Browsing by Author "Mendonca, Eneida"

Now showing 1 - 6 of 6
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    Electronic Health Record (EHR) Data Quality and Type 2 Diabetes Mellitus Care
    (2022-06) Wiley, Kevin Keith, Jr.; Vest, Joshua; Blackburn, Justin; De Groot, Mary; Menachemi, Nir; Mendonca, Eneida
    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.
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    Exploring Racial and Age Disproportionalities in COVID-19 Positive Pediatric Cohort
    (Indiana Medical Student Program for Research and Scholarship (IMPRS), 2020-12-15) Freeman, Emily; Song, Yiqiang; Allen, Katie; Hui, Siu; Mendonca, Eneida; Department of Pediatrics, IU School of Medicine
    Background: Social and health inequities place marginalized populations at increased risk of contracting the novel coronavirus 2019 (COVID-19). While COVID-19 literature continues to accumulate, there remains a lack of comprehensive epidemiological data on COVID-19 in children. The study aims to identify demographic trends in disease severity amongst COVID-19 positive pediatric patients. Methods: We analyzed the medical records of 2217 laboratory-confirmed COVID-19 pediatric patients, ages 0-18, across Indiana. Working with Regenstrief Institute Center of Biomedical Informatics, data was extracted from the databases of Indiana Network for Patient Care, Indiana University Health, and Eskenazi Health from February 28th, 2020 to July 13th, 2020. Factors of interest were age, race, and ethnicity. The study assessed the clinical outcome of disease severity which was defined by one of the following clinical designations: outpatient management exclusively, emergency care without hospital admission, non-pediatric intensive care unit (PICU) hospitalization, PICU hospitalization, and death. Results: The laboratory confirmed COVID-19 pediatric cohort was composed of 12.2% (N= 270) Black or African American, 49.3% (N=1094) white, and 3.2% (N= 71) American Indian/Alaska Native, Asian/Pacific Islander, and Multiracial combined group. 34.4% of Black or African American patients required emergency (12.2%) or inpatient care (22.2%) while 24.4% white patients required emergency (7.0%) or inpatient care (17.3%). 17.6% of the cohort was 0-5 years old, 24.8% was 6-12 years old, and 57.6% was 13-18 years old. 30.9% of the 0-5 age group required emergency or inpatient care while the percentages of the 6-12 age group and 13-18 age group requiring emergency or inpatient care were 20.6% and 18.9%, respectively. Conclusion: While our data is preliminary and requires additional validation, our exploration of racial and age disproportionalities in pediatric coronavirus severity serves to expand on the current COVID-19 literature and understanding of this virus.
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    Quantifying Electronic Health Record Data Quality in Telehealth and Office-Based Diabetes Care
    (Thieme, 2022) Wiley, Kevin K.; Mendonca, Eneida; Blackburn, Justin; Menachemi, Nir; De Groot, Mary; Vest, Joshua R.; Health Policy and Management, School of Public Health
    Objective: Data derived from the electronic health record (EHR) are commonly reused for quality improvement, clinical decision-making, and empirical research despite having data quality challenges. Research highlighting EHR data quality concerns has largely been examined and identified during traditional in-person visits. To understand variations in data quality among patients managing type 2 diabetes mellitus (T2DM) with and without a history of telehealth visits, we examined three EHR data quality dimensions: timeliness, completeness, and information density. Methods: We used EHR data (2016-2021) from a local enterprise data warehouse to quantify timeliness, completeness, and information density for diagnostic and laboratory test data. Means and chi-squared significance tests were computed to compare data quality dimensions between patients with and without a history of telehealth use. Results: Mean timeliness or T2DM measurement age for the study sample was 77.8 days (95% confidence interval [CI], 39.6-116.4). Mean completeness for the sample was 0.891 (95% CI, 0.868-0.914). The mean information density score was 0.787 (95% CI, 0.747-0.827). EHR data for patients managing T2DM with a history of telehealth use were timelier (73.3 vs. 79.8 days), and measurements were more uniform across visits (0.795 vs. 0.784) based on information density scores, compared with patients with no history of telehealth use. Conclusion: Overall, EHR data for patients managing T2DM with a history of telehealth visits were generally timelier and measurements were more uniform across visits than for patients with no history of telehealth visits. Chronic disease care relies on comprehensive patient data collected via hybrid care delivery models and includes important domains for continued data quality assessments prior to secondary reuse purposes.
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    Retrospective Chart Review Comparing CKD COVID-19 Positive Patient Outcomes to non-CKD Patient Outcomes
    (Indiana University, 2020) Eckert, Nicole; Sankari, Safiya; Allen, Katie; Hui, Siu Lui; Mendonca, Eneida; Health Policy and Management, School of Public Health
    Background/Objective: Since January 2020, there have been over 3 million individuals infected with the coronavirus in the United States, quickly spreading across at least 171 countries. The severity and morbidity of patients with COVID-19 are significantly increased when comorbidities, such as Chronic Kidney Disease (CKD), are present. Because the main target of SARS-CoV-2 is ACE2, patients with CKD may be a more vulnerable population. The goal of this study was to determine if COVID-19 positive patients with CKD had increased mortality, inpatient admission, and ED visitation rates compared to those without CKD. Methods: This retrospective chart review includes patients from over 100 separate healthcare entities who were diagnosed with COVID-19 between January 1, 2020 and July 13, 2020 and are over the age of 18. The subjects were first separated into those diagnosed with CKD and those without, basic descriptive calculations were computed, and a Chi Square test was used to analyze outcomes. Results: The CKD COVID-19 positive population was compromised of 47.5% men and 52.5% women while the non-CKD control group was made up of 45.4 % men, 54.1% women, and 0.5% other. The median Charlson index for the CKD and non-CKD population was 4 and 1, respectively. The interest and control groups were further divided into subpopulations by age and race and analyzed accordingly. Chi square tests demonstrated that there is a statistically significant difference (p<0.05) in all clinical outcomes tested of CKD patients diagnosed with COVID-19 compared to non-CKD patients. The CKD population had increased mortality, inpatient admission, and ED visitation rates when compared. Discussion: This study demonstrates that comorbidities, more specifically CKD, may be associated with a higher severity of COVID-19 than those without. Future studies are needed to explore the relationship more extensively, analyze other outcomes, and manage confounding variables.
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    Ten year trends in hospital encounters for pediatric asthma: an Indiana experience
    (Taylor & Francis, 2021) Rogerson, Colin; He, Tian; Rowan, Courtney; Tu, Wanzhu; Mendonca, Eneida; Pediatrics, School of Medicine
    INTRODUCTION: Pediatric asthma is a common cause of emergency department visits, hospital admissions, and mortality. Population incidence studies have historically used large-scale survey data. We measured these epidemiologic trends using a health information exchange. METHODS: In this retrospective cohort study, we used electronic health record data from a regional health information exchange to study clinical trends in pediatric patients presenting to the hospital for asthma in the State of Indiana. Data was obtained from 2010 to 2019 and included all patients ages 2-18 years. Study participants were identified using international classification of disease codes. The measured outcomes were number of hospital encounters per year, percentage of admissions per year, and mortality rates. RESULTS: Data included 50,393 unique patients and 88,772 unique encounters, with 57% male patients. Over the ten-year period, hospital encounters ranged from 5000 to 8000 per year with no change in trajectory. Between 2010 and 2012, the percent of encounters admitted to the hospital was ∼30%. This decreased to ∼20-25% for 2015-2019. Patient mortality rates increased from 1 to 3 per 1000 patient encounters in 2010-2014 to between 5 and 7 per 1000 patient encounters from 2016 to 2019. White patients had a significantly higher admission percentage compared to other racial groups, but no difference in mortality rate. CONCLUSIONS: Asthma continues to be a common condition requiring hospital care for pediatric patients. Regional health information exchanges can enable public health researchers to follow asthma trends in near real time, and have potential for informing patient-level public health interventions.
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    The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics
    (Wiley, 2023) Quinney, Sara K.; Bies, Robert R.; Grannis, Shaun J.; Bartlett, Christopher W.; Mendonca, Eneida; Rogerson, Colin M.; Backes, Carl H.; Shah, Dhaval K.; Tillman, Emma M.; Costantine, Maged M.; Aruldhas, Blessed W.; Allam, Reva; Grant, Amelia; Abbasi, Mohammed Yaseen; Kandasamy, Murugesh; Zang, Yong; Wang, Lei; Shendre, Aditi; Li, Lang; Obstetrics and Gynecology, School of Medicine
    Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.