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  1. Article: Improving access to healthcare and the impact of medical practice in West Virginia.

    Burdick, Hoyt J

    The West Virginia medical journal

    2013  Volume 109, Issue 4, Page(s) 6

    MeSH term(s) Health Services Accessibility ; Health Status ; Humans ; West Virginia
    Language English
    Publishing date 2013-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
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  2. Article: Change of pace...change of direction.

    Burdick, Hoyt J

    The West Virginia medical journal

    2013  Volume 109, Issue 1, Page(s) 4–5

    MeSH term(s) Humans ; Politics ; Societies, Medical ; State Government ; West Virginia
    Language English
    Publishing date 2013-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
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  3. Article: Deadlines for payments or penalties.

    Burdick, Hoyt J

    The West Virginia medical journal

    2013  Volume 109, Issue 2, Page(s) 4–5

    MeSH term(s) Clinical Coding/standards ; Education, Medical, Continuing/standards ; Electronic Prescribing/economics ; Electronic Prescribing/standards ; Humans ; International Classification of Diseases ; Meaningful Use/economics ; Meaningful Use/standards ; Medicaid/economics ; Medicaid/standards ; Medicare/economics ; Medicare/standards ; Patient Protection and Affordable Care Act/standards ; Reimbursement Mechanisms/standards ; United States ; Value-Based Purchasing/standards ; West Virginia
    Language English
    Publishing date 2013-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
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  4. Article: The obsolete image of the ideal physician.

    Burdick, Hoyt J

    The West Virginia medical journal

    2012  Volume 108, Issue 5, Page(s) 4–5

    MeSH term(s) Clinical Competence/standards ; Delivery of Health Care/standards ; Humans ; Physicians/standards ; United States
    Language English
    Publishing date 2012-09
    Publishing country United States
    Document type Addresses
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
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  5. Article: Money & medicine.

    Burdick, Hoyt J

    The West Virginia medical journal

    2012  Volume 108, Issue 6, Page(s) 4–5

    MeSH term(s) Delivery of Health Care/economics ; Delivery of Health Care/standards ; Health Care Costs ; Humans ; Policy Making ; United States ; Unnecessary Procedures/economics
    Language English
    Publishing date 2012-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
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  6. Article ; Online: A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

    Angier Allen / Anna Siefkas / Emily Pellegrini / Hoyt Burdick / Gina Barnes / Jacob Calvert / Qingqing Mao / Ritankar Das

    Applied Sciences, Vol 11, Iss 5576, p

    2021  Volume 5576

    Abstract: Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have ... ...

    Abstract Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUC adversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.
    Keywords digital twins ; variational autoencoder ; machine learning ; algorithm ; stroke ; disease forecasting ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 310
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

    Lam, Carson / Siefkas, Anna / Zelin, Nicole S / Barnes, Gina / Dellinger, R Phillip / Vincent, Jean-Louis / Braden, Gregory / Burdick, Hoyt / Hoffman, Jana / Calvert, Jacob / Mao, Qingqing / Das, Ritankar

    Clinical therapeutics

    2021  Volume 43, Issue 5, Page(s) 871–885

    Abstract: Purpose: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, ... ...

    Abstract Purpose: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.
    Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.
    Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).
    Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.
    MeSH term(s) Adenosine Monophosphate/analogs & derivatives ; Adenosine Monophosphate/therapeutic use ; Adolescent ; Adrenal Cortex Hormones/therapeutic use ; Adult ; Aged ; Aged, 80 and over ; Alanine/analogs & derivatives ; Alanine/therapeutic use ; Antiviral Agents/therapeutic use ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Young Adult ; COVID-19 Drug Treatment
    Chemical Substances Adrenal Cortex Hormones ; Antiviral Agents ; remdesivir (3QKI37EEHE) ; Adenosine Monophosphate (415SHH325A) ; Alanine (OF5P57N2ZX)
    Language English
    Publishing date 2021-03-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603113-4
    ISSN 1879-114X ; 0149-2918
    ISSN (online) 1879-114X
    ISSN 0149-2918
    DOI 10.1016/j.clinthera.2021.03.016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: The State Innovation Model (SIM) Plan: A Practical Guide for Practitioners.

    Austin, Joshua L / Burdick, Hoyt / Chouinard, Sarah / Coben, Jeffrey / Colenda, Christopher / King, Dana E

    The West Virginia medical journal

    2016  Volume 112, Issue 5, Page(s) 12–15

    MeSH term(s) Budgets/legislation & jurisprudence ; Delivery of Health Care/economics ; Delivery of Health Care/legislation & jurisprudence ; Financing, Organized/legislation & jurisprudence ; General Practice/legislation & jurisprudence ; Humans ; Medicaid/economics ; Medicaid/legislation & jurisprudence ; Medicare/economics ; Medicare/legislation & jurisprudence ; Patient Care Planning/economics ; Patient Care Planning/legislation & jurisprudence ; United States ; West Virginia
    Language English
    Publishing date 2016-09
    Publishing country United States
    Document type Journal Article ; Practice Guideline
    ZDB-ID 431705-1
    ISSN 0043-3284
    ISSN 0043-3284
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.

    Burdick, Hoyt / Lam, Carson / Mataraso, Samson / Siefkas, Anna / Braden, Gregory / Dellinger, R Phillip / McCoy, Andrea / Vincent, Jean-Louis / Green-Saxena, Abigail / Barnes, Gina / Hoffman, Jana / Calvert, Jacob / Pellegrini, Emily / Das, Ritankar

    Journal of clinical medicine

    2020  Volume 9, Issue 12

    Abstract: Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom ... ...

    Abstract Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (
    Language English
    Publishing date 2020-11-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm9123834
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  10. Article ; Online: A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study.

    Allen, Angier / Mataraso, Samson / Siefkas, Anna / Burdick, Hoyt / Braden, Gregory / Dellinger, R Phillip / McCoy, Andrea / Pellegrini, Emily / Hoffman, Jana / Green-Saxena, Abigail / Barnes, Gina / Calvert, Jacob / Das, Ritankar

    JMIR public health and surveillance

    2020  Volume 6, Issue 4, Page(s) e22400

    Abstract: Background: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through ... ...

    Abstract Background: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups.
    Objective: The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups.
    Methods: Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care-III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE).
    Results: The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively).
    Conclusions: This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.
    Keywords covid19
    Language English
    Publishing date 2020-10-22
    Publishing country Canada
    Document type Journal Article
    ISSN 2369-2960
    ISSN (online) 2369-2960
    DOI 10.2196/22400
    Database MEDical Literature Analysis and Retrieval System OnLINE

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