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Browsing by Author "Dixon, Brian"
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Item Cancer reporting: timeliness analysis and process reengineering(2015-11-09) Jabour, Abdulrahman M.; Jones, Josette; Dixon, Brian; Haggstrom, David; Davide, BolchiniIntroduction: Cancer registries collect tumor-related data to monitor incident rates and support population-based research. A common concern with using population-based registry data for research is reporting timeliness. Data timeliness have been recognized as an important data characteristic by both the Centers for Disease Control and Prevention (CDC) and the Institute of Medicine (IOM). Yet, few recent studies in the United States (U.S.) have systemically measured timeliness. The goal of this research is to evaluate the quality of cancer data and examine methods by which the reporting process can be improved. The study aims are: 1- evaluate the timeliness of cancer cases at the Indiana State Department of Health (ISDH) Cancer Registry, 2- identify the perceived barriers and facilitators to timely reporting, and 3- reengineer the current reporting process to improve turnaround time. Method: For Aim 1: Using the ISDH dataset from 2000 to 2009, we evaluated the reporting timeliness and subtask within the process cycle. For Aim 2: Certified cancer registrars reporting for ISDH were invited to a semi-structured interview. The interviews were recorded and qualitatively analyzed. For Aim 3: We designed a reengineered workflow to minimize the reporting timeliness and tested it using simulation. Result: The results show variation in the mean reporting time, which ranged from 426 days in 2003 to 252 days in 2009. The barriers identified were categorized into six themes and the most common barrier was accessing medical records at external facilities. We also found that cases reside for a few months in the local hospital database while waiting for treatment data to become available. The recommended workflow focused on leveraging a health information exchange for data access and adding a notification system to inform registrars when new treatments are available.Item Identify Opiod Use Problem(2018-12) Alzeer, Abdullah Hamad; Jones, Josette; Dixon, Brian; Bair, Matthew; Liu, XiaowenThe aim of this research is to design a new method to identify the opioid use problems (OUP) among long-term opioid therapy patients in Indiana University Health using text mining and machine learning approaches. First, a systematic review was conducted to investigate the current variables, methods, and opioid problem definitions used in the literature. We identified 75 distinct variables in 9 models that majorly used ICD codes to identify the opioid problem (OUP). The review concluded that using ICD codes alone may not be enough to determine the real size of the opioid problem and more effort is needed to adopt other methods to understand the issue. Next, we developed a text mining approach to identify OUP and compared the results with the current conventional method of identifying OUP using ICD-9 codes. Following the institutional review board and an approval from the Regenstrief Institute, structured and unstructured data of 14,298 IUH patients were collected from the Indiana Network for Patient Care. Our text mining approach identified 127 opioid cases compared to 45 cases identified by ICD codes. We concluded that the text mining approach may be used successfully to identify OUP from patients clinical notes. Moreover, we developed a machine learning approach to identify OUP by analyzing patients’ clinical notes. Our model was able to classify positive OUP from clinical notes with a sensitivity of 88% on unseen data. We concluded that the machine learning approach may be used successfully to identify the opioid use problem from patients’ clinical notes.Item Living kidney donor follow-up in a statewide health information exchange: health services utilization, health outcomes and policy implications(2016-05-24) Henderson, Macey Leigh; Stone, Cynthia L.; Dixon, Brian; Harle, Chris; Menachemi, Nir; Holmes, Ann; Fry-Revere, SigridLiving donors have contributed about 6,000 kidneys per year in the past 10 years, but more than 100,000 individuals are still waiting for a kidney transplant. Living kidney donors undergo a major surgical procedure without direct medical benefit to themselves, but comprehensive follow-up information on living donors’ health is unfortunately limited. Expert recommendations suggest capturing clinical information beyond traditional sources to improve surveillance of co-morbid conditions from living kidney donors. Currently the United Network for Organ Sharing is responsible for collecting and reporting follow-up data for all living donors from U.S. transplant centers. Under policy implemented in February of 2013, transplant centers must submit follow-up date for two years after donation, but current processes often yield to incomplete and untimely reporting. This dissertation uses a statewide Health Information Exchange as a new clinical data source to 1) retrospectively identify a cohort of living kidney donors, 2) understand their follow-up care patterns, and 3) observe selected clinical outcomes including hypertension, diabetes and post-donation renal function.Item Young people in recovery from substance use disorders: an analysis of a recovery high school's impact on student academic performance & recovery success(2017-12-18) Knotts, Adam Christopher; Stone, Cynthia; Dixon, Brian; Harle, Chris; Pfeifle, Bill; Rattermann, Mary JoThe purpose of this dissertation was to produce knowledge on the academic performance and recovery success of students enrolled in a Recovery High School. The study site was Hope Academy, located in Indianapolis, IN, and at the time of this publication, one of just five schools in the U.S. accredited by the Association of Recovery Schools. Students enrolled between Fall 2010 and Spring 2017 were evaluated using academic test scores (NWEA-MAP), a measure of recovery success (GAIN-SS), as well as key informant interviews with 13 students and five staff members. It was concluded that recovery school students displayed similar levels of academic growth when compared to a nationallyrepresentative matched Virtual Comparison Group, t-stat = +0.849 (p=0.397). This finding provides evidence that even after experiencing a relapse, recovery school students were capable of achieving similar levels of academic growth as their peers not in recovery from substance use disorders. Interview participants provided more context to the quantitative findings with first-hand accounts of the impact the recovery school had on students.