Vivian West

Patient Satisfaction with Telemedicine

The objective of this study was to evaluate patient satisfaction when telemedicine is used for clinical consultations. Patient satisfaction data from 495 real-time interactive telemedicine clinical consultations at the Telemedicine Center at East Carolina University School of Medicine in Greenville, NC were collected and evaluated. Patient satisfaction was examined in relation to patient age, gender, race, income, education, and insurance. Overall patient satisfaction was found to be 98.3%. Because so few patients were dissatisfied with their telemedicine consultation, correlation with the sociodemographic variables was limited. Patients are highly satisfied with consultations through telemedicine, and report that care was easier to obtain. The sample size in this study is larger than other reported telemedicine studies, but its findings are consistent with those of previous studies. In non-telemedicine settings where patient satisfaction has been studied, several significant factors have been correlated ...

Model selection for a medical diagnostic decision support system: a breast cancer detection case

There are a number of different quantitative models that can be used in a medical diagnostic decision support system (MDSS) including parametric methods (linear discriminant analysis or logistic regression), non-parametric models (K nearest neighbor, or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. Practitioners are left to either choose a favorite model or to test a small subset using cross validation methods. This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS. The topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level. These targets can then be used in model selection, variable reduction, parameter determination, and to assess the adequacy of the clinical measurement system. These ideas are applied to a successful model selection for a real-world breast cancer database. Diagnostic accuracy results are reported for individual models, for ensembles of neural networks, and for stacked predictors.

Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application

The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system based on supervised learning algorithms. This research investigates the potential of various selection strategies from a population of 24 classification models to form ensembles in order to increase the accuracy of decision support systems for the early detection and diagnosis of breast cancer. Our results suggest that ensembles formed from a diverse collection of models are generally more accurate than either pure-bagging ensembles (formed from a single model) or the selection of a ‘‘single best model.’’ We find that effective ensembles are formed from a small and selective subset of the population of available models with potential candidates identified by a multicriteria process that considers the properties of model generalization error, model instability, and the independence of model decisions relative to other ensemble members. � 2003 Elsevier B.V. All rights reserved.

Innovative information visualization of electronic health record data: a systematic review

Objective This study investigates the use of visualization techniques reported between 1996 and 2013 and evaluates innovative approaches to information visualization of electronic health record (EHR) data for knowledge discovery. Methods An electronic literature search was conducted May–July 2013 using MEDLINE and Web of Knowledge, supplemented by citation searching, gray literature searching, and reference list reviews. General search terms were used to assure a comprehensive document search. Results Beginning with 891 articles, the number of articles was reduced by eliminating 191 duplicates. A matrix was developed for categorizing all abstracts and to assist with determining those to be excluded for review. Eighteen articles were included in the final analysis. Discussion Several visualization techniques have been extensively researched. The most mature system is LifeLines and its applications as LifeLines2, EventFlow, and LifeFlow. Initially, research focused on records from a single patient and visualization of the complex data related to one patient. Since 2010, the techniques under investigation are for use with large numbers of patient records and events. Most are linear and allow interaction through scaling and zooming to resize. Color, density, and filter techniques are commonly used for visualization. Conclusions With the burgeoning increase in the amount of electronic healthcare data, the potential for knowledge discovery is significant if data are managed in innovative and effective ways. We identify challenges discovered by previous EHR visualization research, which will help researchers who seek to design and improve visualization techniques.

IMPROVING DIAGNOSTIC ACCURACY USING A HIERARCHICAL NEURAL NETWORK TO MODEL DECISION SUBTASKS

A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density, recursive partitioning, and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. This research investigates the decision accuracy of neural network models for the differential diagnosis of six erythematous-squamous diseases. Conditions where a hierarchical neural network model can increase diagnostic accuracy by partitioning the decision domain into subtasks that are easier to learn are specifically addressed. Self-organizing maps (SOM) are used to portray the 34 feature variables in a two dimensional plot that maintains topological ordering. The SOM identifies five inconsistent cases that are likely sources of error for the quantitative decision models; the lower bound for the diagnostic decision error based on five errors is 0.0140. The traditional application of the quantitative models cited above results in diagnostic error levels substantially greater than this target level. A two-stage hierarchical neural network is designed by combining a multilayer perceptron first stage and a mixture-of-experts second stage. The second stage mixture-of-experts neural network learns a subtask of the diagnostic decision, the discrimination between seborrheic dermatitis and pityriasis rosea. The diagnostic accuracy of the two stage neural network approaches the target performance established from the SOM with an error rate of 0.0159.

Organizational and Environmental Factors Affecting the Utilization of Telemedicine in Rural Home Healthcare

ABSTRACT In the last five years, home health agencies have become increasingly interested in telemedicine as a potential means to meet the future healthcare needs of their aged and chronically ill clientele. This case study examines the organizational and environmental conditions that affected the implementation of a telemedicine program in one rural home healthcare organization. Several factors restricted the utilization of telemedicine, including Medicares Prospective Payment System and corresponding documentation (Outcome Assessment and Information Set), the organization controlling grant funding for the program, and several environmental factors. Findings suggest that in rural communities, older homecare patients may have less opportunity to benefit from telemedicine. The study demonstrates the importance of environmental and organizational factors when implementing a telemedicine program. Recommendations are offered for home healthcare organizations considering development of telemedicine programs.