{
  "id": "AIBD-0188",
  "title": "nEmesis-guided inspections find 64% more C-or-worse venues than standard inspections in Las Vegas",
  "summary": "SNHD double-blind trial from 2 January 2015: Twitter-mined nEmesis flags yielded 9 vs 6 demerits per inspection and 11 vs 7 venues graded C or worse (64% more).",
  "date_observed": "2015-03-31",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
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  "account": "On 2 January 2015 the Southern Nevada Health District (SNHD) began a controlled experiment with nEmesis, a University of Rochester system. It applies a support-vector-machine language model to geotagged tweets to rank Las Vegas food venues by foodborne-illness risk. During the first three months environmental health specialists inspected 142 venues, half chosen by nEmesis, half under the standard protocol. Inspectors were blind to which was which. Adaptive inspections uncovered 9 demerits per inspection versus 6 (p = 0.019). They found 11 venues graded C or worse versus 7 for controls, a 64% improvement. The abstract summarizes this as 63% more effective. Side findings included an unpermitted seafood establishment and a food handler working with an influenza-like illness. The authors estimate, from CDC outbreak averages, that adaptive inspections saved 71 infections and 4.4 hospitalizations over the three months. They estimate all of Las Vegas's 35,000+ annual inspections made adaptive could prevent over 9,126 cases and 557 hospitalizations a year. SNHD performed 5 foodborne-illness investigations between 2 January and 31 March 2015, against 11 in the same window the year before.",
  "category": "benefit created",
  "unexpected": false,
  "purpose": "Rank Las Vegas food venues by predicted foodborne-illness risk, inferred from Twitter posts, so that health inspectors can prioritize the riskiest venues.",
  "system": "nEmesis (SVM language model over geotagged tweets with a web interface for inspectors)",
  "model_version": null,
  "developer": "University of Rochester (Adam Sadilek, Henry Kautz and colleagues)",
  "deployer": "Southern Nevada Health District",
  "location": "Las Vegas, United States",
  "domain": "Food safety / public health",
  "segment": "Restaurant inspection targeting",
  "benefit_type": "Improved Task Accuracy",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "organizational",
      "depth": 2,
      "reason": "In 142 blinded inspections the nEmesis arm found 9 vs 6 demerits per inspection (p = 0.019). It found 11 vs 7 venues graded C or worse, an unpermitted seafood establishment and a sick food handler."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "The 71 infections and 4.4 hospitalizations said to be saved are estimates from CDC outbreak averages. The drop in foodborne-illness investigations (5 vs 11) is an uncontrolled year-on-year comparison, not a measured outcome."
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  "ai_role": {
    "status": "assessed",
    "statement": "The AI chose which venues to inspect. The comparison was a controlled double-blind design with 142 venues split between nEmesis and standard protocol. So the difference in findings is attributable to the AI's venue selection. The developers (Rochester) are distinct from the deployer (SNHD), though SNHD staff co-authored the paper. The paper was peer-reviewed as an IAAI deployed paper."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Sadilek A, Kautz H, DiPrete L, Labus B, Portman E, Teitel J, Silenzio V. Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media. Proceedings of the AAAI Conference on Artificial Intelligence 30(2):3982-3989 (2016). doi:10.1609/aaai.v30i2.19072",
      "title": "Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media",
      "url": "https://ojs.aaai.org/index.php/AAAI/article/view/19072",
      "date": "2016-02-18",
      "written_by": "makers",
      "quote": "We determined that adaptive inspections uncover significantly more demerits: 9 vs. 6 per inspection (p-value of 0.019)."
    }
  ],
  "replication": {
    "produced_by": [
      "Dense geotagged social-media posting across a metropolitan area",
      "A health department willing to assign half of a matched inspection sample to algorithm-flagged venues",
      "A graded inspection protocol (demerits, letter grades) that makes arms comparable"
    ],
    "must_hold": [
      "Access to geotagged public posts and venue locations as available through the Twitter and Google Places APIs in 2015",
      "Inspectors blinded to the source of the assignment",
      "Enough flagged venues per day to schedule adaptive inspections"
    ],
    "echoes": [
      {
        "date": "2018-11-06",
        "setting": "Las Vegas (May-August 2016) and Chicago (November 2016-March 2017), Google's FINDER using aggregated web search and location data",
        "model_version": "FINDER (successor model by the same lead author, not nEmesis)",
        "outcome": "recurred",
        "source": "Sadilek et al. 2018, npj Digital Medicine, doi:10.1038/s41746-018-0045-1"
      }
    ]
  },
  "paired_incident": null,
  "headline": "nEmesis-guided inspections find 64% more C-or-worse venues",
  "metric": "found 11 venues graded C or worse versus 7 for the control arm, a 64% improvement",
  "comparator": null,
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later report of SNHD continuing or expanding nEmesis after the 2015 experiment was found; the 2016 FINDER deployment is a different model.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
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    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Improved Task Accuracy 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"
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      "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/University_of_Rochester; https://www.southernnevadahealthdistrict.org/about-us/maps/southern-nevada-health-district-main-facility/",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    }
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  "confidence": "claimed",
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}