location: nan, 'Thailand 
year: 2002 source: HSRI 
title: Geographic information system 
abstract:Geographic Information SystemThe problems of primary health care shortages in Bangkok and its vicinity has been intensified due to the explosion of population, resulting from rural urban migration. The concentration of medical facilities within Bangkok’s CBD further complicates the patients’ traveling problems. Such dilemmas could be solved by careful spatial planning of primary health care service facilities. Patients’ pattern of travelling to health care facilities depends primarily on the decision making process, by which the patient choose his/her primary health care service. This research comprises two phases. The first phase aims to elucidate the factors affecting the patients’ choice towards the type of facilities, to model the spatial pattern of the patients’ travelling behavior from the collected data, and to reach the service catchment areas for each type of primary health care facilities, using Geographic Information System (GIS).   	The second phase of this study attempts to experiment, using GIS and the data obtained from phase I of this study, the modeling of spatial pattern of primary health care, in accordance with the current government’s “equal access to primary health care” program. The model is bound to be utilized as a tool to assign patients to their nearest respective facilities and to build a patient referral network. 	The study is based on Walter Christaller’s Central Place Theory, which attempts to explain the consumer’s purchasing behavior vis-à-vis the spatial pattern of retail facilities. The theory looks at three primary factors—distance of retail facilities from consumer’s home, range of goods (the center’s hierarchy), and the spatial location of facilities. Applying Christaller’s theory to health facility consuming behavior, this research assumes three primary variables affecting the travelling pattern of patients. First, the range of treatment in a particular facility, which comprises two indicators: public/private run hospital, hospital size, and its specialization of treatment. The second factor has been the patients’ attributes, which indicators are sex, income level, occupation, state of being able to be reimbursed, state of being insured, and the acquaintance to the medical personals. The final factor has been the types of sickness, which can be categorized as acute sickness, chronic sickness, and accident.  	This research integrates the primary and secondary data, by which the GIS model is achieved to explain the patients’ travelling pattern.  Structured questionnaires is utilized to obtain the primary data while locational data from the Bangkok Metropolitan Administration is used to identify the spatial location of each health care facility. 	The questionnaire survey based on a random sampling of 4464 patients from private and public hospital, health care centers, and medical clinics, which are drawn form 13 districts in Bangkok. The result is used to represent the patients’ decision making pattern toward the choosing of primary health care. 	Factors such as range of treatment, patients’ attributes, and types of sicknesses are found significantly related to each other, and therefore, determining the pattern of journey to health facilities. Low income patients and civil servants, who cannot afford to pay and who are able to be reimbursed, have the constraints to use only government run facilities, and therefore, must travel longer distance to get there. Higher income patients have the choice to travel shorter distance to private run facilities in their local areas. Age also found significantly related to travelling distance due to the factor of dependency and acquaintance to medical personals.  The younger cohort (0-19 years of age) travels the shortest distance, while the independent group (20-60 years of age) travels a little longer than the first group. Elderly patients travel the longest distance due to the need of specialized care and the acquaintance to medical personals. In terms of sickness types, chronic patients travel the longest distance in viewing that they are in no hurry and are already assigned a medical doctor. Acute patients travel the shortest distance due to urgency, while accident patients must balance between short distance and reliable medical care, and therefore, travel a little longer than the second group.   	From the above finding, the research then models the patients’ travelling pattern by means of GIS buffering, to obtain the catchment areas of public / private hospitals, health care centers, and medical clinics.  The out of reached areas are then identified. Modeling according to the type of sickness and patients personal attributes are also charted to identify the respective groups’ pattern of traveling.  	The second phase of study aims to experiment the modeling of patients assignment to the most proximal health care facilities in accordance to the current government’s “equal access to primary health care” campaign. Using the Bangkok Guide base map and the data obtained from phase I, the study compare the carrying capacity of health care facilities and the spatial organization of population (population density) to sort out the out of service areas and the patient assignment. 	Since the present health care policy predetermine the travelling pattern to health care facilities, ignoring the factor of range of treatment and other factors, a referral network is therefore needed for cases beyond the capability of the local facility enable them to be transferred to a higher order hospital. A referral network is also charted using the existing GIS information gained from the previous phase to optimize the travelling distance to their respective host hospitals. 	In short, this research applies Chistallers’s approach using GIS as a tool of health care facility planning. One can conclude from this experimentation that modeling could be helpful for the prediction of health care sufficiency and the planning of the spatial organization of the facilities in the future.