The field of bioinformatics has exploded over the past decade. Hopes have run high for the impact on preventive, diagnostic, and therapeutic capabilities of genomics and proteomics. As time has progressed, so has our understanding of this field. Although the mapping of the human genome will certainly have an impact on health care, it is a complex web to unweave. Addressing simpler "Single Nucleotide Polymorphisms" (SNPs) is not new, however, the complexity and importance of polygenic disorders and the greater role of the far more complex field of proteomics has become more clear. Proteomics operates much closer to the actual cellular level of human structure and proteins are very sensitive markers of health. Because the proteome, however, is so much more complex than the genome, and changes with time and environmental factors, mapping it and using the data in direct care delivery is even harder than for the genome. For these reasons of complexity, the expected utopia of a single gene chip or protein chip capable of analyzing an individual's genetic make-up and producing a cornucopia of useful diagnostic information appears still a distant hope. When, and if, this happens, perhaps a genetic profile of each individual will be stored with their medical record; however, in the mean time, this type of information is unlikely to prove highly useful on a broad scale. To address the more complex "polygenic" diseases and those related to protein variations, other tools will be developed in the shorter term. "Top-down" analysis of populations and diseases is likely to produce earlier wins in this area. Detailed computer-generated models will map a wide array of human and environmental factors that indicate the presence of a disease or the relative impact of a particular treatment. These models may point to an underlying genomic or proteomic cause, for which genomic or proteomic testing or therapies could then be applied for confirmation and/or treatment. These types of diagnostic and therapeutic requirements are most likely to be introduced into clinical practice through traditional forms of clinical practice guidelines and clinical decision support tools. The opportunities created by bioinformatics are enormous, however, many challenges and a great deal of additional research lay ahead before this research bears fruit widely at the care delivery level.