Data Consult Service: Can we use observational data to address immediate clinical needs?
暂无分享,去创建一个
George Hripcsak | Anna Ostropolets | Ruijun Chen | Philip Zachariah | Patrick Ryan | P. Zachariah | G. Hripcsak | A. Ostropolets | Ruijun Chen | Patrick Ryan
[1] Jason W Busse,et al. What is your research question? An introduction to the PICOT format for clinicians. , 2012, The Journal of the Canadian Chiropractic Association.
[2] George Hripcsak,et al. Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) , 2020, J. Am. Medical Informatics Assoc..
[3] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .
[4] Ross D. Williams,et al. Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network , 2020, PloS one.
[5] M. Schuemie,et al. Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications , 2017, Drug Safety.
[6] William DuMouchel,et al. Interpreting observational studies: why empirical calibration is needed to correct p-values , 2013, Statistics in medicine.
[7] Charles E. Leonard,et al. Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review , 2018, Drug Safety.
[8] George Hripcsak,et al. Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network , 2017, Epilepsia.
[9] George Hripcsak,et al. Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study , 2020, The Lancet Rheumatology.
[10] L. Logan,et al. Ceftriaxone-Associated Biliary and Cardiopulmonary Adverse Events in Neonates: A Systematic Review of the Literature , 2017, Pediatric Drugs.
[11] S. Schneeweiss,et al. Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs , 2016, Clinical pharmacology and therapeutics.
[12] Vijay Sivaraman,et al. Bringing cohort studies to the bedside: framework for a 'green button' to support clinical decision-making. , 2015, Journal of comparative effectiveness research.
[13] George Hripcsak,et al. Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping , 2019, J. Biomed. Informatics.
[14] P. Gorman,et al. Clinical questions raised by clinicians at the point of care: a systematic review. , 2014, JAMA internal medicine.
[15] George Hripcsak,et al. Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin , 2018, JAMA network open.
[16] G. Caughey,et al. Documentation of penicillin adverse drug reactions in electronic health records: inconsistent use of allergy and intolerance labels , 2017, Internal medicine journal.
[17] M. Ebell,et al. Analysis of questions asked by family doctors regarding patient care , 1999, BMJ.
[18] G. Hripcsak,et al. Characterizing physicians’ information needs related to a gap in knowledge unmet by current evidence , 2020, JAMIA open.
[19] George Hripcsak,et al. PheValuator: Development and evaluation of a phenotype algorithm evaluator , 2019, J. Biomed. Informatics.
[20] Paul A. Harris,et al. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability , 2016, J. Am. Medical Informatics Assoc..
[21] J. Karnon,et al. Variation in Clinical Practice: A Priority Setting Approach to the Staged Funding of Quality Improvement , 2016, Applied Health Economics and Health Policy.
[22] Rod J. Rohrich,et al. The Levels of Evidence and Their Role in Evidence-Based Medicine , 2011, Plastic and reconstructive surgery.
[23] G. Hripcsak,et al. Metformin Is Associated With a Lower Risk of Atrial Fibrillation and Ventricular Arrhythmias Compared With Sulfonylureas , 2021, Circulation. Arrhythmia and electrophysiology.
[24] George Hripcsak,et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis , 2019, The Lancet.
[25] Amit X Garg,et al. Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes , 2013, BMC Nephrology.
[26] S. Cuschieri. The STROBE guidelines , 2019, Saudi journal of anaesthesia.
[27] David A Cook,et al. Barriers and decisions when answering clinical questions at the point of care: a grounded theory study. , 2013, JAMA internal medicine.
[28] Himanshu Sharma,et al. Developing a portable natural language processing based phenotyping system , 2018, BMC Medical Informatics and Decision Making.
[29] Anna Ostropolets,et al. A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time , 2020, J. Am. Medical Informatics Assoc..
[30] Spiros C. Denaxas,et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential , 2017, European heart journal.
[31] James M. Walker,et al. Bridging the inferential gap: the electronic health record and clinical evidence. , 2007, Health affairs.
[32] Catherine P. Bradshaw,et al. Assessing the Generalizability of Randomized Trial Results to Target Populations , 2015, Prevention Science.
[33] J. Busse,et al. Evidence-based medicine and precision medicine: Complementary approaches to clinical decision-making , 2018, Precision clinical medicine.
[34] M. Graber,et al. Diagnosis: Interpreting the Shadows , 2017 .
[35] N B Giuse,et al. Clinical medical librarianship: the Vanderbilt experience. , 1998, Bulletin of the Medical Library Association.
[36] N. Shah,et al. A 'green button' for using aggregate patient data at the point of care. , 2014, Health affairs.
[37] Banafsheh Sadeghi,et al. Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States. , 2010, Thrombosis research.
[38] Martijn J Schuemie,et al. Evaluating large-scale propensity score performance through real-world and synthetic data experiments. , 2018, International journal of epidemiology.
[39] Lucy Savitz,et al. Challenges in Using Electronic Health Record Data for CER: Experience of 4 Learning Organizations and Solutions Applied , 2013, Medical care.
[40] D. Madigan,et al. A Systematic Statistical Approach to Evaluating Evidence from Observational Studies , 2014 .
[41] Wendy Novicoff,et al. Information exchange in the NICU: what sources of patient data do physicians prefer to use? , 2004, Int. J. Medical Informatics.
[42] Alison Callahan,et al. It is time to learn from patients like mine , 2019, npj Digital Medicine.
[43] P. Falzer. Evidence-based medicine's curious path: From clinical epidemiology to patient-centered care through decision analysis. , 2020, Journal of evaluation in clinical practice.
[44] Yu-Chuan Li,et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers , 2015, MedInfo.
[45] K. Bollaerts,et al. Bias due to differential and non-differential disease- and exposure misclassification in studies of vaccine effectiveness , 2018, PloS one.
[46] Patrick B. Ryan,et al. Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets , 2016, EGEMS.
[47] G. Guyatt,et al. Drug treatments for covid-19: living systematic review and network meta-analysis , 2020, BMJ.
[48] George Hripcsak,et al. How Confident Are We about Observational Findings in Healthcare: A Benchmark Study. , 2020, Harvard data science review.
[49] Rae Woong Park,et al. Characterizing treatment pathways at scale using the OHDSI network , 2016, Proceedings of the National Academy of Sciences.
[50] Andrew E. Williams,et al. An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza , 2020, medRxiv.
[51] The spectre of Berkson's paradox: Collider bias in Covid‐19 research , 2020 .
[52] George Hripcsak,et al. Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study , 2020, J. Am. Medical Informatics Assoc..
[53] R. Kelishadi,et al. Personal, technical and organisational factors affect whether physicians seek answers to clinical questions during patient care: a literature review. , 2020, Health information and libraries journal.
[54] Richard Smith. What clinical information do doctors need? , 1996, BMJ.
[55] J. Johnston,et al. A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results , 2015, Trials.
[56] Melissa A. Basford,et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. , 2013, Journal of the American Medical Informatics Association : JAMIA.
[57] George Hripcsak,et al. Next-generation phenotyping of electronic health records , 2012, J. Am. Medical Informatics Assoc..