Birth of identity: Understanding changes to birth certificates and their value for identity resolution

pre-printIntroduction Identity information is often used to link records within or among information systems in public health and clinical settings. The quality and stability of birth certificate identifiers impacts both the success of linkage efforts and the value of birth certificate registries for identity resolution. Objective Our objectives were to describe: (1) the frequency and cause of changes to birth certificate identifiers as children age, and (2) the frequency of events (ie, adoptions, paternities, amendments) that may trigger changes and their impact on names. Methods We obtained two deidentified datasets from the Utah birth certificate registry: (1) change history from 2000 to 2012, and (2) occurrences for adoptions, paternities, and amendments among births in 1987 and 2000. We conducted cohort analyses for births in 1987 and 2000, examining the number, reason, and extent of changes over time. We conducted cross-sectional analyses to assess the patterns of changes between 2000 and 2012. Results In a cohort of 48 350 individuals born in 2000 in Utah, 3164 (6.5%) experienced a change in identifiers prior to their 13th birthday, with most changes occurring before 2 years of age. Cross-sectional analysis showed that identifiers are stable for individuals over 5 years of age, but patterns of changes fluctuate considerably over time, potentially due to policy and social factors. Conclusions Identities represented in birth certificates change over time. Specific events that cause changes to birth certificates also fluctuate over time. Understanding these changes can help in the development of automated strategies to improve identity resolution

A domain analysis model for eIRB systems: addressing the weak link in clinical research informatics

pre-printInstitutional Review Boards (IRBs) are a critical component of clinical research and can become a significant bottleneck due to the dramatic increase, in both volume and complexity of clinical research. Despite the interest in developing clinical research informatics (CRI) systems and supporting data standards to increase clinical research efficiency and interoperability, informatics research in the IRB domain has not attracted much attention in the scientific community. The lack of standardized and structured application forms across different IRBs causes inefficient and inconsistent proposal reviews and cumbersome workflows. These issues are even more prominent in multi-institutional clinical research that is rapidly becoming the norm. This paper proposes and evaluates a domain analysis model for electronic IRB (eIRB) systems, paving the way for streamlined clinical research workflow via integration with other CRI systems and improved IRB application throughput via computer-assisted decision support

Improving access to longitudinal patient health information within an emergency department

pre-printWe designed and implemented an electronic patient tracking system with improved user authentication and patient selection. We then measured access to clinical information from previous clinical encounters before and after implementation of the system. Clinicians accessed longitudinal information for 16% of patient encounters before, and 40% of patient encounters after the intervention, indicating such a system can improve clinician access to information. We also attempted to evaluate the impact of providing this access on inpatient admissions from the emergency department, by comparing the odds of inpatient admission from an emergency department before and after the improved access was made available. Patients were 24% less likely to be admitted after the implementation of improved access. However, there were many potential confounders, based on the inherent pre-post design of the evaluation. Our experience has strong implications for current health information exchange initiatives

Enhancing continuity of care through an emergency medical card at Intermountain Healthcare: using the continuity of care record standard

posterComplex and fragmented healthcare systems hamper provision of effective care where it is needed most. 1 In most instances, continuity of care is rarely considered during referral, transfer, or discharge of patients from one caregiver to another. 2,3 The dearth of pertinent current and historical health information at the point of care may lead to medical errors, adverse events, and poor outcomes

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed

PhD

dissertationThe overwhelming amount and diversity of clinical data that may be collected from patients have necessitated the development and use of sophisticated processing methods in order to make the data useful in medical applications. Artificial neural networks (ANNs) are one such method that has found growing success in the medical field. In the past, techniques for optimizing neural networks have focused almost completely on ANN training algorithms and architectures in order to improve performance on specific applications. However, a more data-centric view of optimization may prove more valuable in making ANNs useful to a wider community of medical researchers. Four specific characteristics of medical data processing are addressed: (1) large numbers of classes into which we wish to categorize data; (2) nonuniform distribution of classes; (3) features from input data with non-Gaussian distributions and widely variable ranges; (4) large, complex data sets from which it is difficult to choose appropriate data for model development. These characteristics present potential problems for ANN development. It is proposed that these problems may be overcome by using new data normalization techniques, implementing hierarchical networks, and using distance metrics to choose the most useful training patterns. These proposals should be generalizable to many medical applications because they focus on solutions to data problems and not specific applications. The specific proposals are applied to two practical medical applications: blood pressure determination and classification of breathing circuit faults. The proposals are compared with standard ANN training techniques. For each of the proposals, there was a significant increase in training performance, or network classification capability, or both. It is concluded that the proposals should be generalizable to other medical applications and that a data-centric view of optimization is valuable to ANN development in medical applications