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1.  Nocturia, Sleep and Daytime Function in Stable Heart Failure 
Journal of Cardiac Failure  2012;18(7):569-575.
To evaluate nocturia severity and nocturia-related differences in sleep, daytime symptoms and functional performance among patients with stable heart failure (HF).
Method & Results
In this cross-sectional observational study we recruited 173 patients [M age = 60.3 ±16.8 years; n = 60 (35%) female; left ventricular ejection fraction M = 32 ±14.6] with stable chronic HF from HF disease management programs in the Northeastern United States. Participants reported nocturia and completed a Six Minute Walk test (6 MWT), one night of ambulatory polysomnography (PSG), and the Medical Outcomes Study SF 36, Epworth Sleepiness, Pittsburgh Sleep Quality Index, Multi-Dimensional Assessment of Fatigue, and the Centers for the Epidemiological Studies of Depression scales. Participants reported no (n = 30/17.3%), one or more (n = 87/50.2%), and three or more (n = 56/32.4%) nightly episodes of nocturia. There were decreases in sleep duration and efficiency, stages REM and 3–4 sleep, physical function, and 6 MWT distance; and increases in the percent wake after sleep onset, insomnia symptoms, fatigue and sleepiness across levels of nocturia severity.
Nocturia is common, severe, and closely associated with decrements in sleep and functional performance and increases in fatigue and sleepiness in patients with stable HF.
PMCID: PMC3389347  PMID: 22748491
heart failure; insomnia; nocturia; sleep; fatigue; physical function; quality of life
2.  Pharmacogenomics in the Curricula of Colleges and Schools of Pharmacy in the United States 
To assess the breadth, depth, and perceived importance of pharmacogenomics instruction and level of faculty development in this area in schools and colleges of pharmacy in the United States.
A questionnaire used and published previously was further developed and sent to individuals at all US schools and colleges of pharmacy. Multiple approaches were used to enhance response.
Seventy-five (83.3%) questionnaires were returned. Sixty-nine colleges (89.3%) included pharmacogenomics in their PharmD curriculum compared to 16 (39.0%) as reported in a 2005 study. Topic coverage was <10 hours for 28 (40.6%), 10-30 hours for 29 (42.0%), and 31-60 hours for 10 (14.5%) colleges and schools of pharmacy. Fewer than half (46.7%) were planning to increase course work over the next 3 years and 54.7% had no plans for faculty development related to pharmacogenomics.
Most US colleges of pharmacy include pharmacogenomics content in their curriculum, however, the depth may be limited. The majority did not have plans for faculty development in the area of pharmacogenomic content expertise.
PMCID: PMC2829155  PMID: 20221358
pharmacogenomics education; pharmacogenetics education; curriculum
3.  A draft framework for measuring progress towards the development of a national health information infrastructure 
American public policy makers recently established the goal of providing the majority of Americans with electronic health records by 2014. This will require a National Health Information Infrastructure (NHII) that is far more complete than the one that is currently in its formative stage of development. We describe a conceptual framework to help measure progress toward that goal.
The NHII comprises a set of clusters, such as Regional Health Information Organizations (RHIOs), which, in turn, are composed of smaller clusters and nodes such as private physician practices, individual hospitals, and large academic medical centers. We assess progress in terms of the availability and use of information and communications technology and the resulting effectiveness of these implementations. These three attributes can be studied in a phased approach because the system must be available before it can be used, and it must be used to have an effect. As the NHII expands, it can become a tool for evaluating itself.
The NHII has the potential to transform health care in America – improving health care quality, reducing health care costs, preventing medical errors, improving administrative efficiencies, reducing paperwork, and increasing access to affordable health care. While the President has set an ambitious goal of assuring that most Americans have electronic health records within the next 10 years, a significant question remains "How will we know if we are making progress toward that goal?" Using the definitions for "nodes" and "clusters" developed in this article along with the resulting measurement framework, we believe that we can begin a discussion that will enable us to define and then begin making the kinds of measurements necessary to answer this important question.
PMCID: PMC1177954  PMID: 15953388
4.  Developing and Testing a Model to Predict Outcomes of Organizational Change 
Health Services Research  2003;38(2):751-776.
To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects.
Data Sources
Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation.
A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success.
Data Collection
For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes.
Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84.
A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted.
PMCID: PMC1360903  PMID: 12785571
Organizational change; Bayesian model; improvement; empirical evaluation

Results 1-4 (4)