Group Health Research Institute (GHRI) hosts regular seminars where investigators from the Institute and our collaborating institutions present their research findings.
Upcoming scientific seminars and events
Sept. 28–30, 2014
1st Seattle Symposium on Health Care Data Analytics: Confronting statistical challenges of using health record data to conduct health research.
Hosted by GHRI and the University of Washington Department of Biostatistics.
Past seminars and events
Aug. 18, 2014
“Toward a learning healthcare delivery system: leveraging implementation research and delivery system science to improve performance.”
Brian S. Mittman, PhD, Senior Scientist, VA Center for Implementation Practice and Research Support, U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System and Senior Scientist, Department of Research and Evaluation, Kaiser Permanente Southern California
Abstract: This presentation describes the goals and attributes of a “learning healthcare delivery system” and contrasts these with prevailing integrated delivery systems’ characteristics and performance. It then describes tools and resources from the fields of implementation science, delivery system science and improvement science—and summarizes models for research-operations partnership initiatives to facilitate application of these tools to delivery system reform and improvement. It targets researchers, operations leaders and other stakeholders interested in leveraging research insights and approaches to achieve the vision of a learning healthcare delivery system.
Tues., June 10, 2014
“On the front lines of a paradigm shift: The value of qualitative perspectives for the emerging field of Patient Centered Outcomes Research (PCOR).”
Clarissa Hsu, PhD, Research Associate/Research Program Manager, Center for Community Health and Evaluation (CCHE), Group Health Research Institute
Abstract: Patient centered care and patient centered research has received increasing attention as a result of the Affordable Care Act’s (ACA) focus on the Triple Aim of improving the quality of patient care, advancing population-based health and cost containment. By prescribing the formation of the Patient Centered Outcomes Research Institute (PCORI) and related funds for dissemination of PCOR results, the ACA is supporting the emergence of PCOR as a new approach to health care research. PCOR asks both researchers and those delivering care to fundamentally rethink their assumptions and values regarding what they know and the methods used to gather evidence to inform future innovations aimed at improving health and health care.
Qualitative methods use open-ended inquiry and observation to learn about lived experiences and how people make sense and derive meaning from those experiences. As such, these methods offer unique perspectives and approaches that are well suited to gaining new insights about what matters to patients and how patients and clinical staff experience health care processes.
This presentation will explore the different ways that qualitative work can inform both the way we conceptualize PCOR and insights that are generated in this emerging field. Dr. Hsu will highlight several projects she has led or worked on that have an explicit focus on PCOR and/or patient perspectives. Her goal is to provide concrete examples of how our work at GHRI is intersecting with the field of PCOR and launch a dialog around how researchers at GHRI can continue to be leaders and innovators in this exciting new area of research.
Thurs., May 8, 2014
15th Annual Hilde and Bill Birnbaum Endowed Lecture and Panel Discussion
“Affordable health care for all: How will our nation and region deliver on the promise?”
Thurs., April 17–Fri. April 18, 2014
2014 Latino Health Conference
Mon., Feb. 3, 2014
“Development and evaluation of prognostic models in chronic heart failure.”
Benjamin French, PhD, Assistant Professor, Biostatistics and Epidemiology, University of Pennsylvania
Abstract: Recent clinical research regarding chronic heart failure has focused on identifying prognostic models to predict future morbidity and mortality. Accurate models could be used to counsel patients more effectively and to guide personalized treatment strategies over time. Development of a prognostic model typically requires specification of an appropriate statistical model and is most frequently achieved using standard regression methods such as Cox regression. The prognostic model can be evaluated using time-dependent receiver operating characteristic methods, or risk reclassification methods adapted for censored survival outcomes. In this seminar, Dr. French will
- illustrate the application of these methods to derive a multi-biomarker risk score and evaluate its prediction accuracy for terminal events in chronic heart failure: death, cardiac transplantation, and placement of a ventricular assist device.
- compare the performance of alternative model accuracy methods using simulations, both to evaluate power and to quantify the potential loss in accuracy associated with use of a sub-optimal regression model to derive the multi-marker score.
- discuss ongoing research directions, including the development of new methods to evaluate prediction accuracy for recurrent events (e.g., hospitalization) and their linked outcomes (e.g., total cost and length of hospital stay).
Tues., Dec. 10, 2013
“Consistent estimation of covariate effects for some between-/within-cluster covariate decomposition methods when data are missing at random.”
John Neuhaus, PhD, Professor of Biostatistics, Division of Biostatistics, University of California, San Francisco
Abstract: Investigators often gather longitudinal data to assess changes in responses over time within subjects and to relate these changes to within-subject changes in predictors. Missing data are common in such studies and predictors can be correlated with subject-specific effects. Maximum likelihood methods for generalized linear mixed models provide consistent estimates when the data are “missing at random” (MAR) but can produce inconsistent estimates in settings where the random effects are correlated with one of the predictors. On the other hand, conditional maximum likelihood methods (and closely related maximum likelihood methods that partition covariates into between- and within-cluster components) provide consistent estimation when random effects are correlated with predictors but can produce inconsistent covariate effect estimates when data are MAR. Using theory, simulation studies, and fits to example data this talk shows that decomposition methods using complete covariate information produce consistent estimates. In some practical cases these methods, that ostensibly require complete covariate information, actually only involve the observed covariates. These results offer an easy-to-use approach to simultaneously protect against bias from either cluster-level confounding or MAR missingness in assessments of change.
Tues., Nov. 12, 2013
“Improving diagnosis and monitoring in primary care: new technologies, new methods.”
Matthew J. Thompson, MBChB, MPH, DPhil, MRCGP,
Helen D. Cohen Endowed Professorship in Family Medicine, Vice Chair for Research, Department of Family Medicine, University of Washington
Abstract: Diagnostic research has been an under researched area of primary care practice and is emerging from a focus on diagnostic accuracy to consider wider aspects and implications of testing. Meanwhile, many of the technological advances in diagnostics (speed, scope, size, availability, cost) have largely bypassed primary care in most countries. Dr. Thompson will be presenting results from a portfolio of research conducted in the UK at the Centre for Monitoring and Diagnosis in Oxford, which aims to improve the accuracy and efficiency of diagnosis and monitoring in primary care settings. Thompson plans to cover three main areas
- New diagnostic technologies in primary care—what to learn from prioritizing, assessing evidence, and conducting field studies across multiple clinical areas—where are the gaps in the ‘bench to bedside/clinic’ pathways, and why does the diagnostics industry need our help?
- Monitoring of chronic disease has emerged as a major component of workload in primary care, yet evidence on frequency and actions based on monitoring have not been evidence based. How can applying statistical processes to monitoring improve efficiency of care?
- An update on some new methodological issues in diagnostic research—including diagnostic systematic reviews, handling intermediate test results, and communicating diagnostic accuracy.
Tues., October 22, 2013
“Will the true BP (blood pressure) please stand up?”
Beverly B. Green, MD, MPH, Associate Investigator, GHRI and Family Physician, Group Health
Andrea Cook, PhD, Associate Investigator, GHRI
Melissa Anderson, MS, Biostatistician, GHRI
Abstract: The Electronic Communications and Home Blood Pressure Monitoring Trial (e-BP), four-year results, and differences in BP outcomes based on electronic health record data and research measurements.
The e-BP Comparative Effectiveness Trial compared home BP monitoring, this plus pharmacist team care delivered via secure e-mail to usual care and results were published in JAMA. The study was chosen by the British Medical Journal as a finalist for 1 of the 10 most important studies of 2008. We will discuss long-term results of the trial, methodological questions, and result implications.
Tues., October 8, 2013
“Impact of mandatory behavioral screening for children insured by Medicaid in Massachusetts.”
Robert Penfold, PhD, Assistant Investigator, GHRI
Abstract: In Massachusetts, as part of a settlement of a class action suit, Rosie D vs. Patrick, the state mandated behavioral health (BH) screening in primary care for all children up to 21 years of age who were covered by MassHealth (Medicaid). Clinicians were required to conduct behavioral health screening with validated tools at well-child visits. Use of a screen was to be reported using Current Procedural Terminology (CPT) code 96110 in billing claims for the visit, and providers were to receive payment for the procedure. While the recommendation was to screen at well-child visits, consistent with EPSDT regulations, screens were also reimbursed if used at non-well child visits.
We investigated the impact of the mandate on rates of newly identified behavioral health problems and rates of subsequent behavioral health utilization using logistic regression and interrupted time series analyses. Among continuously enrolled children with evidence of screening, 43 percent with positive screens had no BH history. This ‘newly identified’ group was more likely to be female, younger, minority, and from rural residences Factors predicting positive modifiers included, gender (male), age (older), foster care, BH history, and Hispanic ethnicity.
The mandate was also associated with dramatic increases in the rate of non-physician behavioral health care utilization. However, we observed no measurable impact on the rate of specialty mental health care use or use of psychotropic medications. There was also some indication that Emergency Department visits with a coded mental health diagnosis increased in the post-mandate period.
Tues., September 24, 2013
“Marginal structural modeling to estimate the effect of long-term physical activity on cardiovascular disease and mortality.”
Susan Shortreed, PhD, Assistant Investigator, Biostatistics Unit, GHRI
Abstract: The majority of studies estimating the effect of physical activity on cardiovascular disease (CVD) and mortality assess physical activity at a single time point. The impact of long-term physical activity is likely to differ. In this talk, Dr. Shortreed will
- review marginal structural models, which are used to estimate cumulative exposure effects from longitudinal data. This review will include a discussion of propensity scores.
- overview the differences between marginal structural models and traditional adjusted time-varying outcome models.
- describe and report the results of an analysis using data collected from the Framingham Heart Study. This analysis considered physical activity measured at three time points and used marginal structural modeling to estimate the effect of long-term physical activity on incident CVD, all-cause mortality and CVD-attributable mortality.
Weds., August 14, 2013
“The development, implementation, and evaluation of the Keele STarT BACK Tool for stratifying back pain patients in terms of clinical complexity.”
Nadine Foster, DPhil, Jonathan Hill, PhD, and Gail Sowden, MSc, Keele University, England
Abstract: Come hear about the creation, implementation, and evaluation of a paradigm-shifting approach to primary care for back pain developed by researchers at Keele University in England. Rather than focusing primarily on the degree of acuity/chronicity of back problems, the Keele model gives greater emphasis to consideration of the psycho-social factors that have consistently been found more predictive of patient outcomes than have clinical measures. In essence, this tool uses patient responses to nine simple questions to determine the level of each patient’s complexity and then uses this information to select a treatment option that is most appropriate for that complexity level. This approach has been found to improve patient outcomes and decrease costs of care in National Health Service general practices. Group Health is preparing to conduct the first evaluation of this risk stratification approach in a U.S. health care system.
Tues., July 23, 2013
“Individualizing prevention in older adults.”
Sei Lee, MD, MAS, Assistant Professor, Geriatrics, Department of Medicine, University of California, San Francisco and Physician, Division of Geriatrics, San Francisco VA Medical Center
Abstract: Preventive interventions, by definition, seek to prevent adverse events in the future. However, preventive interventions often impose risks on patients immediately. The time between the risks of preventive interventions (usually immediate) and the benefits is the “lagtime to benefit” and can be many years. For patients with limited life expectancy, preventive interventions may expose them to the risks, with little chance they would benefit. We will examine how to determine mortality risk in multimorbid older adults and quantify the lagtime to benefit for common preventive interventions to identify which patients are most appropriate for specific preventive interventions.
Tues., July 9, 2013
“Primary care transformation: Lessons from the Safety Net Medical Home Initiative.”
Katie Coleman, MSPH, Research Associate, MacColl Center for Healthcare Innovation, GHRI
Abstract: Interested in reducing health disparities? Improving patient health outcomes? Better coordinating care? So are we! Join us as we share lessons learned from our five-year effort to develop patient-centered medical homes in 65 safety-net practices across the country.
Scientific seminars are held at the Metropolitan Park East Building (MPE), 1730 Minor Avenue, in Seattle. For directions to GHRI and contact information see our Contact Us page.