Intensive insulin therapy (IIT), a nurse-driven protocol combining frequent blood glucose testing and insulin administration to tightly control blood glucose, became the standard of critical care following a 2001 study. Many institutions subsequently implemented computer-based clinical decision support systems (CDSSs) for IIT. However, recent studies question IITs benefit and safety. Whereas previous research investigated effects of patient characteristics on IIT performance, this dissertation evaluated IIT CDSS with respect to the interaction of people, process, and technology. An organizational analysis using institutional theory explored the influence of peers, regulators, and professions in IIT adoption. A literature review and case study demonstrated the underreported role of social, organizational, and contextual factors affecting IIT CDSS. A quantitative analysis of system records established the frequency and effect of blood glucose data mismatches as well as characteristics and effects of nurse dosing overrides on IIT CDSS performance. An ethnographic study of nurse workflow yielded understanding of how IIT CDSS functions with respect to other clinical information systems and care processes. Using a mixed quantitative and qualitative approach informed by social theory, this research demonstrates how sociotechnical interactions affect IIT CDSS and may be leveraged to improve care delivery.

Family Health Information (FHI) helps identify individuals who are at increased risk for adverse health conditions due to inherited genetic or environmental predisposition. Appropriate stratification of patients based on familial risk relies on the clinicians ability to ascertain and the patients ability to report complete and accurate FHI. Complicating this factor, the collection of detailed FHI often requires more time than is available in the typical patient encounter in the primary care setting. As a result, FHI is often inconsistently and ineffectively communicated during clinical encounters, leading to FHI that is often incomplete, thus limiting its potential use for clinical decision-making. Yet, FHI epitomizes a cost effective strategy, and is critical to the emerging practice of genome-informed and personalized medicine. This thesis describes the development and evaluation of the www.MyFamilyatVanderbilt.com (MyFaV), a web-based portal for ascertaining structured FHI directly from patients.

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naïve model) is oversimplified, breaking all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models are too computationally intensive for on-the-fly use in database search algorithms. We created an ordinal-regression based model called Basophile that reflects the relative importance of basic residues and fragment length in charge retention during CID/HCD fragmentation of charged peptides. The model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly charged precursors. When compared with the Naïve model and Protein Prospectors prediction model, Basophile has shown an average of 26% and 28% more identifications in triply-charged precursors on ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be easily incorporated into any database search software for shotgun proteomic identification.

The utilization of clinical decision support (CDS) is increasing among healthcare facilities that have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations rarely occurs, and misuse or flaws in system design are often not recognized or corrected. Through retrospective nephrologist adjudication of acute kidney injury (AKI) CDS alerts, we identified patient and knowledgebase factors that contributed to inappropriate, or false positive, alerts. We also estimated the rate of inappropriate provider responses, which occurred in the setting of both true and false positive alerts. We found that few alerts were determined to be inappropriate. Unintended adverse consequences, or inappropriate provider responses resulting from inappropriate alerts, were rare. Retrospective review often occurs too late to make critical corrections or initiate redesign efforts. We developed a real-time, web-based surveillance tool for nephrotoxic and renally cleared medications that integrates provider responses to CDS recommendations with relevant medication ordering, administration, and therapeutic monitoring data. The surveillance view displays all currently admitted, eligible patients and provides brief demographics with triggering order, laboratory, and CDS interactions to facilitate the identification of high-risk patient conditions, such as an imminent adverse drug event (ADE) or potential ADE (pADE). The patient detail view displays a detailed timeline of orders, order administrations, laboratory values, and CDS interactions for an individual patient and allows users to understand provider actions and patient condition changes occurring in conjunction with CDS interactions. We evaluated the surveillance tool with a randomized trial, where intervention patients were monitored on the surveillance tool daily by a clinical pharmacist and control patients received only existing CDS and standard of care. Despite interventions made by the study pharmacist from the surveillance tool, we found no significant change in the timeliness of provider modifications or discontinuations of targeted medications or occurrence of pADEs or ADEs. We concluded that clinical pharmacist surveillance of AKI-related medication alerts did not improve the timeliness or quality of provider responses or patient outcomes.

There is little research on how clinically relevant concepts are transferred from a patient, through a healthcare provider, and then to a resultant clinical note. This study tested whether clinical information flow, defined as the transfer of concepts from patient to note, can be traced. The study's investigators also analyzed the impact of a clinical documentation tool on note content. Healthcare providers, designated as clinical simulation study subjects, generated clinical notes via two documentation tools. The simulation utilized standardized patient scenario descriptions (PSDs). Independent physician reviewers identified clinical concepts present in the PSDs and in resultant clinic notes. Reviewers identified a total of 256 unique clinical concepts across all PSDs. Of these, a total of 122 unique concepts overlapped for the PSDs and resultant notes from both documentation tools. Additionally, the dictation-based and computer-based notes shared 103 distinct concepts not found in the PSDs. This study's findings suggest that both computer-based and dictation-based tools are subject to clinical concept loss. Templates may have eased documentation, partly explaining the greater concept count for computer-based notes. This study found that tracing information flow in a clinical simulation encounter is a valid method for evaluating medical documentation tools. Clinical note template availability also likely impacts healthcare provider documentation.

Shotgun liquid chromatography/tandem mass spectrometry (LC-MS/MS) technology provides data sets rich in the type of information required proteomic quantitation; however, these data are not fully exploited by existing tools. We present a statistical model for combining MS precursor intensity data with MS/MS spectral count data and obtaining a single p-value using Fishers Method of combining p-values. Our model is demonstrated using a new tool, IDPQuantify, which generates MS/MS spectral count data and MS persistent peptide isotopic distribution (PPID) intensity data for peptide group-level difference testing. Using the iPRG 2009 ABRF E. coli data set with known differences in protein content between cohorts, we compared the performance of existing candidate statistical tests using either spectral counts or PPIDs alone. We then compared the performance of our combined model with our candidate tests. Spectral count-based tests showed lower sensitivity but higher specificity than PPID-based tests. In comparison, our combined model yielded a slight drop in sensitivity coupled with an enormous improvement in specificity compared to the PPID-based test alone. We also observed that shared peptide groups tended to yield erroneous rejections of the null hypothesis more often than unshared peptide groups.

Context aware displays are becoming more and more common in one’s daily lifeand play an active role in order to collaborate with people and systems. In most ofthe shared student corridors, tenants often use notice boards to share all kinds of information which is less obsolete in the digital age. This paper presents adesign-oriented research to find out an effective way to fulfill the needs of thetenants who are living in shared corridors. We tried to design a context awaredisplay which will help the tenants to communicate with one another efficiently,share resources and reduce the conflicts. An extensive investigation was carriedout for data gathering to find out the problems of the tenants, behavior and effectsof conflicts on the social life of the tenants living in shared student corridors.Based on the findings we built a design prototype.

The history of technology has shown that with the advance of science almost any manual human task could also be done by a machine. This story of success gives hope for the subject area of artificial intelligence and Cognitive simulation. It is easily comprehensible that the automation of manual tasks is very successful, since it is of very obvious nature. Exactly this factor is the biggest problem in understanding cognitive processes and other products of our mind, that they are not obvious at all. AIl scientists assume that the human brain conducts tasks comparable to a digital computer and must therefore be reproducible as a computer. This view is supported by psychologists who use basic information processing models adapted from computer science to explain the human thought process (Lindsay et. al., 1977). Unfortunately, psychologists are still not completely sure of the way our mind works. We are well aware of the outcome and can predict some of them, but the working procedure behind our decisions remain a mystery. Hubert Dreyfus (Dreyfus, 1999) critically reviewed the psychological, epistemological and ontological grounded expectations of Artificial Intelligence workers. It is his conclusion that the enduring failure of AI to technologically reproduce the function of the human brain serves as empirical evidence against the Foundations of AI itself. According to the Author, it has also not been scientifically proven by the AI community that “the mind must obey a heuristic program”. In fact, psychology suggests that humans commonly make decisions without even considering the situation or their set of standards (Smith, 2003). Dreyfus proves that “arguments which are supposed to show that formalization must be possible are either incoherent or self-contradictory”. Therefore it seems to me, that the current state of the art in AI and Cognitive simulation is at the very limits of technology. For this reason it seems relevant to explore to what extend the current findings and technological solutions can be used to support the activity of the human brain, since it is not possible to replace the human brain by a computational device.

Functional Magnetic Resonance Imaging (fMRI) has the potential to unlock many of the mysteries of the brain. Although this imaging modality is popular for brain-mapping activities, clinical applications of this technique are relatively rare. For clinical applications, classification models are more useful than the current practice of reporting loci of neural activation associated with particular disorders. Also, since the methods used to account for anatomical variations between subjects are generally imprecise, the conventional voxel-by-voxel analysis limits the types of discoveries that are possible. This work presents a classification-based framework for knowledge discovery from fMRI data. Instead of voxel-centric knowledge discovery, this framework is segment-centric, where functional segments are clumps of voxels that represent a functional unit in the brain. With simulated activation images, it is shown that this segment-based approach can be more successful for knowledge discovery than conventional voxel-based approaches. The spatial coherence principle refers to the homogeneity of behavior of spatially contiguous voxels. Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) a new algorithm based on the spatial coherence principle is presented here for functional segmentation. With benchmark data, it is shown that the ACEIC method can achieve higher segmentation accuracy than Probabilistic Independent Component Analysis a popular method used for fMRI data analysis. The spatial coherence principle can also be exploited for voxel-centric image-classification problems. Spatially Coherent Voxels (SCV) is a new feature selection method that uses the spatial coherence principle to eliminate features that are unlikely to be useful for classification. For a Substance Use Disorder dataset, it is demonstrated that feature selection with SCV can achieve higher classification accuracies than conventional feature selection methods.

Background: Disease and patient care processes often create characteristic mathematical and temporal patterns in time-stamped clinical events and observations, but existing medical record systems have a limited ability to recognize or visualize these patterns. System Design: This dissertation introduces the process-oriented approach to clinical data analysis and visualization. This approach aims to support specifying, detecting, and visualizing mathematical and temporal patterns in time-stamped patient data for a broad range of clinical tasks. It has two components: a pattern specification and detection strategy called PROTEMPA (Process-oriented Temporal Analysis); and a pattern visualization strategy called TPOD (Temporal Process-oriented Display). Evaluation: A study in the clinical research domain evaluated PROTEMPAs ability to identify and categorize patients based on diagnosis, disease severity, and disease progression by scanning for patterns in clinical laboratory results. A cognitive study in the patient care domain evaluated PROTEMPA and TPODs ability to help physicians review cases and make decisions using case presentation software that displays laboratory results in either a TPOD-based display or a standard laboratory display. Results: PROTEMPA successfully identified laboratory data patterns in both domains. TPOD successfully visualized these patterns in the patient care domain. In the patient care study, subjects obtained more clinical concepts from the TPOD-based display, but TPOD had no effect on decision-making speed or quality. Subjects were split on which laboratory display they preferred, but expressed a desire to gain more familiarity with the TPOD-based display. Subjects reviewed data in the standard laboratory display for a variety of purposes, and interacted with the display in a complex fashion. Conclusions: The process-oriented approach successfully recognized and visualized data patterns for two distinct clinical tasks. In clinical research, this approach may provide significant advantages over existing methods of data retrieval. In patient care, comparative evaluation of novel data displays in context provides insights into physicians preferences, the process of clinical decision-making by physicians, and display usability. TPODs influence on concept acquisition is promising, but further research is needed regarding physicians use of laboratory data for results review in order to determine how a process-oriented display might be deployed most beneficially.