Correlation between impact and relevance scores was determined using the Spearman rank correlation coefficient. The internal consistency of the total score (summed over the 6 Ds) for each condition was determined by calculating Cronbach's alpha. Using this summary index score, the conditions were rank ordered for each age category. The frequency count and weighted score for each condition and age category were then multiplied together (in order to have positive signs after multiplication, negative z-scores were eliminated by the linear transformation of adding 4 to each score). This transformation gives all variables the same mean and standard deviation. For each condition, a standard normal deviate (position of score on a standard normal distribution with mean of 0 and standard deviation of 1) was calculated. In phase four, a summary index score was developed and the conditions ranked according to this index. Respondents were asked to note the relevance and potential impact of EMS for each condition/outcome category on a 5-point scale ranging from (1) low to (5) high impact. Two questions were included in the adult and child questionnaire: 1) For each of the following conditions, how would you rate the relevance of the following 6 outcome categories? and 2) For each of these conditions, how would you rate the potential impact of EMS (including both basic and advanced EMT care) on each outcome? The six outcome categories were defined as: survival (death), impaired physiology (disease), limit disability (disability), alleviate discomfort (discomfort), satisfaction (dissatisfaction), and cost (destitution). Respondents were asked to complete two questionnaires, one for patients less than 15 years of age and another for patients 15 years of age or older (see Appendix II). Lacking meaningful outcome data, the investigators obtained expert opinions from 37 EMS researchers and leaders regarding the relevance and potential impact of EMS (see Appendix I). In phase three, the relevance of various outcomes and the potential impact of EMS on these outcomes, for each condition, was determined.
Data from Jthrough Jwere obtained from Alabama, Mississippi, Oklahoma, Illinois and eleven central California counties and used for the frequency analysis. EMS Data Systems collects data from various EMS systems across the country using optically-scanned data entry forms and data sets similar to that promulgated by NHTSA. (Phoenix, Arizona) was selected to provide frequency data.
No local, state or federal databases were suitable for use due to inconsistent data definitions, inconsistent data formatting, and variation in inclusion criteria.
In phase two, frequency data were obtained for all the conditions identified. A preliminary list of such conditions was identified using the NHTSA Uniform Data Conference data element items "Provider Impression" (data element 50), "Signs and Symptoms Present" (data element 52) and "Injury Site and Type" (data element 53). An EMS condition was defined as an illness, injury or combination of signs and symptoms that caused EMS activation. Identify conditions that should take precedence in EMS outcomesÄuring phase one of this portion of the project, a list of EMS conditions was developed. Department of Transportation - National Highway Traffic Safety Administration (NHTSA) - Emergency Medical Services Outcomes Evaluation - 5.0 Project Findings - DOT HS 809 603 - July 2003