To improve the quality of healthcare planning, healthcare systems face challenges in identifying clusters of similar hospitals while considering varying factors. Clustering hospitals based on their admission behavior would be helpful whereas diagnosis of patients is vital in understanding variation in admission. Therefore, grouping hospitals that show similar behavior on their admission distribution while considering similarity among disease symptoms in admission is the objective of our study. This is achieved by a Disease Network of Hospital Networks model which is used to represent hospital admission distribution of multiple diseases as different hospital networks that correspond to disease nodes in a top-layer disease network. This disease network that was extracted from the Human Symptoms Disease Network models the similarity among different disease-specific hospital networks. We assume that disease-specific hospital networks have different underlying clustering structure while share the same underlying clustering structure if corresponding diseases share similar symptoms. Experiments were conducted on 14,534,016 electronic health records of monthly admission of 160 diseases over 4 years at 301 hospitals in California. Results of clustering 160 disease-specific hospitals networks that share similar symptoms among corresponding diseases show consistent behavior among these networks when similarity among diseases is considered in clustering process. Patterns of consistent behavior were lacking in results when similarity among diseases is not considered.