Introduction: Conducting research in rural and remote areas is compounded by challenges associated with accessing relatively small populations spread over large geographical areas. Open-ended questions provided in a postal survey format are an advantageous way of including rural and remote residents in research studies. This method means that it is possible to ask for in-depth perspectives, from a large sample, in a relatively resource-efficient way. Such questions are frequently included in population-based surveys; however, they are rarely analysed. The aim of this article is to explore word cloud analysis, to evaluate the utility of automated programs to supplement the analysis of open-ended survey responses. Methods: Participants from the Australian Rural Mental Health Study completed the open-ended question 'What health services would you like to see the local health district providing that are currently not available in your area?' A word cloud analysis was then undertaken using the program Wordle; the size of the word in the cloud illustrates how many times, in proportion to other words, a word has appeared in responses, and provides an easily interpretable visual illustration of research results. Results: In total, 388 participants provided a response to the free-text question. Using the word cloud as a visual guide, key words were identified and used to locate relevant quotes from the full open-text responses. 'Mental health' was the most frequent request, cited by 81 people (20.8%). Following mental health, requests for more 'specialists' (n=59) and 'services' (n=53) were the second and third most frequent responses respectively. Visiting specialists were requested by multiple respondents (n=14). Less frequent requests illustrated in the word cloud are important when considering representatives from smaller population groups such as those with specific health needs or conditions including 'maternity' services (n=13), 'cancer' (n=10), 'drug and alcohol' services (n=8), and 'aged care' (n=7) services are all core services even though they were being called for by fewer people. This lesser frequency may suggest that these services are already considered as available in some rural and remote communities. Conclusions: This research aimed to determine whether meaningful and informative data could be obtained from short responses from open-ended survey questions using an automated data analysis technique to supplement a more in-depth analysis. The findings showed that, while not as detailed as interview responses, the open-ended survey questions provided sufficient information to develop a broad overview of the health service priorities identified by this large rural sample. Such automated data analysis techniques are rarely employed; however, the current research provides valuable support for their utility in rural and remote health research. This research has implications for researchers interested in engaging rural and remote residents, demonstrating that meaningful information can be extracted from short survey response data, contributing a resource-efficient supplement to a more detailed analysis. Open-ended questions are often asked in population-based studies yet they are rarely analysed, posing both an opportunity and a challenge for researchers using such participant-driven responses. The lessons learned from the methodology applied can be transferred to other population-based survey studies more widely.