Decision Support for Patients

Medical Care Research and Review (Impact Factor: 2.62). 11/2012; 70(1 Suppl). DOI: 10.1177/1077558712461182
Source: PubMed


Providing a patient with decision support involves helping that person to choose among two or more elective health care options. "Values Clarification" and "Preference Elicitation" are integral to the full decision-support process. During values clarification, the patient and clinician gain insight into the importance that the patient ascribes to the options' positive and negative characteristics. During preference elicitation, the patient identifies which options are, overall, personally most favored (and, by corollary, which are least favored). This article identifies the roles that values clarification/preference elicitation (VC/PE) play in the full process of patients' decision support, outlines various approaches to fostering VC/PE, and poses some fundamental and applied research questions about VC/PE. It also argues that, in order to proceed to answer the posed research questions, investigators in the field of patients' decision support require a systematic set of criteria for comparing the performance of different VC/PE techniques.

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Available from: Hilary A Llewellyn-Thomas, Jun 25, 2015
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    • "There are no standard criteria for studying values concordance and a recent Cochrane review [1] shows that there is substantial heterogeneity among the measures that authors have used to date. We agree with the growing number of researchers calling for further study into the “active ingredients” of values clarification [64] and the creation of standard measures for analyzing values congruence [62,65]. Such research will assist us in identifying what proportion of people make values congruent decisions when they use DCIDA in comparison to conventional tools. "
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    ABSTRACT: Background Patient decision aids (PtDA) are developed to facilitate informed, value-based decisions about health. Research suggests that even when informed with necessary evidence and information, cognitive errors can prevent patients from choosing the option that is most congruent with their own values. We sought to utilize principles of behavioural economics to develop a computer application that presents information from conventional decision aids in a way that reduces these errors, subsequently promoting higher quality decisions. Method The Dynamic Computer Interactive Decision Application (DCIDA) was developed to target four common errors that can impede quality decision making with PtDAs: unstable values, order effects, overweighting of rare events, and information overload. Healthy volunteers were recruited to an interview to use three PtDAs converted to the DCIDA on a computer equipped with an eye tracker. Participants were first used a conventional PtDA, and then subsequently used the DCIDA version. User testing was assessed based on whether respondents found the software both usable: evaluated using a) eye-tracking, b) the system usability scale, and c) user verbal responses from a ‘think aloud’ protocol; and useful: evaluated using a) eye-tracking, b) whether preferences for options were changed, and c) and the decisional conflict scale. Results Of the 20 participants recruited to the study, 11 were male (55%), the mean age was 35, 18 had at least a high school education (90%), and 8 (40%) had a college or university degree. Eye-tracking results, alongside a mean system usability scale score of 73 (range 68–85), indicated a reasonable degree of usability for the DCIDA. The think aloud study suggested areas for further improvement. The DCIDA also appeared to be useful to participants wherein subjects focused more on the features of the decision that were most important to them (21% increase in time spent focusing on the most important feature). Seven subjects (25%) changed their preferred option when using DCIDA. Conclusion Preliminary results suggest that DCIDA has potential to improve the quality of patient decision-making. Next steps include larger studies to test individual components of DCIDA and feasibility testing with patients making real decisions.
    BMC Medical Informatics and Decision Making 08/2014; 14(1):62. DOI:10.1186/1472-6947-14-62 · 1.83 Impact Factor
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    • "Consensus recommendations have indicated that decision aids should include some method to help patientsa consider how they value key aspects of the decision with which they are faced [1]. These recommendations are based on the belief that, by clarifying individuals’ values, the medical treatments that people actually receive will be more reflective of their personal preferences and treatment goals [2,3]. Whether such recommendations have strong theoretical and empirical justification remains controversial [4,5]. "
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    ABSTRACT: Background Consensus guidelines have recommended that decision aids include a process for helping patients clarify their values. We sought to examine the theoretical and empirical evidence related to the use of values clarification methods in patient decision aids. Methods Building on the International Patient Decision Aid Standards (IPDAS) Collaboration’s 2005 review of values clarification methods in decision aids, we convened a multi-disciplinary expert group to examine key definitions, decision-making process theories, and empirical evidence about the effects of values clarification methods in decision aids. To summarize the current state of theory and evidence about the role of values clarification methods in decision aids, we undertook a process of evidence review and summary. Results Values clarification methods (VCMs) are best defined as methods to help patients think about the desirability of options or attributes of options within a specific decision context, in order to identify which option he/she prefers. Several decision making process theories were identified that can inform the design of values clarification methods, but no single “best” practice for how such methods should be constructed was determined. Our evidence review found that existing VCMs were used for a variety of different decisions, rarely referenced underlying theory for their design, but generally were well described in regard to their development process. Listing the pros and cons of a decision was the most common method used. The 13 trials that compared decision support with or without VCMs reached mixed results: some found that VCMs improved some decision-making processes, while others found no effect. Conclusions Values clarification methods may improve decision-making processes and potentially more distal outcomes. However, the small number of evaluations of VCMs and, where evaluations exist, the heterogeneity in outcome measures makes it difficult to determine their overall effectiveness or the specific characteristics that increase effectiveness.
    BMC Medical Informatics and Decision Making 11/2013; 13(Suppl 2-Suppl 2):S8-. DOI:10.1186/1472-6947-13-S2-S8 · 1.83 Impact Factor
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    ABSTRACT: Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 × k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.
    BMC Medical Informatics and Decision Making 04/2013; 13 Suppl 1(Suppl 1):S8. DOI:10.1186/1472-6947-13-S1-S8 · 1.83 Impact Factor
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