New Query Tool Searches EHR Unstructured Data

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.

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As a result, the summary screens in EHRs often lack key patient data that might be relevant to medical decision making. "For a complete picture of the patient, you have to look at both structured and unstructured data," said Michael Doyle, president and CEO of QPID, in an interview with InformationWeek Healthcare.

Doctors are frustrated, he said, by having to plow through reams of electronic documents to find what they're looking for. This method is neither timely nor efficient, and it can be a patient safety issue. For example, he said, if a radiologist is going to do an MRI, he might be unaware that a patient has a piece of metal in his knee, because that information is in an old transcribed note.

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.

QPID's accuracy rate is high, he said. The system is now used in 15 departments at MGH, as well as in other Partners hospitals and clinics. QPID currently performs 53 million searches a month involving 7,000 patients a day, and 5,000 doctors are using the platform.

According to Doyle, QPID works with any EHR and can improve its search capabilities, no matter how advanced the system is. Although the application is used with data warehouses at MGH, he says, it can also use HL7 feeds or Web services to extract relevant data.

Besides doing searches for information on individual patients, QPID also can be used to look for data within or across particular departments or across an entire hospital or ambulatory care clinic. It can also be a research tool. For example, Doyle noted, researchers at MGH and Brigham & Women's Hospital used QPID to find out whether doctors had asked 650,000 patients about smoking cessation, finishing the study in six weeks.

In addition, he said, QPID could improve on current computer-assisted coding (CAC) systems. QPID automates the process by finding textual data referring to diagnoses or procedures to support particular billing codes.

MGH is the oldest customer of CAC vendor CodeRyte (now part of 3M); Rick Toren, CodeRyte's former president, is Doyle's partner in QPID. But CodeRyte's technology is unrelated to QPID, Doyle said.

QPID plans to target academic medical centers first and then market its product to other hospitals and healthcare systems, he said. Among the investors in QPID are Matrix Partners, the Partners Innovation Fund and the Massachusetts General Physicians Organization.

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