Electronic Health Records (EHRs) is a health information technology that has already begun to change the way healthcare providers care for patients. EHRs can potentially enhance the quality and efficiency of patient care (Simon et al., 2010); however, some research shows that EHRs do not always do so. The lack of improved efficiency and quality of care can lead to frustrated and dissatisfied users. The effects of different aspects of EHR implementation could affect user satisfaction and perceived quality of EHRs. This study investigates the how time since implementation, training, and leadership affect user satisfaction and perceived quality of the EHR system in clinics in Mississippi. The results of the study indicate that training and leadership have an effect on users’ perceived quality and satisfaction with EHRs. These findings reveal that clinics and EHR providers should focus on training and leadership to improve user satisfaction and perceived quality of EHRs.

The history of human progress is also a history of the growth of evaluative processes. From simple types of self-analysis to complex stages of appraisal wherein survival depends on continuous research and intensive evaluation, human progress has gone hand-in-hand with evaluation. Concomitant with the evaluative process has been the interpretation and transmission of evaluation to the individuals concerned.

Record linkage is the process that joins separately recorded pieces of information for a particular individual from one or more sources. To facilitate record linkage, a reliable computer based approach is ideal. In genealogical research computerized record linkage is useful in combing information for an individual across multiple censuses. In creating a computerized method for linking censuse records it needs to be determined if weights calculated from one geographical area, can be used to link records from another geographical area. Research performed by Marcie Francis calculates field weights using census records from 1910 and 1920 for Ascension Parish Louisiana. These weights are re-calculated to take into account population changes of the time period and then used on five data sets from different geographical locations to determine their robustness. HeritageQuest provided indexed census records on four states. They include California, Connecticut, Illinois and Michigan in addition to Louisiana. Because the record size of California was large and we desired at least five data sets for comparison this state was split into two groups based on geographical location. Weights for Louisiana were re-calculated to take into consideration visual basic code modifications for the field "Place of Origin", "Age" and "Location" (enumeration district). The validity of these weights, were a concern due to the low number of known matches present in the data set for Louisiana. Thus, to get a better feel for how weights calculated from a data source with a larger number of known matches present, weights were calculated for Michigan census records. Error rates obtained using weights calculated from the Michigan data set were lower than those obtained using Louisiana weights. In order to determine weight robustness weights for Southern California were also calculated to allow for comparison between two samples. Error rates acquired using Southern California weights were much lower than either of the previously calculated error rates. This led to the decision to calculate weights for each of the data sets and take the average of the weights and use them to link each data set to take into account fluctuations of the population between geographical locations. Error rates obtained when using the averaged weights proved to be robust enough to use in any of the geographical areas sampled. The weights obtained in this project can be used when linking any census records from 1910 and 1920. When linking census records from other decades it is necessary to calculate new weights to account for specific time period fluctuations.

The purpose of this study was to study the effects of increased teacher knowledge of students' individual characteristics on students' religious attitudes and achievement in Seminary. It was based upon the rationale that when teachers have more knowledge of each student's characteristics, they can be more effective in planning for individual needs. One result will be that students' behavior and attitudes will be affected positively because their needs are more fully satisfied and interests more effectively utilized.

Indiana University-Purdue University Indianapolis (IUPUI) / Due to frequent utilization, high costs, high prevalence, and negative health outcomes, the care of patients managing type 2 diabetes mellitus (T2DM) remains an important focus for providers, payers, and policymakers. The challenges of care delivery, including care fragmentation, reliance on patient self-management behaviors, adherence to care management plans, and frequent medical visits are well-documented in the literature. T2DM management produces numerous clinical data points in the electronic health record (EHR) including laboratory test values and self-reported behaviors. Recency or absence of these data may limit providers’ ability to make effective treatment decisions for care management. Increasingly, the context in which these data are being generated is changing. Specifically, telehealth usage is increasing. Adoption and use of telehealth for outpatient care is part of a broader trend to provide care at-a-distance, which was further accelerated by the COVID-19 pandemic. Despite unknown implications for patients managing T2DM, providers are increasingly using telehealth tools to complement traditional disease management programs and have adapted documentation practices for virtual care settings. Evidence suggests the quality of data documented during telehealth visits differs from that which is documented during traditional in-person visits. EHR data of differential quality could have cascading negative effects on patient healthcare outcomes. The purpose of this dissertation is to examine whether and to what extent levels of EHR data quality are associated with healthcare outcomes and if EHR data quality is improved by using health information technologies. This dissertation includes three studies: 1) a cross-sectional analysis that quantifies the extent to which EHR data are timely, complete, and uniform among patients managing T2DM with and without a history of telehealth use; 2) a panel analysis to examine associations between primary care laboratory test ages (timeliness) and subsequent inpatient hospitalizations and emergency department admissions; and 3) a panel analysis to examine associations between patient portal use and EHR data timeliness.

A record type is a data type consisting of a collection of named fields that combines the flexibility of associative arrays in some dynamically typed languages with the safety guarantees and possible runtime performance of static typing. The structural typing of records is especially suitable for handling semi-structured data such as JSON and XML making efficient records an attractive choice for high-performance computing and large- scale data analytics. It has proven difficult to implement record types in Scala however. Existing libraries suffer from either severe compile-time penalties, large runtime over- head, or other restrictions in usability such as poor IDE integration and hard-to-compre- hend error-messages. This thesis provides a systematic description and comparison of both existing and possible new approaches to records in Scala and Dotty, a new compiler for the Scala 3 language. A novel benchmarking suite is presented, built on top of the Java Microbench- mark Harness (JMH), for measuring runtime and compile-time performance of records running on the Java Virtual Machine and currently supporting Scala, Dotty, Java and Whiteoak. To achieve field access times comparable to nominally typed classes, it is conjectured that width subtyping has to be restricted to explicit coercion and a compilation scheme for such record types is sketched. For unordered record types with width and depth sub- typing however, hashmap-based approaches are found to have the most attractive run- time performance characteristics. In particular, Dotty provides native support for such an implementation using structural refinement types that might strike a good balance be- tween flexibility and runtime performance for records in the future. / En record-typ är en datatyp som består av en en uppsättning namngivna fält som kombinerarflexibiliteten hos associativa arrayer i vissa dynamiskt typade programmeringsspråkmed säkerhetsgarantierna och den potentiella exekveringshastigheten som fås av statisk typning. Records strukturella typning är särskilt väl lämpad för att hantera semistruktureraddata såsom JSON och XML vilket gör beräkningseffektiva records ett attraktivt val för högprestandaberäkningar och storskalig dataanalys. Att implementera records i programmeringsspråket Scala har dock visat sig svårt. Existerande bibliotek lider antingenav långa kompileringstider, långsam exekveringshastighet, eller andra problem med användbarheten såsom dålig integration med olika utvecklingsmiljöer och svårförståddafelmeddelanden. Den här uppsatsen ger en systematisk beskrivning och jämförelse av både existerandeoch nya lösningar för records i Scala och Dotty, en ny kompilator för Scala 3. Ett nyttbenchmarkingverktyg för att mäta exekveringshastigheten och kompileringstiden av recordssom körs på den virtuella Java maskinen presenteras. Benchmarkingverktyget ärbyggt på Java Microbenchmark Harness (JMH) och stöder i nuläget Scala, Dotty, Java ochWhiteoak. För att åstadkomma körtider som är jämförbara med nominellt typade klasser antasatt subtypning på bredden måste begränsas till explicita konverteringsanrop och enskiss till en kompileringsstrategi för sådana records presenteras.  För record-typer med ickeordnade fält och subtypning på bredden och djupet visar sig istället records baseradepå hashtabeller ha de mest attraktiva exekveringstiderna. Dotty tillhandahåller stöd fören sådan implementation med strukturella förfiningstyper som kan komma att träffa enbra balans mellan flexibilitet och exekveringshastighet för records i framtiden.

A personal health record (PHR) allows a patient to exert control over his/her healthcare by enhancing communication with healthcare providers. According to research, patients find value in having access to information contained in their medical records. Often a glossary is required to aid in interpreting the information and understanding the content. However, giving patients the ability to speak with providers about their medical conditions empowers them to participate as informed healthcare consumers. The majority of patients (75%) at Medical Specialists expressed their intention to adopt the PHR if it is made available to them. Although the perceived usefulness of a PHR was a significant determining factor, comfort level with technology, health literacy, and socioeconomic status were indirectly related to intention to adopt as well. Perceived health status was not found to be a significant factor in this population for determining intention to adopt a PHR. The majority of patients in each category of gender, age, marital status, and race/ethnicity (except American Indian/Alaska Native) expressed interest in adopting a PHR, with most categories being above 70%. Findings indicate a broad acceptance of this new technology by the patients of Medical Specialists. Improvement of adoption and use rates may depend on availability of office staff for hands-on training as well as assistance with interpretation of medical information. Hopefully, over time technology barriers will disappear, and usefulness of the information will promote increased demand.

Indiana University-Purdue University Indianapolis (IUPUI) / The dissertation consists of three essays attempting to assess the impact of expanded health insurance policy on cancer care continuum and measure the unbiased program effects after taking care of mis-measured cancer screening self-reports. The first essay examines the impact of the Affordable Care Act’s Medicaid expansion on time to oral cancer treatment initiation since diagnosis, quality of hospital care such as length of stay in the hospital, planned and unplanned readmissions post-surgery, and care outcome such as ninety-day mortality since surgery. The study uses two-way fixed effects linear model analysis under a difference-in-difference estimation setting to show that Medicaid expansion eligibility reduced overall oral cancer treatment initiation timing since diagnosis, including radiation initiation as well as first surgery of the primary site. It also shortened the length of stay in the hospital post-surgery. The second essay assesses the value of electronic medical records from Indiana health information exchange (IHIE) and survey self-reports of Indiana residents seen at Indiana University Health in measuring population-based cancer screening for colorectal, cervical, and breast cancer. Between the two measures of screening, the study examines association using Spearman’s rank correlation and concordance using Percent Agreement and Gwet’s Agreement Coefficient. Health information exchange and self-reports, both provided unique information in measuring cancer screening, and the most robust measurement approach entails collecting screening information from both HIE and patient self-report. In this study, we find evidence of measurement error in self-reports in terms of reporting bias. The majority of the publicly available datasets collect information on cancer screening behavior through patient interviews which are self-reported and may suffer from potential measurement errors. The third essay uses a nationwide population-based database and examines the true, unbiased impact of Medicaid expansion on cancer screening for breast, colorectal, cervical, and prostate cancers after correcting for any bias due to possible misclassification of the self-reported screening status. This study conducts a modified two-way fixed effects probit model under a difference-in-difference estimation setting to identify and correct the errors in the self-reports and estimate the unbiased program effect which shows positive impact on cancer screening with increased effect sizes.

<p> A simple model, expressed in terms of cloud amounts and heights, and air temperature (recorded hourly at Ft. St. John, B.C.) and daily radiosonde records (from St. Nelson, B.C.), is used in an attempt to approximate incoming solar and net radiation values at a nearby pasture site, where hourly measurements were recorded. Results from the model indicate that measured values on a daily basis were estimated within 20%, while five and ten day running means were calculated within 10% of the measured means. </p> / Thesis / Bachelor of Arts (BA)

Structured electronic health record (EHR) data are commonly incomplete and can lack diagnostic detail. Clinical reports, on the other hand, are typically comprehensive and contain a wealth of detailed medical information. Pathologists invest considerable time and specialized training to create information-rich pathology reports, but the necessary manual review of these reports for clinical or research use is a high barrier to their routine utilization. The automated extraction of clinical targets directly from pathology reports would allow for the structured aggregation of relevant patient data that are not currently routinely captured in the EHR. In this dissertation, I apply recently developed transformer models to predict clinical targets from cancer pathology report text. In the first chapter, I present a pathology report corpus that I fully processed and made publicly available, and perform a proof-of-concept cancer type classification. In the second chapter, I discuss a set of cancer stage classification models that I fine-tune on the pathology report corpus and then externally validate on reports from Columbia University Irving Medical Center (CUIMC). In the last chapter, I explore additional applications for this methodology, developing a generalizable model to classify prostate cancer reports into primary Gleason score categories, applying a transformer model to classify reports into diagnosis categories for a Barrett’s esophagus patient cohort in a low-data environment, and performing a proof-of-concept prediction of adverse drug events from 1D drug representations.