PATIENT RECRUITMENT PLATFORM
A method, according to some implementations, includes determining, via at least one processor, a plurality of possible sites for recruiting patients from for a trial and determining, via the at least one processor and for each of one or more subgroupings of the plurality of possible sites, a predicted patient recruitment value. The method may include determining, via the at least one processor, a candidate subgrouping of the plurality of possible sites having a predicted patient recruitment value that globally optimizes a desired site selection criteria, and transmitting, via the at least one processor, data corresponding to the candidate subgrouping.
1 . A method comprising:
determining, via at least one processor, a plurality of possible sites for recruiting patients from for a trial;
determining, via the at least one processor and for each of one or more subgroupings of the plurality of possible sites, a predicted patient recruitment value;
determining, via the at least one processor, a candidate subgrouping of the plurality of possible sites having a predicted patient recruitment value that globally optimizes a desired site selection criteria; and
transmitting, via the at least one processor, data corresponding to the candidate subgrouping.
2 . The method of claim 1 , wherein determining the predicted patient recruitment value for each of the subgroupings of the plurality of possible sites comprises:
simulating, via the at least one processor, each of the subgroupings.
3 . The method of claim 2 , wherein simulating each of the one or more subgroupings is based at least in part on use of different types of engines.
4 . The method of claim 3 , wherein the difference between the types of engines is based at least in part on a version number.
5 . The method of claim 2 , further comprising:
determining one or more site selection parameters, wherein simulating each of the one or more subgroupings is based at least in part on the one or more site selection parameters.
6 . The method of claim 5 , wherein the one or more site selection parameters are based at least in part on at least one of:
a country;
a state/province;
a county;
a city;
a zip code; or
a patient enrollment matriculation number.
7 . The method of claim 2 , further comprising:
determining, via the at least one processor, the desired site selection criteria, wherein simulating each of the one or more subgroupings is based at least in part on the determined site selection criteria.
8 . The method of claim 7 , wherein the determined site selection criteria is based at least in part on at least one of:
a number of required patients;
a start date of the trial;
an end date of the trial; or
a total cost of the trial.
9 . The method of claim 1 , wherein determining the candidate subgrouping of the plurality of possible sites has a predicted patient recruitment value that globally optimizes the desired site selection criteria comprises use of one or more of:
a convex hull engine;
a Pareto engine;
a Monte Carlo engine; or
a simulated annealing engine.
10 . The method of claim 1 , wherein determining the candidate subgrouping of the plurality of possible sites has a predicted patient recruitment value that globally optimizes the desired site selection criteria is based at least in part on a machine learning engine.
11 . The method of claim 1 , further comprising:
evaluating historical trial design selections to identify one or more trial design parameters based at least in part on one or more trial design criteria determined from a user via an interactive interface,
obtaining trial design simulation results based at least in part on a quick search data structure and the one or more trial design parameters;
generating a substitute for at least some of the trial design simulation results based at least in part on a relationship between the trial design simulation results and supplemental data;
generating a performance surface based at least in part on a set of trial designs corresponding to trial design simulation results;
evaluating one or more trial designs based at least in part on the performance surface; and
calculating a score based on normalized score component values corresponding to the trial design simulation results.
12 . An apparatus comprising:
a site selection data processing circuit structured to interpret possible site selection data identifying a plurality of possible sites for recruiting patients from for a trial;
a patient recruitment determination circuit structured to determine a predicted patient recruitment value for each of one or more subgroupings of the plurality of possible sites;
a site searching circuit structured to determine which subgrouping of the plurality of possible sites has a predicted patient recruitment value that globally optimizes a desired site selection criteria; and
a site selection provisioning circuit structured to transmit the subgrouping of the plurality of possible sites that has the predicted patient recruitment value that globally optimizes the desired site selection criteria.
13 . The apparatus of claim 12 , wherein the patient recruitment determination circuit is further structured to determine the predicted patient recruitment value for each of the one or more subgroupings of the plurality of possible sites by simulating each of the subgroupings.
14 . The apparatus of claim 13 , wherein simulating each of the one or more subgroupings is based at least in part on use of different types of engines.
15 . The apparatus of claim 12 , further comprising:
a user input circuit structured to interpret user input data; and
a criteria determining circuit structured to determine the desired site selection criteria based at least in part on the user input data.
16 . The apparatus of claim 12 , wherein the site searching circuit comprises at least one of:
a convex hull engine;
a Pareto engine;
a Monte Carlo engine; or
a simulated annealing engine.
17 . A system comprising:
a database storing site selection data identifying a plurality of possible sites for recruiting patients from for a trial;
a server structured to:
access the site selection data stored in the database;
determine a patient recruitment value for each of one or more subgroupings of the plurality of possible sites; and
determine which subgrouping of the plurality of possible sites has a patient recruitment value that globally optimizes a desired site selection criteria; and
an electronic display structured to depict the determined subgrouping.
18 . The system of claim 17 , wherein the server is further structured to:
determine the patient recruitment value for each of one or more subgroupings of the plurality of possible sites by simulating each of the subgroupings.
19 . The system of claim 18 , wherein simulating each of the one or more subgroupings is based at least in part on use of different types of engines.
20 . The system of claim 18 , wherein the server is further structured to:
determine the desired site selection criteria, wherein simulating each of the one or more subgroupings is based at least in part on the determined site selection criteria.