SAMSI DDDHC Workshop: My experience

The following blog entry is from John Olaomi, Associate Professor from the University of South Africa. He recently attended the Data-Driven Decisions in Healthcare Opening Workshop.

John Olaomi

John Olaomi, Associate Professor from U. of South Africa and guest blogger.

In my quest for knowledge and international exposures into the current trend in statistics and its applications, I first got in contact with SAMSI in 2007, responding to the 2008-2009 Postdoc position request, thanks to Google Search.  I was invited to participate in 2008-2009 Workshop on Sequential Monte Carlo Methods but could not, due to last minute funding challenges.  Since then, I have always been notified about all SAMSI programs.

The Data-Driven Decisions in Healthcare (DDDHC) opening workshop was another opportunity to participate in SAMSI programs, which I eventually succeeded with, thanks to the funding from my institution, the University of South Africa. From the organization to the execution and the forming of working groups, the workshop was a real success.  The tutorials and the technical sessions were informative, practical and posed many relevant research challenges which is really worth coming for.  Meeting erudite scholars and colleagues (both academic and professional) that can contribute to one’s academic prowess is priceless.

people looking at a poster

There was a poster session and reception held Monday night for the DDDHC opening workshop.

Technically, the expositions on Comparative Effectiveness Research (CER)- Observational Studies, Patient Flow, Personalized Healthcare and Healthcare Databases really opened my eyes to many applications and the need for statisticians to be at the forefront to achieving the healthcare goals.  Although the presentations were good, my expectations were that a lot of methodological issues (with Statistical and Operations Research perspectives) will be raised and tackled.  This was lacking, as virtually all presentations were like 80 percent healthcare issues and 20 percent (statistical or operations research) methodology, which I think should be vise-versa. This is necessary to avoid errors of the third kind: the error committed by giving the right answer to the wrong problem (Kimball, 1957).

In all, I think my trip really worth it. Thanks to SAMSI for the opportunity and the good facilitation.

Olaomi, John. O. (PhD)
Associate Professor
Department of Statistics,
University of South Africa (UNISA)
P. O. Box 392 UNISA 0003
Pretoria, South Africa

Data-Driven Decisions in Healthcare – An Overview of the Opening Workshop

The following is from Ray Falk, from OptInference LLC, who participated in the opening workshop for SAMSI’s Data-Driven Decisions in Healthcare program.

Ray Falk looking at Banafsheh Behzad’s poster as she explains the research she is doing at the Data-Driven Decisions in Healthcare Opening Workshop.

I found the kick-off workshop, Data-Driven Decisions in Healthcare, tightly focused on methodologically tractable projects with ‘shovel-ready’ data sources, primarily in the areas of operational patient, provider, and (material) resource allocation and scheduling and patient-centered (personalized) effectiveness assessment.

Avi Mandelbaum

Avi Mandelbaum of Technion gave a lecture at the opening workshop.

Inference, analysis and control of service systems were developed via extension of existing results and observations from call center management (Avi Mandelbaum), including stabilization of performance under time-varying conditions (Yunan Liu).  Prodigiously comprehensive tracking of patient flows through the ICU (Carri Chan) and the remaining non-intensive departments of a hospital (Jim Dai) were related to specific insights regarding options for intervention (including proportional and absolute threshold effects), both for providers and patients.  An interesting discussion addressed dependence of service times on (new vs. readmitted) patient status (Guodong Pang).

Bob Obenchain

Bob Obenchain at the poster session Monday evening.

Personalized medicine was addressed from several perspectives, including heterogeneous treatment effects (with a heuristic re-sampling based approaches from Bob Obenchain and Sheldon Jacobson), appropriately targeted decision analysis based on high-dimensional modeling from large populations (centered on ROC curves from validation data sequestered from model development), and availability of comprehensive data, as well as methodologies for data acquisition and alignment, inference, and modeling(OMOP) (David Madigan, Patrick Ryan) .  Assessment and prediction from retrospective observational studies and tracking data was addressed via meta-analysis, propensity matching or weighting, and spontaneous event reporting (with no identifiable population at risk) (Alan Menius)

Guidance and policy for the national initiative in patient-centered evidence-based assessment of medical effectiveness was addressed via standards elaborated via PCORI (Constantine Gatsonis, Sally Morton).  Issues and perspectives included:
Value of Information for  prioritization, Efficiency of adaptive experimental design, individually heterogeneous treatment effects, observational vs. prospective randomized trials, data infrastructure (storage of electronic records and ontologies for integrated access), and technology assessment and dissemination, hyper-inclusive access to results for review (beyond standard publications in English).  An auxiliary resource is the Society for Medical Decision Making (Turgay Ayer).

– Ray Falk