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Aggregated EMR: Proven to Enhance Study Site Enrollment
In this Electronic Medical Records (EMR) blog series, we explained the basics of Aggregated EMR, which leverages natural language processing (NLP) and machine learning algorithms to aggregate both structured and unstructured EMR data. We also spoke about how biopharmaceutical sponsors can take an active approach in AE detection, and how EMR can pinpoint patients based on specific queries. This article will elaborate on how Aggregated EMR can create a collaborative culture that supports clinical trial subject enrollment amongst academic institutions and satellite sites.
Clinical Research at Academic Institutions is Disconnected
Many academic research institutions tend to affiliate themselves with smaller practices in order to generate referral infrastructures and expand reach within patient communities; this is especially the case in New York State, where major hospital systems have satellite site affiliations. The pitfall in this model for clinical trials involve the fact that many qualified candidates go unnoticed, as clinical trials tend to be centrally operated at large academic institutions, and not at satellite sites. Further, many medical institutions tend to encounter qualified candidates for a particular trial that is conducted at another academic institution, and the patient never gets referred to the right trial due to the communications gap between academic institutions.
Addressing the Communications Gap
Collaboration is critical, and Aggregated EMR can significantly facilitate connections between academic institutions and create a collective community of physicians and research staff. Figure 1 demonstrates how Aggregated EMR can establish a collaborative system between academic institutions and satellite sites/other academic institutions for clinical trial subject referrals and enrollment.
Figure 1: Aggregated EMR Process on Academic Medical Research Centers
The process involves academic research institutions allowing access to their EMR systems for Aggregated EMR querying; it is important to emphasize that the patient’s EMR does leave the hospital’s firewall, and any private health information (PHI) is automatically redacted from a patient’s EMR. Correspondingly, the Aggregated EMR system (with trial-specific auto queries) sends notifications of qualified patients to research staff on a regular basis. Research staff can, then, e-screen a patient’s EMR, submit referral requests to physicians participating in the network, and if the physician agrees to refer the patient, research staff can screen/enroll the patient in the trial.
What about HIPAA Violations and Data Access?
It is natural for medical institutions to be concerned about sharing patient data with other institutions. Nonetheless, with today’s advanced technologies, Natural Language Processing and Machine Learning can do wondrous things. For example, Aggregated EMR can automatically detect any private health information, and redact the information from a patient’s EMR. In addition, this advanced technology can focus the search across EMR data specifically to the information of interest, avoiding the need to peruse data that may contain PHI. Moreover, the patient’s EMR never leaves a hospital system’s firewall, which is HIPAA compliant.
EMR Auto Querying: Rallying Patients from Every Corner
The benefit of the Aggregated EMR system is that it can pick up patients as soon as they are seen at any institution/satellite site participating in the Aggregated EMR network. This allows for timely patient engagement, and clinical trial subject enrollment. Moreover, case studies are showing that this system has improved the efficiency of medical chart review screening by more than 60%, which reduces study staff burden and improves clinical operational productivity. Furthermore, the system can create collaborative networks and communities between academic research institutions and research staff to efficiently enroll patients in the right clinical trials.