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The BD Intelliport™ System is an automated record keeping system that incorporates patient safety features that are aligned with hospital patient records and protocols. The system is comprised of an injection port and software that enables the identification, measurement, alerting and documentation of the administration of medications to patients.

The BD Intelliport™ System allows the clinician to record anesthesia-related medication administration events during pre-procedure, intra-procedure and recovery phase. The system is indicated for use by healthcare professionals in a hospital or medical center setting with patients who are receiving manually administered bolus intravenous injections as part of their care to facilitate documentation of the medications.

The BD Intelliport™ System is intended for patients with body weights >20 kg.

Do not use the BD Intelliport™ System with blood, blood products, biologics, or chemotherapeutics.

BD Intelliport™ System integrates into an intravenous line and automatically captures information about the anesthesia medications administered to the patient. It wirelessly transmits anesthesia medication administration information to the patient's Electronic Medical Record (EMR) via hospital server applications (Gateway software). The BD Intelliport™ System provides core technologies that enable key functions of the system:
• Medication Identification: Informs clinician of medication and concentration along with any informational notifications such as patient allergy and expired medication reminders. This occurs when syringes with the correct type of RFID encoded label are attached.
• Dose Measurement: Measures volume of drug administered to the patient through the system, then calculates dose weight.
• Automatic Charting: Wirelessly transmits measured doses to the EMR.

The following are the main system components:
• BD Intelliport™ Injection Site which is comprised of the following two components:

• BD Intelliport™ Sensor
• BD Intelliport™ Reader
  • BD Intelliport™ Mount (optional accessory)
  • BD Intelliport™ 2-Bay Charger (accessory)
  • BD Intelliport™ Gateway

The provided FDA 510(k) clearance letter and summary for the BD Intelliport™ System (K243062) describes the performance testing conducted to demonstrate substantial equivalence to its predicate device (K182092). However, it does not provide specific acceptance criteria or reported device performance values in a quantifiable table format, nor does it detail a standalone study with quantitative results, or a multi-reader multi-case (MRMC) comparative effectiveness study.

The document primarily focuses on demonstrating substantial equivalence through a comparison of technological characteristics and a list of performance tests completed.

Here's an attempt to structure the answer based on the available information, with caveats where data is missing:

Acceptance Criteria and Device Performance

The 510(k) summary states that "The subject device, the BD Intelliport™ System, has met all predetermined acceptance criteria for the non-clinical and human factors testing conducted in accordance with relevant FDA guidance, recognized consensus standards, and internal requirements." However, the specific numerical acceptance criteria and the quantitative reported device performance values for most tests are not explicitly stated in the provided document.

The only quantitative performance criteria and reported values mentioned are for "Volume measurement accuracy" and "Volume Measurement Resolution."

Note: The document states these are "Identical" to the predicate device, implying the reported performance matches the specified criteria.

Study Information

Due to the nature of a 510(k) summary, detailed study reports with specific quantitative results (beyond volume measurement accuracy) are not included. The document generally refers to "performance testing" and "human factors evaluation."

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated for each test. For "Volume Measurement Performance Window," the volume range tested was 0.5ml to 30ml, and average push speed 10ml/min to 400 ml/min. This implies tests were conducted across this range, but the number of injections or trials is not provided.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be non-clinical (laboratory-based) and human factors studies. The human factors testing likely involved simulated clinical environments.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable/Not provided within the scope of this 510(k) summary. These types of details are usually found in full study reports, not the summary itself, especially for a device that primarily automates record-keeping and measurement, rather than making diagnostic assessments that require expert ground truth. The human factors testing involved "intended device users," but their specific qualifications or roles in establishing "ground truth" (in a diagnostic sense) are not outlined.

3. Adjudication method for the test set:

   • Not applicable/Not provided. Adjudication methods (like 2+1, 3+1) are typically used in studies where there is disagreement in expert interpretation of diagnostic data. This device automates measurements and record-keeping, so such a method is not relevant.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not explicitly reported or appears not to have been the primary method for demonstrating substantial equivalence. The device's primary function is "automated record keeping" and facilitating "documentation of the medications." It improves efficiency and accuracy of recording medication administration, rather than assisting human readers in interpreting medical images or data. The human factors evaluation assessed "critical tasks completed by intended device users," implying usability and user performance with the system, not a comparison of expert diagnostic accuracy with and without AI.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • The document implies that standalone (algorithm-only) performance testing was conducted for various technical attributes. For example, "Bolus volume measurement accuracy," "Sensor flow rate," "Decoding response time," "Wifi functionality," and "Dose transmission time" are intrinsic functions of the system and its algorithms, which would have been tested independently of a human operator to verify their technical specifications. The "Flow Algorithm" itself was updated and "qualified through verification testing." However, explicit, detailed results from a standalone study with acceptance criteria are not presented in a formal table like format for all algorithm-driven functions.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For the technical performance tests (e.g., volume measurement), the "ground truth" would have been established using calibrated instruments and reference standards (e.g., known volumes, known flow rates) in a laboratory setting.
   • For the human factors evaluation, the "ground truth" would be defined optimal task performance and safety outcomes as determined by medical device standards and clinical best practices.

7. The sample size for the training set:

   • The document does not provide information on a training set size. The BD Intelliport System involves embedded software and algorithms (e.g., flow algorithm, RFID reading, EMR communication). While such systems often involve development and testing cycles, the summary does not detail a specific "training set" like one would find for a machine learning or AI algorithm that learns from data in a traditional sense. The "Flow Algorithm" was updated and "qualified through verification testing," which implies validation against known physical models or experimental data, rather than a statistical "training set" in the context of deep learning.

8. How the ground truth for the training set was established:

   • As no "training set" is explicitly mentioned for a machine learning model, this question is not applicable in the context of the provided information. The "ground truth" for verifying the updated flow algorithm would have been established through physical experiments and engineering measurements with known parameters (e.g., precise drug volumes, flow rates) to ensure the algorithm accurately processes the sensor data.

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The BD Intelliport™ system is an automated record keeping system that incorporates patient safety features that are aligned with hospital patient records and protocols. The system is comprised of an injection port and software that enables the identification, measurement, alerting and documentation of the administration of medications to patients.

The BD Intelliport™ system allows the clinician to record anesthesia-related, medication administration events in the pre-op, intra-op, and PACU. The system is indicated for use by healthcare professionals in a hospital or medical center setting with patients who are receiving manually administered bolus intravenous injections as part of their care to facilitate documentation of the medications.

The BD Intelliport™ system is intended for patients whose body weights are >20kg.

The BD Intelliport™ system is not intended for use with blood, blood products, biologics, or chemotherapeutics.

The BD Intelliport™ system is not intended for use with refrigerated medications (excluding cefazolin)

Like the cited predicate device, the BD Intelliport Medication Management System, Version 1.2 is a system of hardware and software components that measures the delivered volume of intravenous (IV) bolus medications associated with anesthesiarelated, medication administration events in the pre-op, intra-op, and PACU, and provides automated documentation of medication identity, concentration, dose, delivered volume, and delivery timestamp to the hospital electronic medical record (EMR) system. There are two modes: EMR mode and Standalone mode. Standalone mode allows for printing dose history reports, but is not connected to an EMR

The device is organized by three subsystems, each of which includes several physical/software components:

• . Injection Site subsystem
• . Tablet subsystem
• . Gateway subsystem

The Injection Site subsystem is composed of two physical components (the Base and the ultrasonic Sensor) and the software residing inside the Base. The Tablet subsystem is composed of one physical component (the Tablet) and the software residing inside the Tablet. The Gateway subsystem consists of the Gateway software and the hospital server hardware on which the Gateway software resides. The device also includes an accessory for charging up to five Bases simultaneously.

During treatment, the clinician connects an intelligent injection port (the Injection Site) to a patient's fluid-delivery line and performs standard drug-delivery activities. The clinician injects the druq using BD Luer Lok, 1-60ml size syringes only, and then flushes with a separate normal, saline syringe. Flushing after each administration ensures all medication is administered to patient since there is a 0.334 ml dead space.

The device can be used with both encoded and non-coded syringes. If an encoded syringe is used, the device reads the two-dimensional barcode adhered to the syringe. The barcode contains information about the content in the syringe including the drug name and concentration. If a noncoded syringe is used, then the end user is prompted by the Tablet software to pick the drug name and concentration from the Encoded Drug List (EDL). When an encoded syringe is first connected to the device, the name of the drug is read back to the clinician and an allergy alert will trigger on the Injection Site base if the patient is allergic to the medication based on the patient's allergies record in their EHR (electronic health record). As the drug is injected, the device measures the volume of the injected drug via an ultrasonic sensor and records the time the drug was administered. Drug delivery information is stored and inserted into the patient's EHR by way of the Tablet and Gateway software in the EMR mode and available for printing in the Standalone mode.

Please find the requested information regarding the acceptance criteria and the study that proves the device meets the acceptance criteria for the BD Intelliport System, Version 1.2.

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not explicitly state the sample size for the test set in quantitative terms for the identified performance criteria. For volume measurement accuracy, it states "Testing was conducted on a set of drugs commonly used in Anesthesia, bracketed based upon speed of sound and solvent/dissolved solids composition," implying multiple drugs were tested.

The document does not provide information on the country of origin of the data or whether the data was retrospective or prospective. The study appears to be bench and simulated human use testing, rather than clinical data from human patients.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not specify the number of experts or their qualifications for establishing ground truth within the context of the technical performance testing (e.g., volume accuracy). For human factors testing, it refers to "intended device users" and "clinicians," but does not detail their number or specific qualifications for establishing ground truth beyond their role as users.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe any specific adjudication method for establishing ground truth for the technical performance test sets. For human factors testing, it implies evaluation by human factors experts (though not explicitly stated as 'adjudication').

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was performed. The device is described as an "automated record keeping system" and the studies reported are primarily non-clinical performance testing and simulated human use testing, not studies comparing human reader performance with and without AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

Yes, standalone (algorithm only) performance was assessed for aspects like volume measurement accuracy. The "Modification of the volume measurement algorithm to improve accuracy and precision" implies testing of the algorithm itself. The system also has a "Standalone mode" which allows for printing dose history reports without EMR connection, suggesting functionality independent of real-time human interaction for certain tasks.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For volume measurement accuracy, the ground truth would likely be established through precise, laboratory-grade measurements of administered fluid volumes using calibrated equipment, comparing these to the device's reported measurements. The document mentions "Testing was performed to ensure that the device volume measurements meet the volume accuracy specification with real drugs," which supports this interpretation. For software functionalities like error messages and drug tables, the ground truth would be based on predefined software requirements and verification of their correct implementation.

8. The sample size for the training set

The document does not provide information about a "training set" or its sample size. The studies described are primarily verification and validation testing of the device's performance against specifications, rather than development or training of a machine learning model.

9. How the ground truth for the training set was established

As no training set is described in the provided text, there is no information on how its ground truth was established.

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