Accuracy of Heart Disease Prevalence Estimated from Claims Data Compared With an Electronic Health Record

Keywords

Medicine, Confidence interval, Population, Psychological intervention, Medical record

References

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• Are Claims Data Accurate Enough to Identify Patients for Performance Measures or Quality Improvement? The Case of Diabetes, Heart Disease, and DepressionLeif I. Solberg, Karen I Engebretson, JoAnn M. Sperl‐Hillen — 2006
• Self-report as an indicator of incident disease.Tuula Oksanen, Mika Kivimäki, Jaana Pentti — 2010
• Identifying opportunities for a medical group to improve outcomes for patients with coronary artery disease and heart failure: an exploratory study.Courtney Jordan Baechler, Thomas E. Kottke — 2011
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• International classification of diseases: ninth revision.National Cancer Institute — 1978
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Cited by

• Validation of colonoscopic findings from a structured endoscopic documentation database against manually collected medical records dataOtto S. Lin, Danielle La Selva, Jae-Myung Cha — 2016
• Enhancing Learning, Innovation, Adaptation, and Sustainability in Health Care Organizations: The ELIAS Performance Management FrameworkD. David Persaud — 2014
• The Electronic Health Record for Translational ResearchLuke V. Rasmussen — 2014
• Agreement of Medicaid claims and electronic health records for assessing preventive care quality among adultsJohn Heintzman, Steffani R. Bailey, Megan Hoopes — 2014
• Proton pump inhibitor use by aspirin-treated coronary artery disease patients is not associated with increased risk of cardiovascular events.Laura A. Fortuna, Pamala A. Pawloski, Emily D. Parker — 2016
• Measuring Preventive Care DeliverySteffani R. Bailey, John Heintzman, Miguel Marino — 2016
• Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015Theo Vos, Christine A. Allen, Megha Arora — 2016
• Asphyxia in the Newborn: Evaluating the Accuracy of ICD Coding, Clinical Diagnosis and Reimbursement: Observational Study at a Swiss Tertiary Care Center on Routinely Collected Health Data from 2012-2015Olga Endrich, Carole Rimle, Marcel Zwahlen — 2017
• A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease casesSantiago Esteban, Manuel Rodríguez Tablado, Ricardo Ignacio Ricci — 2017
• Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016Theo Vos, Amanuel Alemu Abajobir, Kalkidan Hassen Abate — 2017
• Medicaid coverage accuracy in electronic health recordsMiguel Marino, Heather Angier, Steele Valenzuela — 2018
• Systemic nature of spinal muscular atrophy revealed by studying insurance claimsScott Lipnick, Denis Agniel, Rahul Aggarwal — 2019
• Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016Catherine O. Johnson, Minh Nguyen, Gregory A. Roth — 2019
• An Algorithm That Identifies Coronary and Heart Failure Events in the Electronic Health RecordThomas E. Kottke, Courtney Jordan Baechler — 2013
• Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic reviewElliott Bosco, Leon Hsueh, Kevin W. McConeghy — 2021
• Design and Development of a Hospital-Based Coronary Artery Disease (CAD) Registry in IranAli Garavand, Reza Rabiei, Hassan Emami — 2023
• PREVENTING CHRONIC DISEASEH. Madanat, M. Molina, Rachel Mintle — 2013
• HEALTH CARE REFORM ORIGINAL INVESTIGATION Comparative Effectiveness of 2 �-Blockers in Hypertensive Patients

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