QPID (Queriable Patient Inference Dossier) is a health intelligence platform incorporating an EHR (electronic health record) search engine, a scalable library of HIPAA-compliant search queries, and a programming system for application and query development. In February 2013, group members launched a company that uses the platform to retrieve and integrate EHR data into clinical practice.
Using QPID, we have developed and deployed a suite of department-specific applications that substantially improve the integration of EHR information into clinical and administrative work, leading to improved efficiency, safety, and workflow.
This programmable, ontology driven search engine can be used to extract detailed information from a single patient's record or be run against an entire care unit census. Unique to QPID is the ability to search EHRs by clinical concepts and automate complex structured queries whose results can then be integrated into a web browser or other software application. This platform has immense potential with regards to data mining for clinical, research, and administrative uses and has already been implemented within multiple departments at Mass General.
Unique features of QPID:
The Department of Gastroenterology uses a unique QPID alert system to prescreen high-risk patients scheduled for conscious sedation.
The Department of Anesthesia has incoporated QPID as a tool to prescreen and manage patients with diabetes in the operating room.
The Emergency Departments at both Massachusetts General Hospital and Brigham and Women's Hospital utilize a custom built QPID application to rapidly gather and organize salient patient information resulting in better informed healthcare decisions.
QPID Palliative Care
Using published criteria, custom built QPID search queries identify inpatients who might be appropriate referrals for Palliative Care. This has reduced the burden on the small palliative care team, improved patient care, and helped MGH meet pay-for-performance screening targets.
QPID MRI Safety
QPID searches have been developed to prescreen patients scheduled for MRI and alerts healthcare providers to possible contraindications improving safety and limiting workflow disruptions.
QPID Contrast Related Adverse Events Module
A multivariable logistic regression-based prediction score for assessing the risk of contrast media induced nephropathy (CMIN) has been developed based on QPID functionality. This module will be incorporated into the Radiology Order Entry (ROE) system within the next year with the hope of reducing the rate of CMIN throughout the health care enterprise.
Contextual information regarding indications for imaging exams is often lacking. Using QPID, patient specific abstracts can now be generated which are tailored towards a radiologist's needs to improve the quality of interpretation and speed interpretation time.
Back row, left to right: Stefaan Heyvaert, Neeraj Joshi, Gaurav Singal, Abraham Lin, Sarita Nair. Front Row, left to right: Garry Choy, Arun Krishnaraj, Mike Zalis, Mitch Harris, Jim McGaffigan.
Michael E. Zalis, MD
Associate professor, Harvard Medical School
Director, QPID Informatics
Mitchell A. Harris, PhD
Chief architect and chief technology officer, QPID Informatics
Stefaan Heyvaert, MA
Abraham Lin, AB
Jim McGaffigan, MBA
QPID Business Operations Manager
Sarita Nair, MS
Neeraj Joshi, MS
Gaurav Singal, MD
Resident Physician, MGH
Clincial Developer, QPID Informatics
Garry Choy, MD, MSc
Instructor of Radiology, Harvard Medical School
Clinical developer, QPID Informatics
Arun Krishnaraj, MD, MPH
Instructor of Radiology, Harvard Medical School
Clinical developer, QPID Informatics fellow
Position: Developer with Emphasis on Systems Management
Academic research group based at MGH seeks software engineer to assist in implementation of a rich internet, medically oriented search application with a web based front end and database-linked backend. Duties will include general application development work and will include some system administration, including hardware, virtual environment and db management duties. Applicants will work with existing team of developers and managers and will report to chief technology officer of the group.
Experience managing and configuring virtual environments, databases, and web applications.
Position: Developer with Emphasis on Natural Language Processing / Machine Learning
Academic research group based at MGH seeks software engineer to assist in development and implementation of a rich internet, medically oriented search application with a web based front end and database-linked backend. Duties will include application development, deployment and support, with emphasis on natural language processing and machine learning. Applicants will work with existing team of developers and managers and will report to chief technology officer of the group.
Complete list of publications coming soon
RSNA 2011 abstracts