.

Based on the total time (TT) and total cost (TC), commutation details defines a priority index called Accessibility Index. Comparing the accessibility indices for the different routes from one point to other, one can identify the best possible route in terms of time and cost.

Service Availability Constraint

The service model describes the services rendered by the different health centers. It identifies the name of the health center, its type (i.e., PHC, BPHC, RH, SDH, etc.), diseases/ ailments that are treated and the criticality level of the disease that can be handled. For a given patient, with a certain level of criticality and originating from a particular location, one can determine the possible destination health service centers from the service model.

Distress Factor

Distress factor is a quantitative measure of the amount of distress or discomfort that one has to bear in order to travel from one point to other. The distress factor for any two points is defined by the condition of the roads, the time in waiting for the availability of transport (worst case consideration) and the number of transport changes that one has to undergo. In case of Sundarbans all of these three factors are again dependent on the season (navigable waterways) and the time of the day (occurrence of tides).

Thus the Accessibility Index between any two points computed in the commutation model varies inversely with the distress factor between them, and combining this distress factor with the commutation details, one can redefine Accessibility Index as Qualitative Accessibility Index.

Disease Constraint

The Disease Constraint model defines all the factors (both clinical and spatial) that must be met for treating the diseases with different level of criticality. This includes the allowable time (maximum) necessary to get a particular treatment, the condition of the road required to transfer the patient to the health center, and the maximum number of transport changes or relocations that can be allowed.

The Referral (Computation) Model

The model is thematically multi-sliced with a combination of spatial and non-spatial slices intertwined on hierarchical information architecture. These slices are mostly conceptual. In terms of an implementation under a GIS system, multiple slices are flattened into a few GIS layers for efficiency of storage, visualization and computation.

At the base slice there is a spatial layer. This is where the whole story starts and this is where the story should end as well. This layer has a set of node points

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