The purpose of this study was to examine the acoustic characteristics of children's speech and voices that account for listeners' ability to identify gender. In Experiment I, vocal recordings and gross physical measurements of 4-, 8-, 12-, and 16-year olds were taken (10 girls and 10 boys per age group). The speech sample consisted of seven nondiphthongal vowels of American English (/ae/ "had," /E/ "head," /i/ "heed," /I/ "hid," /a/ "hod," /inverted v/ "hud," and /u/ "who'd") produced in the carrier phrase, "Say /hVd/ again." Fundamental frequency (f0) and formant frequencies (F1, F2, F3) were measured from these syllables. In Experiment II, 20 adults rated the syllables produced by the children in Experiment I based on a six-point gender rating scale. The results from these experiments indicate (1) vowel formant frequencies differentiate gender for children as young as four years of age, while formant frequencies and f0 differentiate gender after 12 years of age, (2) the relationship between gross measures of physical size and vocal characteristics is apparent for at least 12- and 16-year olds, and (3) listeners can identify gender from the speech and voice of children as young as four years of age, and with respect to young children, listeners appear to base their gender ratings on vowel formant frequencies. The findings are discussed in relation to the development of gender identity and its perceptual representation in speech and voice.

Effective lifestyle health management may increase low-cost health claims activity, because many patients improve at self-care. As demonstrated here, preventive initiatives result in lower cumulative costs and decrease the risk of high-cost or outlier claim events.

Organizations across industry sectors continue to develop data resources and utilize analytic techniques to enhance efficiencies in their operations. One example of this is evident as Managed Care Organizations (MCOs) enhance their care and disease management initiatives through the utilization of population segmentation techniques. This article proposes a classification system for population segmentation techniques for care and disease management and provides an evaluation process for each. The three proposed operational areas for Managed Care Organizations are: 1) Risk Status: early identification of high-risk patients, 2) Treatment Status: compliance with treatment protocols, and 3) Health Status: severity of illness or episodes of care groupings, all of which require particular analytic methodologies to leverage data resources. By applying this classification system an MCO can improve its ability to clarify internal goals for population segmentation, more accurately apply existing analytic methodologies, and produce more appropriate solutions.

Healthcare has become a data-intensive business. Over the last 30 years, we have seen significant advancements in the areas of health information technology and health informatics as well as healthcare modeling and artificial intelligence techniques. Health informatics, which is the science of health information,1 has made great progress during this period (American Medical Informatics Association). Likewise, data mining, which has been generally defined as the application of technology and statistical/mathematical methods to uncover relationships and patterns between variables in data sets, has experienced noteworthy improvements in computer technology (e.g., hardware and software) in addition to applications and methodologies (e.g., statistical and biostatistical techniques such as neural networks, regression analysis, and classification/segmentation methods) (Kudyba & Hoptroff, 2001). Though health informatics is a relatively young science, the impact of this area on the health system and health information technology industry has already been seen, evidenced by improvements in healthcare delivery models, information systems, and assessment/diagnostic tools.

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Healthcare has become a data-intensive business. Over the last 30 years, we have seen significant advancements in the areas of health information technology and health informatics as well as healthcare modeling and artificial intelligence techniques. Health informatics, which is the science of health information,1 has made great progress during this period (American Medical Informatics Association). Likewise, data mining, which has been generally defined as the application of technology and statistical/mathematical methods to uncover relationships and patterns between variables in data sets, has experienced noteworthy improvements in computer technology (e.g., hardware and software) in addition to applications and methodologies (e.g., statistical and biostatistical techniques such as neural networks, regression analysis, and classification/segmentation methods) (Kudyba & Hoptroff, 2001). Though health informatics is a relatively young science, the impact of this area on the health system and health information technology industry has already been seen, evidenced by improvements in healthcare delivery models, information systems, and assessment/diagnostic tools.

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