CONCLUSION: Most numerate women were able to learn numeric information from the video but had difficulty manipulating this information if they were not given the correct response in the video. Non-numerate women were unable to learn numeric information even when given the correct response. Although women in their forties are supposed to be informed of the benefits/harms of mammography to make decisions about screening, many women may have difficulty understanding this information if it is relayed numerically. The sample consisted of a primarily well educated group of 30 ± 50 year old (75%) professional men (37%) and women (63%). Almost all of the sample had been born in China (93%), claimed Mandarin Chinese as their native language (88%), and had resided in the United States an average of 8.9 (SD=7) years. Although most of the respondents had health insurance (93%) and more than half (69%) had a primary care provider, only 37% stated that they would have a routine check-up with this provider if they``felt fine.'' However 54% of the females in our sample reported having annual pap screening and breast exam by their physicians. Over half of the sample (63%) reported problems with understanding medical terms. Less educated respondents were more likely to be hesitant to see the doctor due to language difficulties (p = 0.001) and more likely to use western medicine for a cold (p = 0.023). Respondents with lower incomes were also more likely to be hesitant to see the doctor due to language difficulties (p = 0.036) but respondents with higher incomes were more more likely to have a primary care physician (p < 0.001). CONCLUSION: Although our sample was well educated and had adequate access to health care services, the inability to understand medical terminology may be a prime contributor to the under-utilization of health care services. A more diverse and larger sample is needed to further assess the utilization patterns from all socioeconomic levels. In addition, health literacy levels of Chinese Americans need to be assessed in order to provide culturally competent health care to this population. PURPOSE: Group A -hemolytic streptococci (GABHS) are cultured in 10 ± 25% of adults with sore throat. GABHS is the main cause of sore throat requiring antibiotic therapy. The Infectious Disease Society of America recommends either penicillin or erythromycin as first-line agents to prevent complications and reduce symptoms. Our goals were: 1) define the rate and type of antibiotics given to patients with sore throat by community primary care physicians over a ten-year period, and 2) determine predictors of antibiotic prescription and use of nonrecommended antibiotics for sore throat. METHODS: The National Ambulatory Medical Care Survey (NAMCS) collects data from office-based physician practices in the United States including patient demographics, patients' reasons for visit, physician diagnoses, and medications prescribed. Using NAMCS from 1989 to 1998, we analyzed 2,141 adult primary care visits with a chief complaint of sore throat. We estimated national antibiotic prescription rates over time and determined both the most frequent diagnoses made and the most frequent antibiotics prescribed. We evaluated significant predictors of antibiotic prescription and non-recommended treatment using multivariate logistic regression. RESULTS: An estimated 6.8 million adult patients each year made acute visits for sore throat to office-based, primary care physicians between 1989 and 1998. The most common diagnoses were acute pharyngitis (42%), acute upper respiratory tract infection (21%), acute tonsillitis (8%), and streptococcal infection (6%). Antibiotics were prescribed to 73% (95% CI 70 ± 76%) of patients. The most common antibiotics prescribed were aminopenicillins (23% of visits), cephalosporins (14%), penicillin (14%), and erythromycin (10%). Over ten-years the proportion of patients receiving any antibiotic decreased while the proportion of patients receiving non-recommended treatment increased ( 1989 ± 1992 1993 ± 1995 1996 ± 1998 NHANES included 157 women with a personal history of breast cancer. Eleven of these survivors (7.0%) were currently using HRT. CONCLUSION: A surprisingly large fraction of breast cancer survivors use HRT. The concordance between two estimates obtained using very different methods argues against random or data error. Other possible explanations include: 1) the patient opts to accept the putative risks of recurrence in an informed manner, 2) the prescriber does not know that HRT is contraindicated in breast cancer, 3) the prescriber rejects the theory of hormone induced recurrence, 4) the prescriber does not know the patient is a breast cancer survivor, or 5) the patient self-prescribes. We believe that HRT use in breast cancer survivors is a sentinel event that may represent medical error. In stage 1, multi-pattern regressions were performed to impute PCS and MCS using SF-12 items only (simple model), and then using SF-12 items plus patients demographics and comorbidities (enhanced model) in an attempt to reduce bias from the simple model. Distribution and variation of the imputed SF-12 values were evaluated. Based on confidence bands of the imputed SF-12 values, a cut point of number of missing SF-12 items was selected for using the simple or enhanced model to impute SF-12 PCS and MCS. In stage 2, patients with no missing SF-12 items who also had demographic and comorbidity data were randomly classified into 11 subgroups. Permutations of missing patterns (missing 1 to 11 out of the total 12 items) on SF-12 items were generated to simulate missingness (missing 1 to 11 SF-12 items) for these patients. The imputed PCS and MCS were compared to the observed values for these patients separately by the identified cut point. The analysis was performed separately for chronic and non-chronic subgroups. RESULTS: Results: In stage 1, the standard errors of means of imputed summary scores increased almost monotonically by the number of missing SF-12 items, ranging from 0.11 to 4.55 for PCS and 0.09 to 2.18 for MCS in the non-chronic subgroup, and from 0.28 to 5.84 for PCS and 0.24 to 5.62 for MCS in the chronic subgroup. A significantly consistent increase of the width of confidence bands (about 50% in standard error) occurred between 5 and 6 items missing. Of the 13,438 patients, 10,766 (8949 non-chronic and 1817 chronic) patients had non-missing data for all SF-12 items as well as demographic and comorbidities. Productmoment correlations between the imputed and the observed scores were large, ranging from 0.73 to 0.97. The correlations from imputation with the enhanced model were consistently higher than the correlations from the simple model. The increments of correlations by the enhanced model were statistically significant for patients with more than 6 missing SF-12 items (7%, P < 0.05). CONCLUSION: Conclusion: For patients with partially missing SF-12 items, direct imputation of summary SF-12 physical and mental scores is efficient. Adding patients' demographics and comorbidity information to the imputation model for PCS and MCS can improve the imputed value and reduce bias. The enhancement of adding patients' demographics and comorbidities to the imputation models for PCS and MCS is necessary and important for patients with 6 or more SF-12 items missing. PURPOSE: Most clinicians rely on the history to make an accurate diagnosis and prescribe appropriate treatment. However, as increasing administrative and financial pressures shorten the outpatient clinical visit, the opportunity to take a careful history is becoming more abbreviated. This study prospectively evaluated whether accurate history taking, under precisely controlled conditions, actually leads to better outpatient clinical practice. METHODS: We prospectively collected data in the primary care general internal medicine clinics at two VA-university teaching hospitals. Greater than 95% of residents and attendings consented to participate Ð and 20 were randomly selected as subjects. Eight unannounced standardized patients (SPs) were introduced into the clinic of each selected provider presenting four common conditions: COPD, Low Back Pain, diabetes mellitus and coronary artery disease (i.e., 2 SPs per condition). Thus, every physician saw an identical set of cases. All visits were new patient appointments. Visits were scored by the SP using a closed-ended checklist of quality criteria; other criteria were assessed by abstracting the medical record. Scoring was based on explicit criteria derived from national guidelines and expert panels. Separate scores were generated for History Taking, the Physical Examination, Testing (laboratory and imaging studies), and Diagnosis plus Treatment. Scores were expressed as a percentage of criteria that were correct. Multiple linear regression models were used to predict if better history taking led to more accurate diagnosis and treatment plans, more correct testing, and appropriate physical examinations. The models controlled for case, site, and level of training. RESULTS: Physicians who scored higher on history taking (that is, took more accurate histories) made more accurate diagnoses and more appropriate treatment decisions (p = 0.006). By contrast, more accurate physical examinations and diagnostic test ordering did not predict better diagnosis and treatment (p > 0.05). The quality of diagnosis and treatment varied between the two sites (p < 0.001) but not by level of training or case. Taking an accurate history did predict the appropriateness of the physical examination (p = 0.004), but it did not predict testing (p > 0.05). CONCLUSION: Under controlled conditions, where case mix was specified by experimental design, our results show that accurate history taking predicted when residents and attendings were more likely to make the correct diagnosis and prescribe the best treatment. While accurate history taking also guided the physical examination, the examination itself did not lead to subsequently higher process scores. The site effects were also strong. These findings demonstrate that successful completion of the history effectively predicts of diagnostic and treatment accuracy. Emphasis on development of history taking skills in residency programs, and adequate visits time allocations in practice settings, should contribute to higher quality clinical care. were able to recall their PCP's name. When asked,``When you are sick and need care right away, how quickly do they see you?'' 70% answered the same day or the next day. Regarding ED usage, 49% of patients called triage before coming to the ED; those with CMA insurance were less likely to call (42% vs 75%, p =. 04). Of those that called, 96% reported they were told to go to the ED (``callers''). ED nurses triaged 59% of``callers'' and 44% of non``callers'' to Level 1 or 2 (out of 4; 1 is high). Eighty per cent of respondents rated``How well doctor listens to you,''`D octors respect for you,'' and``Doctors ability to figure out what is the matter'' as excellent or very good. There were no significant differences in ratings between PCPs and ED physicians.