Suresh K. Bhavnani

Domain-specific search strategies for the effective retrieval of healthcare and shopping information

An increasing number of users are performing searches on the Web in unfamiliar domains such as healthcare. However, because many users lack domain-specific search knowledge, their searches are often ineffective. An important remedy is to make domain-specific search knowledge in these new domains explicit and available. Towards that goal, healthcare and online shopping experts were observed while they performed search tasks within and outside their domains of expertise. The study: (1) identified domain-specific search strategies in each domain; (2) demonstrated that such knowledge is not automatically acquired from using general-purpose search engines. These results suggest that users should benefit from Strategy Portals that provide domain-specific knowledge to perform searches in unfamiliar domains.

The strategic use of complex computer systems

Several studies show that despite experience, many users with basic command knowledge do not progress to an efficient use of complex computer applications. These studies suggest that knowledge of tasks and knowledge of tools are insufficient to lead users to become efficient. To address this problem, we argue that users also need to learn strategies in the intermediate layers of knowledge lying between tasks and tools. These strategies are (a) efficient because they exploit specific powers of computers, (b) difficult to acquire because they are suggested by neither tasks nor tools, and (c) general in nature having wide applicability. The above characteristics are first demonstrated in the context of aggregation strategies that exploit the iterative power of computers. A cognitive analysis of a real-world task reveals that even though such aggregation strategies can have large effects on task time, errors, and on the quality of the final product, they are not often used by even experienced users. We identify other strategies beyond aggregation that can be efficient and useful across computer applications and show how they were used to develop a new approach to training with promising results. We conclude by suggesting that a systematic analysis of strategies in the intermediate layers of knowledge can lead not only to more effective ways to design training but also to more principled approaches to design systems. These advances should lead users to make more efficient use of complex computer systems.

From sufficient to efficient usage: an analysis of strategic knowledge

Can good design guarantee the eflicient use of computer tools? Can experience guarantee it? We raise these questions to explore why empirical studies of real-world usage show even experienced users under-utilizing the capabilities of computer applications. By analyzing the use of everyday devices and computer applications, as well as reviewing empirical studies, we conclude that neither good design nor experience may be able to guarantee efficient usage. Efficient use requires task decomposition strategies that exploit capabilities offered by computer applications such as the ability to aggreguteobjects, and to manipulate the aggregates with powerful operators. To understand the effects that strategies can have on performance, we present results from a GOMS analysis of a CAD task. Furthermore, we identify some key aggregation strategies that appear to generalize across applications. Such strategies may provide a framework to enable users to move from a sufficient to a more ef)icient use of computer tools.

Exploring the unrealized potential of computer-aided drafting

A B S T R A C T Despite huge investments by vendors and users, CAD productivity remains disappointing. Our analysis of realworld CAD usage shows that even after many years of experience, users tend to use suboptimal strategies to perform complex CAD tasks. Additionally, some of these strategies have a marked resemblance to manual drafting techniques. Although this phenomenon has been previously reported, this paper explores explanations for its causes and persistence. We argue that the strategic knowledge to use CAD effectively is neither defined nor explicitly taught. In the absence of a well-formed strategy, users often develop a synthetic mental model of CAD containing a mixture of manual and CAD methods. As these suboptimal strategies do not necessarily prevent users from producing clean, accurate drawings, the inefficiencies tend to remain unrecognized and users have little motivation to develop better strategies. To reverse this situation we recommend that the strategic knowledge to use CAD effectively should be made explicit and provided early in training. We use our analysis to begin the process of making this strategic knowledge explicit. We conclude by discussing the ramifications of this research in training as well as in the development of future computer aids for drawing and design.

Strategy hubs: next-generation domain portals with search procedures

Current search tools on the Web, such as general-purpose search engines (e.g. Google) and domain-specific portals (e.g. MEDLINEplus), do not provide search procedures that guide users to form appropriately ordered sub-goals. The lack of such procedural knowledge often leads users searching in unfamiliar domains to retrieve incomplete information. In critical domains such as in healthcare, such ineffective searches can have dangerous consequences. To address this situation, we developed a new type of domain portal called a Strategy Hub. Strategy Hubs provide the critical search procedures and associated high-quality links that enable users to find comprehensive and accurate information. This paper describes how we collaborated with skin cancer physicians to systematically identify generalizeable search procedures to find comprehensive information about melanoma, and how these search procedures were made available through the Strategy Hub for healthcare. A pilot study suggests that this approach can improve the efficacy, efficiency, and satisfaction of even expert searchers. We conclude with insights on how to refine the design of the Strategy Hub, and how it can be used to provide search procedures across domains.

Beyond command knowledge: identifying and teaching strategic knowledge for using complex computer applications

Despite experience, many users do not make efficient use of complex computer applications. We argue that this is caused by a lack of strategic knowledge that is difficult to acquire just by knowing how to use commands. To address this problem, we present efficient and general strategies for using computer applications, and identify the components of strategic knowledge required to use them. We propose a framework for teaching strategic knowledge, and show how we implemented it in a course for freshman students. In a controlled study, we compared our approach to the traditional approach of just teaching commands. The results show that efficient and general strategies can in fact be taught to students of diverse backgrounds in a limited time without harming command knowledge. The experiment also pinpointed those strategies that can be automatically learned just from learning commands, and those that require more practice than we provided. These results are important to universities and companies that wish to foster more efficient use of complex computer applications.

The strategic use of CAD: an empirically inspired, theory-based course

The inefficient use of complex computer systems has been widelyreported. These studies show the persistence of inefficient methodsdespite many years of experience and formal training. To counteractthis phenomenon, we present the design of a new course, called theStrategic Use of CAD. The course aims at teaching studentsefficient strategies to use a computer-aided drafting systemthrough a two-pronged approach. Learning to See teaches students torecognize opportunities to use efficient strategies by studying thenature of the task, and Learning to Do teaches students toimplement the strategies. Results from a pilot experiment show thatthis approach had a positive effect on the strategic behavior ofstudents who did not exhibit knowledge of efficient strategiesbefore the class, and had no effect on the strategic behavior ofthose who did. Strategic training can thus assist users inrecognizing opportunities to use efficient strategies. We presentthe ramifications of these results on the design of training andfuture experiments.

Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations

In applications such as active learning and disease/fault diagnosis, one often encounters the problem of identifying an unknown object through a minimal number of queries. This problem has been referred to as query learning or object/entity identification. We consider three extensions of this fundamental problem that are motivated by practical considerations in real-world,time-critical identification tasks such as emergency response. First, we consider the problem where the objects are partitioned into groups, and the goal is to identify only the group to which the object belongs. Second, we address the situation where the queries are partitioned into groups, and an algorithm may suggest a group of queries to a human user, who then selects the actual query. Third, we consider the problem of object identification in the presence of persistent query noise, and relate it to group identification. To address these problems we show that a standard algorithm for object identification, known as generalized binary search, may be viewed as a generalization of Shannon-Fano coding. We then extend this result to the group-based settings, leading to new algorithms, whose performance is demonstrated through a logarithmic approximation bound, and through experiments on simulated data and a database used for toxic chemical identification.

Why Is It Difficult to Find Comprehensive Information? Implications of Information Scatter for Search and Design

The rapid development of Web sites providing extensive coverage of a topic, coupled with the development of powerful search engines (designed to help users find such Web sites), suggests that users can easily find comprehensive information about a topic. In domains such as consumer healthcare, finding comprehensive information about a topic is critical as it can improve a patient’s judgment in making healthcare decisions, and can encourage higher compliance with treatment. However, recent studies show that despite using powerful search engines, many healthcare information seekers have difficulty finding comprehensive information even for narrow healthcare topics because the relevant information is scattered across many Web sites. To date, no studies have analyzed how facts related to a search topic are distributed across relevant Web pages and Web sites. In this study, the distribution of facts related to five common healthcare topics across high-quality sites is analyzed, and the reasons underlying those distributions are explored. The analysis revealed the existence of few pages that had many facts, many pages that had few facts, and no single page or site that provided all the facts. While such a distribution conforms to other information-related phenomena, a deeper analysis revealed that the distributions were caused by a trade-off between depth and breadth, leading to the existence of general, specialized, and sparse pages. Furthermore, the results helped to make explicit the knowledge needed by searchers to find comprehensive healthcare information, and suggested the motivation to explore distribution-conscious approaches for the development of future search systems, search interfaces, Web page designs, and training.

How cytokines co-occur across asthma patients

Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.