The Socio-Cultural Influences of School Choice

This dissertation explored whether the marketable features of three different charter schools in East Atlanta, Georgia influenced parents’ school selections and whether differences in race, income level, and educational attainment created patterns of interest regarding their selections. The accessible population (N=1865) for this study included parents of elementary and middle school aged children enrolled in three East Atlanta schools. A sample of 150 parents of elementary and middle school age charter school students enrolled in the three schools were randomly selected from the accessible population. One hundred percent of the proposed participants (150) agreed to participate as subjects. Data obtained for this study were analyzed using between-subjects designs and multivariate analysis of variance (MANOVA) statistical procedures. Post hoc analyses were conducted for significant findings. Factors for the designs included race, income level, and educational attainment. The dependent variable included the marketable features of charter schools. Null hypotheses were tested at the .05 probability level. The findings indicated that parents of elementary and middle school age charter school students enrolled in three charter schools located in East Atlanta, Georgia, rated the importance of the marketable features of charter schools differently based on their race. However, there was no statistically significant difference in participants’ rating of the importance of the marketable features of charter schools based on their income level or educational attainment. The findings and implications of the present study contribute to the knowledge base surrounding school choice. Limitations of the study were discussed and recommendations for future research were presented

Endorsement, Prior Action, and Language: Modeling Trusted Advice in Computerized Clinical Alerts

The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low, with up to 95% often ignored. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians' lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To mitigate this gap, we introduce a model to guide the design of trust-based clinical alerts-based on what physicians value when trusting advice from peers in clinical activities. We discuss three key dimensions to craft trusted alerts: using colleagues' endorsement, foregrounding physicians' prior actions, and adopting a suitable language. We exemplify our approach with emerging alert designs from our ongoing research with physicians and contribute to the current debate on how to design effective alerts to improve patient safety

How Good Are Provider Annotations?: A Machine Learning Approach

Introduction: CMS-2728 form (Medical Evidence Report) assesses 23 comorbidities chosen to reflect poor outcomes and increased mortality risk. Previous studies questioned the validity of physician reporting on forms CMS-2728. We hypothesize that reporting of comorbidities by computer algorithms identifies more comorbidities than physician completion, and, therefore, is more reflective of underlying disease burden. Methods: We collected data from CMS-2728 forms for all 296 patients who had incident ESRD diagnosis and received chronic dialysis from 2005 through 2014 at Indiana University outpatient dialysis centers. We analyzed patients' data from electronic medical records systems that collated information from multiple health care sources. Previously utilized algorithms or natural language processing was used to extract data on 10 comorbidities for a period of up to 10 years prior to ESRD incidence. These algorithms incorporate billing codes, prescriptions, and other relevant elements. We compared the presence or unchecked status of these comorbidities on the forms to the presence or absence according to the algorithms. Findings: Computer algorithms had higher reporting of comorbidities compared to forms completion by physicians. This remained true when decreasing data span to one year and using only a single health center source. The algorithms determination was well accepted by a physician panel. Importantly, algorithms use significantly increased the expected deaths and lowered the standardized mortality ratios. Discussion: Using computer algorithms showed superior identification of comorbidities for form CMS-2728 and altered standardized mortality ratios. Adapting similar algorithms in available EMR systems may offer more thorough evaluation of comorbidities and improve quality reporting

Understanding Advice Sharing among Physicians: Towards Trust-Based Clinical Alerts

Safe prescribing of medications relies on drug safety alerts, but up to 96% of such warnings are ignored by physicians. Prior research has proposed improvements to the design of alerts, but with limited increase in adherence. We propose a different perspective: before re-designing alerts, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (22 participants), and corroborated our findings with a survey (37 participants). Drivers that guide physicians in trusting peer advice include: timeliness of the advice, collaborative language, empathy, level of specialization and medical hierarchy. Based on these findings, we introduce seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating and agency laden. Our work contributes to novel alert design strategies to improve the effectiveness of drug safety advice

Explainable Prediction of Medical Codes from Clinical Text

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignmentComment: NAACL 201

From Critique to Collaboration: Rethinking Computerized Clinical Alerts

poster abstractThe safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low—with up to 96% of such alerts ignored daily. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians’ lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To address this issue, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (n = 22) and corroborated our findings with a survey (n = 37). Drivers that guided physicians in trusting peer advice included: timeliness of the advice, collaborative language, empathy, level of specialization, and medical hierarchy. Based on these findings, we introduced seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating, and agency laden. Grounded in these results, we then proposed a model to guide the design of trust-based clinical alerts. Our model constitutes of three key dimensions, using colleagues’ endorsement, foregrounding physicians’ prior actions, and adopting a suitable language. Using this model, we iteratively designed, pruned, and validated a set of novel alert designs. We are currently evaluating eleven alert designs in an online survey with physicians. The ongoing survey evaluates the likelihood of alert compliance and the perceived value of our proposed trust-based alerts. Next, we are planning in-lab studies to evaluate physicians’ cognitive load during decision making and measure attention to different trust cues using gaze duration and trajectories. Our work contributes to the current debate on how to design effective alerts to improve patient safety. Acknowledgements. This research material is based on work supported by the National Science Foundation under Grant #1343973. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the NSF

An Interactive User Interface for Drug Labeling to Improve Readability and Decision-Making

FDA-approved prescribing information (also known as product labeling or labels) contain critical safety information for health care professionals. Drug labels have often been criticized, however, for being overly complex, difficult to read, and rife with overwarning, leading to high cognitive load. In this project, we aimed to improve the usability of drug labels by increasing the ‘signal-to-noise ratio’ and providing meaningful information to care providers based on patient-specific comorbidities and concomitant medications. In the current paper, we describe the design process and resulting web application, known as myDrugLabel. Using the Structured Product Label documents as a base, we describe the process of label personalization, readability improvements, and integration of diverse evidence sources, including the medical literature from PubMed, pharmacovigilance reports from FDA adverse event reporting system (FAERS), and social media signals directly into the label

Integration of FHIR to Facilitate Electronic Case Reporting: Results from a Pilot Study

Current approaches to gathering sexually transmitted infection (STI) case information for surveillance efforts are inefficient and lead to underreporting of disease burden. Electronic health information systems offer an opportunity to improve how STI case information can be gathered and reported to public health authorities. To test the feasibility of a standards-based application designed to automate STI case information collection and reporting, we conducted a pilot study where electronic laboratory messages triggered a FHIR-based application to query a patient’s electronic health record for details needed for an electronic case report (eCR). Out of 214 cases observed during a one week period, 181 (84.6%) could be successfully confirmed automatically using the FHIR-based application. Data quality and information representation challenges were identified that will require collaborative efforts to improve the structure of electronic clinical messages as well as the robustness of the FHIR application

Advancing Epidemiological Science Through Computational Modeling: A Review with Novel Examples

Computational models have been successfully applied to a wide variety of research areas including infectious disease epidemiology. Especially for questions that are difficult to examine in other ways, computational models have been used to extend the range of epidemiological issues that can be addressed, advance theoretical understanding of disease processes and help identify specific intervention strategies. We explore each of these contributions to epidemiology research through discussion and examples. We also describe in detail models for raccoon rabies and methicillin-resis-tant Staphylococcus aureus, drawn from our own research, to further illustrate the role of computation in epidemiological modeling