Journal of Integrated Design and Process Science

The number of older adults in the USA is projected to continue growing, thus driving the demand for orthopedic surgery due to diseases like Osteoporosis and related bone fractures. To manage the healthcare costs, there has been increased interest in introducing predictive analytics for the assessment of bone quality and properties. Bone has been described as a nanocomposite with unique mechanical properties, governed by its structural organization and chemical composition. To develop predictive analytics in relation to bone health, there is a need to understand why cortical bone is so resistant to crack propagation and how it ultimately succumbs to fracture. Moreover, bone has a hierarchical structure through multiple length scales in which the variability of the properties at each length scale affects the overall mechanical properties. Since there is no comprehensive model to capture the mechanics of fracture, there is a need to develop a stochastic model incorporating uncertainty in the multiscale of the bone hierarchy. For this study the research question was: Can multiscale probabilistic techniques used in composites be applied to bones? To answer this question, the following specific aims were constructed: survey probabilistic analysis for bones, survey probabilistic analysis for composites, and present probabilistic multiscale techniques for composites that can be transferred to bones. As a methodology, a critical review was conducted on the probabilistic modeling of bone and composite materials. The uncertainties at the different scales were reviewed for bone and composites. An assessment was conducted whether multiscale probabilistic techniques used in composites can be applied to bones. It was shown that there are several studies of deterministically modeling of bone at different scales. It was shown that several probabilistic multiscale models of composites exist. An argument was made that the probabilistic multiscale models of composites may be extended and modified for application in bone. It was argued and shown that multiscale probabilistic techniques used in composites may be extended and modified to apply to bone. The contribution of this work is proposing a predictive analytic method for the assessment of bone quality and properties. The predictive analytics is anchored in probabilistic models for bone adopted and extended from models for composites.

Young adult and adolescent males (YAAM) experience disparate health outcomes as compared to their female counterparts despite wellness promotion services targeted specifically for this population. One major obstacle in bringing about a truer sense of public health is the lack of evidence-based research on health-related attitudes, perceptions, and lived experiences of YAAMs. This implies the presence of systemic issues in the way YAAM health and wellness information is gathered and input into health informatics systems, which therefore leads to a need to improve current strategies of service within this population. A novel methodological approach, known as structured phenomenology, was created to identify the underlying mechanisms contributing to the current health disparities experienced by males. It could also provide a thematic structure from which more accurate and reliable information, as well as communication systems for YAAMs, can be created. A series of semi-structured interviews were conducted to learn about YAAM health behavior decisions and related outcomes. Participant responses produced eight core themes as it related to YAAM health and wellness. These extracted data create an opportunity to better inform prevention and intervention support services for this population in order to improve proximal, intermediate, and distal health outcomes through the creation of an original, validated assessment tool.

This paper proposes an original mining federated data framework (MFDF) which can be used as a conceptual framework to perform exploratory and evaluation analysis of micro and macro level performance measures of hospitals. The framework uses the data mining techniques (statistical tools/machine learning) on enterprise data warehouse (EDW) platform that federates data for hospitals from multiple sources on a continual basis. This scalable and cyclic framework is flexible to test theories and analyze the impact of independent/predictor variables on dependent/response variables by deploying various data/statistical models. The paper presents a brief exploratory analysis performed based on this framework to understand the relationships between patient perceptions of care and hospital performance scores indicate that there is a positive correlation between patient satisfaction and hospital total performance scores; albeit the relationship of patient satisfaction scores with other domain scores such as safety, timeliness, effectiveness etc. cannot be ascertained.

Correctional environments are not often associated with health care delivery or health information technology (health IT). Nevertheless, a great deal of healthcare occurs within jails and prisons. In many of these institutions, correctional health providers have access to health IT to provide a practical solution to the complex health challenges of justice-involved individuals. This case study will detail how one jail planned and implemented an integrated health IT system that interfaced an electronic health record (EHR) with both the correctional IT system and existing health IT systems, including a pharmacy system and a health information exchange (HIE). These interfaces involved technical challenges unique to corrections that required technical solutions as well as manual solutions, when systems could not be electronically integrated. Health IT also allowed for monitoring of contracted services for healthcare within the correctional facility. As with many technical solutions, health IT in corrections brought with it the possibility of changing correctional healthcare in unanticipated ways.