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While the effectiveness of the VAMTA during the pilot study testing can be estimated from statistics on voice accuracy and duration of medical encounters, the applicability of the TTF model to the VAMTA system can be determined by an analysis of the end-user survey instrument responses Multiple regression analysis reveals the effects of VAMTA utility, quality performance, task characteristics, and individual characteristics on user evaluations of VAMTA (Table 48)
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TABLE 47 VAMTA T&E Data Female Percentage Accurate with Video Sex F F F F F F F F F F F 13:35:43 14:26:53 10:49:42 11:18:13 13:35:02 14:28:28 10:49:39 9:45:02 10:41:12 10:45:40 11:06:50 14:08:34 14:32:18 11:01:15 11:23:27 13:42:16 14:33:37 11:01:28 10:07:50 10:47:18 10:51:11 11:12:58 947% 996% 992% 984% 984% 996% 850% 935% 980% 976% 984% Maximum Accurate 996% Minimum Time Voice 0:05:09 Maximum Time Voice 0:32:51 Number Right with Punctuation and Video 234 246 245 243 243 246 210 231 242 241 243 Time Started Voice Time Stopped Voice Time to Complete Voice 0:32:51 0:05:25 0:11:33 0:05:14 0:07:14 0:05:09 0:11:49 0:22:48 0:06:06 0:05:31 0:06:08
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TABLE 48 One-Way ANOVA for Regression Analysis Degrees of Freedom 4 29 33
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Source Regression Residual Total
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Sum of Squares 2664 2867 5531
Mean Squares 0666 0099
F Value 6735
p>F 0001 a
Signi cant at P = 0001
The results indicate that overall end-user evaluations of VAMTA are consistent with the TTF model The F value was 6735 and the model was signi cant at the p 0001 level of signi cance The R-square for the model was 0410 This indicates that model-independent variables explain 41% of the variance in the dependent variable The individual contributions of each independent variable factor are shown in Table 49 While the Table 48 data reveals the suitability of the TTF model, Table 49 reveals another nding Based on the data shown in Table 49, according to the case study user evaluations of VAMTA, utility and quality performance are the major factors that affect the management of smart data by VAMTA The VAMTA case revealed ndings related to the effectiveness of the survey instrument and the VAMTA itself As a result of the case study, the VAMTA follow-up questionnaire has been proved to be a valid survey instrument By examining end-user responses from completed surveys, analysts were able to measure multiple variables and determine if the TTF model was applicable to the VAMTA system The survey s effectiveness should extend to its use in future studies of VAMTA s performance in providing smart data for preventive healthcare in a national setting Analysis of the actual end-user responses supplied during the case study con rmed that the TTF model of smart data does apply to VAMTA In the case survey responses, the VAMTA system received high ratings in perceived usefulness and perceived ease of use This suggests that the VAMTA shows promise in medical applications for providing smart data The survey responses also revealed that utility and quality
TABLE 49 Individual Contribution of the Study Variables to the Dependent Variable p>F Source Ease of use Navigation Application Operation Understandable
Degrees of Freedom 1 1 1 1 1
Sum of Squares 1577 2122 0211 0912 0965
Mean Square 1577 2122 380 0912 0965
F Value 5834 8459 459 3102 3196
0022 a 0007 a 00000 b 00000 b 0008 a 00085 a
Signi cant at p 005 Signi cant at p 0001
TODAY S POSSIBILITIES
performance are the major factors affecting the management of information by VAMTA In the future, end-users who want to improve the management of healthcare information through use of VAMTA will need to focus on utility and quality performance as measured by perceived usefulness and perceived ease of use of the VAMTA system In addition to providing ndings related to TTF and the utility and quality performance of VAMTA, the case study demonstrated ways in which the VAMTA system itself can be improved For example, additional training with the application and corrections of misrecognitions will improve the overall accuracy rate of this product The combined ndings resulting from the case study have laid the groundwork for further testing of VAMTA This additional testing is necessary to determine the system s performance in an actual medical setting and to de ne more clearly the variables affecting the TTF model when applied to delivering smart data in that setting In the case study, efforts were focused on de ning the smart data technology construct for preventive healthcare applications [4] It is our contention that applying smart data is accomplished in three dimensions beginning with healthcare executives embracing the idea to employ accounting for patient and other data as a catalyst for change and improvement in the healthcare delivery process In this case, we have followed the data through processes and arrived at outcomes that identify problems and help us to realize opportunities for improving collection of healthcare data We have also demonstrated that this data collection should be done in the most direct manner, by focusing on smart data that in turn focuses on the primary business principles This case also demonstrates that smart data has three dimensions These dimensions include optimization performance in an enterprise context, interoperability of technology, and data-aligned methods and algorithms The case also illustrates that problems and opportunities will be solved and pursued through application of improved data engineering applications, such as VAMTA, which improve data exchange strategies and technical implementations, including decision support While our case accomplished many insights, limited work was done to de ne tasks and individual characteristics of the end-users of the application To complete this work, future research should focus on de ning the task and individual characteristics constructs for the TTF model for measuring user evaluations of IT delivering smart data, in preventive healthcare Case Study: Enterprise Logistics Information Trading Exchange (ELITE) Now, we will share a case study experience that further supports some of these parameters and lessons learned We have said that smart data is a product of data engineering, so we will de ne the technical skills and position descriptions that comprise data engineering disciplines derived from our experience When starting and staf ng the ELITE program, we had to plan for work to be done and to assign labor categories to the work Work was categorized into four main activities and with subordinate activities: