VisualizingHealthcareSystemDynamics(HSD)inBiomedicalBigData Acknowledgements Biomedical Big Data, such as electronic health records (EHR) and administrative claims, are records of patients’ interactions with the healthcare system; for example, the date of a diagnosis is when a physician entered the code into the EHR, not when the patient developed the disease. Most researchers are either unaware of the distinction or naively treat it as noise. However, using data visualizations, we show that these subtle effects of the healthcare system on observational clinical data actually contain valuable information that could benefit biomedical research, clinical care, and health care policy. NickBenik1;KatyBorner,PhD2;NickBrown1;DanielHalsey2;IsaacKohane,MD,PhD1;GriffinWeber,MD,PhD1 1HarvardMedicalSchool;2IndianaUniversityhttp://HealthcareSystemDynamics.org AddingHSDtothei2b2Platform Abstract This work was conducted with support from National Institutes of Health, National Cancer Institute, RFAͲHGͲ14Ͳ020, Targeted Software Development: Data Visualization, Grant Number 1 U01 CA198934Ͳ01. PrototypeVisualizations 3DWLHQW3DWKRSK\VLRORJ\ +HDOWKFDUH6\VWHP'\QDPLFV 1RUPDO $EQRUPDO 1RUPDO +LJKHVW6XUYLYDO 0RGHUDWH6XUYLYDO $EQRUPDO 0RGHUDWH6XUYLYDO /RZHVW6XUYLYDO 3DWLHQW3DWKRSK\VLRORJ\ +HDOWKFDUH6\VWHP'\QDPLFV 'DWD4XDOLW\ 3DWLHQW'HPRJUDSKLFV 'LDJQRVHV /DERUDWRU\7HVW5HVXOWV 9LWDO6LJQV *HQHWLF0DUNHUV 1XPEHURI2EVHUYDWLRQV 7LPHRI'D\RI2EVHUYDWLRQV 7LPH%HWZHHQ2EVHUYDWLRQV &RVWRID7HVWRU7UHDWPHQW &OLQLFDO6HWWLQJ&OLQLFLDQ7\SH 'DWD(QWU\(UURUV 'LFWDWLRQ0LVWDNHV 'DWD&RPSUHVVLRQ/RVV 8QVWUXFWXUHG'DWD 0LVVLQJ'DWD Hypothesis Every observation in EHR data combines both patient pathophysiology and healthcare system dynamics (HSD). Each predict patient outcomes. PredictingSurvivalUsingHSD The table indicates the fraction of patients alive 3 years after a white blood cell (WBC) test. Columns correspond to patient pathophysiology categories and rows correspond to HSD categories. An abnormally high WBC in the evening has a higher survival (85.74%) than a normal WBC in the morning (82.91%)! Lowest survival combines both abnormal patient pathophysiology and abnormal HSD. FactCountGrowthCurves Fact count is the total number of diagnoses, medications, procedures, etc. in a patient’s record. Fact count rate grows over time, but at different rates for different patients (blue curves). Patients with a normal fact count (50th percentile) have the highest 3 year survival. For example, a 70 yearͲold man with a median fact count has the same survival rate (90%) as a 45 yearͲold man in the 95th fact count percentile and a 58 yearͲold man in the 5th fact count percentile. 0DOH )HPDOH DerivingNormalLabValueRangesUsingHSD Clinicians repeat laboratory tests more frequently (an HSD variable) if the values are abnormal. As the values become more extreme, the variance in repeat interval across patients decreases. Thus, more clinically useful normal ranges for lab tests can be defined based physician ordering behavior. Subgroups of patients can also be identified from the HSD data (right, pediatric age groups). HSD concepts (e.g., fact count, day of the week, etc.) are added to the i2b2 ontology and can be used in queries in combination with patient pathophysiology concepts (e.g., diagnoses, lab values, etc.). Download from http://HealthcareSystemDynamics.org PatientSurvivalbyWhiteBloodCell(WBC)CountandTimeofDay AgeandFactCountDistributionsofMultiplePatientCohorts This project integrates two widely adopted open source software platforms for querying clinical data repositories (Informatics for Integrating Biology and the Bedside, i2b2, https://www.i2b2.org) and developing modular data analysis and visualization tools (Science of Science, Sci2, https://sci2.cns.iu.edu). Below are screenshots of HSD visualizations built using these two systems. For more information visit htt