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DissertationDOI

Measuring the speed and efficacy of clinical decision-making when comparing two different data visualizations for medications

01 Jan 2016-

TL;DR: This chapter describes the development of the method and some of the basic principles that went into its development.
Abstract: ....................................................................................................................... viii CHAPTER 1: BACKGROUND OF THE STUDY ........................................................................
Topics: Data visualization (51%)

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Public Abstract
First Name:Andrew
Middle Name:Hargrove
Last Name:Hutson
Adviser's First Name:Suzanne
Adviser's Last Name:Boren
Co-Adviser's First Name:
Co-Adviser's Last Name:
Graduation Term:SP 2016
Department:Informatics
Degree:PhD
Title:Measuring the Speed and Efficacy of Clinical Decision-Making When Comparing Two Different Data
Visualizations for Medications
Background: The percentage of patients with polypharmacy needs is increasing among a growing patient
population. As a result, the amount of time health care professionals require to make clinical decisions
based on current and past medications is increasing. Health care professionals need methods for
increasing the speed of clinical decision making without sacrificing the quality of care. The goal of this
study is to demonstrate how modifying the data visualization for patient medication histories will change
decision making speed or efficacy.
Methods: We compared two groups across five randomized blocks. Group 1 responded to questions based
on the control data visualizations derived from an existing electronic health record. Group 2 responded to
questions based on the experimental data visualization based on a medication history developed by a team
led by Dr. Jeffrey Belden. All medical information presented to both groups is identical.
Each block represents a core clinical task associated with leveraging the medication history for a clinical
decision extrapolated from anecdotal scenarios in primary care. Block 1 asks the participant to identify
current prescriptions. Block 2 asks the participant to identify past prescriptions. Block 3 asks the participant
to identify the length of time a patient has been prescribed a specific drug. Block 4 asks the participant to
identify all new prescriptions in a given time interval. Block 5 asks the participant to identify a dosage
change for any prescription in a given time interval.
Each block holds two questions, identical in wording, differing only on the visualization presented to the
participant. The survey is configured to randomly present one question from each block to each participant.
Regardless of the question presented, we additionally track the response time for each block measured as
the last click on the survey page before the “submit” or “next” button is clicked. Participants are shown only
one question per page to increase the relevance of time tracking.
Results: Twenty-three participants enrolled in the study. A total of 112 observations were collected across
five randomized blocks. The average task time for control was 1366.3+/-10.35 and the average response
time for treatment 1773.23+/-10.4; however, the T-value was -1.313, thus the results were not statistically
significant. The average task correctness for control was 30.61% and the average task correctness for
treatment was 66.67% with a p-value of 0.000502.
Conclusions: Task correctness saw a significant increase in the probability for a correct response when
using the treatment visualization versus the control visualization. Additional research is required to
determine the effect of the treatment visualization on task time. The findings may have a significant impact
on how medication histories are presented to care provided through the electronic health record.
Citations
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Howard Margolis1Institutions (1)

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TL;DR: A controlled experiment is reported on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework, both of which integrate quantitative data with qualitative abstractions.
Abstract: In many application areas, analysts have to make sense of large volumes of multivariate time‐series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain‐specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color‐coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.

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Dissertation
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Abstract: L’objet principal de cette these concerne le Dossier Patient Informatise (DPI) dans les hopitaux, et plus particulierement les difficultes d’usage apres la phase d’implementation. Notre demarche visait a concevoir, a partir des connaissances academiques en Systeme d’Information, une methode permettant aux acteurs de terrain d’agir sur une problematique specifique. Concretement nous cherchions a savoir comment ameliorer les situations de « misfit », c’est a dire les situations dans lesquelles les professionnels de sante considerent l’usage du DPI comme inadapte a leur metier. Pour ce faire, nous avons cherche a comprendre le bilan mitige du DPI dans la litterature en utilisant les travaux d’Adler & Borys (1996) sur le caractere dual de la formalisation du travail induite par la technologie (habilitante ou coercitive). Une technologie habilitante permet de positionner un utilisateur comme source de solutions, alors qu’une technologie coercitive tend a le considerer comme source de problemes. Dans le premier manuscrit portant sur l’autonomie des professionnels de sante face au systeme d’information hospitalier, nous avons conclu sur la necessite d’une formalisation de type habilitante pour ameliorer l’usage du DPI. Dans le deuxieme manuscrit, nous avons montre que dans les exemples d’experiences negatives du Dossier Patient Informatise rapportes dans la litterature, il etait possible de mettre en evidence les differents aspects d’une formalisation coercitive. Pour argumenter sur le caractere dual de la formalisation du travail induite par le DPI, nous avons rapporte dans le troisieme manuscrit une etude empirique illustrative. Nos donnees montrent que pour un meme DPI, dans un meme hopital, il peut exister des ilots de formalisation habilitante. En sortant du seul domaine d’application de la sante, nous avons approfondi les travaux sur la conceptualisation du misfit entre processus d’affaires et Systeme d’Entreprise (SE) de Strong & Volkoff de 2010, en utilisant les concepts d’affordance et d’actualisation d’affordance utilises par les memes auteurs en 2013 et 2014. Le quatrieme manuscrit presente donc un modele original permettant d’analyser l’usage des technologies de l’information pour un processus d’affaires donne sous la forme d’une combinaison d’actualisations, de non-actualisation ou d’actualisations partielles de multiples affordances du systeme d’entreprise par de multiples acteurs. Dans une perspective realiste critique, le misfit est alors la perception par un individu ou un groupe d’individus que cette combinaison ne repond pas a l’ensemble ou a une partie des objectifs du processus de facon satisfaisante. Nous rapportons ensuite comment, dans le cadre d’une recherche-action, nous avons utilise ce modele pour ameliorer l’usage d’un DPI en post-implementation dans un hopital. L’analyse d’une situation de misfit selon notre methode permet une recombinaison, en recherchant une suite coherente d’ajustements techniques et organisationnels acceptables pour toutes les parties prenantes et dont l’agencement permet bien la disparition du misfit initial, mais aussi de l’ensemble des misfits reveles pendant l’analyse. Cette recombinaison aboutit alors a une formalisation du travail presentant toutes les caracteristiques d’une formalisation habilitante. La discussion de la these aborde les conditions du succes de la methode, le choix des situations sur lesquelles l’appliquer et les perspectives de recherche qui en decoulent.

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References
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John Sweller1Institutions (1)
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1,954 citations


Journal ArticleDOI

1,778 citations


"Measuring the speed and efficacy of..." refers background in this paper

  • ...The rapid change in our ability to store, extract, and analyze data is a direct result of our evolving information infrastructure.([6]) For example, the continuous generation of large, diverse, complex, and/or longitudinal datasets (i....

    [...]


Journal ArticleDOI
06 Feb 2002-JAMA

1,218 citations


"Measuring the speed and efficacy of..." refers background in this paper

  • ...The given environment, combined with an increasing high frequency of polypharmacy patients, lead to mismanagement and medication errors.([9-11])...

    [...]


Journal ArticleDOI
TL;DR: Physicians who use electronic health records believe such systems improve the quality of care and are generally satisfied with the systems, but as of early 2008, electronic systems had been adopted by only a small minority of U.S. physicians, who may differ from later adopters of these systems.
Abstract: BACKGROUND Electronic health records have the potential to improve the delivery of health care services. However, in the United States, physicians have been slow to adopt such systems. This study assessed physicians' adoption of outpatient electronic health records, their satisfaction with such systems, the perceived effect of the systems on the quality of care, and the perceived barriers to adoption. METHODS In late 2007 and early 2008, we conducted a national survey of 2758 physicians, which represented a response rate of 62%. Using a definition for electronic health records that was based on expert consensus, we determined the proportion of physicians who were using such records in an office setting and the relationship between adoption and the characteristics of individual physicians and their practices. RESULTS Four percent of physicians reported having an extensive, fully functional electronic-records system, and 13% reported having a basic system. In multivariate analyses, primary care physicians and those practicing in large groups, in hospitals or medical centers, and in the western region of the United States were more likely to use electronic health records. Physicians reported positive effects of these systems on several dimensions of quality of care and high levels of satisfaction. Financial barriers were viewed as having the greatest effect on decisions about the adoption of electronic health records. CONCLUSIONS Physicians who use electronic health records believe such systems improve the quality of care and are generally satisfied with the systems. However, as of early 2008, electronic systems had been adopted by only a small minority of U.S. physicians, who may differ from later adopters of these systems.

1,097 citations


"Measuring the speed and efficacy of..." refers background in this paper

  • ...Electronic Health Records (EHRs) have grown in functionality and availability over the past two decades.([1-3]) The American Recovery and Reinvestment Act of 2009 mandates all private and public health care providers must adopt and demonstrate “meaningful use” of an electronic health record in...

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