Visualizing healthcare system dynamics in biomedical big data
Welcome to the website for "Visualizing healthcare system dynamics in biomedical big data", an NIH-funded project in the Weber Lab in the Department of Biomedical Informatics at Harvard Medical School. 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, the proposed research will show, using a novel Data Visualization, 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.
Project dates: Jun 1, 2015 - May 31, 2018
For more information: Please contact Griffin M Weber, MD, PhD
Electronic health records (EHR) and administrative claims databases are transforming medical research by giving investigators access to data on millions of individual patients. Compared to manual paper chart review, these databases reduce the time and cost of clinical studies by orders of magnitude, enabling types of research that were unfeasible in the past. However, investigators often incorrectly treat EHR and claims data as simply big versions of clinical trials data. Yet, there are important differences: During clinical trials, patient information is obtained and recorded in a standardized way and checked for accuracy and completeness. In contrast, EHR and claims are observational databases, which reflect not only the health of the patients, but also their interactions with the healthcare system. For example, the date associated with a code for diabetes is when the physician made the diagnosis, not when the patient first developed the disease. These observations are influenced by the dynamics of the healthcare system--when physicians schedule visits with their patients, which tests physicians decide to order, what codes need to be recorded to get reimbursed for procedures, etc. By ignoring this dimension of the data or naively treating it as noise, investigators risk both misinterpreting the true patient pathophysiology and losing valuable information content. In prior work we showed that analysis of the "healthcare system dynamics" (HSD) dimension of observational databases can actually be more useful than the patient pathophysiology in predicting survival, selecting matched control cohorts, identifying healthy patients, and defining normal ranges of laboratory tests. Yet, conveying the concept of HSD to researchers and helping them use it effectively is difficult. Therefore, focusing on the topic area of Data Visualization, this proposal addresses this challenge of separating healthcare system dynamics from pathophysiology in observational databases, so that Big Data researchers can use both dimensions to generate new knowledge about patient health. To do this, we bring together informatics and data visualization experts who developed two widely adopted open source software platforms for querying clinical data repositories (Informatics for Integrating Biology and the Bedside, i2b2) and developing modular data analysis and visualization tools (Science of Science, Sci2). We will leverage these systems to perform three Specific Aims: (1) Create an extensible ontology for visualizing the HSD dimensions of biomedical Big Data. (2) Develop a prototype interactive visualization to enable investigators to study HSD in Big Data. The visualization will be simple and familiar to investigators, but innovative in that for the first time HSD will be treated as its own informative component of the data. By literally placing HSD on its own dimension, the visualization will show investigators its value and teach them how to use it for research. (3) Demonstrate and evaluate the visualizations using three sources of biomedical Big Data: EHR data from two hospital systems in Boston with a total of 7 million patients and nationwide claims data from Aetna health insurance with 34 million patients.
The script below adds HSD extensions to i2b2 version 1.7 or higher. The first part of the script should be run on the i2b2 clinical research chart (CRC) cell, and the second part should be run on the i2b2 ontology (ONT) cell. The script requires Microsoft SQL Server 2008 (or newer).
The following HSD concepts are included in the database script:
- Fact Count (the total number of data facts a patient has)
- Fact Count Percentile (in the range 0%-99%)
- Time Period - Weekday (day of the week, Sunday to Saturday)
- Time Period - Month (month of the year, January to December)
Download ontology: HSD_Extensions_For_i2b2.txt
Launch an i2b2 instance with the HSD ontology in a new browser window/tab. Use the default username and password. The underlying data is the demo dataset that comes with the i2b2 software, available at http://www.i2b2.org.
The items below illustrate our newest software tools. However, they are still under development, and therefore will be unavailable at certain times and might differ from the final software.
- Our development i2b2 HSD website contains an expanded HSD ontology (e.g., "hour of the day"), an HSD "fact count" visualization plugin, and an HSD laboratory test visualization plugin.
- Download the ontology used in the development website.
- Try an example of our laboratory test heatmap visualization illustrating patient survival rates following a white blood cell (WBC) count laboratory test. Patients are grouped by the value of the test result as well as the time of day of the test, in either 24 one-hour blocks or 3 eight-hour blocks.
Consistent with our Data Sharing Policy, we are pleased to share datasets that we have been using to develop and test our software.
- Our first dataset contains 100,000 records for each of 97 common laboratory tests and 1 million white blood cell (WBC) count tests. The data come from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, between 1986 and 2004. Instructions on how to obtain the dataset can be found here.
- Our second dataset contains more than 8 million records for 272 laboratory tests, along with three-year survival rates and 30-day readmission rates of the patients. The manuscript describing the data is currently under review. We will make the dataset available here upon acceptance to the journal.