How Researchers Are Strengthening Grant Proposals Using Deep 6 AI

grant writing banner 1
Deep 6 AI Blog

Deep 6 AI Blog

Get insights about clinical trials, patient identification, health IT, data science, machine learning, and more.

With such tight budgets in healthcare organizations, investigators must get creative to be able to execute their research goals. For academic medical institutions and research hospitals, applying for grants can be a daunting yet crucial aspect of the process to getting a clinical trial up and running. A grant is a form of federal financial assistance that goes towards funding “ideas and projects to provide public services and stimulate the economy.” There are many different types of grant agencies, but in health research the vast majority of public federal funding comes from the National Institutes of Health (NIH), awarding about $32 billion a year in grants for human health research. With the NIH, there is a specialized online system to submit grants which takes applicants through a structured step by step process. Other funding sources will also provide guidelines by which to carefully abide. A successful grant applicant will have started the feat of securing funding by studying up on the source, their eligibility, and the application requirements. After being certain that they and their proposed study are a match for this opportunity, the researcher can begin filling out the grant.

   

Steps to Writing a Grant for a Clinical Trial

 

An investigator must outline their study design in detail within a grant application. To decide whether a grant should be granted, awarding agencies reviewing an application want to feel secure in their decision to support a project and that it will yield answers to the posed hypothesis. One of the most important aspects of the study design that can help ensure this security in the decision is the inclusion and exclusion criteria, which determine whether a patient is a right fit to participate in a trial. These criteria are so crucial because they will inform the ability to recruit the intended number of subjects for the study. Having an adequate amount of patients participating is how researchers learn the efficacy of a treatment being tested. It must be clearly outlined in the grant application how you plan to recruit the participants that you need, whether it by existing patients, advertisement, or another method. Guaranteeing that you have at your disposal all the resources needed to conduct the trial effectively is a crucial consideration required within an application. These resources include money, tools, staff, and patients. All of these determinations must also be made quickly and accurately, in order to meet both internal and external deadlines and to pass peer reviews and grant agency reviews. Additionally, a good grant application will include preliminary data for the study.

 

Being able to say how many patients one can include in a study can be difficult to predict. It is also extremely significant in that the sample size will affect the amount of funding a project is awarded. If advertising the study, there will be a high number of screen failures to sort through for many reasons, from bringing anyone desperate for a new treatment to just the intricacies of the inclusion/exclusion criteria that are difficult for the average layperson to decide if they’re a match for. In searching through existing patients, the number of matching patients is also tricky to predict for a physician who sees hundreds of patients, especially in studies where the criteria are very detailed and specific. Even if they do have the right pool of patients existing in the research organization’s database, the process of pre-screening the patients by searching through their medical charts one by one within the electronic medical records (EMR) is tedious and time-consuming, especially before grant funding is in place. If it becomes too complicated to gather data for a grant proposal, it can hamper a team’s ability to apply for the grants they need.

   

Bringing Deep 6 AI into Your Grant Writing Process

 

Our clients have been harnessing the speed and self-service nature of the Deep 6 AI platform to strengthen their grant proposals. One of the favorite uses of our software for clinical trial patient recruitment is the ability to easily determine the feasibility of a trial. Deep 6 AI is a self-service tool that allows research staff to generate counts and lists of patients that match complex criteria by searching unstructured data in the EMR. This allows you to find and validate all patients in the database at once within minutes, whereas typically a trial coordinator would have to pre-screen one patient at a time by reviewing their entire medical chart and history. This traditional manual process makes it incredibly difficult to immediately be able to come up with an accurate number of existing patients who match the intended criteria for a prospective trial. Using Deep 6 AI, an investigator can almost instantly know what their patient population looks like. This also allows time before the grant is due for adjustments to be made to certain inclusion or exclusion criteria which may be affecting the potential pool of trial participants in a drastic or unwanted way. Telling a grant reviewer that you are sure you can recruit not only the needed number of patients but also ones that perfectly match your criteria gives your application a huge advantage in terms of confidence to complete the study as it was designed.

 

Deep 6 AI can help determine a clinical trial’s feasibility even with difficult populations to identify, such as cross checking a patient with a required lengthy list of criteria or like finding patients who may not be at the physician’s top of mind because they don’t make frequent medical visits. This is particularly valuable for new medical concepts or challenging concepts to pin down, such as COVID-19. Having come into the world so quickly and with such force, it has been an arduous journey for leaders in healthcare to determine the list of symptoms or proper way of diagnosing a patient with the novel coronavirus, and even more tricky to accurately identify these patients with all the scattered data in the EMR. Because the Deep 6 AI tool searches unstructured data, it can find patients that would be missed by other search methods, making our tool particularly help for innovative research projects, such as those involving COVID-19.

 

All of these supportive capabilities of Deep 6 AI are pertinent details to include within a grant proposal in order to prove to the agency that you possess the tools to efficiently conduct the study you want to take on. By incorporating the software into your study design, you are telling the reviewer you know this trial is feasible, you can accrue the right patients for it, you can edit inclusion/exclusion criteria at ease if need be, and you can start the study as soon as you’d like without having to wait months or more to fill out the trial with participants. We’ve been able to vouch for our clients having these abilities by writing letters of support to attach to the grant application. With the COVID-19 pandemic happening, many research organizations and companies are conducting research in the hopes of finding a treatment. Today, shorter study times and more accuracy are undeniably vital factors in ensuring the world is supplied with the means to fight this virus, as soon as possible. An investigator with a successful grant may be the first step to achieving this goal and saving lives.

 

See our platform’s capabilities in action by requesting a demo. We’ll be discussing more interesting use cases for the Deep 6 AI platform in the coming weeks. Sign up for our newsletter so you don’t miss the next one!

   

About the Author: Nour Malki

 

Nour Malki is a business development representative at Deep 6 AI, which uses AI and NLP to allow clinical researchers to find patients that fit any set of complex criteria in real time. Nour works to facilitate clinical and academic partnerships between Deep 6 AI and research institutions nationwide. She has a background in biotechnology and the healthcare field, previously holding various positions centered around technology transfer and clinical research in cancer hospitals, academic medical centers, as well as small clinics.

 

 

Share this post

Share on linkedin
Share on facebook
Share on twitter
Share on print
Share on email