# US 2021/0241861 A1 · Patient recruitment platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241861 A1 - Document ID: US20210241861A1 - Status: Published application - Kind: A1 - Application number: 17/163,431 - Filed: 2021-01-30 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Applicants: Cytel Inc. - Inventors: Jaydeep Bhattacharya; James Bolognese; Alexandre Buer; Eric Edwards; Stanley Y. Huang; Yannis Jemiai; Cyrus Mehta; Nitin Patel; Anne Pelz; Ajay Prabhakar Sathe; Joshua A. Schultz; Pralay Senchaudhuri - Web page: https://patentgrep.com/patent/US20210241861A1 ## CPC - G16H 10/20: for electronic clinical trials or questionnaires - G06F 30/10: Geometric CAD - G06F 30/12: characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 5/04: Inference or reasoning models - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06Q 10/06315: {Needs-based resource requirements planning or analysis} - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/0674: {using process simulation} - G06Q 10/101: Collaborative creation, e.g. joint development of products or services - G06Q 30/0205: {based on location or geographical consideration} - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - G16H 40/67: for remote operation - G16H 50/50: for simulation or modelling of medical disorders - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range - G06F 2111/02: CAD in a network environment, e.g. collaborative CAD or distributed simulation - G06F 2111/04: Constraint-based CAD - G06F 2111/06: Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] - G06F 2111/08: Probabilistic or stochastic CAD - G06F 2111/16: Customisation or personalisation - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 2) 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. ### Claim 4 (depends on 3) The method of claim 3, wherein the difference between the types of engines is based at least in part on a version number. ### Claim 5 (depends on 2) 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. ### Claim 6 (depends on 5) 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. ### Claim 7 (depends on 2) 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. ### Claim 8 (depends on 7) 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. ### Claim 9 (depends on 1) 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. ### Claim 10 (depends on 1) 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. ### Claim 11 (depends on 1) 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. ### Claim 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. ### Claim 13 (depends on 12) 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. ### Claim 14 (depends on 13) 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. ### Claim 15 (depends on 12) 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. ### Claim 16 (depends on 12) 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. ### Claim 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. ### Claim 18 (depends on 17) 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. ### Claim 19 (depends on 18) 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. ### Claim 20 (depends on 18) 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.