[show abstract][hide abstract] ABSTRACT: Objective: Computerized clinical reminder (CCR) systems can improve preventive service delivery by providing patient-specific reminders at the point of care. However, adherence varies between individ-ual CCRs and is correlated to resolution time amongst other factors. This study aimed to evaluate how a proposed CCR redesign providing information explaining why the CCRs occurred would impact pro-viders' prioritization of individual CCRs. Design: Two CCR designs were prototyped to represent the original and the new design, respectively. The new CCR design incorporated a knowledge-based risk factor repository, a prioritization mecha-nism, and a role-based filter. Sixteen physicians participated in a controlled experiment to compare the use of the original and the new CCR systems. The subjects individually simulated a scenario-based pa-tient encounter, followed by a semi-structured interview and survey. Measurements: We collected and analyzed the order in which the CCRs were prioritized, the per-ceived usefulness of each design feature, and semi-structured interview data. Results: We elicited the prioritization heuristics used by the physicians, and found a CCR system nee-ded to be relevant, easy to resolve, and integrated with workflow. The redesign impacted 80% of phy-sicians and 44% of prioritization decisions. Decisions were no longer correlated to resolution time gi-ven the new design. The proposed design features were rated useful or very useful. Conclusion: This study demonstrated that the redesign of a CCR system using a knowledge-based risk factor repository, a prioritization mechanism, and a role-based filter can impact clinicians' decision making. These features are expected to ultimately improve the quality of care and patient safety.
[show abstract][hide abstract] ABSTRACT: A computerized clinical reminder (CCR) system is a type of decision support tool to remind healthcare providers of recommended
actions. In our prior study, we found a linear correlation between resolution time and adherence rate. This correlation implies
a potentially biased clinical decision making. This study aimed to redesign the Veterans Affairs (VA) CCR system in order
to improve providers’ situation awareness and decision quality. The CCR redesign incorporated a knowledge-based risk factor
repository and a prioritization mechanism. Both CCR designs were prototyped and tested by 16 physicians in a controlled lab
in the Indianapolis VA Medical Center. The results showed that 80% of the subjects changed their prioritization decisions
after being introduced to the modified design. Moreover, with the modified design, the correlation between resolution time
and adherence rate was no longer found. The redesign improved the subjects’ situation awareness and assisted them in making
more informed decisions.
Digital Human Modeling, Second International Conference, ICDHM 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings; 01/2009
[show abstract][hide abstract] ABSTRACT: This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 04/2007; · 2.18 Impact Factor
[show abstract][hide abstract] ABSTRACT: Electronic decision support systems are an important tool for improving performance and improving quality of care. We investigated the relationship between physicians' estimated resolution times for computerized clinical reminders and adherence rates in VA outpatient settings. We surveyed 10 expert physician users to assess the resolution times of four targeted CCRs for three cases: pessimistic (worst case), expected (average), and optimistic times (best case). ANOVA test shows that physicians' adherence rates for the four CCRs differed significantly (p = 0.01). CCR adherence rate and resolution time were highly linearly correlated (R-square= 0.876 for the best case, R-square= 0.997 for the average case, and R-square= 0.670 for the worst case). This study suggested that future efforts in designing CCRs need to take resolution time into consideration during design, usability testing and implementation phases.
[show abstract][hide abstract] ABSTRACT: This study compared the accuracy of three Singular Value Decomposition (SVD) based models developed for classifying injury
narratives. Two SVD-Bayesian models and one SVD-Regression model were developed to classify bodies of free text. Injury narratives
and corresponding E-codes assigned by human experts from the 1997 and 1998 US National Health Interview Survey (NHIS) were
used on all three models. Using the E-code categories assigned by experts as the basis for comparison all methods were compared.
Further experiments showed that the performance of the equidistant Bayes model and regression model improved as more SVD vectors
were used for the input. The regression model was compared to a fuzzy Bayes model. It was concluded that all three models
are capable of learning from human experts to accurately categorize cause-of-injury codes from injury narratives, with the
regression-based model being the strongest, while all were dominated by multiple-word fuzzy Bayes model.
Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design, Symposium on Human Interface 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings Part I; 01/2007
[show abstract][hide abstract] ABSTRACT: Objective: Computerized clinical reminders (CCRs) are useful tools for alerting healthcare providers of upcoming or overdue events. Previous studies have reported that clinicians will be more likely to comply with a CCR when it is perceived to be useful. We sought to identify and measure factors potentially important in affecting clinician's perceived usefulness for CCRs. Methods: We conducted a cross-sectional survey of 261 VA EHR users from 104 different VA facilities. The survey assessed users' demographics and attitudes toward CCR. Data were analyzed using a regression tree algorithm. Cross validation was used to enhance the robustness of the findings. Perceived usefulness was predicted well by three CCR factors: (1) the perceived ease in use (2) help in delivering care more effectively, and (3) respondents' responsibility for