By doing so, it imposes no risk for revealing personally identifiable information. These approaches are usually based on some probability-based logic and completely bypass the use of real patient-level data. To circumvent these challenges, some organizations and individuals have developed different approaches to synthesize clinical data. This is particularly challenging in healthcare, where health records contain highly sensitive information and are strictly protected by laws and organizational policies. However, obtaining real-world data can be costly and often presents ethical challenges such as privacy concerns. However, its validity has not been fully examined, which poses questions for its broader adoption.Īccess to data is essential for research, implementation and training across disciplines. To output a more realistic data set, we propose that synthetic data generators should consider important quality measures in their logic and model when clinicians may deviate from standard practice.Ĭlinical data synthesis is an emerging technique that has the potential to boost clinical research, system implementation and training, while protecting patient privacy. ![]() Synthea and other synthetic patient generators do not currently model for deviations in care and the potential outcomes that may result from care deviations. However, its capabilities to model heterogeneous health outcomes post services are limited. ![]() Results show that Synthea is quite reliable in modeling demographics and probabilities of services being offered in an average healthcare setting. No Synthea residents had complications after Hip/Knee Replacement (Massachusetts: 2.9%, national: 2.8%) or had their blood pressure controlled after being diagnosed with hypertension (Massachusetts: 74.52%, national: 69.7%). Using an expanded logic, this rate increased to 5.7%. Of the 409 eligible patients, 0.7% of died within 30 days after COPD exacerbation, versus 7% reported in Massachusetts and 8% nationally. Of the total Synthea Massachusetts population ( n = 1,193,439), 394,476 were eligible for the “colorectal cancer screening” quality measure, and 248,433 (63%) were considered compliant, compared to the publicly reported Massachusetts and national rates being 77.3 and 69.8%, respectively. Calculated rates were then compared with publicly reported rates based on real-world data of Massachusetts and United States. Four quality measures, Colorectal Cancer Screening, Chronic Obstructive Pulmonary Disease (COPD) 30-Day Mortality, Rate of Complications after Hip/Knee Replacement, and Controlling High Blood Pressure, were selected based on clinical significance. We selected a representative 1.2-million Massachusetts patient cohort generated by Synthea. We examined an open-source well-documented synthetic data generator Synthea, which was composed of the key advancements in this emerging technique. This study fills this gap by calculating clinical quality measures using synthetic data. ![]() However, its validity has not been fully examined, and no previous study has validated it from the perspective of healthcare quality, a critical aspect of a healthcare system. It protects patient confidentiality, deepens our understanding of the complexity in healthcare, and is a promising tool for situations where real world data is difficult to obtain or unnecessary. Clinical data synthesis aims at generating realistic data for healthcare research, system implementation and training.
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