{
  "id": "AIBD-0066",
  "title": "TREWS machine-learning sepsis early warning system: provider-confirmed alerts within 3 hours associated with lower in-hospital mortality across five hospitals",
  "summary": "Prospective five-hospital study: sepsis alerts confirmed within 3 h had 3.3% absolute lower in-hospital mortality (CI 1.7–5.1).",
  "date_observed": "2022-07-21",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "The Targeted Real-time Early Warning System (TREWS) is a machine-learning sepsis alert developed at Johns Hopkins University. It was deployed in the electronic health record (EHR) at five US hospitals including Johns Hopkins, Howard County General Hospital and Suburban Hospital. A prospective multi-site cohort study (Nature Medicine, 21 July 2022) monitored 590,736 patients. The analysis focused on 6,877 patients with sepsis identified by the alert before antibiotics were started. After adjustment for presentation and severity, patients whose alert a provider confirmed within three hours had 3.3% absolute (18.7% relative) lower in-hospital mortality. They had less organ failure and shorter stays than patients whose alert was not confirmed within three hours. No individual patient is described.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "TREWS was built to identify patients developing sepsis early so that treatment can start sooner; the mortality association is its intended benefit.",
  "system": "TREWS (Targeted Real-time Early Warning System)",
  "model_version": null,
  "developer": "Johns Hopkins University (Suchi Saria's group)",
  "deployer": "Johns Hopkins Medicine (five hospitals)",
  "location": "Baltimore, United States",
  "domain": "Healthcare - sepsis / critical care",
  "segment": "EHR-integrated early warning system",
  "benefit_type": "Improved Physical Health",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": null,
      "reason": "Cohort-level association (mortality, organ failure, length of stay) without an individually attributable event."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "The study reports adjusted associations across 590,736 monitored patients, not a discrete population-level event."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "The study is observational. It compares patients whose TREWS alert was confirmed within 3 hours with those confirmed later or not at all, adjusting for presentation and severity. It supports an association, not causation. A medRxiv critique titled “Randomized Clinical Trials or Convenient Controls: TREWS or FALSE?” (Nemati et al., 2022) questions the comparison design. The sources include only the paper's abstract, not its competing-interests statement."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Adams R, Henry KE, Sridharan A, et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine 28(7):1455-1460. doi:10.1038/s41591-022-01894-0.",
      "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
      "url": "https://europepmc.org/abstract/MED/35864252",
      "date": "2022-07-21",
      "written_by": "makers",
      "quote": "During the study, 590,736 patients were monitored by TREWS across five hospitals."
    },
    {
      "kind": "peer-reviewed study",
      "citation": "Adams R et al. (2022). Nature Medicine 28(7):1455-1460 (second quotation from abstract).",
      "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
      "url": "https://europepmc.org/abstract/MED/35864252",
      "date": "2022-07-21",
      "written_by": "makers",
      "quote": "whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction"
    }
  ],
  "replication": {
    "produced_by": [
      "EHR-integrated deployment with alerts presented to bedside providers",
      "A provider confirmation workflow linked to sepsis treatment"
    ],
    "must_hold": [
      "Providers evaluate alerts promptly (the benefit was seen only for alerts confirmed within 3 hours)",
      "Comparable patient mix and alert thresholds"
    ],
    "echoes": []
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "123",
    "title": "Epic Systems’s Sepsis Prediction Algorithms Revealed to Have High Error Rates on Seriously Ill Patients",
    "url": "https://incidentdatabase.ai/cite/123",
    "severity": null,
    "group": "personal",
    "basis": "Same system class and task, EHR-integrated machine-learning sepsis prediction: Epic's model showed high false-positive and false-negative rates on seriously ill patients, the sepsis-patient cohort TREWS's confirmed alerts benefited."
  },
  "headline": "Confirmed TREWS sepsis alerts are linked to 3.3% lower mortality",
  "metric": "3.3% absolute (18.7% relative) reduction in in-hospital mortality",
  "comparator": {
    "against": "alerted sepsis patients whose alert was not confirmed by a provider within 3 h",
    "before": "patients whose alert was not confirmed by a provider within 3 h",
    "after": "reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction)",
    "basis": "measured",
    "quote": "Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h.",
    "url": "https://europepmc.org/abstract/MED/35864252"
  },
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later development appears in the cited source.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Paired with AI Incident Database incident 123 (high confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/123",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Verification sweep: the Nature Medicine study is written by TREWS's developers at Johns Hopkins; those sources are now marked as written by the makers. The record is admitted on its peer-reviewed or independent source.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Added what the benefit is measured against from the cited sources.",
      "source": "https://europepmc.org/abstract/MED/35864252",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Johns_Hopkins_University; https://en.wikipedia.org/wiki/Johns_Hopkins_Medicine",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-22",
      "change": "Organization name standardized to Johns Hopkins Medicine.",
      "source": null,
      "editor": "AIBD"
    }
  ],
  "confidence": "claimed",
  "shade": "low",
  "claim_status": "The makers' claim",
  "claim_basis": "Sources: the makers' peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0066",
    "catalog_version": "1.0.0",
    "source_fingerprint": "185690fa4197527956053e0cbf3f73df70fd0c5530d6616ea774a007afe949ac",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0066-source-0001",
      "AIBD-0066-source-0002"
    ],
    "families": {
      "strategic assessments": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "evaluations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "monetary observations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "institutional decisions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      }
    },
    "fields": {
      "actors[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "developer": "Johns Hopkins University (Suchi Saria's group)",
            "deployer": "Johns Hopkins Medicine (five hospitals)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "developer",
            "deployer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "actors[].ownership_control": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "ai_contribution": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ai_role": {
              "status": "assessed",
              "statement": "The study is observational. It compares patients whose TREWS alert was confirmed within 3 hours with those confirmed later or not at all, adjusting for presentation and severity. It supports an association, not causation. A medRxiv critique titled “Randomized Clinical Trials or Convenient Controls: TREWS or FALSE?” (Nemati et al., 2022) questions the comparison design. The sources include only the paper's abstract, not its competing-interests statement."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "ai_relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "category": "general benefit",
            "ai_role": {
              "status": "assessed",
              "statement": "The study is observational. It compares patients whose TREWS alert was confirmed within 3 hours with those confirmed later or not at all, adjusting for presentation and severity. It supports an association, not causation. A medRxiv critique titled “Randomized Clinical Trials or Convenient Controls: TREWS or FALSE?” (Nemati et al., 2022) questions the comparison design. The sources include only the paper's abstract, not its competing-interests statement."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role",
            "category"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].alternatives": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].assessor_date": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].depth": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].display_confidence": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "confidence": "low",
            "evidence_word": "claimed",
            "claim_status": "The makers' claim",
            "basis": "Sources: the makers' peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.",
            "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review."
          }
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Derived from the existing case display properties; not a new evidentiary judgment.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rationale": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "personal",
                "reason": "Cohort-level association (mortality, organ failure, length of stay) without an individually attributable event."
              },
              {
                "group": "societal",
                "reason": "The study reports adjusted associations across 590,736 monitored patients, not a discrete population-level event."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rubric_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].status_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].target": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "personal",
                "reason": "Cohort-level association (mortality, organ failure, length of stay) without an individually attributable event."
              },
              {
                "group": "societal",
                "reason": "The study reports adjusted associations across 590,736 monitored patients, not a discrete population-level event."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "availability": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "brief": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_kind": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_links[]": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "answer": "Same system class and task, EHR-integrated machine-learning sepsis prediction: Epic's model showed high false-positive and false-negative rates on seriously ill patients, the sepsis-patient cohort TREWS's confirmed alerts benefited.",
            "links": [
              {
                "database": "AI Incident Database",
                "identifier": "123",
                "title": "Epic Systems’s Sepsis Prediction Algorithms Revealed to Have High Error Rates on Seriously Ill Patients",
                "url": "https://incidentdatabase.ai/cite/123",
                "group": "personal",
                "basis": "Same system class and task, EHR-integrated machine-learning sepsis prediction: Epic's model showed high false-positive and false-negative rates on seriously ill patients, the sepsis-patient cohort TREWS's confirmed alerts benefited.",
                "relation": "comparable_harm"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Existing paired_incident classification; no new source review.",
          "record_fields": [
            "paired_incident"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "claims[].evidence_links": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_strength": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].missingness": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].proposition": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "cross_border_flows[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "decisions[].action_outcome": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].grounds": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].institution_task": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].success_criterion": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "deployment_stage": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "economics[].amount": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].costs[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].counterfactual": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].currency_price_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].gross_net": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].measure_type": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].period_unit": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].realization": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].recipient_payer": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].return_payback": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].valuation_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "evaluations[].ai_uplift": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].comparator": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].design": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].effect": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].exposure": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].measurement_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].metric": {
        "state": "not_assessed",
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "metric"
          ],
          "source_refs": []
        },
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations",
        "display": {
          "answer": "3.3% absolute (18.7% relative) reduction in in-hospital mortality",
          "editor_note": ""
        }
      },
      "evaluations[].reproduction_materials": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].setting_sample": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].uncertainty_limitations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "external_references[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "paired_incident": {
              "database": "AI Incident Database",
              "identifier": "123",
              "title": "Epic Systems’s Sepsis Prediction Algorithms Revealed to Have High Error Rates on Seriously Ill Patients",
              "url": "https://incidentdatabase.ai/cite/123",
              "group": "personal",
              "basis": "Same system class and task, EHR-integrated machine-learning sepsis prediction: Epic's model showed high false-positive and false-negative rates on seriously ill patients, the sepsis-patient cohort TREWS's confirmed alerts benefited."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "paired_incident"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "follow_up[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "later": {
              "status": "unknown",
              "note": "No later development appears in the cited source."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "later"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "human_oversight": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "id": "AIBD-0066"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "id"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "infrastructure_dependencies[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiaries": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiary_count": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].benefit_type": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "benefit_type": "Improved Physical Health"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "The Targeted Real-time Early Warning System (TREWS) is a machine-learning sepsis alert developed at Johns Hopkins University. It was deployed in the electronic health record (EHR) at five US hospitals including Johns Hopkins, Howard County General Hospital and Suburban Hospital. A prospective multi-site cohort study (Nature Medicine, 21 July 2022) monitored 590,736 patients. The analysis focused on 6,877 patients with sepsis identified by the alert before antibiotics were started. After adjustment for presentation and severity, patients whose alert a provider confirmed within three hours had 3.3% absolute (18.7% relative) lower in-hospital mortality. They had less organ failure and shorter stays than patients whose alert was not confirmed within three hours. No individual patient is described.",
            "benefit_type": "Improved Physical Health"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].distribution": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].duration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "personal",
                "reason": "Cohort-level association (mortality, organ failure, length of stay) without an individually attributable event."
              },
              {
                "group": "societal",
                "reason": "The study reports adjusted associations across 590,736 monitored patients, not a discrete population-level event."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].status": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].tradeoffs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].unexpected": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "unexpected": false,
            "purpose": "TREWS was built to identify patients developing sepsis early so that treatment can start sooner; the mortality association is its intended benefit."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "unexpected",
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "location": "Baltimore, United States",
            "developer_country": "United States",
            "deployer_country": "United States"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "location",
            "developer_country",
            "deployer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "TREWS was built to identify patients developing sepsis early so that treatment can start sooner; the mortality association is its intended benefit."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "record_dates": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "date_observed": "2022-07-21",
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Paired with AI Incident Database incident 123 (high confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/123",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Verification sweep: the Nature Medicine study is written by TREWS's developers at Johns Hopkins; those sources are now marked as written by the makers. The record is admitted on its peer-reviewed or independent source.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against from the cited sources.",
                "source": "https://europepmc.org/abstract/MED/35864252",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
                "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Johns_Hopkins_University; https://en.wikipedia.org/wiki/Johns_Hopkins_Medicine",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-22",
                "change": "Organization name standardized to Johns Hopkins Medicine.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "date_observed",
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.attempts[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "EHR-integrated deployment with alerts presented to bedside providers",
                "A provider confirmation workflow linked to sepsis treatment"
              ],
              "must_hold": [
                "Providers evaluate alerts promptly (the benefit was seen only for alerts confirmed within 3 hours)",
                "Comparable patient mix and alert thresholds"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "revision_history[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Paired with AI Incident Database incident 123 (high confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/123",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Verification sweep: the Nature Medicine study is written by TREWS's developers at Johns Hopkins; those sources are now marked as written by the makers. The record is admitted on its peer-reviewed or independent source.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against from the cited sources.",
                "source": "https://europepmc.org/abstract/MED/35864252",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
                "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Johns_Hopkins_University; https://en.wikipedia.org/wiki/Johns_Hopkins_Medicine",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-22",
                "change": "Organization name standardized to Johns Hopkins Medicine.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Healthcare - sepsis / critical care",
            "segment": "EHR-integrated early warning system"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "domain",
            "segment"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].access_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].citation_locator": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "peer-reviewed study",
                "citation": "Adams R, Henry KE, Sridharan A, et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine 28(7):1455-1460. doi:10.1038/s41591-022-01894-0.",
                "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
                "url": "https://europepmc.org/abstract/MED/35864252",
                "date": "2022-07-21",
                "written_by": "makers",
                "quote": "During the study, 590,736 patients were monitored by TREWS across five hospitals."
              },
              {
                "kind": "peer-reviewed study",
                "citation": "Adams R et al. (2022). Nature Medicine 28(7):1455-1460 (second quotation from abstract).",
                "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
                "url": "https://europepmc.org/abstract/MED/35864252",
                "date": "2022-07-21",
                "written_by": "makers",
                "quote": "whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].id": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "AIBD-0066-source-0001",
            "AIBD-0066-source-0002"
          ],
          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Stable within the fingerprinted record version; no source content or source review has been added.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "sources[].language": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "peer-reviewed study",
                "citation": "Adams R, Henry KE, Sridharan A, et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine 28(7):1455-1460. doi:10.1038/s41591-022-01894-0.",
                "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
                "url": "https://europepmc.org/abstract/MED/35864252",
                "date": "2022-07-21",
                "written_by": "makers",
                "quote": "During the study, 590,736 patients were monitored by TREWS across five hospitals."
              },
              {
                "kind": "peer-reviewed study",
                "citation": "Adams R et al. (2022). Nature Medicine 28(7):1455-1460 (second quotation from abstract).",
                "title": "Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis",
                "url": "https://europepmc.org/abstract/MED/35864252",
                "date": "2022-07-21",
                "written_by": "makers",
                "quote": "whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "strategic_assessments[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "strategic assessments"
      },
      "summary": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "Prospective five-hospital study: sepsis alerts confirmed within 3 h had 3.3% absolute lower in-hospital mortality (CI 1.7–5.1).",
            "account": "The Targeted Real-time Early Warning System (TREWS) is a machine-learning sepsis alert developed at Johns Hopkins University. It was deployed in the electronic health record (EHR) at five US hospitals including Johns Hopkins, Howard County General Hospital and Suburban Hospital. A prospective multi-site cohort study (Nature Medicine, 21 July 2022) monitored 590,736 patients. The analysis focused on 6,877 patients with sepsis identified by the alert before antibiotics were started. After adjustment for presentation and severity, patients whose alert a provider confirmed within three hours had 3.3% absolute (18.7% relative) lower in-hospital mortality. They had less organ failure and shorter stays than patients whose alert was not confirmed within three hours. No individual patient is described."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "summary",
            "account"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "TREWS (Targeted Real-time Early Warning System)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "system"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].provider": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "application_developer": "Johns Hopkins University (Suchi Saria's group)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "developer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "taxonomy_crosswalks[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "TREWS machine-learning sepsis early warning system: provider-confirmed alerts within 3 hours associated with lower in-hospital mortality across five hospitals",
            "headline": "Confirmed TREWS sepsis alerts are linked to 3.3% lower mortality"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "185690fa4197527956053e0cbf3f73df70fd0c5530d6616ea774a007afe949ac"
          ],
          "role": "SHA-256 of normalized original case data"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "The Targeted Real-time Early Warning System (TREWS) is a machine-learning sepsis alert developed at Johns Hopkins University. It was deployed in the electronic health record (EHR) at five US hospitals including Johns Hopkins, Howard County General Hospital and Suburban Hospital. A prospective multi-site cohort study (Nature Medicine, 21 July 2022) monitored 590,736 patients. The analysis focused on 6,877 patients with sepsis identified by the alert before antibiotics were started. After adjustment for presentation and severity, patients whose alert a provider confirmed within three hours had 3.3% absolute (18.7% relative) lower in-hospital mortality. They had less organ failure and shorter stays than patients whose alert was not confirmed within three hours. No individual patient is described.",
            "replication": {
              "produced_by": [
                "EHR-integrated deployment with alerts presented to bedside providers",
                "A provider confirmation workflow linked to sepsis treatment"
              ],
              "must_hold": [
                "Providers evaluate alerts promptly (the benefit was seen only for alerts confirmed within 3 hours)",
                "Comparable patient mix and alert thresholds"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 29,
      "reviewed": 0,
      "record_mapped": 29,
      "not_assessed": 62,
      "complete": false,
      "by_state": {
        "value_present": 29,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 62,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}