Browsing by Subject "Electronic medical records"

Now showing 1 - 6 of 6
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    Changes of glucose levels precede dementia in African Americans with diabetes but not in Caucasians
    (Elsevier, 2018-12) Hendrie, Hugh C.; Zheng, Mengjie; Lane, Kathleen A.; Ambuehl, Roberta; Purnell, Christianna; Li, Shanshan; Unverzagt, Frederick W.; Murray, Michael D.; Balasubramanyam, Ashok; Callahan, Chris M.; Gao, Sujuan; Psychiatry, School of Medicine
    INTRODUCTION Changes in glucose levels may represent a powerful metabolic indicator for dementia in African Americans with diabetes. It is unclear whether these changes also occur in Caucasians. METHODS A secondary data analysis using electronic medical records from 5228 African Americans and Caucasians 65 years and older. Mixed effects models with repeated serum glucose measurements were used to compare changes in glucose levels between African Americans and Caucasian patients with and without incident dementia. RESULTS African Americans and Caucasians with diabetes had significantly different changes in glucose levels by dementia status (p<0.0001). African Americans experienced a significant decline in glucose levels before the dementia diagnosis (estimated glucose decline 1.3421 mg/dL per year, p<0.0001) than those who did not develop dementia. Caucasians with and without dementia showed stable glucose levels over time (p=0.3071). DISCUSSION Significant changes in glucose levels precede dementia in African American patients with diabetes but not in Caucasians.
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    Comparison of health information exchange data with self-report in measuring cancer screening
    (BMC, 2023-07-25) Bhattacharyya, Oindrila; Rawl, Susan M.; Dickinson, Stephanie L.; Haggstrom, David A.; Economics, School of Liberal Arts
    Background: Efficient measurement of the receipt of cancer screening has been attempted with electronic health records (EHRs), but EHRs are commonly implemented within a single health care setting. However, health information exchange (HIE) includes EHR data from multiple health care systems and settings, thereby providing a more population-based measurement approach. In this study, we set out to understand the value of statewide HIE data in comparison to survey self-report (SR) to measure population-based cancer screening. Methods: A statewide survey was conducted among residents in Indiana who had been seen at an ambulatory or inpatient clinical setting in the past year. Measured cancer screening tests included colonoscopy and fecal immunochemical test (FIT) for colorectal cancer, human papilloma virus (HPV) and Pap tests for cervical cancer, and mammogram for breast cancer. For each screening test, the self-reported response for receipt of the screening (yes/no) and 'time since last screening' were compared with the corresponding information from patient HIE to evaluate the concordance between the two measures. Results: Gwet's AC for HIE and self-report of screening receipt ranged from 0.24-0.73, indicating a fair to substantial concordance. For the time since receipt of last screening test, the Gwet's AC ranged from 0.21-0.90, indicating fair to almost perfect concordance. In comparison with SR data, HIE data provided relatively more additional information about laboratory-based tests: FIT (19% HIE alone vs. 4% SR alone) and HPV tests (27% HIE alone vs. 12% SR alone) and less additional information about procedures: colonoscopy (8% HIE alone vs. 23% SR alone), Pap test (13% HIE alone vs. 19% SR alone), or mammography (9% HIE alone vs. 10% SR alone). Conclusion: Studies that use a single data source should consider the type of cancer screening test to choose the optimal data collection method. HIE and self-report both provided unique information in measuring cancer screening, and the most robust measurement approach involves collecting screening information from both HIE and patient self-report.
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    Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange
    (MDPI, 2023-05-05) Holler, Emma; Chekani, Farid; Ai, Jizhou; Meng, Weilin; Khandker, Rezaul Karim; Ben Miled, Zina; Owora, Arthur; Dexter, Paul; Campbell, Noll; Solid, Craig; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and Technology
    This study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011–2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: −1 to −365 days and −180 to −365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011–2013, 2011–2015, and 2011–2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011–2017 data from −1 to −365 and −180 to −365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.
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    Glucose level decline precedes dementia in elderly African Americans with diabetes
    (Elsevier, 2017-02) Hendrie, Hugh C.; Zheng, Mengjie; Li, Wei; Lane, Kathleen; Ambuehl, Roberta; Purnell, Christianna; Unverzagt, Frederick W.; Torke, Alexia; Balasubramanyam, Ashok; Callahan, Chris M.; Gao, Sujuan; Psychiatry, School of Medicine
    INTRODUCTION: High blood glucose levels may be responsible for the increased risk for dementia in diabetic patients. METHODS: A secondary data analysis merging electronic medical records (EMRs) with data collected from the Indianapolis-Ibadan Dementia project (IIDP). Of the enrolled 4105 African Americans, 3778 were identified in the EMR. Study endpoints were dementia, mild cognitive impairment (MCI), or normal cognition. Repeated serum glucose measurements were used as the outcome variables. RESULTS: Diabetic participants who developed incident dementia had a significant decrease in serum glucose levels in the years preceding the diagnosis compared to the participants with normal cognition (P = .0002). They also had significantly higher glucose levels up to 9 years before the dementia diagnosis (P = .0367). DISCUSSION: High glucose levels followed by a decline occurring years before diagnosis in African American participants with diabetes may represent a powerful presymptomatic metabolic indicator of dementia.
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    Prescriptions of opioid-containing drugs in patients with chronic cough
    (Sage, 2024) Weiner, Michael; Liu, Ziyue; Schelfhout, Jonathan; Dexter, Paul; Roberts, Anna R.; Griffith, Ashley; Bali, Vishal; Weaver, Jessica; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. Objectives: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. Design: Retrospective cohort study. Methods: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. Results: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. Conclusion: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.
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    TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease
    (Oxford University Press, 2024) Li, Zuotian; Liu, Xiang; Tang, Ziyang; Jin, Nanxin; Zhang, Pengyue; Eadon, Michael T.; Song, Qianqian; Chen, Yingjie V.; Su, Jing; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. Materials and methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Discussion: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.