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1.  Unimanual to Bimanual: Tracking the Development of Handedness from 6 to 24 Months 
Infant behavior & development  2013;36(2):181-188.
Manual skills change dramatically over the first two years of life, creating an interesting challenge for researchers studying the development of handedness. A vast body of work to date has focused on unimanual skills during the period from the onset of reaching to walking. The current study sought to connect such early unimanual hand use to later role-differentiated bimanual manipulation (RDBM), in which one hand stabilizes the object for the other hand’s action. We examined hand use in 38 children over 16 monthly visits using a validated measure for assessing hand preference for acquiring objects when children were 6 to 14 months old. We also developed a new measure for assessing RDBM preference presented when children were 18 to 24 months old. The new measure reliably elicited RDBM actions in both toddlers and an adult control group (N =15). Results revealed that some children show preferences for acquiring objects as infants; these preferences are stable and persist into their second year as new skills appear. Moreover, children with no hand preference during infancy shifted to left or right lateralized hand use as toddlers. Despite a higher incidence of left-handedness compared to adult norms, the majority of children were right-handed by 2 years of age.
doi:10.1016/j.infbeh.2013.01.009
PMCID: PMC3615031  PMID: 23454419
handedness; hand preference; infant; toddler; bimanual; manipulation
2.  Building better guidelines with BRIDGE-Wiz: development and evaluation of a software assistant to promote clarity, transparency, and implementability 
Objective
To demonstrate the feasibility of capturing the knowledge required to create guideline recommendations in a systematic, structured, manner using a software assistant. Practice guidelines constitute an important modality that can reduce the delivery of inappropriate care and support the introduction of new knowledge into clinical practice. However, many guideline recommendations are vague and underspecified, lack any linkage to supporting evidence or documentation of how they were developed, and prove to be difficult to transform into systems that influence the behavior of care providers.
Methods
The BRIDGE-Wiz application (Building Recommendations In a Developer's Guideline Editor) uses a wizard approach to address the questions: (1) under what circumstances? (2) who? (3) ought (with what level of obligation?) (4) to do what? (5) to whom? (6) how and why? Controlled natural language was applied to create and populate a template for recommendation statements.
Results
The application was used by five national panels to develop guidelines. In general, panelists agreed that the software helped to formalize a process for authoring guideline recommendations and deemed the application usable and useful.
Discussion
Use of BRIDGE-Wiz promotes clarity of recommendations by limiting verb choices, building active voice recommendations, incorporating decidability and executability checks, and limiting Boolean connectors. It enhances transparency by incorporating systematic appraisal of evidence quality, benefits, and harms. BRIDGE-Wiz promotes implementability by providing a pseudocode rule, suggesting deontic modals, and limiting the use of ‘consider’.
Conclusion
Users found that BRIDGE-Wiz facilitates the development of clear, transparent, and implementable guideline recommendations.
doi:10.1136/amiajnl-2011-000172
PMCID: PMC3240753  PMID: 21846779
Controlled natural language; controlled terminologies and vocabularies; developing/using clinical decision support (other than diagnostic) and guideline systems; developing/using wireless and in-the-field applications (mHealth); guideline development; guidelines; implementation; knowledge bases; knowledge representations; ontologies; portable; practice guidelines
3.  How “Should” We Write Guideline Recommendations? Interpretation of Deontic Terminology in Clinical Practice Guidelines: Survey of the Health Services Community 
Quality & safety in health care  2010;19(6):509-513.
Objective
To describe the level of obligation conveyed by deontic terms (words such as “ should,” “may,” “must,” and “is indicated”) commonly found in clinical practice guidelines.
Design
Cross sectional electronic survey.
Setting
Researchers developed a clinical scenario and presented participants with recommendations containing 12 deontic terms and phrases.
Participants
All 1332 registrants of the 2008 annual conference of the US Agency for Healthcare Research and Quality.
Main Outcome Measures
Participants indicated the level of obligation they believed guideline authors intended by using a slider mechanism ranging from “No obligation” (leftmost position recorded as 0) to “Full obligation” (rightmost position recorded as 100.)
Results
445/1332 registrants (36%) submitted the on-line survey. 254/445 (57%) reported they had experience developing clinical practice guidelines.133/445 (30%) indicated they provided healthcare. “Must” conveyed the highest level of obligation (median = 100) and least amount of variability (interquartile range = 5.) “May” (median = 37) and “may consider” (median = 33) conveyed the lowest levels of obligation. All other terms conveyed intermediate levels of obligation characterised by wide and overlapping interquartile ranges.
Conclusions
Members of the health services community believe guideline authors intend variable levels of obligation when using different deontic terms within practice recommendations. Ranking of a subset of terms by intended level of obligation is possible. Matching deontic terminology to intended recommendation strength can help standardise the use of deontic terminology by guideline developers.
doi:10.1136/qshc.2009.032565
PMCID: PMC2982946  PMID: 20702437
deontic; practice guidelines
4.  GEM at 10: A Decade’s Experience with the Guideline Elements Model 
The Guideline Elements Model (GEM) was developed in 2000 to organize the information contained in clinical practice guidelines using XML and to represent guideline content in a form that can be understood by human readers and processed by computers. In this work, we systematically reviewed the literature to better understand how GEM was being used, potential barriers to its use, and suggestions for improvement. Fifty external and twelve internally produced publications were identified and analyzed. GEM was used most commonly for modeling and ontology creation. Other investigators applied GEM for knowledge extraction and data mining, for clinical decision support for guideline generation. The GEM Cutter software—used to markup guidelines for translation into XML— has been downloaded 563 times since 2000. Although many investigators found GEM to be valuable, others critiqued its failure to clarify guideline semantics, difficulties in markup, and the fact that GEM files are not usually executable.
PMCID: PMC3243287  PMID: 22195106
5.  Developing a Decision Support System for Tobacco Use Counseling Using Primary Care Physicians 
Informatics in primary care  2008;16(2):101-109.
Background
Clinical decision support systems (CDSS) have the potential to improve adherence to guidelines, but only if they are designed to work in the complex environment of ambulatory clinics as otherwise physicians may not use them.
Objective
To gain input from primary care physicians in designing a CDSS for smoking cessation to ensure that the design is appropriate to a clinical environment before attempts to test this CDSS in a clinical trial. This approach is of general interest to those designing similar systems.
Design and Approach
We employed an iterative ethnographic process that used multiple evaluation methods to understand physician preferences and workflow integration. Using results from our prior survey of physicians and clinic managers, we developed a prototype CDSS, validated content and design with an expert panel, and then subjected it to usability testing by physicians, followed by iterative design changes based on their feedback. We then performed clinical testing with individual patients, and conducted field tests of the CDSS in two primary care clinics during which four physicians used it for routine patient visits.
Results
The CDSS prototype was substantially modified through these cycles of usability and clinical testing, including removing a potentially fatal design flaw. During field tests in primary care clinics, physicians incorporated the final CDSS prototype into their workflow, and used it to assist in smoking cessation interventions up to eight times daily.
Conclusions
A multi-method evaluation process utilizing primary care physicians proved useful for developing a CDSS that was acceptable to physicians and patients, and feasible to use in their clinical environment.
PMCID: PMC2706833  PMID: 18713526
Smoking cessation; Medical Informatics; Qualitative Research; Guidelines
6.  Embedding the Guideline Elements Model in Web Ontology Language 
The Guideline Elements Model (GEM) uses XML to represent the heterogeneous knowledge contained in clinical practice guidelines. GEM has important applications in computer aided guideline authoring and clinical decision support systems. However, its XML representation format could limit its potential impact, as semantic web ontology languages, such as OWL, are becoming major knowledge representation frameworks in medical informatics. In this work, we present a faithful translation of GEM from XML into OWL. This translation is intended to keep the knowledge model of GEM intact, as this knowledge model has been carefully designed and has become a recognized standard. An OWL representation would make GEM more applicable in medical informatics systems that rely on semantic web. This work will also be the initial step in making GEM a guideline recommendation ontology.
PMCID: PMC2815420  PMID: 20351934
7.  Bridging the Guideline Implementation Gap: A Systematic, Document-Centered Approach to Guideline Implementation 
Objective: A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems.
Design: This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification.
Results: The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system.
Conclusion: Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge.
doi:10.1197/jamia.M1444
PMCID: PMC516249  PMID: 15187061
8.  The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation 
Background
Clinical practice guidelines are not uniformly successful in influencing clinicians' behaviour toward best practices. Implementability refers to a set of characteristics that predict ease of (and obstacles to) guideline implementation. Our objective is to develop and validate a tool for appraisal of implementability of clinical guidelines.
Methods
Indicators of implementability were identified from the literature and used to create items and dimensions of the GuideLine Implementability Appraisal (GLIA). GLIA consists of 31 items, arranged into 10 dimensions. Questions from 9 of the 10 dimensions are applied individually to each recommendation of the guideline. Decidability and Executability are critical dimensions. Other dimensions are Global, Presentation and Formatting, Measurable Outcomes, Apparent Validity, Flexibility, Effect on Process of Care, Novelty/Innovation, and Computability. We conducted a series of validation activities, including validation of the construct of implementability, expert review of content for clarity, relevance, and comprehensiveness, and assessment of construct validity of the instrument. Finally, GLIA was applied to a draft guideline under development by national professional societies.
Results
Evidence of content validity and preliminary support for construct validity were obtained. The GLIA proved to be useful in identifying barriers to implementation in the draft guideline and the guideline was revised accordingly.
Conclusion
GLIA may be useful to guideline developers who can apply the results to remedy defects in their guidelines. Likewise, guideline implementers may use GLIA to select implementable recommendations and to devise implementation strategies that address identified barriers. By aiding the design and operationalization of highly implementable guidelines, our goal is that application of GLIA may help to improve health outcomes, but further evaluation will be required to support this potential benefit.
doi:10.1186/1472-6947-5-23
PMCID: PMC1190181  PMID: 16048653
9.  A Wireless, Handheld Decision Support System To Promote Smoking Cessation in Primary Care 
Tobacco use remains a relatively unaddressed cause of disease and death in the daily care of patients by physicians. To overcome the barriers that physicians face in addressing tobacco use and its treatment in the primary care setting, we have developed a clinical decision support system that is readily accessible through the use of familiar wireless handheld devices and supportive of treatment through the implementation of the Tobacco Use and Dependence Treatment Guideline recommendations. We adopted the Information Management Services model to ensure that the application would effectively implement the guideline. The techniques used here are readily adaptable to implementing a broad range of clinical guidelines.
PMCID: PMC1560449  PMID: 16779096
10.  Comprehensive Categorization of Guideline Recommendations: Creating an Action Palette for Implementers 
Transforming guideline recommendations into executable statements for computerized decision support systems requires a clear understanding of what tasks must be performed. We sought (a) to determine whether a limited set of action types could be defined to comprehensively categorize activities recommended by the majority of clinical guidelines, (b) to describe the relative frequency of these action types, and (c) to create a library of recommendations for future validation activities. We randomly selected test and validation sets of 50 recommendations each from the National Guideline Clearinghouse and randomly extracted 3 recommendations from each guideline. We tested the ability of a preliminary palette of action types to categorize guideline-prescribed activities and expanded it to accommodate several unanticipated actions. Ultimately, the following actions were sufficient to categorize all 405 actions: Prescribe, Perform therapeutic procedure, Educate/Counsel, Test, Dispose, Refer/Consult, Conclude, Monitor, Document, Advocate, Prepare, and No recommendation. These action types can be used to construct a framework for design of clinical decision support systems.
PMCID: PMC1480124  PMID: 14728166

Results 1-10 (10)