Table 4.1 | Barriers to Implementing Electronic Databases |
Table 4.2 | Health Information Privacy Principles |
Table 5.1 | Tan's Health Information Processing System and CDC-Provided Information Technology Specifications for Emergency and Bioterrorism Preparedness |
Table 6.1 | Nominal Bandwidth Requirements for Different Telemedicine Applications |
Table 6.2 | Nominal File Sizes of Common Medical Images |
Table 7.1 | E-Rehabilitation Applications at Integris Jim Thorpe Rehabilitation Center |
Table 7.2 | Clinical E-Rehabilitation Encounters at Integris Jim Thorpe Rehabilitation Center |
Table 8.1 | Major Socioeconomic and Health Indicators of Ethiopia and Sub-Saharan Africa |
Table 8.2 | Information and Communication Technology for Health in Ethiopia |
Table 10.1 | Decision Outcomes Included in Diagnosis Support System |
Table 10.2 | Question Items Included in Diagnosis Support System |
Table 10.3 | Evaluation Framework for an E-Diagnosis Support System |
Table 10.4 | Comparative Analysis of Performance: E-Diagnosis Support System Versus Human Experts |
Table 10.5 | Summary of Clinical Efficacy Evaluation Results for E-Diagnosis Support System |
Table 11.1 | Types of Attributes After Their Ascent in the Concept Hierarchy |
Table 11.2 | Attributes of Health Care Providers |
Table 11.3 | Results of Clustering Health Care Providers |
Table 11.4 | Comparison of the Clustering Results of Original and Optimal Concept Spaces |
Table 11.5 | Average and Standard Deviation of Credibility Scores |
Table 12.1 | E-Health Business Structures |
Table 12.2 | Opportunities for E-Stakeholders |
Table 12.3 | Challenges for E-Stakeholders |
Table 12.4 | E-Health Value Propositions |
Table 13.1 | Critical Attributes for Health Care Technology Management from Content Analysis |
Table 13.2 | Revised Model of Indicators |
Table 13.3 | Gap Score Differences Between Cluster 1 and Cluster 2 |
Table 13.4 | Indicator Distribution |
Table 14.1 | Sample Data for Hospital |
Table 14.2 | Sample Data for Treatment |
Table 14.3 | Sample Data for Pharmacy |
Table 14.4 | Sample Data for Medicare |
Table 14.5 | An Integrated View of Hospital, Treatment, Pharmacy, and Medicare |
Table 15.1 | E-Technologies in Various Sectors of the Health Care Industry |
Figure 1.1 | Electronic Data Interchange Technology |
Figure 1.2 | Wisconsin Health Information Network (WHIN) |
Figure 2.1 | E-Health Care Systems and Subsystems |
Figure 3.1 | A Rich Picture of an E-Multicommunity Health Promotion Project |
Figure 4.1 | Conceptualization of Electronic Health Records |
Figure 5.1 | Model of E-Public Health Information System |
Figure 5.2 | The Convergence of Geographical Information Systems, Public Health Preparedness and Response, and Epidemiological Surveillance |
Figure 5.3 | An Integrated GIS-Based Public Health Preparedness System |
Figure 6.1 | E-Networking for E-Consumer Informatics |
Figure 6.2 | Comparison of Three E-Network Configurations |
Figure 8.1 | E-Medicine as an Information Clearinghouse |
Figure 8.2 | Conceptual Framework of Issues in Telemedicine Transfer in Ethiopia |
Figure 8.3 | Effects of National ICT Policies on E-Medicine Transfer Outcomes |
Figure 8.4 | Effects of ICT Infrastructure on E-Medicine Transfer Outcomes |
Figure 8.5 | Effects of E-Medicine Implementation Factors on E-Medicine Transfer Outcomes |
Figure 8.6 | Effects of Culture on E-Medicine Transfer Outcomes |
Figure 10.1 | SOAP: A Common Service Process for Managing Patients with Lower Back Pain |
Figure 10.2 | Architecture of a Web-Based Diagnosis Support System |
Figure 10.3 | A Diagnostic Interface That Uses Nonmedical Terms and Graphs to Represent Symptoms |
Figure 11.1 | Examples of Concept Hierarchies at a University |
Figure 11.2 | One Possible Scenario After the Ascent of a Nominal Attribute (Cluster B Moves Toward Cluster A) |
Figure 11.3 | Another Possible Scenario After the Ascent of a Nominal Attribute (Cluster A Shrinks) |
Figure 11.4 | Three Possible Scenarios After the Ascent of an Interval or Ordinal Attribute |
Figure 11.5 | Constructive Clustering Analysis Algorithm |
Figure 11.6 | Concept Hierarchies for Clustering Health Care Providers |
Figure 12.1 | Stakeholders in the E-Health Marketplace |
Figure 13.1 | Technology Management Framework |
Figure 14.1 | Architecture of E-Health Data Integration |
Figure 14.2 | Sample Schema for E-Health Data Integration |
Figure 16.1 | Traditional Process for Analysis of Users' Information Requirements |
Figure 16.2 | Accountability Expectations Framework for E-Health Innovations |
Exhibit 1.1 | Video Image of a Baby as Transmitted Through the Telebaby Video Circuit |
Exhibit 1.2 | Telebaby Login Screen |
Exhibit 1.3 | Telebaby Camera Overview Menu |
Exhibit 1.4 | Parents at Home, with On-Line Telebaby Connection |
Exhibit 1.5 | Duration and Frequency of Telebaby Use |
Exhibit 3.1 | Precede-Proceed Model Applied to E-Health Intervention |
Exhibit 5.1 | Indexes for Community Health Profile |
Exhibit 5.2 | Community Health Profile |
Exhibit 6.1 | Mobile Bluetooth Telemedicine System |
Exhibit 6.2 | Data Flow in Unconfined Mobile Bluetooth Nursing System |
Exhibit 6.3 | Security Architecture for Bluetooth Local Area Network Access |
Exhibit 6.4 | Using a Covert Channel to Transfer Medical Images |
Exhibit 8.1 | Major E-Medicine Programs in Taiwan |
Exhibit 11.1 | Performance of Incremental Neural Networks in Six Experimental Groups |
Exhibit 11.2 | Incremental Ratio and Time Reduction Ratio of the Tested Classes |
Exhibit 12.1 | Information Systems and Health Care Research Works |
Exhibit 13.1 | The Importance of Dichotomous Measures of Effect |
Exhibit 13.2 | Chapter Introduction to Clinical Evidence |
Exhibit 14.1 | Processing at the Transmission End (top) and the Receiving End (bottom), Using the I-SEE Framework |
Exhibit 14.2 | Rate and Distortion Curves for Images, by Number of Consecutive LSBs Selected for Encoding |
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