Clinical Complexity in Medicine: A Measurement Model of Task and Patient Complexity.

BACKGROUND: Complexity in medicine needs to be reduced to simple components in a way that is comprehensible to researchers and clinicians. Few studies in the current literature propose a measurement model that addresses both task and patient complexity in medicine. OBJECTIVE: The objective of this paper is to develop an integrated approach to understand and measure clinical complexity by incorporating both task and patient complexity components focusing on the infectious disease domain. The measurement model was adapted and modified for the healthcare domain. METHODS: Three clinical infectious disease teams were observed, audio-recorded and transcribed. Each team included an infectious diseases expert, one infectious diseases fellow, one physician assistant and one pharmacy resident fellow. The transcripts were parsed and the authors independently coded complexity attributes. This baseline measurement model of clinical complexity was modified in an initial set of coding processes and further validated in a consensus-based iterative process that included several meetings and email discussions by three clinical experts from diverse backgrounds from the Department of Biomedical Informatics at the University of Utah. Inter-rater reliability was calculated using Cohen's kappa. RESULTS: The proposed clinical complexity model consists of two separate components. The first is a clinical task complexity model with 13 clinical complexity-contributing factors and 7 dimensions. The second is the patient complexity model with 11 complexity-contributing factors and 5 dimensions. CONCLUSION: The measurement model for complexity encompassing both task and patient complexity will be a valuable resource for future researchers and industry to measure and understand complexity in healthcare.

Modeling end-users' acceptance of a knowledge authoring tool.

OBJECTIVES: Knowledge bases comprise a vital component in the classic medical expert system model, yet the knowledge acquisition process by which they are created has been characterized as highly iterative and labor-intensive. The difficulty of this process underscores the importance of knowledge authoring tools that satisfy the demands of end-users. The authors hypothesize that the acceptability of a knowledge authoring tool for the creation of medical knowledge base content can be predicted by an accepted model in the information technology (IT) field, specifically the Technology Acceptance Model (TAM). METHODS: An online survey was conducted amongst knowledge base authors who had previously established experience with the authoring tool software. The Likert-based questions in the survey were patterned directly after accepted TAM constructs with minor modifications to particularize them to the software being used. The results were analyzed using structural equation modeling. RESULTS: The TAM performed well in predicting endusers' behavioral intentions to use the knowledge authoring tool. Five out of seven goodness-of-fit statistics indicate that the model represents the behavioral intentions of the authors well. All but one of the hypothesized relationships specified by the TAM were significant with p values less than 0.05. CONCLUSIONS: The TAM provides an adequate means by which development teams can anticipate and better understand what aspects of a knowledge authoring tool are most important to their target audience. Further research involving other behavioral models and an expanded user base will be necessary to better understand the scope of issues that factor into acceptability.

"On-demand" access to a multi-purpose collection of best practice standards.

On-demand" information has been chosen by clinicians as one of their preferred modes of interaction with computers when in need of information about evidence-based practices. However, most of the clinicians' information needs remain unmet, especially due to a lack of easy access to resources that are able to satisfy these needs in a timely manner. We present three scenarios indicating opportunities for a clinical information system to present interdisciplinary standards at the point-of-care. In each scenario, we highlight the importance of context of use and the opportunities offered by the clinical workflow for providing access to relevant "on-demand" information. We also present an XML model for structuring non-physician interdisciplinary standards, in an effort to fulfill the requirements exposed by the three scenarios.

The knowledge authoring tool: an XML-based knowledge acquisition environment.

As part of an enterprise effort to develop new clinical information systems at Intermountain Health Care, we are developing a Knowledge Authoring Tool that facilitates the development of medical knowledge. At present, users of the application can compose order sets and other clinical knowledge documents based upon XML schemas. The flexible nature of the application allows for the authoring of new types of documents once an XML schema and accompanying web form have been developed and stored in a shared repository. The need for a knowledge acquisition tool stems largely from the desire for medical practitioners to be able to write their own content for clinical use. We hypothesize that knowledge content for clinical use can be successfully implemented around XML-based document frameworks containing structured and coded knowledge.

Comparison of two knowledge bases on the detection of drug-drug interactions.

This paper describes a drug ordering decision support system that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The architecture of the system was devised in order to facilitate its use attached to physician order entry systems. The described model focuses in issues related to knowledge base maintenance and integration with external systems. Finally, a retrospective study was performed. Two knowledge bases, developed by different academic centers, were used to detect drug-drug interactions in a dataset with 37,237 drug prescriptions. The study concludes that the proposed knowledge base architecture enables content from other knowledge sources to be easily transferred and adapted to its structure. The study also suggests a method that can be used on the evaluation and refinement of the content of drug knowledge bases.

Design, implementation and evaluation of a clinical decision support system to prevent adverse drug events.

Adverse drug events are known to be a major health problem worldwide. It is estimated that the annual costs related to these events in the United States are greater than the total costs with cardiovascular disease care. Decision support systems that assist drug ordering have demonstrated to be a powerful tool to prevent prescription errors and adverse drug events. On the other hand, some issues related to the development, implementation, configuration, and evaluation of these decision support systems still need further research. This paper presents the development and evaluation of a decision support system prototype that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The structure of the system tries to solve some of the problems described by the literature, such as integration with hospital information systems, adaptability to local needs, and knowledge base maintenance. The proposed model has shown to be an effective method for representing drug-drug interactions. The prototype was evaluated by a retrospective study using a dataset with 37.237 prescriptions. The system was able to detect 10.044 (27.0%) orders containing one or more drug-drug interactions. Among these interactions, 6.4% had high severity. In a future study, it is intended to apply the developed system in a real-time on-line environment, evaluating the benefits achieved in terms of improvement in medical practice and patient outcomes.