Permissions: eIQ Review: Allows licensed users to access eIQ Review in Data Central. Default access is not granted. Access to eIQ Review must be manually enabled in User Management.

eIQ Review Models bring machine learning and statistical monitoring into the clinical trial workflow. By scanning study data, eIQ identifies atypical results, data quality issues, and irregularities that require human review. Each model targets a specific indicator, such as lab-value shifts or anomalous AE durations. Then, the findings are sent to Data Central's eIQ Review, where reviewers can drill down to details, provide feedback, and perform other tasks. By focusing on the records most likely to need attention, eIQ Review Models enable faster data review and problem resolution.

Access eIQ Review

1. Within Data Central, click eIQ Review in the left navigation pane to expand the list of eIQ Review models.
2. Click any of the model names to open a listing of related anomalies; this opens in a new sheet labelled EIQReview. 

Tip: The same Data Central functionality is available, including filtering and sorting columns, using panel icons to export a listing, creating issues, marking records as reviewed, and maximizing / restoring a panel. Additional lists and charts can also be opened on an eIQ Review sheet.

CSM Labs and Vitals

Central Statistical Monitoring (CSM) - Labs & Vitals analyzes laboratory and vital sign SDTM data to flag subjects whose results deviate significantly from study norms. After a test has five subjects with at least five results each, the model flags unusual values and displays them in eIQ Review. Users can adjust a sensitivity slider to control the model's threshold to detect anomalies. This allows reviewers to quickly focus on outlier test results or subjects and drill down into graphical patient profiles to determine whether the findings warrant further review.

Lab and Vitals Tests Containing Anomalies

1. Click CSM Labs and Vitals. The listing opens. This view provides a summary of all tests that have at least one anomalous subject.

   Note: CSM requires at least 5 subjects with 5 tests each to analyze a test type. 

2. The sensitivity value is selected by default, click the threshold icon, and use the slider to adjust the sensitivity value. Changing the sensitivity updates the number of anomalous subjects shown in the listing and dashboards. Use the Reset all tests to default threshold button to return to the default sensitivity value.
3. Select the record for a specific test type and click to open a test-level sheet with three panels.  

Test-level Sheet

Lab and Vitals Tests Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple tests without going to the previous screen. The selected row is highlighted blue.

• Select another record for a specific test type, and the other two panels update based on the selection.

Tip: Hover over a data point in the box and whisker chart to see details in a tooltip.

Anomalous Subjects for <test type> (e.g., ALP): This listing displays the reviewer role(s) (e.g., DM, MDR, etc.), Subject ID, # of Analyzed Measurements, and Site ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed, based on the user's reviewer role.

• From this listing, users can create issues and mark records as reviewed.

Central Subject vs Anomalous Subjects: A box and whisker chart displaying details for the selected test type (e.g., ALP). 

• Click a box for a subject, and the subject-level sheet displays (same sheet as above).

Tip: Use the box and whisker chart to select the subject to start the review.

Subject-level Sheet

Tip: Use the breadcrumbs at the top left to return to the previous sheet and use the test name drop-down to select a different test. 

Anomalous Subjects for <test type> (e.g., ALP): This is the same listing but reduced in size to allow users to select other anomalous subjects. The selected row is highlighted blue.

Central Subject vs Anomalous Subjects: A box and whisker chart displaying details for the selected test type (e.g., ALP) and subject. 

Graphical Patient Profile (GPP): Usual Data Central functionality is available, such as creating issues, and clicking on a data point to open the record details as a floating panel. 

AE Durations

Anomalous AE Durations applies a machine learning model trained on historical data to the study’s SDTM adverse event data. This model flags records whose reported AE durations fall outside expected clinical patterns. This model has a confidence indicator column that shows how confident the model identifies potential data entry errors, such as end dates that appear implausibly too short or too long, and pinpoint outliers that may signal the need for further clinical review. The model supports follow-up actions like creating issues, providing feedback, or marking records as reviewed within eIQ Review.

AEs Containing Anomalies

1. Click AE Durations. The listing opens in the EIQReview sheet. This view provides a summary of all reported AEs that have at least one anomalous AE duration.
2. Select an adverse event record and click to open an AE-level sheet with two panels displayed.

AE-level Sheet

AEs Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple AEs without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific adverse event, and the other panel updates based on the selection.

Anomalous Records for <AEDECOD>(e.g., Arthralgia): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Subject ID, Record ID, Confidence Indicator, Value, and Site ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Select a specific row from the Anomalous Records to open a record-level sheet with five panels.

Record-level Sheet

Anomalous Records for <AEDECOD> (e.g., Arthralgia): This is the same listing but reduced in size to allow users to select multiple records without having to go to the previous screen. The selected row is highlighted blue.

Medical History Listing: Medical History listing.

AE Durations: Box and whisker chart.

Graphical Patient Profile (GPP): Usual Data Central functionality is available, such as creating issues, and clicking on a data point to open the record details as a floating panel.

Record Details: Use this panel to review record details and create issues directly.

Shift in Labs and Vitals

Shift in Labs and Vitals applies a machine learning model to SDTM laboratory and vital sign data, flagging records for unusual changes over time. The model highlights subjects and tests that may signal further clinical review by quantifying the percentage change and assigning a confidence indicator. Reviewers can then focus on the most atypical shifts, inspect trend graphs and medical history, provide feedback on predictions, and create issues directly from eIQ Review.

Lab and Vitals Tests Containing Anomalies

1. Click Shift in Labs and Vitals. The listing opens. This view provides a summary of all labs that have at least one anomalous shift in labs or vitals.
2. Select the record for a specific test type and click to open a test-level sheet with two panels. 

Test-level Sheet

Lab and Vitals Tests Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple lab tests without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific test type, and the other panel updates based on the selection.

Anomalous Records for <test type> (e.g., BASO): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Subject ID, Record ID, Confidence Indicator, Value, % Shift, and Site ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Select a specific row from the Anomalous Records to open a record-level sheet with five panels.

Record-level Sheet

Anomalous Records for <test type> (e.g., BASO): This is the same listing but reduced in size to allow users to select other anomalous records. The selected row is highlighted blue.

Medical History Listing: Medical History listing.

Measurements over time graph: A line chart showing the shift in measurements for labs and vitals across study days.  
The model requires the first two measurements to learn the base trend, therefore the first two dots are grey.

Graphical Patient Profile (GPP): Usual Data Central functionality is available, such as creating issues, and clicking on a data point to open the record details as a floating panel.

Record Details: Use this panel to review record details and create issues directly.

Univariate Outlier Detection 

The Univariate Outlier Detection model is study-agnostic and scans raw clinical domains for atypical values in individual variables selected during configuration. Using anomaly detection algorithms (not historical training data), it assigns confidence scores to each outlier and displays an overall summary and detailed variable-level charts. This model allows reviewers to use this data to spot data entry errors, extreme clinical readings, or early protocol deviations. Users can quickly create issues from eIQ Review, leave feedback, and mark records as reviewed.

Tests Containing Anomalies

1. Click Univariate Outlier Detection. The listing opens. This view provides a summary of all configured variables and groups of variables that are outliers.
2. The confidence value is selected by default, click the threshold icon, and use the slider to adjust the confidence value. Changing the confidence updates the number of outlier subjects shown in the listing and dashboards. Use the Reset all variables to default threshold button to return to the default confidence value.
3. Select the record for a specific variable and click to open a variable-level sheet with three panels. 

Variable-level Sheet

Tests Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple variables without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific variable and the other two panels update based on the selection.

Distribution of data: This chart identifies outlier data points.

• Select an item on the chart to open a record-level sheet with four panels.

Anomalous Records for <variable> (e.g., LBSTRESN-LBCAT-HEMATOLOGY-LBORRESU- hover over to see the full variable in a tooltip):

This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Record ID, Confidence Indicator, Value, and Subject ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Select the record for a specific variable and click to open a record-level sheet with four panels.

Record-level Sheet

Anomalous Records for <variable> (e.g., LBSTRESN-LBCAT-HEMATOLOGY-LBORRESU- hover over to see the full variable in a tooltip): 

This is the same listing but reduced in size to allow users to select other anomalous records. The selected row is highlighted blue.

Medical History Listing: Medical History listing.

Distribution of data for subject chart: A one-line bubble chart displaying all measurement instances. The more instances a measurement contains, the larger the bubble. Bubbles are colored differently depending on how many models detect the instance as an outlier.

Record Details: View the record details, add an Issue by clicking on the plus sign at the top right, scroll to the bottom of the record to view comments, related queries, and related issues. Usual Data Central functionality is available. 

Incorrect Item Type

Incorrect Item Type scans clinical domains for values whose data types do not align with their field definitions, such as text entered into a numeric field. Each mismatch is listed so reviewers can quickly identify entries that could trigger errors. By recognizing these type inconsistencies early, the model enables reviewers to correct problematic records before they proceed further downstream. Within eIQ Review, users can create issues, provide feedback, and mark records as reviewed.

Tests Containing Anomalies

1. Click Incorrect Item Type. The listing opens. This view provides a summary of all configured variables and groups of variables that have incorrect item types.
2. Select the record for a specific variable and click to open a test-level sheet with two panels.

Test-level Sheet

Tests Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple variables without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific variable, and the other panel updates based on the selection.

Anomalous Records for <variable>(e.g., ANALYTEVALUE - hover over to see the full variable in a tooltip):

This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Record ID, Value, and Subject ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.

CM Durations

CM Durations analyzes the SDTM Concomitant Medication (CM) data to flag records whose treatment periods are unusually short, long, or inconsistent with historic medication patterns. Each flagged entry is scored by confidence and paired with dose, indication, and site details. This model helps reviewers spot potential concerns that require further clinical review. Interactive charts and listings allow users to drill down into suspect records, give model feedback, and create issues or mark items as reviewed within eIQ Review.

CMs Containing Anomalies

1. Click CM Durations. The listing opens in the EIQReview sheet. This view provides a summary of all reported CMs that have at least one anomalous CM duration.
2. Select the record for a specific CMDECOD and click to open a CM-level sheet with three panels. 

CM-level Sheet

CMs Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple CMs without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific CMDECOD, and the other two panels update based on the selection.

Anomalous Records for <CMDECOD> (e.g., Ibuprofen): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Subject ID, Record ID, Confidence Indicator, Value, CMINDC, CMDOSE, CMDOSEU, and Site ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Click a row (record) and the record-level sheet opens displaying three panels.

CM Durations chart: A chart displaying the dose by duration (days). Color-coded markers indicate Range Area, Not Anomalous, and Anomalous.

Record-level Sheet

Anomalous Records for <CMDECOD> (e.g., Ibuprofen): This is the same listing but reduced in size to allow users to select multiple records without having to go to the previous screen. The selected row is highlighted blue.

CM Durations: A chart displaying the dose by duration (days). Color-coded markers indicate Range Area, Not Anomalous, and Anomalous.

Graphical Patient Profile (GPP): Usual Data Central functionality is available, such as creating issues, and clicking on a data point to open the record details as a floating panel. 

Domain Classification

Domain Classification analyzes free-text and coded fields in Medical History (MH) and Adverse Event (AE) records. By predicting the most appropriate domain for each entry, it identifies data that may be incorrectly filed, such as an AE listed under MH or vice versa. Reviewers can rely on this model to redirect misclassified records and reduce downstream reconciliation efforts. From eIQ Review, users can also provide model feedback, create issues, or mark items as reviewed.

Tests Containing Anomalies

1. Click Domain Classification. The listing opens in the EIQReview sheet. This view provides a summary of records identified as entered into the incorrect form or domain.
2. Click a row (Data Store / Domain) and the Data Store / Domain-level sheet with four panels displays.

Data Store / Domain-level Sheet

Tests Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple rows without having to go to the previous screen. The selected row is highlighted blue.

• Click another row (Data Store / Domain) and the other panels update based on the selected row.

Anomalous Records for Data Store-Domain (e.g., Clinical-MH): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Subject ID, Record ID, Confidence Indicator, Site ID, Predicted Domain, Misclassified Variables, and Reason. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Click a row (record) and the two panels on the right update.

Domain for Subject (e.g., MH for Subject CLINTEK-064-001-008): The AE or MH listing for the selected subject.

Record Details: View the Record Details of the highlighted record.

• Create an issue on the record or on an anomalous field. Anomalous fields are highlighted red for easy identification. 

Tip: When creating a record-level issue, the Issue Text is auto populated based on the model. The auto-populated text can be used as provided or updated before saving.

CM Consistency

CM Consistency analyzes the SDTM CM domain to spot internal contradictions, such as mismatched dose and unit combinations, implausible routes or frequencies, or fields left blank or coded as Other. A semi-supervised learning model trained on historical medication data flags each record by confidence indicator level and suggests likely corrections. Reviewers can use this model to identify data-entry errors before propagating, ensuring medication information is complete, consistent, and ready for downstream analyses. Users can also provide model feedback, create issues, or mark items as reviewed within eIQ Review.

CM Consistency between Dose, Dose unit, Route of Administration, and Frequency Containing Anomalies

1. Click CM Consistency. The listing opens in the EIQReview sheet. This view provides a summary of records with fields identified as inconsistent, incorrectly entered as other or have missing data, and provides suggestions for correct data entry.
2. Select the record for a specific CMDECOD and click to open a record-level sheet with 3 panels.

CM / Record-level Sheet

CM Consistency between Dose, Dose Unit, Route of Administration, and Frequency Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple rows without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific CMDECOD, and the other two panels update based on selection.

Anomalous Records for <CMDECOD> (e.g., Biotin): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), number of fields containing inconsistencies, Subject ID, Record ID, Confidence Indicator, and Site ID. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can provide feedback on the model prediction, create issues, and mark records as reviewed.
• The Confidence Indicator column displays the confidence level associated with each prediction. Records with the highest confidence levels are listed at the top of the list, ensuring the most anomalous records appear first.
• Click a row (subject) and that subject's CM record displays in the Record Details.

Record Details: View the Details view of the highlighted subject.

• Create an issue on the record or on an anomalous field. Anomalous fields are highlighted based on the Confidence Indicator value for easy identification.
• Hover over the anomalous data variable to view a tooltip that contains the confidence level and description.

CM Indication

CM Indication cross-checks each Concomitant Medication record in the SDTM CM domain against an open-source external reference library of approved medication / indication pairs. This machine learning model assigns a confidence score that reflects how well the study’s recorded data matches accepted usage. Low-scoring (high anomaly) records are flagged for review, allowing reviewers to spot implausible or mismatched records early, correct the entry, and maintain clinical integrity. Users can also provide model feedback, create issues, or mark items as reviewed within eIQ Review.

CMs Containing Anomalies

1. Click CM Indication. The listing opens in the EIQReview sheet. This view provides a summary of Concomitant Medications (CMs) that have an indication identified as anomalous in the recorded study data.
2. Select the record for a specific CMDECOD and click to open a record-level sheet with three panels.

CM / Record-level Sheet

CMs Containing Anomalies: This is the same listing but reduced in size to allow users to select multiple rows without having to go to the previous screen. The selected row is highlighted blue.

• Select another record for a specific CMDECOD, and the other two panels update based on the selection.

Anomalous Records for <CMDECOD> (e.g., Pyridoxine hydrochloride): This listing displays a checkbox (when a reviewer role is assigned), feedback options, assigned reviewer roles (e.g., DM, MDR, etc.), Subject ID, Record ID, confidence Score, CMINDC, Site ID, and Reason. The reviewer role columns have a pre-set filter displaying records that are new and updated since reviewed based on the user's reviewer role.

• From this listing, users can create issues, provide feedback on the model prediction, and mark records as reviewed.
• The Score column displays the confidence level associated with each prediction. Records with the lowest scores are listed at the top of the list, ensuring the most anomalous records appear first.
• Click a row and that subject's preset Graphical Patient Profile (GPP) displays at the right.

Subject Graphical Patient Profile (GPP): Usual Data Central functionality is available, such as creating issues, and clicking on a data point to open the record details as a floating panel. 

Audit Trail Review / Irregular Data Patterns

Selecting the Audit Trail Review Workspace opens the Irregular Data Patterns model.

Irregular Data Patterns examines a Rave study's audit trail logs rather than focusing on clinical values. It identifies unusual site-level activities, such as spikes in data deletions, increases in post-verification edits, or users who modify significantly more records than their peers. Each indicator is presented on a separate sheet within the Audit Trail Review Workspace, allowing reviewers to easily switch between high-level charts and detailed listings. By identifying operational outliers early, this model allows timely intervention, preventing minor workflow issues from escalating into significant issues. Users can also create issues, or mark items as reviewed within this workspace.

1. Click the Workspace drop-down in the master header.
2. Scroll to locate the Audit Trail Review folder.

3. Select the Audit Trail Review Workspace.

   

4. By default, the Data Deletions sheet is in view.
5. To access other sheets within this model, click the sheet name at the bottom of the window. Available sheets include:
   • Data Deletions
   • Data Changes
   • Data Changes per Month
   • Data Changes per User
   • Data Changes after Verify
   • Data Deletions per Month
   • Data Changes per User & Month
   • Changes after Verify per Month