The landscape of N-of-1 trial design and personalized medicine workflows has evolved through a mix of dedicated mobile applications, specialized clinical platforms, and AI-driven data optimization tools. Unlike population-based randomized controlled trials (RCTs), these platforms focus on single-case experimental designs (SCEDs) and individual response trajectories.
1. Dedicated Consumer and Research Mobile Apps
StudyMe : An open-source mobile application designed specifically to empower non-experts and consumers to conduct user-centric N-of-1 self-experiments. It provides accessible language, flexible scheduling, and custom component libraries to help users systematically track and evaluate personal health interventions on their own devices.
2. Clinical and Methodological Consultancy Platforms
N-of-1 Hub : A specialized consultancy and data service provider that works with digital health, pharmaceutical, and academic institutions. They assist in designing, executing, and analyzing N-of-1 trials and single-case experimental designs (SCEDs), helping translate intensive longitudinal patient data and wearable metrics into actionable, individual-level treatment evidence.
3. AI and Phenotypic Personalized Medicine (PPM) Platforms
CURATE.AI : An artificial intelligence platform geared toward dynamic dose and intervention optimization. Rather than relying on large population data sets, CURATE.AI uses an individual's own real-time phenotypic response data (such as drug dosage vs. clinical outcome) to calibrate a personal profile. It continuously updates as the patient's condition changes, acting as an active management tool for oncology, hypertension, and digital therapeutics.
QPOP (Quadratic Phenotypic Optimization Platform) : Frequently paired with phenotypic workflows like CURATE.AI, QPOP assists in mapping optimal drug combinations using small-data sets derived strictly from the individual patient.
4. General Digital Health & Remote Monitoring Infrastructure
While not standalone "N-of-1 platforms," platforms leveraging the Mayo Clinic Platform ecosystem or custom ecological momentary assessment (EMA) apps are frequently adapted for N-of-1 protocols. They utilize wireless patient monitoring, digital diaries, and wearable data feeds to meticulously log the fluctuating symptom responses necessary for single-patient trial validation.
If you are looking to build or implement a workflow, let me know:
Are you designing an academic/clinical research study or a commercial digital health product?
What specific therapeutic area or data types (wearables, patient-reported outcomes, blood markers) are you looking to track?
I can help narrow down the optimal methodology or technical architecture.
Yes. The ecosystem splits into true N-of-1 trial platforms and broader personalized/precision-medicine platforms. They solve somewhat different problems.
Platform
N-of-1 design/execution
Personalized-medicine workflow
What it is particularly suited for
StudyU
Yes — core capability
Yes, at the individual-treatment level
Designing, randomizing, running, analyzing, and returning results from digital N-of-1 trials
Eureka
Yes — demonstrated in N-of-1 studies
Yes
Research-grade mobile N-of-1 studies, PROs, sensors, remote participation
REDCap
Partial / build-it-yourself
Yes
Data capture, longitudinal questionnaires, study infrastructure; usually needs another layer for randomization/analysis
OpenClinica
Partial / configurable
Yes
Clinical-trial infrastructure, ePRO/eCOA, randomization, EHR integration and APIs
Its StudyU Designer lets researchers/clinicians configure digital N-of-1 trials, while the participant app handles the actual study. The platform supports automated individual-level analysis and returning results to participants. It is open source and supports self-hosting.
A particularly interesting aspect is that StudyU is being used in contemporary N-of-1 research rather than merely being a generic survey platform; a 2025 digital-health study used it to deliver interventions, collect daily PROs, and provide individual results.
2. Eureka — research-grade N-of-1 infrastructure
Eureka is another important option, particularly if you're building an academic/clinical research program rather than a consumer-facing personalized-treatment product.
The NIH-supported platform provides a participant-facing mobile app, study-management portal, secure backend, remote consent/data capture, and integrations with devices/apps. Researchers have used it to conduct N-of-1 studies involving dietary interventions and atrial-fibrillation triggers.
Its architecture is therefore closer to:
clinical/research protocol + mobile data collection + sensors + longitudinal analysis
than to a general-purpose precision-medicine decision engine.
3. REDCap — excellent infrastructure, but not an N-of-1 engine
REDCap is frequently part of N-of-1 workflows, but I wouldn't classify it as a turnkey N-of-1 platform.
For example, published N-of-1 studies have used REDCap for screening, enrollment, questionnaires and clinical data while a dedicated application handled the actual individualized trial workflow.
4. OpenClinica — more capable clinical-trial foundation
OpenClinica is interesting if you want something closer to a regulated clinical-research stack.
It provides EDC, eConsent, ePRO/eCOA, randomization, EHR-to-EDC integration and APIs.
That means you could potentially construct an N-of-1 workflow around it, but it isn't marketed primarily as an individualized-treatment optimization system in the way StudyU is.
Broader personalized-medicine platforms
If your definition of personalized medicine includes "given this patient's molecular/clinical characteristics, what treatment options are relevant?", the landscape changes substantially.
5. Tempus — broad precision-oncology workflow
Tempus is one of the more comprehensive commercial examples.
Its platform combines molecular testing, clinical/EHR data, treatment information and clinical-trial matching. Its EHR integrations can deliver structured genomic results into clinical workflows, while Tempus One provides patient summaries and molecular/clinical-trial information to clinicians.
It also has pharmacogenomics capabilities, including medication-safety, dosing and toxicity-risk information.
patient → randomized treatment A/B periods → estimate that patient's causal treatment effect.
6. Foundation Medicine — genomic precision oncology
Foundation Medicine is another major precision-medicine workflow, particularly around cancer.
Its comprehensive genomic profiling is used to identify actionable alterations, relevant targeted therapies and clinical trials. It also offers pharmacogenetic testing.
Again, this is precision treatment selection based on biomarkers, not an N-of-1 experimental-treatment platform.
The key distinction
There are really three technological layers here:
PERSONALIZED MEDICINE STACK
┌─────────────────────────────────────────────────┐
│ Treatment decision / precision medicine │
│ Tempus • Foundation Medicine │
│ "What therapy fits this patient?" │
└───────────────────────┬─────────────────────────┘
│
┌───────────────────────▼─────────────────────────┐
│ Individual treatment experimentation │
│ StudyU • Eureka • custom N-of-1 applications │
│ "Which treatment works better FOR THIS PERSON?"│
└───────────────────────┬─────────────────────────┘
│
┌───────────────────────▼─────────────────────────┐
│ Data / clinical research infrastructure │
│ REDCap • OpenClinica • EHR • sensors • APIs │
│ "Collect and integrate the longitudinal data." │
└─────────────────────────────────────────────────┘
This distinction matters because personalized medicine and N-of-1 medicine aren't synonymous. An N-of-1 trial estimates an individual's response by experimentally varying treatment over time; precision-medicine platforms often personalize treatment by using biomarkers, genomics, phenotype, prior outcomes, guidelines, or population evidence.
If you're evaluating these for building a product
I'd investigate StudyU, Eureka, and OpenClinica/REDCap first if the core product is an N-of-1 therapy optimization workflow. I'd investigate Tempus and Foundation Medicine if the core product is precision-treatment decision support, especially oncology.
One particularly interesting product opportunity sits between the two: a platform that combines longitudinal patient data + biomarker/clinical context + formal N-of-1 experimentation + Bayesian/causal analysis to continuously update an individual's treatment recommendation. The existing ecosystem appears considerably more mature on the data collection and precision-oncology sides than on that fully integrated adaptive N-of-1 side. The AHRQ literature likewise identifies randomization, adaptive/sequential designs, longitudinal data, patient access to results, and integration with EHR/device data as important components of a scalable N-of-1 platform.
If you tell me whether you're looking at this from a researcher, clinician, startup/product, or pharma perspective, I can also produce a deeper comparison of ~15 platforms covering randomization, crossover designs, Bayesian analysis, wearables, EHR integration, APIs, HIPAA/GDPR, adaptive dosing, and patient-facing treatment recommendations.
Yes. There are several platforms, but they fall into two somewhat different categories: purpose-built N-of-1 platforms and broader decentralized/personalized-research platforms that can implement N-of-1 workflows.
Platform
N-of-1 design
Data collection
Analysis/results
Customization
Best fit
StudyU
Strong
Mobile/web
Yes
High
Researchers/clinicians wanting true digital N-of-1 trials
Eureka
Yes, demonstrated in research
Mobile, sensors, devices
Study-dependent
High
Academic/decentralized clinical research
Trialist
Yes
Mobile/web
Yes
Moderate
Chronic-pain N-of-1 research
StudyMe
Yes
Mobile
Descriptive results
High
Patient-driven/self-directed N-of-1 experiments
PACO
Can support individual experiments
Mobile/web
Limited built-in analysis
High
Behavioral/self-tracking research
movisensXS
Can implement single-case designs
Mobile/sensor data
Usually external
High
Intensive longitudinal/sensor research
OpenClinica
Not N-of-1-specific
EDC/web
Primarily study-data management
High
Building a regulated/custom clinical workflow
1. StudyU
studyu.health is probably the closest match if by "N-of-1 therapy design" you mean actually specifying treatment periods, outcomes, schedules, running the experiment, and analyzing the individual's response.
It has a web-based StudyU Designer for researchers/clinicians and a participant app. The platform is open source and supports customizable digital N-of-1 trials.
The underlying research platform explicitly supports interventions, outcome measures, schedules, and participant-level analysis rather than merely collecting longitudinal data.
2. Eureka
Eureka is more of a decentralized/mobile research infrastructure than an N-of-1 product. It has nevertheless been used to run genuine N-of-1 studies, including the I-STOP-AFib study. Its architecture includes a participant-facing app, study-management portal, secure backend, and integrations with mobile/sensor data.
It's particularly interesting if you're building research-grade personalized medicine studies at scale, rather than giving an individual clinician a turnkey N-of-1 prescribing tool.
3. Trialist
Trialist is an earlier purpose-built N-of-1 application developed around personalized comparative-effectiveness studies in chronic pain. It allowed patients to design studies, track symptoms, and generate results for discussion with clinicians.
It's historically important, although it isn't as compelling a current platform choice as StudyU.
4. StudyMe
StudyMe takes a more patient-centric approach. Users can define their goal, interventions, measures, and schedule, run the N-of-1 experiment, and inspect the resulting data. Its analysis is primarily descriptive rather than a sophisticated clinical-trial statistical engine.
5. Broader personalized-medicine infrastructure
Platforms such as Vibrent, Evidation, Sage Bionetworks, CareEvolution, and similar decentralized-trial infrastructure can support individualized longitudinal research, but they're generally not turnkey N-of-1 treatment-design systems. They provide components such as recruitment, mobile data capture, adherence/reminders, sensor integration, and study management that can be assembled into personalized trials.
One important distinction
If you're looking specifically for clinical treatment optimization, rather than simply personalized health tracking, I'd separate the requirements into:
Personalized decision support: translating the result into the next treatment decision
EHR/clinical data integration
Regulatory/privacy: HIPAA/GDPR, audit trails, electronic consent, etc.
That's where the market gets much thinner. The literature notes that conventional clinical-trial management systems aren't inherently designed for N-of-1 workflows, because an N-of-1 system needs to handle things like within-person randomization, repeated treatment periods, patient goals, and individualized reporting.
If you're evaluating these platforms for a specific project, I can also make a more detailed comparison of ~10 platforms across randomization, crossover/ABAB support, Bayesian analysis, EHR integration, wearables, APIs, HIPAA/GDPR, open-source status, and commercial availability.
The landscape of n-of-1 therapy design and personalized medicine workflows is shifting from academic theory toward dedicated digital health frameworks and software platforms . An n-of-1 trial treats a single individual as a complete entity—cycling through randomized intervention and control periods to figure out what uniquely works best for that specific patient rather than relying on population averages.
Several notable platforms, software engines, and open-source ecosystems support n-of-1 trials and personalized optimization workflows:
StudyU Platform
What it is: A fully functional, open-source digital health platform specifically built for designing and conducting n-of-1 trials and single-case experimental designs (SCEDs).
Components: It features the StudyU Designer (a collaborative web application for researchers and clinicians to digitally specify, configure, and publish trials) paired with the StudyU App (a patient-facing smartphone app for tracking interventions and reporting real-time health data).
Best for: Researchers, clinicians, and digital health developers wanting an open-source, self-hostable ecosystem with high data privacy standards. You can explore the tool at the StudyU Platform or access its configuration workspace via the StudyU Designer.
CURATE.AI / CURATE.Dtx Engine
What it is: A pioneering artificial intelligence platform and optimization tool designed to continuously calibrate personalized dosing or therapeutic regimens. Rather than a static one-size-fits-all plan, it uses small amounts of individual patient data over time to map out individualized response profiles.
Application: It has been deployed across clinical trials in oncology, digital therapeutics (DTx), hypertension, and cardiorespiratory performance optimization. Regulatory bodies (such as Singapore's HSA) have evaluated specific implementations of the CURATE.AI software framework as classified medical devices for dosage/intensity recommendations.
Best for: Advanced digital therapeutic tracking and AI-driven dynamic individualized dose optimization.
N-of-1 Hub
What it is: A specialist clinical and methodological consultancy/collaboration platform that partners with pharma, med-tech, digital health, and academic researchers.
Services & Workflow Support: While not a standalone software download, they provide rigorous operational scaffolding, setting up dedicated databases, generating individual randomization sequences, and executing specialized n-of-1 data analytics.
Best for: Clinical teams and organizations needing end-to-end design protocols, training, and complex statistical analysis for n-of-1 trials. Learn more via the N-of-1 Hub.
What it is: For custom enterprise or institutional workflows, much of the heavy lifting in n-of-1 analysis is driven via specialized open-source statistical libraries (such as R packages designed for single-case data analysis like scan or multilevel modeling frameworks). Many hospital systems build internal personalized dashboards that feed electronic health record (EHR) data into localized Bayesian n-of-1 analytical models.
Would you like to explore how to set up a study on StudyU , or are you looking for guidance on the statistical modeling methods used to analyze n-of-1 trial data?
Supporting platforms for n-of-1 trial design and personalized medicine workflows bridge the gap between traditional population-based clinical data and individual, patient-centric optimization. While many commercial electronic health record (EHR) systems lack native n-of-1 trial capabilities, dedicated digital health frameworks, open-source software, and AI-driven platforms have emerged to fill this space.
Dedicated N-of-1 and Personalized Trial Platforms
StudyU : An open-source platform specifically built for designing and conducting digital n-of-1 trials . It splits into the StudyU Designer (a web app for researchers/clinicians to map out intervention sequences, schedules, and metrics) and a companion mobile app for real-time patient tracking and feedback.
StudyMe : A mobile-centric framework designed for user-centric n-of-1 trials, allowing individuals and clinicians to structure comparative tests on lifestyle, supplements, or minor therapeutic interventions.
Legacy/Condition-Specific Apps : Smaller niche tools like TummyTrials (for IBS dietary tracking) and SleepCoacher (for iterative sleep adjustments) demonstrate how single-condition n-of-1 workflows can be operationalized via guided micro-experiments.
AI and Mathematical Modeling Frameworks for N-of-1 Dosing
CURATE.AI : A proprietary AI/machine learning platform that treats each patient as a discrete case rather than relying on population big data. It uses proprietary algebraic-neural modeling based strictly on an individual's incoming clinical or digital therapeutic data to dynamically adjust and optimize drug dosing (e.g., in oncology or post-transplant care).
QPOP (Quantitative Personalized Optimization Platform) : A computational platform used to analyze individual multi-omics or ex-vivo drug response profiles to predict optimal drug combinations for patients failing standard therapies.
Academic and Enterprise Clinical Trial Solutions
Custom EDC (Electronic Data Capture) systems : Many academic medical centers adapt traditional flexible enterprise EDC platforms (such as REDCap or ClinCapture) by building custom repeating branching logic and randomized crossover schedules to mimic n-of-1 trial constraints manually.
If you're looking to implement a workflow, tell me:
Are you designing a clinical research study or looking for a clinical care tool for individual patients?
What type of intervention (pharmacological, dietary, digital) are you hoping to track?
I can help narrow down whether an open-source framework or a custom clinical protocol fits best.
Yes. The ecosystem is still relatively small, and it helps to distinguish true N-of-1 trial platforms from broader personalized-medicine/genomics platforms.
Platforms specifically suited to N-of-1 / single-patient experiments
studyu.health — probably the clearest fit if you want an end-to-end N-of-1 workflow. It provides a web-based StudyU Designer for specifying trials and a participant app for running them, collecting outcomes, and analyzing/reporting results. It is open source and designed around personalized interventions.
StudyMe — a research-developed mobile application for people to create and run highly customizable N-of-1 experiments themselves. It is particularly oriented toward self-experimentation rather than clinical-trial infrastructure.
PACO — a mobile/web experience-sampling platform that has been used for flexible individual-level intervention studies. It can collect data for N-of-1 designs, although the literature notes that it does not provide the same integrated statistical analysis/reporting workflow as StudyU.
movisensXS — flexible mobile data-collection infrastructure that can support individual-level/N-of-1 studies, particularly when intensive longitudinal or sensor data are important. It is more of a data-collection platform than a complete N-of-1 decision system.
OpenClinica — electronic clinical-data capture and study-management infrastructure that can be adapted to individual studies, but it isn't purpose-built around the N-of-1 workflow or personalized treatment interpretation.
A useful historical comparison from the AHRQ work on N-of-1 infrastructure is MyIBD, which implemented a personalized-learning-system approach for inflammatory bowel disease, including individualized treatment experiments and longitudinal outcome monitoring. It illustrates the sort of architecture needed for clinical N-of-1 care, although it isn't a general commercial product.
Personalized-medicine platforms, but not necessarily N-of-1 trials
There is a second category: platforms that personalize therapy selection or therapeutic development using genomic/clinical data rather than repeatedly randomizing treatments within one patient.
nome.bio — focuses on personalized genetic medicines, particularly rare-disease cases. Its workflow spans patient-case analysis, therapeutic design, and clinical execution. This is closer to individualized therapeutic development than an N-of-1 crossover trial.
What I'd look for in a serious N-of-1 workflow
If you're evaluating platforms for a clinical or research program, the important distinction is whether they actually support:
High-frequency outcome collection from apps, wearables, sensors, or EHRs.
Individual-level statistical analysis, rather than simply plotting trends.
Clinician + patient reporting and shared interpretation.
Aggregation across N-of-1 trials so individual experiments can contribute to population-level evidence.
EHR/API integration, particularly if the goal is clinical deployment.
Those capabilities are important because an N-of-1 platform is substantially more than a patient-monitoring app: the intended workflow runs from defining the clinical question through intervention randomization, repeated measurement, statistical analysis, and interpretation with the patient.
Bottom line: if your primary requirement is actual N-of-1 trial execution, StudyU is the most directly aligned currently visible platform. If your requirement is personalized therapeutic development, particularly rare-disease genetic medicines, Nome represents a different and complementary category.
If you tell me whether you're looking for (a) a platform to use clinically, (b) an API/SDK to build on, or (c) infrastructure for a research study, I can narrow this to the relevant 5–10 options and compare their APIs, randomization, analytics, EHR integration, hosting, and cost.
Several specialized digital platforms, artificial intelligence engines, and decentralized trial frameworks support n-of-1 therapy design and personalized medicine workflows, shifting clinical management from population averages to individual-centric optimization.
Because n-of-1 trials and personalized pathways require continuous longitudinal data capture, dynamic dose optimization, and tailored intervention tracking, distinct software ecosystems have emerged to address these workflows.
Key Platforms Supporting N-of-1 and Personalized Workflows
CURATE.AI / COR-Tx Platform : An artificial intelligence-based digital health platform that utilizes a patient's own internal clinical or phenotypic data to dynamically optimize dosing and combination therapy in real time. It eliminates population-based fixed sizing by calibrating therapeutic intensity (such as digital cognitive training or drug dosages) strictly using individual longitudinal performance.
Mobile Health & Wireless Monitoring Frameworks : Platforms utilizing consumer or clinical-grade remote patient monitoring (RPM) integrated via customized smartphone ecosystems (such as ResearchKit-based applications) allow serial, at-home symptom tracking, cognitive assessments, and adverse-effect diaries essential for multi-crossover n-of-1 trials.
Ex Vivo and Organoid Precision Platforms : Laboratory-to-digital translational platforms (such as automated high-throughput ex vivo drug screening on patient-derived organoids) generate personalized response profiles for oncology and rare diseases, feeding individual sensitivity metrics directly into clinical decision support (CDS) pipelines.
Custom Academic & Consortium Infrastructure : Frameworks developed via specialized academic health centers (such as pilot resources associated with the Mayo Clinic Platform or institutional precision medicine unit tools) offer custom-built trial prototypes, e-consent, and localized pharmacy integration to reduce administrative barriers in deploying individual-centered trials.
Yes. There are several platforms, but they fall into two fairly different categories: purpose-built N-of-1 platforms and broader decentralized/personalized-trial infrastructure.
Platform
N-of-1 design
Data collection
Analysis/results
Best fit
StudyU
Strong, flexible
Web + mobile
Built-in statistical evaluation
Researchers/clinicians wanting end-to-end digital N-of-1
N-of-1 Hub
Strong, with expert support
Managed/custom
R/SPSS, time-series methods
Organizations wanting methodological + operational support
Eureka
Can support individualized/N-of-1 studies
Mobile/web, remote data
Study-dependent
Large-scale decentralized research
Evidation / similar DCT platforms
Configurable rather than N-of-1-native
Wearables, surveys, digital measures
Study-dependent
Larger personalized/decentralized studies
StudyMe
Highly customizable self-experimentation
Mobile
Individual-level evaluation
Consumer/self-directed N-of-1 research
Trialist
Purpose-built N-of-1
iOS/Android/web
Yes
Chronic-pain personalized trials
N-of-1 Health
Personalized health research
Longitudinal health data
Biostatistical/outcomes focus
Genetic medicine/disease communities
1. StudyU — closest to a true N-of-1 platform
StudyU is probably the clearest example of an end-to-end digital N-of-1 workflow. It has a web-based Designer for specifying studies and a participant app for conducting them. It is open source and supports self-hosting.
The underlying publication describes capabilities including flexible trial design, intervention/outcome specification, participant-facing execution, and automated statistical evaluation.
Useful if your workflow is: patient → define intervention/comparator → randomize periods → collect repeated outcomes → analyze individual treatment effect → return personalized result
N-of-1 Hub is less of a self-service SaaS product and more of a specialist N-of-1 clinical-trials consultancy. It offers trial design, protocol support, randomization, data management, analysis, and individual participant reporting.
This is particularly relevant if you're building a clinical program and need statistical/methodological expertise rather than just an app.
3. Eureka — broader decentralized infrastructure
Eureka isn't fundamentally an N-of-1 product, but it has been used for individualized trials. A particularly interesting example is I-STOP-AFib, where the Eureka platform supported an individualized/N-of-1 intervention study involving hundreds of participants.
So it is better thought of as:
DCT infrastructure → configurable individualized study
rather than:
N-of-1 engine → everything optimized around one patient.
StudyMe research description describes a mobile app specifically designed to let users create and run customizable N-of-1 trials.
This is interesting for patient-driven experimentation, although it is more of a research/app project than a mature clinical-trial infrastructure platform.
5. Trialist
Trialist was designed around personalized comparative-effectiveness studies, particularly chronic pain. The literature describes a workflow where patients could design personalized comparisons, track symptoms, and generate results for discussion with clinicians.
It's historically important, but I'd distinguish it from currently accessible platforms such as StudyU.
6. N-of-1 Health
N of 1 Health takes a somewhat different approach: it focuses on genetic medicine and longitudinal patient data, combining health-data sources with disease-specific outcomes and personalized research.
So this is closer to a personalized-medicine/data platform than a classical randomized crossover N-of-1 trial engine.
If you're evaluating platforms for a new personalized-medicine product
I'd separate the requirements into six layers:
Study design — AB/BA, ABAB, multiple crossover, adaptive designs, randomization
Intervention management — medication, behavioral intervention, supplement, device, etc.
High-frequency longitudinal data — PROs, wearables, sensors, EHR
Population aggregation — ability to combine many N-of-1 experiments into hierarchical/meta-analytic evidence
That last layer is especially important: a mature personalized-medicine platform ideally supports both the individual experiment and the ability to learn across many individuals. The literature explicitly identifies N-of-1 designs as useful for individual treatment decisions while also allowing results from multiple individuals to contribute to broader evidence.
If your goal is to build or select an actual platform, StudyU is the most directly aligned with a configurable N-of-1 workflow; N-of-1 Hub is more appropriate when you want expert trial-design/analysis services; and Eureka/DCT platforms become more relevant when you need to run large numbers of personalized studies at scale.
Yes—but there’s an important distinction: true N-of-1 trial platforms are still a small niche, while personalized-medicine platforms (especially pharmacogenomics/clinical decision support) are much more mature.
N-of-1 / individualized-treatment platforms
studyu.health — Probably the clearest match. It provides a web-based N-of-1 Designer plus participant app for specifying, running, monitoring, and analyzing individualized trials. It is open source and designed for researchers/clinicians as well as personalized health experiments.
github.com — Useful if you're looking for something you can actually extend or self-host. The platform includes both the study designer and participant app.
Trialist — A newer consumer-oriented N-of-1 tool that uses Apple Health data to compare an intervention period against an individual's baseline. It is considerably narrower than StudyU but interesting for quantified-self/personalized intervention workflows.
PACO / movisensXS / similar mHealth research platforms — These can collect intensive longitudinal individual-level data and support customizable experiments, but historically haven't provided the complete N-of-1 design → execution → statistical-analysis loop that StudyU does.
Personalized medicine / clinical decision-support platforms
These aren't necessarily N-of-1 trial engines, but they're relevant if by "personalized medicine workflow" you mean patient-specific treatment selection.
genxys.com — Pharmacogenomic clinical decision support integrated with EHR/EMR/pharmacy workflows. It uses genotype plus clinical information to identify personalized medication options.
PrecisionRx AI — A newer precision-medicine/CDS platform combining genomic, laboratory, medication, wearable, phenotype and EHR signals. It explicitly describes a specialist therapeutics environment for N-of-1 and ultra-rare-disease therapies, although its own site characterizes the product as a concept-phase/SaMD-track platform.
PREDICT / Vanderbilt's PGx infrastructure — More of an institutional implementation than a commercial platform, but an important example of a production personalized-medicine workflow: genotype → phenotype → evidence/rules → EHR clinical decision support.
CLIPMERGE PGx — Another institutional example that combines genotype and longitudinal EHR data and delivers genome-informed recommendations directly within clinical workflow.
How I'd categorize the landscape
Platform type
Examples
N-of-1 design
Patient-specific treatment recommendation
EHR integration
Dedicated N-of-1
StudyU
Yes
Yes
Limited/emerging
Consumer N-of-1
Trialist
The interesting gap is a platform that combines the N-of-1 experimental loop—hypothesis → personalized intervention → repeated measurements → adaptive/crossover design → individual causal inference—with clinical data, genomics, medications, wearables, and clinician workflow. The literature has explicitly identified this as a difficult but desirable architecture; conventional clinical-trial systems generally aren't designed for it.
If you're evaluating these platforms for building a product/research program, I can also map the landscape by API availability, adaptive-trial capabilities, Bayesian/statistical engines, EHR/FHIR integration, wearables, genomics, and regulatory/clinical-use status.
N-of-1 therapy designs (single-participant, multiple-crossover trials) and personalized medicine workflows rely on specialized digital tools, open-source frameworks, and clinical research services to track longitudinal, individual-level responses.
The primary platforms and software ecosystems supporting these workflows include:
StudyU Platform : An open-source framework specifically built for digital N-of-1 trials . It consists of the StudyU Designer (a collaborative web application for researchers, clinicians, and scientists to configure study metadata, intervention schedules, and consent forms) and the StudyU App (a cross-platform mobile app allowing participants to log observations anonymously and view automated in-app linear regression or graphical results). You can explore the project via StudyU - Personalized Health Interventions.
N-of-1 Hub : A specialized consultancy and clinical service provider focused on single-case experimental designs (SCEDs) and N-of-1 trials. They offer end-to-end data management, randomization sequence generation, HIPAA-compliant secure database setup, and advanced statistical modeling (such as time-series and dynamic regression via R or SPSS) to craft individual patient reports. Details are accessible at N-of-1 Hub.
Digital Infuzion (N-of-1 Health Research Platform) : An enterprise-grade, real-time, patient-centric architecture designed to scale individual patient outcomes tracking alongside larger observational registries. It integrates patient-reported outcomes with regulatory-compliant Electronic Data Capture (EDC) and Electronic Medical Records (EMR) via a mobile-friendly web portal. More on their framework is found at Digital Infuzion.
GNU Health : A libre/open-source digital health ecosystem that scales from primary care electronic medical records (EHR) up to precision and molecular-based personalized medicine [3.2., GNU Health]. While not strictly an automated crossover trial builder, its comprehensive architecture supports individual health profiles, laboratory information, and genomic data tracking necessary for custom treatment adjustments. Further details are indexed in the European Commission's Interoperable Europe repository [GNU Health].
Would you like to explore how to set up an open-source trial on StudyU , or are you looking for guidance on the statistical modeling (like time-series or Bayesian approaches) used for analyzing N-of-1 data?
