From training software to medical device

What we'd propose

• Digital Lab

  IoT-Enabled Diagnostic Hardware Standardisation Across Provider Network

  We deploy a certified IoT diagnostic hardware ecosystem across the APD Medical provider network — calibrated headphones, measurement microphones, and audio interface verification with cloud-synchronised calibration records — ensuring that every diagnostic session captures data with known, consistent hardware characteristics, and eliminating the equipment-driven variance that currently confounds the platform's normative comparisons.

  • IoT headset with embedded calibration verification

    Every headset calibrated and verified before each session

    Deploy Bluetooth-connected calibrated headphones with embedded calibration verification — a built-in tone generator and measurement microphone confirm that the delivered stimulus matches the platform's reference values within specified tolerances before every diagnostic session, with the verification record stored in the patient data record.

  • Cloud calibration registry for provider equipment

    Provider hardware status tracked and alerts issued on drift

    Build a cloud-based calibration registry that tracks the calibration status of every piece of hardware in every provider's setup — issuing alerts when calibration drifts outside acceptable tolerances and requiring re-calibration verification before the next diagnostic session can be recorded as valid.

  • Acoustic environment qualification check

    Room noise levels verified before each session

    Implement a pre-session acoustic environment qualification check using the headset's measurement microphone — assessing ambient noise level, reverberation characteristics, and sound isolation of the room, and flagging sessions where the environment falls below the minimum acoustic standard required for valid auditory training.

  • Diagnostic data quality improves because every session's hardware characteristics are verified and recorded — the normative database is no longer confounded by unquantified equipment variation across providers.
  • The cloud calibration registry gives APD Medical visibility into which providers are operating with out-of-specification equipment, enabling proactive maintenance rather than retrospective data quality review.
  • The acoustic environment qualification creates a feedback loop that trains providers to select appropriate therapy environments — improving session quality across the network over time without requiring centralised enforcement.

  We've done this before

  • Restoration: Establishing or Re-establishing a Complex Lab Ecosystem
  • MTP Library for Modular Automation

  See how we do Digital Lab
• Digital Lab

  Provider Network Digital Operating System

  We build a digital operating system for the APD Medical provider network — automated certification tracking, AI-driven competency monitoring based on diagnostic accuracy patterns, and patient referral management — transforming the network from a collection of independent practitioners into a data-driven clinical ecosystem where patient outcomes drive referrals and provider development.

  • Provider competency monitoring dashboard

    Diagnostic accuracy tracked per provider over time

    Build a provider performance dashboard that tracks diagnostic accuracy patterns, session completion rates, and patient outcome trajectories for each provider — identifying which providers are most effective at which patient types and surfacing skill gaps that targeted training interventions can address.

  • Automated certification and credential tracking

    Provider certifications tracked and expiry-alerted automatically

    Implement an automated certification tracking system for provider credentials, training completions, and annual competency assessments — eliminating the manual tracking that currently allows expired certifications to go undetected and creating an auditable record for regulatory compliance.

  • AI-assisted patient referral routing

    Patients routed to optimal providers based on outcome data

    Build a referral routing engine that matches new patients to providers based on the provider's demonstrated accuracy with similar patient profiles — moving referrals from a reputation-based model to a data-driven model that improves patient outcomes by matching each patient to the provider most effective for their specific presentation.

  • Patient outcomes improve because referrals are routed based on demonstrated provider effectiveness data rather than geographic convenience or historical relationship — each patient is more likely to reach the provider who is most effective for their specific auditory processing profile.
  • The certification tracking system reduces the regulatory and liability risk of operating with expired credentials — an automated alert before expiry is substantially more reliable than a provider's personal calendar management.
  • APD Medical's competitive advantage in provider quality becomes visible and defensible — the outcome data that the platform generates is a moat that competitors cannot replicate without building the same longitudinal dataset.

  We've done this before

  • Bridging the Gap Between Scientists and Algorithms
  • Proactive Support & Change Management Framework for a Digital Lab

  See how we do Digital Lab
• Enterprise AI

  FHIR-Compliant Healthcare Integration and SaMD Certification Platform

  We architect and implement the FHIR-compliant integration layer and quality management infrastructure required for Software as a Medical Device certification under EU MDR — enabling the hospital EHR connectivity, insurance reimbursement, and clinical care pathway integration that the current pedagogical classification prevents.

  • FHIR-compliant API gateway for Neuroflow platform

    Hospital EHR systems can exchange data with Neuroflow

    Deploy a FHIR-compliant API gateway that exposes Neuroflow diagnostic results, therapy session summaries, and patient progress data through standardised FHIR resources — enabling bidirectional integration with hospital EHR systems and primary care clinical workflows.

  • Clinical validation study design and data platform

    Clinical evidence generated for MDR technical file

    Design and implement the clinical validation study architecture — patient recruitment tracking, outcome measurement protocols, and statistical analysis pipeline — to generate the clinical evidence that the EU MDR technical documentation requires for SaMD certification.

  • Post-market surveillance infrastructure

    Real-time safety monitoring after MDR certification

    Build the post-market surveillance system required for an active medical device — adverse event tracking, complaint management workflows, and periodic safety update report generation — ensuring that the platform continuously meets the safety monitoring obligations that MDR certification requires.

  • The hospital integration enables the clinical care pathway revenue stream — Neuroflow becomes a prescribed, reimbursed digital therapeutic rather than a self-pay educational tool, dramatically expanding the addressable patient population.
  • The MDR certification differentiates APD Medical from the international NeuroFlow competitor that has not achieved medical device classification — CE marking is a substantive competitive moat that the Polish platform can establish before international competitors enter the neuro-audiology specific market.
  • The post-market surveillance data becomes a continuous improvement engine — every safety signal and adverse event is tracked and analysed, providing the evidence base for future algorithm improvements and MDR post-market surveillance reports.

  We've done this before

  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES
  • Scaling Biotech Manufacturing via Universal IT/OT Architecture

  See how we do Enterprise AI
• Enterprise AI

  Predictive Auditory Analytics Engine

  We develop a predictive diagnostic engine using machine learning models trained on the longitudinal Neuroflow platform dataset — identifying sub-clinical auditory processing patterns that precede disorder manifestation, enabling earlier intervention and personalised therapy intensity calibration for each patient's specific developmental trajectory.

  • Longitudinal outcome trajectory modelling

    Each patient's therapy response predicted from history

    Build predictive models that analyse each patient's historical diagnostic data, therapy session responses, and outcome trajectories to predict how the patient's auditory processing capabilities are likely to develop — enabling therapy intensity and frequency to be calibrated to the individual patient's predicted trajectory rather than to population averages.

  • Sub-clinical pattern detection for early intervention

    Risk indicators flagged before disorder becomes clinical

    Implement a sub-clinical pattern detection system that identifies early markers of auditory processing difficulty in children before referral for formal assessment — enabling the early intervention window that has the highest therapeutic efficacy.

  • Provider decision support dashboard

    Therapy recommendations generated from patient data

    Build a provider-facing decision support dashboard that presents the predictive model's assessment alongside the provider's own clinical evaluation — giving providers the AI-augmented insight that helps them make more accurate diagnoses and more effective therapy recommendations without replacing clinical judgement.

  • Children who would otherwise be assessed only after significant developmental delay are identified earlier — the early intervention window that has the highest therapeutic efficacy is used rather than missed.
  • Therapy personalisation improves because each patient's predicted trajectory is used to calibrate intensity — the platform's fundamental competitive advantage over one-size-fits-all auditory training is amplified by AI-driven personalisation.
  • The longitudinal dataset that the predictive engine requires becomes more valuable as it grows — every completed therapy case improves the model's accuracy, creating an increasing returns dynamic where the platform with the most data delivers the best outcomes.

  We've done this before

  • Real-Time Process Intelligence & KPI Visualization
  • Transforming Raw Bioprocess Data into Actionable Insights

  See how we do Enterprise AI
• Digital Lab

  Environmental Monitoring for Therapy Session Validity

  We implement a sensor ecosystem integrated with the Neuroflow therapy platform that monitors session acoustic environment quality and patient biometric state — automatically qualifying whether each session meets the conditions required for valid auditory stimulus delivery and flagging or pausing sessions where environmental conditions compromise therapeutic integrity.

  • Ambient noise monitoring during therapy sessions

    Background noise measured continuously during every session

    Integrate ambient noise sensing using the diagnostic headset's built-in microphone — continuously monitoring background noise levels during therapy sessions and automatically flagging sessions where ambient noise exceeds the acoustic threshold required for valid stimulus delivery, with the environmental quality record stored alongside the session data.

  • Biometric feedback integration for patient engagement

    Heart rate and attention indicators monitored during sessions

    Implement biometric wearable integration — heart rate and electrodermal activity sensors — to monitor patient engagement and stress levels during therapy sessions, providing both a real-time feedback signal for therapy adaptation and a data channel for outcome analysis.

  • Session validity score in the patient record

    Every session rated for environmental and biometric quality

    Build a session validity scoring system that rates each completed therapy session on acoustic environment quality, patient biometric engagement, and stimulus delivery verification — providing a data quality filter for the AI model training data and giving clinicians a reliable signal about which sessions produced clinically useful results.

  • AI model training data quality improves because sessions with compromised acoustic environments are flagged and excluded — the diagnostic algorithms are trained on high-quality data that actually reflects what the patient heard, not what the platform assumes they heard.
  • Providers receive objective evidence about the acoustic quality of their practice environments — this feedback drives improvement in the physical spaces where therapy is delivered without requiring APD Medical to audit every provider location.
  • The session validity score creates a defensible evidence base for therapy efficacy — when outcomes are questioned by insurers, clinicians, or regulators, APD Medical can demonstrate that the therapy was delivered under valid conditions.

  We've done this before

  • Bioreactor Control: Computer Vision for Non-Invasive Foam Management
  • Fully Automated Autosampling Control System

  See how we do Digital Lab