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Intended Use Statement:

The eCareManager System is a software tool intended for use by trained medical staff providing supplemental remote support to bedside care teams in the management and care of in-hospital patients. The software collects, stores and displays clinical data obtained from the electronic medical record, patient monitoring systems connected through networks. Using this data, clinical decisions are generated that aid in understanding the patient's current condition and changes over time. The eCareManager System does not provide any alarms. It is not intended to replace bedside vital signs alarms or proactive patient care from clinicians.

All information and notifications provided by the eCareManager System are intended to support the judgement of a medical professional and are not intended to be the sole source of information for decision making.

Indications for Use Statement:

The eCareManager software is solely indicated for use in hospital environment or remote locations with clinical professionals. It is not indicated for home use.

The eCareManager System is a collection of software applications designed to facilitate the delivery of high-quality critical care services with the assistance of a Telehealth Center Program (THC). The THC provides an organizational and technology platform to transform critical care by redesigning the way critical care is structured and managed. The THC Care Team is a multi-professional team that includes both bedside and THC remote clinicians working together to ensure the best care is provided. The eCareManager System provides a robust toolset that helps ICU and Acute Care clinicians plan, document, and standardize care around best practices.

Population management and communications facilitate a collaborative approach to delivery of in-patient care. The system's clinical decision support applications further aid in the proactive delivery of care. Using data received from the hospital's systems, clinical decision support algorithms provide cues that assist in the early detection of changes in patient condition.

The Visicu, Inc. eCareManager 4.5 is a software tool intended for use by trained medical staff to provide supplemental remote support to bedside care teams in the management and care of in-hospital patients. This device generates clinical decision support notifications to aid in understanding the patient's current condition and changes over time, using clinical data from electronic medical records, patient monitoring systems, and ancillary systems. It is not intended to provide alarms or replace bedside vital signs alarms or proactive patient care from clinicians. The information and notifications are meant to support the judgment of a medical professional and are not the sole source for decision-making. The software is indicated for use in hospital environments or remote locations with clinical professionals and is not for home use.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not explicitly state quantitative acceptance criteria for device performance in a table format. Instead, it relies on a qualitative assessment of successful completion of verification and validation activities. The "Reported Device Performance" is implied by the statement "All predetermined acceptance criteria were met, demonstrating that the device performs appropriately per defined specifications, and correctly incorporates all required safety mitigations."

However, the document mentions two key device functions under review: Automated Acuity and Discharge Readiness Score, suggesting these would have had specific performance requirements, though not detailed here. The "Comparisons of Technological Characteristics" section also outlines new features and enhancements, and their successful implementation implies they met their intended design specifications.

2. Sample Size Used for the Test Set and Data Provenance:

The document does not specify a separate "test set" sample size in terms of patient data. The non-clinical testing described focuses on software verification and validation activities. It states that "eCareManager was tested in accordance with Philips verification processes" and "Software verification and validation tests were successfully executed on the eCareManager." This implies that the validation was conducted on the software itself rather than a specific patient data set. There is no mention of the country of origin of data or whether it was retrospective or prospective, as the testing was primarily on the software functionality.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

The document does not mention the use of experts to establish a "ground truth" for a patient-data-based test set, as the testing described is primarily software verification and validation. While the device's purpose is to aid medical professionals, the validation focuses on the software's functional correctness rather than the accuracy of its clinical decision support against expert consensus in a clinical setting.

4. Adjudication Method for the Test Set:

No adjudication method is described, as the non-clinical testing detailed does not involve human readers evaluating output against a ground truth from patient data. The testing was focused on software functionality and compliance with specifications.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No MRMC comparative effectiveness study is mentioned. The document primarily reports on software verification and validation activities, not a study involving human readers with and without AI assistance to assess improved effectiveness.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

Yes, the testing described appears to be primarily stand-alone algorithm performance. The "Non-Clinical Testing" section details "Software verification and validation activities" including "Unit Testing, Integration Testing and System Level Testing (Functional and Regression Testing, Migration Testing, Localization Testing, Performance/Load Testing and Compatibility Testing included the following System Level Testing: User/Commercial Requirements Validation, Essential Performance Testing and Human Factors/Usability Testing." This indicates that the software's functionality was evaluated independently to ensure it performed according to its defined specifications and safety mitigations.

7. The Type of Ground Truth Used:

For the software verification and validation, the "ground truth" would be the defined specifications and requirements for the eCareManager 4.5 software. The success of the testing means that the software's outputs and behaviors matched these predetermined specifications.

8. The Sample Size for the Training Set:

The document does not provide information about a "training set" sample size. The eCareManager system uses clinical data from electronic medical records and patient monitoring systems to generate clinical decision support notifications. While this implies underlying algorithms that may have been trained, the regulatory submission documentation focuses on the verification and validation of the software system as a whole, rather than the development and training of specific AI/ML models within it. Thus, a training set is not mentioned.

9. How the Ground Truth for the Training Set Was Established:

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

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Intended Use Statement:

The eCareManager System is a software tool intended for use by trained medical staff providing supplemental remote support to bedside care teams in the management and care of in-hospital patients. The software collects, stores and displays clinical data obtained from the electronic medical record, patient monitoring systems and ancillary systems connected through networks. Using this data, clinical decision support notifications are generated that aid in understanding the patient's current condition and changes over time. The eCareManager System does not provide any alarms. It is not intended to replace bedside vital signs alarms or proactive patient care from clinicians.

All information and notifications provided by the eCareManager System are intended to support the judgement of a medical professional and are not intended to be the sole source of information for decision making.

Indications for Use Statement:

The eCareManager software is indicated for use in hospital environment or remote locations with clinical professionals. It is not indicated for home use.

The eCareManager system is a software platform that enables enterprise telehealth. The system includes interface features to acquire patient data from the electronic medical record and bedside devices which can be shared between the bedside and remote care teams. Population management and communication features facilitate a collaborative approach to delivery of in-patient care. The system's clinical decision support features further aid in the proactive delivery of care. Using data received from the hospital's systems, clinical decision support algorithms provide cues that assist in the early detection of changes in patient condition.

The provided document is a 510(k) premarket notification for a software device called eCareManager 4.1. It details the device's intended use, comparison with a predicate device (eCareManager 4.0), and summarizes performance testing. However, this document does not contain the specific acceptance criteria or detailed results of a study proving the device meets those criteria, as typically found in a clinical study report for an AI/ML medical device.

The eCareManager system described is a "telehealth software system" that provides "clinical decision support notifications" based on collected clinical data. It is explicitly stated that the system "does not provide any alarms" and "is not intended to replace bedside vital signs alarms or proactive patient care from clinicians," nor is it "intended to be the sole source of information for decision making." This suggests its role is primarily informational and supportive, not diagnostic or a direct intervention.

Given the nature of the device as clinical decision support software without direct diagnostic or therapeutic action, the FDA submission focuses on showing substantial equivalence to a previous version and that the changes do not raise new safety or effectiveness concerns, rather than proving a specific performance metric against a "ground truth" as would be done for an AI diagnostic device.

Therefore, much of the requested information regarding detailed acceptance criteria, specific performance metrics (like sensitivity, specificity, AUC), sample sizes for test sets, data provenance, expert adjudication, MRMC studies, standalone performance, and ground truth establishment is not present in this 510(k) summary.

The document states:

• "Changes to the Automated Acuity Score calculation have been validated using clinical data collected under an observational, non-human subject evaluation. The evaluation demonstrated substantial equivalence of the modified calculation with the unmodified, predicate device version."
• "Test results demonstrated that eCareManager software release 4.1 meets all device specifications and user needs."

This indicates that some form of validation was done, but the specifics of how "substantial equivalence" was demonstrated in terms of precise metrics for the "Automated Acuity Score" or what "device specifications and user needs" were measured are not provided in this public summary.

Based on the available information:

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

The document does not provide a table of explicit acceptance criteria with numerical performance targets and corresponding reported device performance values (e.g., sensitivity, specificity, accuracy, etc.) for AI/ML performance. Instead, it states that "Test results demonstrated that eCareManager software release 4.1 meets all device specifications and user needs" and that the "evaluation demonstrated substantial equivalence of the modified calculation with the unmodified, predicate device version." These are high-level conclusions without quantified metrics for specific performance criteria.

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

• Sample Size for Test Set: Not specified. The document mentions "clinical data collected under an observational, non-human subject evaluation" was used to validate changes to the Automated Acuity Score.
• Data Provenance: Not specified (e.g., country of origin).
• Retrospective or Prospective: "Retrospective" is mentioned for "Vital Signs Monitoring" under "Clinical Decision Support Features" in Table 5-1, but it's unclear if this refers to the data used for the validation study or just a general characteristic of how vital signs are handled by the system. The validation itself is described as "observational, non-human subject evaluation," which typically implies retrospective analysis of existing data.

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)

Not applicable/Not specified. Given the device's function as clinical decision support that generates "notifications" and "cues" to "aid in understanding the patient's current condition," and explicitly "not intended to be the sole source of information for decision making," the ground truth wouldn't typically be established by expert consensus on, for example, image interpretation, but rather on the clinical condition of the patient as recorded in their EMR or other systems. The validation focused on the "Automated Acuity Score," and it's not clear that human experts were involved in establishing a "ground truth" for this algorithm's output, beyond perhaps verifying consistency with existing clinical assessments or outcomes data.

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

Not applicable/Not specified. There's no indication of any expert adjudication process for the "clinical data" used in validation.

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

No such study appears to have been performed or reported in this summary. The device is a "telehealth software system" providing "clinical decision support notifications," not an AI diagnostic tool intended to assist human readers differentiate between medical conditions from images or other complex data. The validation focused on the "substantial equivalence of the modified calculation with the unmodified, predicate device version."

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

The "Automated Acuity Score" calculation was validated using "clinical data." This suggests a standalone evaluation of the algorithm's output against some measure (likely derived from the clinical data it processes or against the predicate's output), although the specific metrics used are not stated. The device functions as a software tool that generates notifications, meaning its core function is algorithm-driven, so an algorithm-only evaluation of its calculations (like the Acuity Score) would be inherent to its validation.

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

Not explicitly stated. For the "Automated Acuity Score," the validation aimed to demonstrate "substantial equivalence... with the unmodified, predicate device version." This implies that the "ground truth" for the new version's performance was its consistency or agreement with the predicate's output or a clinical outcome measure derived from the "clinical data" itself that the score is meant to reflect (e.g., patient condition changes, length of stay, etc.). The summary only states "clinical data collected under an observational, non-human subject evaluation."

8. The sample size for the training set

Not applicable/Not specified. The document describes the device validation for a new version (eCareManager 4.1) against a predicate (eCareManager 4.0). It validates "changes to the Automated Acuity Score calculation." This would typically involve re-training or fine-tuning the algorithm, but the size of any training data used for the development of this calculation (or the updated parameters) is not disclosed in this summary, which focuses on validation of the product.

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

Not applicable/Not specified. As mentioned, the document describes product validation for an updated version, not the initial development or training process.

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eCareCoordinator and its accessories are indicated for use by patients and by care teams for collecting and reviewing patient data from patients who are capable and willing to engage in use of this software, to transmit medical and non-medical information through integrated technologies.

eCareCoordinator (eCC) is a software-only telemedicine system, designed to enable the support of patients in the home setting. eCC is intended to support the clinician with monitoring of remote patients. Clinicians use eCC to manage populations of ambulatory care patients, while keeping primary care physicians informed of patient status. eCC is a software-only device and does not contain any patient-contacting components.

The provided document, a 510(k) Pre-Market Notification for eCareCoordinator 1.5, does not contain a typical "acceptance criteria" table with reported device performance in a quantitative manner as one might expect for a diagnostic or interventional device. This is likely because the device is a software-only telemedicine system for data aggregation and communication facilitation, rather than a device with direct physiological measurement or diagnostic capabilities requiring numerical performance metrics (e.g., sensitivity, specificity).

Instead, the document focuses on demonstrating substantial equivalence to a previously cleared predicate device (eCareCoordinator 1.0) through a comparison of technological characteristics and verification/validation activities.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned, a specific table of quantitative acceptance criteria and performance metrics for the eCareCoordinator 1.5 is not provided in the document. The "performance" is demonstrated through verification and validation efforts, ensuring the device meets its specifications and user needs. The comparison table (Table 5-1 on pages 5-6) lists features and provides a categorical comparison (e.g., "Same", "Enhanced user interfaces", "Added video communications"), but not numerical performance.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in terms of patient data or clinical images for evaluating performance. The testing mentioned refers to:

• "Philips verification and validation processes"
• "detailed functional, system level and usability testing"

Given the nature of the device as a software-only telemedicine system for data aggregation and communication, a traditional test set with patient data provenance is not applicable in the way it would be for an AI diagnostic algorithm. The "test set" here would refer to the internal testing of the software's functionality and usability.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not applicable and therefore not provided in the document. Since there's no clinical performance testing involving patient data for diagnostic or treatment decisions, there's no ground truth to be established by experts in the context of diagnostic accuracy. The device is described as an "informational tool only and is not to be used as a substitute for professional judgment of healthcare providers."

4. Adjudication Method for the Test Set

This information is not applicable and therefore not provided in the document for the reasons stated above.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A MRMC comparative effectiveness study was not done. The document states:

• "Clinical Performance testing for Philips eCareCoordinator 1.5 was not performed, as there were no new clinical applications that had hazards or risk mitigations that required clinical performance testing to support equivalence."

Therefore, there is no effect size reported for human readers improving with or without AI assistance, as the device is not an AI diagnostic tool and no such study was conducted.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

A standalone performance study of an "algorithm" in the typical sense (e.g., for diagnostic accuracy) was not done. The device is a "software-only telemedicine system" that facilitates data aggregation and communication for human care teams, not an autonomous algorithm making diagnostic or treatment decisions. The document explicitly states: "eCareCoordinator does not send any real time alarms and is not intended to provide automated treatment decisions. This software is an informational tool only..."

7. Type of Ground Truth Used

Not applicable/Not explicitly stated for clinical performance. The "ground truth" for this type of software would be its adherence to functional and user requirements during internal testing. For example, a ground truth for a communication feature would be "did the video call successfully connect and transmit audio/video as designed?" rather than "was the diagnosis correct?".

8. Sample Size for the Training Set

This information is not applicable and therefore not provided. The eCareCoordinator 1.5 is a traditional software system, not an AI/machine learning model that undergoes "training" on a dataset in the way a diagnostic algorithm would.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable as there is no training set mentioned for this traditional software.

Summary of Device Acceptance and Study:

The acceptance of eCareCoordinator 1.5 for 510(k) clearance is based on demonstrating substantial equivalence to its predicate device (eCareCoordinator 1.0). This was supported by:

• Non-clinical testing: This involved internal "Philips verification and validation processes," including Risk Analysis, Product Specifications, Design Reviews, Verification & Validations.
• Conclusion: "Test results demonstrated that eCareCoordinator 1.5 meets all specifications and user needs." This implies that the software functions as designed and fulfills its intended purpose as a telemedicine system for data aggregation and user communication.

The document emphasizes that the enhanced features (like two-way video communication and Bluetooth connectivity for measurements) do not change the intended use or introduce new questions of safety or effectiveness, thus obviating the need for clinical performance testing typically required for devices with direct clinical impact.

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Intended Use Statement:

The eCareManager System is a software tool intended for use by trained medical staff providing supplemental remote support to bedside care teams in the management and care of in-hospital patients. The software collects, stores and displays clinical data obtained from the electronic medical record, patient monitoring systems and ancillary systems connected through networks. Using this data, clinical decision support notifications are generated that aid in understanding the patient's current condition and changes over time. The eCareManager System does not provide any alarms. It is not intended to replace bedside vital signs alarms or proactive patient care from clinicians.

All information and notifications provided by the eCareManager System are intended to support the judgement of a medical professional and are not intended to be the sole source of information for decision making.

Indications for Use Statement:

The eCareManager software is indicated for use in hospital environment or remote locations with clinical professionals. It is not indicated for home use.

The eCareManager (eCM) system is a software platform that enables enterprise telehealth. The system includes an interface to acquire patient data from the electronic medical record and bedside devices. eCM provides a history of the patient population in clinic and provides a clinical decision support feature to aid in the proactive delivery of consultative care for the patient

The provided text describes the eCareManager 4.0.1, a telehealth software, but does not contain a table of acceptance criteria or detailed results of a study proving the device meets specific performance criteria. The document is a 510(k) summary for FDA clearance, focusing on demonstrating substantial equivalence to predicate devices rather than reporting on specific performance metrics against pre-defined acceptance criteria.

However, based on the information provided, here's what can be extracted and inferred regarding performance data and studies:

1. Table of Acceptance Criteria and Reported Device Performance:

No specific acceptance criteria or quantitative reported device performance metrics are explicitly stated in a table format within the provided document. The document primarily focuses on demonstrating substantial equivalence through comparison with predicate devices and mentions that "Verification and validation activities have been conducted to establish the performance, functionality, and usability characteristics of the new device with respect to the predicate, intended use and defined requirements."

It does highlight key features like the Sepsis Screening Prompt and validates its performance against the IntelliVue Protocol Watch SSC. The comparison table (Table 5-2) implies that the eCareManager's sepsis screening model "yielded higher discrimination for identifying severe sepsis than traditional criteria as used in the Intellivue SSC model," suggesting a performance improvement, but without specific metrics or acceptance thresholds.

2. Sample Size Used for the Test Set and Data Provenance:

The document states: "Clinical validation for the Philips eCareManager software release 4.0.1 included both model development and post-implementation studies to evaluate the performance of the Sepsis Screening Prompt algorithm." However, it does not provide details on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:

The document does not specify the number of experts used to establish the ground truth for the test set or their qualifications. It generally refers to "trained medical staff" and "clinicians" as the intended users and those who would follow up on sepsis assessments.

4. Adjudication Method for the Test Set:

The document does not describe any adjudication method used for establishing ground truth in the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The focus is on the algorithm's performance and its role in providing "supplemental remote support" and "clinical decision support notifications."

6. Standalone (Algorithm Only) Performance Study:

Yes, a standalone performance evaluation of the Sepsis Screening Prompt algorithm was done. The document states "Clinical validation for the Philips eCareManager software release 4.0.1 included both model development and post-implementation studies to evaluate the performance of the Sepsis Screening Prompt algorithm." The comparison with the IntelliVue Protocol Watch SSC (K113657), which also has a sepsis screening feature, indicates a standalone assessment of the eCareManager's algorithm.

7. Type of Ground Truth Used:

The document itself does not explicitly state the type of ground truth used for the sepsis screening algorithm. However, given that the algorithm is based on "2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med 2003 Vol 31, No.4" and assesses criteria like "Temperature, Heart Rate, Respiratory Rate, White Blood Cell Count/Bands, Mental Status, Serum Glucose, Ileus, INR, Lactate," it is highly probable that the ground truth for training and evaluation was established based on clinical criteria, patient outcomes, and potentially expert clinical review or diagnosis of sepsis. It references "higher discrimination for identifying severe sepsis," which implies a comparison against a clinical gold standard for sepsis diagnosis.

8. Sample Size for the Training Set:

The document does not provide the sample size for the training set used for the model development of the Sepsis Screening Prompt algorithm.

9. How the Ground Truth for the Training Set Was Established:

The document states the sepsis screening criteria are based on the "2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med 2003 Vol 31, No.4." This strongly suggests that the ground truth for the training set was established using consensus-based clinical definitions and guidelines for sepsis. It is implied that patient data meeting these established criteria would have been used to train and validate the algorithm.

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eCareCoordinator and its accessories are indicated for use by patients and by care teams for collecting and reviewing patient data from patients who are capable and willing to engage in use of this software, to transmit medical and non-medical information through integrated technologies.

eCareCoordinator (eCC) is a software-only telemedicine system. eCareCoordinator (eCC) is a combination of technology and clinical programs designed to enable the support of patients in the home setting, eCC is intended to support the clinician with monitoring of remote patients. Clinicians use eCC to manage populations of ambulatory care patients, while keeping primary care physicians informed of patient status.

eCC is comprised of the following primary components:

• . eCareCoordinator (eCC): eCC is the platform supporting the Clinical User Interface. eCC is a cloud-based system, which is used to acquire patient data from home devices, as well as provide a population management triage dashboard to enable the clinician's team to prioritize and manage populations of patients.
• . eCareCompanion (eCP): eCP is a patient application element of eCareCoordinator used to engage patients in their own health. eCP is a mobile application which runs on an commercial off-the-shelf (COTS) Android tablet. Patients manually input measurements (including weight, blood pressure, pulse, blood glucose concentration, SpO2, temperature, prothrombin time (PT), coagulation ratio (INR), and transthoracic impedance) from measurement devices into the COTS tablet containing eCP. The COTS tablet wirelessly communicates with eCC to transmit the data stored by eCP to eCC.

Here's an analysis of the acceptance criteria and study information based on the provided text, focusing on the eCareCoordinator (eCC) device:

Important Note: The provided document is a 510(k) Summary, which primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, not necessarily extensive performance validation against strict clinical acceptance criteria with statistical power. Therefore, some of the requested information (like specific effect sizes for MRMC studies, detailed sample sizes for "test sets" in a diagnostic accuracy sense, or specific ground truth methodologies for training data) are typically not found in such summaries for devices of this nature. The "studies" described here are mainly for verification and validation of the software's functionality and usability.

Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" for diagnostic performance in the way one might expect for an AI diagnostic device (e.g., sensitivity, specificity thresholds). Instead, the performance objective is to demonstrate that the eCareCoordinator (eCC) performs "as intended" and is "substantially equivalent" to its predicate devices. The "performance data" section states: "Bench tests were executed to verify and validate eCC. Verification testing consisted of verification of the system-level requirements and verification of the sub-system level requirements. Validation testing consisted of formative usability testing and summative usability testing. The verification and validation test results confirm that eCC performs as intended."

The acceptance criteria are implicitly tied to the functional and usability requirements, ensuring the system reliably collects, transmits, and presents patient data, and facilitates patient/clinician interaction as designed.

Additional Information:

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

   • The document mentions "bench tests" and "usability testing." It does not specify a "test set" in terms of a dataset for diagnostic accuracy, nor does it provide a sample size for data points or patients used in these tests.
   • Data provenance (country of origin, retrospective/prospective) is not mentioned for validation testing. Given the nature of a telemedicine system for data aggregation, the "data" itself might be simulated or test data for functional verification, and participants for usability studies.

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

   • This information is not provided. The device is a "telemedicine system" for data aggregation and communication, not a diagnostic AI that generates a "ground truth" to be compared against expert readings. The "ground truth" for its type of validation would likely be correct data transmission, correct display, and appropriate user interaction as per design specifications, rather than a clinical diagnosis.

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

   • Not applicable as this is not a diagnostic performance study requiring adjudication of expert readings against an AI output.

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 MRMC study was conducted or mentioned. The device is not presented as an AI-assisted diagnostic tool for human readers. It's a system to facilitate data collection and communication.

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

   • The "bench tests" and "verification testing" would likely constitute a standalone evaluation of the software's functionality. However, the device's purpose inherently involves human interaction (patients entering data, clinicians reviewing it), so a complete "standalone" clinical performance evaluation without any human interaction would not be meaningful for this device type. The document states it's an "informational tool only and is not to be used as a substitute for professional judgment of healthcare providers."

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

   • For the functional aspects (data aggregation, transmission, display), the "ground truth" would be the expected system behavior and correct data handling as defined by the software requirements and design specifications.
   • For usability, the "ground truth" would be successful completion of tasks by users and positive feedback on the user experience without critical errors. This is not a clinical ground truth in the diagnostic sense.

7. The sample size for the training set:

   • Not applicable. The eCareCoordinator is described as a "software-only telemedicine system" and a "platform supporting the Clinical User Interface," and "engages patients in their own health" by having them manually input measurements. It does not appear to employ machine learning that would require a "training set" of data to develop algorithms for diagnostic or predictive purposes.

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

   • Not applicable, as there is no mention of a training set or machine learning algorithms in the traditional sense described for this device.

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The VISICU ARGUS System is intended for use in data collection, storage and clinical information management with independent bedside devices, and ancillary systems that are connected either directly or through networks.

The VISICU ARGUS System is intended to provide patient information and surveillance of monitored patients at the point of care location and at a remote supplementary care location through wide area networking technology and dedicated telephone lines.

The VISICU ARGUS System is solely intended for use in a hospital environment. It is not intended to be used in a home environment.

Not Found

This document is a 510(k) clearance letter from the FDA for the VISICU Argus System, dated July 24, 2001. It indicates that the device has been found substantially equivalent to a legally marketed predicate device. However, the provided text does not contain any information regarding acceptance criteria, device performance studies, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth establishment.

Therefore, I cannot provide the requested table and study details from the given text.

The document primarily focuses on:

• The FDA's decision to clear the device for marketing.
• The device's regulation number, class, and product code.
• The intended indications for use of the VISICU ARGUS System, which include data collection, storage, clinical information management, and patient information/surveillance at point of care and remote locations within a hospital environment.

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