Clinical Decision Support for Integrated Cyber-Physical Systems: A Mixed Methods Approach

We describe the design and implementation of a clinical decision support system for assessing risk of cerebral vasospasm in patients who have been treated for aneurysmal subarachnoid hemorrhage. We illustrate the need for such clinical decision support systems in the intensive care environment, and propose a three pronged approach to constructing them, which we believe presents a balanced approach to patient modeling. We illustrate the data collection process, choice and development of models, system architecture, and methodology for user interface design. We close with a description of future work, a proposed evaluation mechanism, and a description of the demo to be presented

Taking Control of Castleman Disease: Leveraging Precision Medicine Technologies to Accelerate Rare Disease Research

Castleman disease (CD) is a rare and heterogeneous disorder characterized by lymphadenopathy that may occur in a single lymph node (unicentric) or multiple lymph nodes (multicentric), the latter typically occurring secondary to excessive proinflammatory hypercytokinemia. While a cohort of multicentric Castleman disease (MCD) cases are caused by Human Herpes Virus-8 (HHV-8), the etiology of HHV-8 negative, idiopathic MCD (iMCD), remains unknown. Breakthroughs in “omics” technologies that have facilitated the development of precision medicine hold promise for elucidating disease pathogenesis and identifying novel therapies for iMCD. However, in order to leverage precision medicine approaches in rare diseases like CD, stakeholders need to overcome several challenges. To address these challenges, the Castleman Disease Collaborative Network (CDCN) was founded in 2012. In the past 3 years, the CDCN has worked to transform the understanding of the pathogenesis of CD, funded and initiated genomics and proteomics research, and united international experts in a collaborative effort to accelerate progress for CD patients. The CDCN’s collaborative structure leverages the tools of precision medicine and serves as a model for both scientific discovery and advancing patient care

Using a Mystery-Caller Approach to Examine Access to Prostate Cancer Care in Philadelphia

<div><p>Purpose</p><p>Prior work suggests that access to health care may influence the diagnosis and treatment of prostate cancer. Mystery-caller methods have been used previously to measure access to care for health services such as primary care, where patients’ self-initiate requests for care. We used a mystery-caller survey for specialized prostate cancer care to assess dimensions of access to prostate cancer care.</p><p>Materials and Methods</p><p>We created an inventory of urology and radiation oncology practices in southeastern Pennsylvania. Using a ‘mystery caller’ approach, a research assistant posing as a medical office scheduler in a primary care office, attempted to make a new patient appointment on behalf of a referred patient. Linear regression was used to determine the association between time to next available appointment with practice and census tract characteristics.</p><p>Results</p><p>We successfully obtained information on new patient appointments from 198 practices out of the 223 in the region (88.8%). Radiation oncology practices were more likely to accept Medicaid compared to urology practices (91.3% vs 36.4%) and had shorter mean wait times for new patient appointments (9.0 vs 12.8 days). We did not observe significant differences in wait times according to census tract characteristics including neighborhood socioeconomic status and the proportion of male African American residents.</p><p>Conclusions</p><p>Mystery-caller methods that reflect real-world referral processes from primary care offices can be used to measure access to specialized cancer care. We observed significant differences in wait times and insurance acceptance between radiation oncology and urology practices.</p></div

Characteristics of practices in southeastern Pennsylvania and the surrounding counties.

<p>Characteristics of practices in southeastern Pennsylvania and the surrounding counties.</p

Multivariable linear regression model examining the association between wait time (in days) and practice characteristics.

<p>Multivariable linear regression model examining the association between wait time (in days) and practice characteristics.</p