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1.  Implementation Pearls from a New Guidebook on Improving Medication Use and Outcomes with Clinical Decision Support 
Effective clinical decision support (CDS) is essential for addressing healthcare performance improvement imperatives, but care delivery organizations (CDO) typically struggle with CDS deployment. Ensuring safe and effective medication delivery to patients is a central focus of CDO performance improvement efforts, and this article provides an overview of best-practice strategies for applying CDS to these goals. The strategies discussed are drawn from a new guidebook, co-published and co-sponsored by more than a dozen leading organizations. Developed by scores of CDS implementers and experts, the guidebook outlines key steps and success factors for applying CDS to medication management. A central thesis is that improving outcomes with CDS interventions requires that the CDS five rights be addressed successfully. That is, the interventions must deliver the right information, to the right person, in the right format, through the right channel, at the right point in workflow. This paper provides further details about these CDS five rights, and highlights other important strategies for successful CDS programs.
PMCID: PMC3316472  PMID: 19894486
Clinical decision support; medication management; performance improvement
3.  Information management to enable personalized medicine: stakeholder roles in building clinical decision support 
Background
Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies.
Discussion
Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine.
Summary
This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.
doi:10.1186/1472-6947-9-44
PMCID: PMC2763860  PMID: 19814826
4.  Grand Challenges in Clinical Decision Support v10 
Journal of biomedical informatics  2007;41(2):387-392.
There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: Improve the human-computer interface; Disseminate best practices in CDS design, development, and implementation; Summarize patient-level information; Prioritize and filter recommendations to the user; Create an architecture for sharing executable CDS modules and services; Combine recommendations for patients with co-morbidities; Prioritize CDS content development and implementation; Create internet-accessible clinical decision support repositories; Use freetext information to drive clinical decision support; Mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare.
doi:10.1016/j.jbi.2007.09.003
PMCID: PMC2660274  PMID: 18029232
5.  A Roadmap for National Action on Clinical Decision Support 
This document comprises an AMIA Board of Directors approved White Paper that presents a roadmap for national action on clinical decision support. It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder.
doi:10.1197/jamia.M2334
PMCID: PMC2213467  PMID: 17213487
6.  Patient-Care Questions that Physicians Are Unable to Answer 
Objective
To describe the characteristics of unanswered clinical questions and propose interventions that could improve the chance of finding answers.
Design
In a previous study, investigators observed primary care physicians in their offices and recorded questions that arose during patient care. Questions that were pursued by the physician, but remained unanswered, were grouped into generic types. In the present study, investigators attempted to answer these questions and developed recommendations aimed at improving the success rate of finding answers.
Measurements
Frequency of unanswered question types and recommendations to increase the chance of finding answers.
Results
In an earlier study, 48 physicians asked 1062 questions during 192 half-day office observations. Physicians could not find answers to 237 (41%) of the 585 questions they pursued. The present study grouped the unanswered questions into 19 generic types. Three types accounted for 128 (54%) of the unanswered questions: (1) “Undiagnosed finding” questions asked about the management of abnormal clinical findings, such as symptoms, signs, and test results (What is the approach to finding X?); (2) “Conditional” questions contained qualifying conditions that were appended to otherwise simple questions (What is the management of X, given Y? where “given Y” is the qualifying condition that makes the question difficult.); and (3) “Compound” questions asked about the association between two highly specific elements (Can X cause Y?). The study identified strategies to improve clinical information retrieval, listed below.
Conclusion
To improve the chance of finding answers, physicians should change their search strategies by rephrasing their questions and searching more clinically oriented resources. Authors of clinical information resources should anticipate questions that may arise in practice, and clinical information systems should provide clearer and more explicit answers.
doi:10.1197/jamia.M2398
PMCID: PMC2244897  PMID: 17460122
7.  Recommendations for Clinical Decision Support Deployment: Synthesis of a Roundtable of Medical Directors of Information Systems 
Background: Ample evidence exists that clinical decision support (CDS) can improve clinician performance. Nevertheless, additional evidence demonstrates that clinicians still do not perform adequately in many instances. This suggests an ongoing need for implementation of CDS, in turn prompting development of a roadmap for national action regarding CDS.
Objective: Develop practical advice to aid CDS implementation in order to improve clinician performance. Method: Structured group interview during a roundtable discussion by medical directors of information systems (N = 30), with subsequent review by participants and synthesis.
Results: Participant consensus was that CDS should be comprehensive and should involve techniques such as order sets and facilitated documentation as well as alerts; should be subject to ongoing feedback; and should flow from and be governed by an organization’s clinical goals.
Conclusion: A structured roundtable discussion of clinicians experienced in health information technology can yield practical, consensus advice for implementation of CDS.
PMCID: PMC2655795  PMID: 18693858
8.  Clinical Decision Support in Electronic Prescribing: Recommendations and an Action Plan 
Clinical decision support (CDS) in electronic prescribing (eRx) systems can improve the safety, quality, efficiency, and cost-effectiveness of care. However, at present, these potential benefits have not been fully realized. In this consensus white paper, we set forth recommendations and action plans in three critical domains: (1) advances in system capabilities, including basic and advanced sets of CDS interventions and knowledge, supporting database elements, operational features to improve usability and measure performance, and management and governance structures; (2) uniform standards, vocabularies, and centralized knowledge structures and services that could reduce rework by vendors and care providers, improve dissemination of well-constructed CDS interventions, promote generally applicable research in CDS methods, and accelerate the movement of new medical knowledge from research to practice; and (3) appropriate financial and legal incentives to promote adoption.
doi:10.1197/jamia.M1822
PMCID: PMC1174880  PMID: 15802474
9.  Answering Physicians' Clinical Questions: Obstacles and Potential Solutions 
Objective: To identify the most frequent obstacles preventing physicians from answering their patient-care questions and the most requested improvements to clinical information resources.
Design: Qualitative analysis of questions asked by 48 randomly selected generalist physicians during ambulatory care.
Measurements: Frequency of reported obstacles to answering patient-care questions and recommendations from physicians for improving clinical information resources.
Results: The physicians asked 1,062 questions but pursued answers to only 585 (55%). The most commonly reported obstacle to the pursuit of an answer was the physician's doubt that an answer existed (52 questions, 11%). Among pursued questions, the most common obstacle was the failure of the selected resource to provide an answer (153 questions, 26%). During audiotaped interviews, physicians made 80 recommendations for improving clinical information resources. For example, they requested comprehensive resources that answer questions likely to occur in practice with emphasis on treatment and bottom-line advice. They asked for help in locating information quickly by using lists, tables, bolded subheadings, and algorithms and by avoiding lengthy, uninterrupted prose.
Conclusion: Physicians do not seek answers to many of their questions, often suspecting a lack of usable information. When they do seek answers, they often cannot find the information they need. Clinical resource developers could use the recommendations made by practicing physicians to provide resources that are more useful for answering clinical questions.
doi:10.1197/jamia.M1608
PMCID: PMC551553  PMID: 15561792
10.  Obstacles to answering doctors' questions about patient care with evidence: qualitative study 
BMJ : British Medical Journal  2002;324(7339):710.
Objective
To describe the obstacles encountered when attempting to answer doctors' questions with evidence.
Design
Qualitative study.
Setting
General practices in Iowa.
Participants
9 academic generalist doctors, 14 family doctors, and 2 medical librarians.
Main outcome measure
A taxonomy of obstacles encountered while searching for evidence based answers to doctors' questions.
Results
59 obstacles were encountered and organised according to the five steps in asking and answering questions: recognise a gap in knowledge, formulate a question, search for relevant information, formulate an answer, and use the answer to direct patient care. Six obstacles were considered particularly salient by the investigators and practising doctors: the excessive time required to find information; difficulty modifying the original question, which was often vague and open to interpretation; difficulty selecting an optimal strategy to search for information; failure of a seemingly appropriate resource to cover the topic; uncertainty about how to know when all the relevant evidence has been found so that the search can stop; and inadequate synthesis of multiple bits of evidence into a clinically useful statement.
Conclusions
Many obstacles are encountered when asking and answering questions about how to care for patients. Addressing these obstacles could lead to better patient care by improving clinically oriented information resources.
What is already known on this topicDoctors are encouraged to search for evidence based answers to their questions about patient care but most go unansweredStudies have not defined the obstacles to answering questions in a systematic mannerA comprehensive description of such obstacles has not been presentedWhat this study addsFifty nine obstacles were found while attempting to answer clinical questions with evidence; six were particularly salientThe obstacles were comprehensively described and organised
PMCID: PMC99056  PMID: 11909789
11.  A taxonomy of generic clinical questions: classification study 
BMJ : British Medical Journal  2000;321(7258):429-432.
Objective
To develop a taxonomy of doctors' questions about patient care that could be used to help answer such questions.
Design
Use of 295 questions asked by Oregon primary care doctors to modify previously developed taxonomy of 1101 clinical questions asked by Iowa family doctors.
Setting
Primary care practices in Iowa and Oregon.
Participants
Random samples of 103 Iowa family doctors and 49 Oregon primary care doctors.
Main outcome measures
Consensus among seven investigators on a meaningful taxonomy of generic questions; interrater reliability among 11 individuals who used the taxonomy to classify a random sample of 100 questions: 50 from Iowa and 50 from Oregon.
Results
The revised taxonomy, which comprised 64 generic question types, was used to classify 1396 clinical questions. The three commonest generic types were “What is the drug of choice for condition x?” (150 questions, 11%); “What is the cause of symptom x?” (115 questions, 8%); and “What test is indicated in situation x?” (112 questions, 8%). The mean interrater reliability among 11 coders was moderate (κ=0.53, agreement 55%).
Conclusions
Clinical questions in primary care can be categorised into a limited number of generic types. A moderate degree of interrater reliability was achieved with the taxonomy developed in this study. The taxonomy may enhance our understanding of doctors' information needs and improve our ability to meet those needs.
PMCID: PMC27459  PMID: 10938054
12.  Analysis of questions asked by family physicians regarding patient care 
Western Journal of Medicine  2000;172(5):315-319.
Objectives To characterize the information needs of family physicians by collecting the questions they asked about patient care during consultations and to classify these in ways that would be useful to developers of knowledge bases. Design An observational study in which investigators visited physicians for two half-days and collected their questions. Taxonomies were developed to characterize the clinical topic and generic type of information sought for each question. Setting Eastern Iowa. Participants Random sample of 103 family physicians. Main outcome measures Number of questions posed, pursued, and answered; topic and generic type of information sought for each question; time spent pursuing answers; and information resources used. Results Participants asked a total of 1,101 questions. Questions about drug prescribing, obstetrics and gynecology, and adult infectious disease were most common, comprising 36% of the total. The taxonomy of generic questions included 69 categories; the three most common types, comprising 24% of all questions, were “What is the cause of symptom X?” “What is the dose of drug X?” and “How should I manage disease or finding X?” Answers to most questions (n = 702 [64%]) were not immediately pursued, but of those pursued, most (n = 318 [80%]) were answered. Physicians spent an average of less than 2 minutes pursuing an answer, and they used readily available print and human resources. Only two questions led to a formal literature search. Conclusions Family physicians in this study did not pursue answers to most of their questions. Questions about patient care can be organized into a limited number of generic types, which could help guide the efforts of knowledge-base developers.
PMCID: PMC1070879  PMID: 18751285
13.  Analysis of questions asked by family doctors regarding patient care 
BMJ : British Medical Journal  1999;319(7206):358-361.
Objectives
To characterise the information needs of family doctors by collecting the questions they asked about patient care during consultations and to classify these in ways that would be useful to developers of knowledge bases.
Design
Observational study in which investigators visited doctors for two half days and collected their questions. Taxonomies were developed to characterise the clinical topic and generic type of information sought for each question.
Setting
Eastern Iowa.
Participants
Random sample of 103 family doctors.
Main outcome measures
Number of questions posed, pursued, and answered; topic and generic type of information sought for each question; time spent pursuing answers; information resources used.
Results
Participants asked a total of 1101 questions. Questions about drug prescribing, obstetrics and gynaecology, and adult infectious disease were most common and comprised 36% of all questions. The taxonomy of generic questions included 69 categories; the three most common types, comprising 24% of all questions, were “What is the cause of symptom X?” “What is the dose of drug X?” and “How should I manage disease or finding X?” Answers to most questions (702, 64%) were not immediately pursued, but, of those pursued, most (318, 80%) were answered. Doctors spent an average of less than 2 minutes pursuing an answer, and they used readily available print and human resources. Only two questions led to a formal literature search.
Conclusions
Family doctors in this study did not pursue answers to most of their questions. Questions about patient care can be organised into a limited number of generic types, which could help guide the efforts of knowledge base developers.
Key messagesQuestions that doctors have about the care of their patients could help guide the content of medical information sources and medical trainingIn this study of US family doctors, participants frequently had questions about patient care but did not pursue answers to most questions (64%)On average, participants spent less than 2 minutes seeking an answer to a questionThe most common resources used to answer questions included textbooks and colleagues; formal literature searches were rarely performedThe most common generic questions were “What is the cause of symptom X?” “What is the dose of drug X?” and “How should I manage disease or finding X?”
PMCID: PMC28191  PMID: 10435959
14.  Online, Health-Related Discussion Groups 
doi:10.1046/j.1525-1497.1997.00093.x
PMCID: PMC1497151  PMID: 9276659

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