We were pleased to rub shoulders with our colleagues and customers at last week’s AMIA (American Medical Informatics Association) annual meeting. Of note were posters presented by two teams, one led by Gaurav Singal and the other by Shann-Chirag Gandhi. The teams presented case studies of QPID at work to support clinicians with patient information extracted from medical records.
In QPIDMed: A Search-Driven Automated Chart Biopsy Dashboard, Dr. Gaurav Singal and his research partners at the Massachusetts General Hospital (MGH) described the QPID EHR and Medicine Portal applications. The problem with the modern-day EHR, simply put, is that critical information is hard to find. As the poster aptly summarizes:
Critical information is frequently buried beneath volumes of data, frequently leading to errors in patient management.
The authors provide several clinical scenarios in which simply storing data in an EHR has failed, including “a patient who presented with a fracture bleeding profusely because of chronic anticoagulation – with notes documenting warfarin should have been stopped years ago.” The problem is not only the volume of data, we believe, but that notes such as this one are found in unstructured fields that are laborious to comb through without a smart NLP-based search engine such as QPID.
Queries can be developed within QPID that can identify clinical concepts, not just keywords. QPID queries showed on average over 90% sensitivity and 99% specificity, according to the authors, exceeding performance of simple keyword searches.
The Medicine Portal (or what the authors’ cleverly call an automated “chart biopsy” dashboard) was deployed to the MGH Department of Medicine in September 2012. Usage is heaviest at patient admission, but extends into a “long tail” of many days into hospitalization.
The authors plan to study nuances of usage to guide development of additional “contextually aware” dashboards such as those optimized for rounds, floors, the ICU and so forth. Further, they plan to study impact on clinical outcomes and test ordering.
Detailed knowledge of a patient’s past medical history can be crucial for optimal study interpretation. Manual electronic record searching is time-consuming and can lead to potentially lower-quality, less-efficient interpretations.
The authors used QPID to extract information pertinent to the interpretation of liver, prostate, and rectal MRIs and display that information in an organized graphical interface which included interpretation guidelines.
Patient historical data that is pertinent to the radiologist can be “distilled and converted” to fast, accurate search queries using QPID. For example, in this case, queries designed for magnetic resonance (MR) imaging studies encompassed relevant imaging studies, laboratory/pathology results, medication and allergy lists; admission, progress, operative/procedural and discharge notes; as well as other unstructured/structured data.
For the study, nine MR search algorithms related to liver, prostate and rectum exams were run on a test sample of 20 patient records. High levels of Positive Predictive Value (0.86 +/- 0.02) and Negative Predictive Value (0.91 +/- 0.01 were reported.
We are looking forward to follow-up research from our esteemed customers.
Two recent studies caught our eye. Both tell us that half of physicians are still dissatisfied with their EHR systems. Those who are unhappy cite a negative impact on their productivity.
IDC Insights reports that “inappropriate form factors and user interfaces” contribute to the productivity drain. Similarly, in an AMA-commissioned RAND study, researchers found that “poor usability” is a common source of dissatisfaction.
To translate: Physicians are frustrated because they spend less time with patients and more time related to updating and reviewing records in systems that are not designed to make things easier for them.
QPID alleviates this pain. The QPID platform was designed to deliver patient summaries directly to the point of care for rapid assimilation of the key facts that impact decisions. Instead of searching through multiple EHR screens in the hopes of finding all the information to piece together the patient story, QPID delivers an “at a glance” view of the pertinent labs, drugs, symptoms and common causes. For example:
The QPID Medicine Portal automatically displays answers to some 300 questions an expert clinician wants answered about a newly admitted patient. The screen is organized for rapid review by body system, lab results and common workups.
For emergency department staff in a hurry, the ED Portal instantly delivers vital information on allergies, conditions, medications and more that can drive life-saving decisions. And QPID finds this even in unstructured notes written in medical “street language.”
Provider organizations take note: 60% of costs in healthcare are labor, so solving the problem of productivity is more than just about making physicians and other clinicians happy. It’s a bottom line decision.
Mike and his colleague Mitch Harris conceived, designed and developed the QPID software as a direct result of Mike’s experience on the front lines. Like many clinicians, Mike wanted to make the best decisions for patients when analyzing an image, but he found that it was taking far too long to dig out what he needed from the patient’s electronic health record. He thought that it had to be possible to automatically extract the relevant information from the EHR, but after investigating found that no one had figured out how to do it yet. Thus QPID was born.
As Mike notes in the abstract for his presentation:
Electronic health record data, whether in discretized (structured field) or unstructured forms presents a potentially overwhelming amount of information for a Radiologist to consume at the time of clinical encounter. This applies for both in- and out-patient settings, and spans a broad range of sub-specialty and acuity scenarios. Consuming and understanding this data in an efficient way is essential for efficient, high quality care delivery, especially since most Radiologists have little prior familiarity with their patients.