New Query Tool Searches EHR Unstructured Data by Ken Terry.
From the post:
A new electronic health record “intelligence platform” developed at Massachusetts General Hospital (MGH) and its parent organization, Partners Healthcare, is being touted as a solution to the problem of searching structured and unstructured data in EHRs for clinically useful information.
QPID Inc., a new firm spun off from Partners and backed by venture capital funds, is now selling its Web-based search engine to other healthcare organizations. Known as the Queriable Patient Inference Dossier (QPID), the tool is designed to allow clinicians to make ad hoc queries about particular patients and receive the desired information within seconds.
Today, 80% of stored health information is believed to be unstructured. It is trapped in free text such as physician notes and reports, discharge summaries, scanned documents and e-mail messages. One reason for the prevalence of unstructured data is that the standard methods for entering structured data, such as drop-down menus and check boxes, don’t fit into traditional physician workflow. Many doctors still dictate their notes, and the transcription goes into the EHR as free text.
and,
QPID, which was first used in the radiology department of MGH in 2005, incorporates an EHR search engine, a library of search queries based on clinical concepts, and a programming system for application and query development. When a clinician submits a query, QPID presents the desired data in a “dashboard” format that includes abnormal results, contraindications and other alerts, Doyle said.
The core of the system is a form of natural language processing (NLP) based on a library encompassing “thousands and thousands” of clinical concepts, he said. Because it was developed collaboratively by physicians and scientists, QPID identifies medical concepts imbedded in unstructured data more effectively than do other NLP systems from IBM, Nuance and M*Modal, Doyle maintained.
Take away points for data search/integration solutions:
- 80% of stored health information (need)
- traditional methods for data entry….don’t fit into traditional physician workflow (user requirement)
- developed collaboratively by physicians and scientists (semantics originate with users, not top down)
I am interested in how QPID conforms (or not) QPID to local medical terminology practices.
To duplicate their earlier success, conforming to local terminology practices is critical.
If for no other reason it will give physicians and other health professionals “ownership” of the vocabulary and hence faith in the system.