How Can Expedited Patient Recruitment Affect the Bottom Line of a Research Center?

Feb 16, 2021, 08:00 AM by Sophia Kapchinsky, PhD.

Knowing whether a patient cohort already exists before starting recruitment can be done through an organized database of EHRs. Using a centralized database of de-identified EHRs, which include digital notes about health, socio-economic and genetic data, I.o.T data, etc., reduces the chance that researchers will fail to meet their recruitment targets, exceed the planned timeline, or that the study will fail to meet statistical significance. Visibility on patient numbers directly supports making informed decisions and impacts the direction of the research question.

Logibec_NOAH_recherche-cliniqueThe ability to identify patient cohorts before commencing the study has also been shown to support grant applications and increase the likelihood of receiving funding. This means that financial expenditure can be reduced by knowing the feasibility of the research study and having insight on the number of patients who match certain inclusion and exclusion criteria.

The time needed to accurately identify a patient cohort using Logibec NOAH is about 5 minutes. This is in sharp contrast to long recruitment periods used in traditional methods that can last anywhere from 3 months onwards and often yield poor results, as mentioned above.

Logibec NOAH eliminates this lengthy process. The saved time results in not only faster and better decision making, but also avoid unnecessary administrative costs associated with maintaining studies and extending recruitment periods. Using cohort identification tools, such as Logibec NOAH, has been shown to result in a higher enrollment rate than traditional recruitment methods.  

Facilitating patient identification can lead to an increased number of successful research studies, publications, funding applications, and patients getting access to new medical treatments.


Build a collaborative research network while positively affecting the bottom line

When searching for patient cohorts, Logibec NOAH automatically provides the number of patients with specific inclusion and exclusion criteria, as well as, the collaborating research centers that house the patient data. This information facilitates researchers to connect, share clinical and research information, and build new cross-center collaborations.  

In addition, direct contact with respective centers and physician referrals have been shown to be cost-effective recruitment strategies, and therefore reducing the likelihood that the patient recruitment timeline and budget will be extended.

Partnerships with other institutions can also lead to more robust grant submissions which, supported with concrete data on patient cohorts obtained through Logibec NOAH, increases the likelihood of obtaining funding.


Improving ROI and research footprint using new technology with Logibec NOAH