Eligibility Criteria Structuring
Converting protocol inclusion and exclusion criteria from prose into queryable logic, distinguishing what can be assessed from records and what requires screening contact.
Custom Software
Hire AI Clinical Trial Matching Engineers
AI clinical trial matching engineers build systems that surface potentially eligible patients for research studies. They parse eligibility criteria, screen against clinical records, and present candidates with the evidence behind each match, working so a research coordinator or investigator confirms eligibility for every patient before any approach.
Matching is a recall problem, not a precision problem. A system that misses an eligible patient has denied them an option; one that surfaces an ineligible patient has cost a coordinator a few minutes. That asymmetry should drive every threshold decision, and systems tuned the other way quietly narrow access. Taction Software builds toward recall, and our hire dedicated developers hub covers adjacent roles.
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What Trial Matching Engineers Build
The work spans criteria structuring, candidate identification, and coordinator workflow. Criteria are written in prose for human readers and must be converted into something queryable, which is where most of the difficulty concentrates. The work below reflects that. Coordinator workflow appears prominently because a list of candidates without a screening workflow becomes a spreadsheet nobody maintains after the first month.
Record-Based Candidate Screening
Querying structured and narrative record content to identify patients who may meet criteria, with the supporting evidence attached to each match.
Prescreening Prioritization for Coordinators
Ranking candidates by how likely they are to qualify and how much screening effort each requires, so limited coordinator time is spent productively.
Criteria Gap Identification
Reporting which criteria could not be assessed from available data, so coordinators know what to verify rather than assuming the system checked everything.
Trial Feasibility Analysis
Estimating how many patients in your population might meet criteria before a study is accepted, which informs whether the site can realistically enroll.
Screening Workflow and Outcome Tracking
Recording screening decisions, reasons for exclusion, and enrollment outcomes, which supports both operations and reporting on why candidates did not proceed.
Research and Clinical Context This Role Requires
Trial matching touches research regulation, patient privacy, and equity in research access simultaneously. Screening records to identify candidates involves specific permissions. Approaching patients requires appropriate authorization. And research populations have historically underrepresented several groups, which a matching system can either reduce or entrench. The context below spans the healthcare work you assign.
01
Recall Matters More Than Precision
Missing an eligible patient removes an option from them. Surfacing an ineligible one costs coordinator minutes. Thresholds should reflect that asymmetry explicitly.
02
Screening Records Requires Authorization
Using clinical records to identify research candidates operates under specific permissions and institutional review approval. That authorization is established before engineering, not during.
03
Eligibility Is Determined by Investigators
The system surfaces candidates. Eligibility determination is made by the investigator or delegated coordinator against the protocol, documented per research requirements.
04
Criteria Frequently Cannot Be Fully Assessed
Many criteria depend on information not in the record. Systems implying full assessment mislead coordinators into skipping verification they must perform.
05
Equity in Research Access Is at Stake
Matching that relies on data more complete for frequently seen patients will surface those patients disproportionately, entrenching existing underrepresentation in research.
06
Patients Are Never Excluded by Software
Systems identify possible candidates. They do not exclude patients from consideration, and no predicted likelihood of completion or adherence factors into who is surfaced.
Technical Skills for Matching Systems
This work combines clinical NLP, structured querying, and workflow engineering. Criteria structuring is the specialist skill, since protocol language is precise, conditional, and frequently ambiguous in ways that require interpretation. The competencies below reflect that. Weight criteria structuring and evidence provenance above matching sophistication, because a match a coordinator cannot verify quickly will either be trusted blindly or ignored.
Criteria Parsing and Structuring
Converting protocol prose into logic with explicit handling for criteria that cannot be assessed from data, which is where most matching systems overstate their coverage.
Structured and Narrative Record Querying
Combining coded data queries with text extraction, since many criteria depend on details documented only in notes rather than in structured fields.
Evidence Provenance for Every Match
Attaching the specific record content supporting each criterion, so coordinators verify against the source rather than re-screening the entire chart.
Recall-Weighted Threshold Design
Tuning toward surfacing possible candidates rather than filtering aggressively, with coordinator effort managed through prioritization rather than through exclusion.
Clinical Data Access Integration
Querying record systems under appropriate controls. Our healthcare integration work covers the data access this requires.
Screening Workflow and Audit Infrastructure
Recording screening decisions and outcomes with the audit trail research operations and monitoring require.
How We Evaluate Trial Matching Engineers
The distinguishing question is how they handled criteria that could not be assessed. Engineers who reported those gaps explicitly built systems coordinators can trust; those who ignored them produced matches implying verification that never occurred. Our assessment centers on criteria structuring, provenance, and threshold reasoning. Our delivery process includes review points for reassessing fit.
Handling of Unassessable Criteria
We ask what happened when a criterion was not in the data. Systems silently ignoring criteria produce matches coordinators believe were screened more thoroughly than they were.
Threshold Direction
We ask whether they optimized recall or precision. Candidates who filtered aggressively to reduce coordinator workload narrowed patient access to studies.
Evidence Presentation Design
We ask how coordinators verified a match. Matches without linked record evidence require full chart review, which eliminates the time saving entirely.
Criteria Structuring Experience
We ask about an ambiguous criterion and how they resolved it. Engineers who interpreted protocol language alone made decisions belonging to the investigator.
Equity Awareness in Matching
We ask whether they examined who was being surfaced. Systems favoring frequently seen patients entrench underrepresentation without anyone noticing.
Verified Research Systems Experience
We describe which matching systems each engineer built and what supported real studies. We do not claim research credentials for engineers who lack them.
Engagement Options for Matching Projects
Engagements should begin with criteria structuring for a small number of active studies rather than with a general matching platform, because criteria conversion is where the effort concentrates and general platforms tend to overstate what they assess. Structures below reflect that. We also confirm authorization for record screening before any engineering begins.
Criteria Structuring for Active Studies
Converting criteria for a handful of current studies, reporting explicitly what can and cannot be assessed. This establishes realistic expectations before platform work.
A Single Engineer With Research Coordinator Input
Suits matching for a defined study portfolio with coordinator workflow input, since coordinators determine whether the output is usable in practice.
Feasibility Analysis Capability
Building population feasibility estimation helps decide which studies to accept, which is valuable independent of any patient-level matching capability.
Augmenting Your Research Informatics Team
Where you own research operations, staff augmentation adds engineering capacity working within your existing authorization and screening practices.
Full Team for Research Enablement Programs
A dedicated healthcare development team suits programs spanning criteria management, matching, coordinator workflow, and outcome reporting across a study portfolio.
Fixed-Scope Matching Delivery
Where studies and criteria are defined, a fixed-scope build under our engagement models delivers matching with evidence provenance and screening workflow.
Tell Us How Many Studies and Who Screens
Share your active study count, coordinator capacity, and your authorization for record screening. Coordinator capacity determines how many candidates the system should surface.
Authorization, Investigator Authority, and Boundaries We Maintain
Trial matching touches research regulation and patient access simultaneously. We build to HIPAA-aligned practices where HIPAA applies; software cannot be HIPAA certified. Record screening for research operates under your institutional authorization and review board approval. Eligibility determination and every decision about approaching a patient remain with investigators and their delegated staff.
01
Authorization Established Before Screening
Record screening for research candidates proceeds only under your documented institutional authorization and review board approval. Engineering does not begin before that.
02
Investigators Determine Eligibility
The system surfaces possible candidates with evidence. Investigators and delegated coordinators determine eligibility against the protocol and document that determination.
03
Unassessable Criteria Reported Explicitly
Criteria that could not be checked are listed for each candidate, so coordinators verify rather than assuming the system screened the full protocol.
04
No Patient Excluded by Prediction
Systems identify possible candidates only. We do not build exclusion based on predicted adherence, completion likelihood, or any characteristic beyond protocol criteria.
05
Sensitive Study Category Handling
Studies involving behavioral health, substance use, or genetic information carry additional confidentiality requirements. We built CHIPSS, a behavioral health system, where such handling was foundational.
06
Applications We Would Not Build
We would not build trial-dropout prediction used to exclude enrollment, systems that screen out patients by predicted cost or adherence, or matching that determines eligibility without investigator review.
Cost to Hire Engineers and Build Matching Capability
Cost concentrates in criteria structuring and record querying rather than in matching logic. Each study’s criteria require individual conversion, and narrative extraction for criteria not in structured data is the substantial technical work. We publish no figures on enrollment improvement or screening time, because those depend on your studies, population, and coordinator capacity. What we deliver is screening instrumentation for your own measurement.
MVP or Single Module
$40,000 to $80,000
Matching for a small study portfolio with criteria structuring, record querying, evidence provenance, gap reporting, and a coordinator screening workflow.
Full Platform Build
$80,000 to $200,000
Matching across a study portfolio with criteria management, narrative extraction, prioritization, feasibility analysis, screening workflow, and outcome tracking.
Enterprise Deployment
Starting at $200,000
Multi-site research enablement with data across several clinical environments, governance documentation, and integration into research operations systems.
Discovery Phase Scoping
Discovery is paid and time-boxed. It produces an authorization review, criteria assessability analysis for representative studies, data availability finding, and an itemized fixed-scope estimate.
Cost Drivers to Expect
Study count and criteria complexity, proportion of criteria requiring narrative extraction, data source count, coordinator workflow integration, feasibility analysis scope, and research governance cycles.
Ongoing Support Costs
Studies open and close continuously. Budget for criteria conversion as the portfolio changes, extraction maintenance, and periodic review of which candidates are being surfaced.
Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly.
Why Build Matching Capability With Taction
Two questions matter. Whether the vendor reports unassessable criteria honestly, and whether they will refuse to build exclusion logic. Taction Software has built healthcare software since 2013, more than twelve years, with over 200 healthcare projects delivered and ISO 27001 certification. Leadership brings more than twenty years of personal experience in the field, which is separate from company age. Our wider case for Taction sits elsewhere.
Clinical Record Understanding
We built Voyant Health, an EHR platform. Our healthcare case studies reflect knowledge of how clinical detail is recorded, which determines what criteria can be assessed.
Sensitive Data Segmentation Experience
We built CHIPSS, a behavioral health system. Studies involving sensitive categories require the access controls that work established.
Experience Under Regulatory Registration
We built Revive Ease and PainKare, both FDA-registered applications. That work informs how we document system behavior in regulated research contexts.
ISO 27001 Certified Security Management
Taction Software holds ISO 27001 certification covering our information security management practices. It certifies our internal processes and does not determine your organization’s compliance position.
We Report What We Could Not Check
Every candidate carries a list of criteria the system could not assess. That honesty makes the output less impressive and considerably more useful to coordinators.
We Will Decline Exclusion Logic
Where clients want dropout or adherence prediction used to filter candidates, we refuse. Narrowing research access by predicted behavior is not something we will build.
FAQs
Frequently Asked Questions
We confirm your authorization for record screening, review your study portfolio and coordinator capacity, then present matched candidates. You interview and approve each engineer before placement.
A small study portfolio runs $40,000 to $80,000, portfolio-wide matching $80,000 to $200,000, and multi-site enablement starts at $200,000. Cloud, licensing, and inference are itemized separately.
Our delivery history includes the Voyant Health EHR platform, the CHIPSS behavioral health system, and the FDA-registered applications Revive Ease and PainKare, within more than 200 healthcare projects delivered since 2013.
No. It surfaces possible candidates with supporting evidence and lists criteria it could not assess. Investigators and coordinators determine eligibility against the protocol.
No. Using predicted adherence or completion to exclude patients narrows research access based on characteristics outside protocol criteria, and we decline that work.
NLP engineers build extraction broadly. Trial matching adds criteria structuring, recall-weighted thresholds, research authorization requirements, and coordinator screening workflow around that extraction.
Share your active study count and criteria complexity, your coordinator capacity, your record screening authorization, your data sources, and the engagement model you have in mind. We will assess which criteria are actually checkable and report that honestly before scoping. We do not promise instant matching or any enrollment figure.