{
  "docId": "019dd923-5ca1-7489-b638-e91d3b8478cc",
  "docSlug": "9fbedbfea0231cbe",
  "documentTitle": "AI Healthcare Errors",
  "authorId": "McKinsey",
  "authorName": "McKinsey",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.333,
  "pageNumber": 17,
  "pageCount": 26,
  "prevPage": 16,
  "nextPage": 18,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 4,
  "notes": "The slide uses a 'before-after' framing implicitly by contrasting the initial problem (Summary) with the achieved results (Impact).",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "screenshot"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b638-e91d3b8478cc/17",
  "deckHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc#slide-17",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "€15-20M saved in absenteeism costs",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-d3bc-73c4-a2ba-3665459dca0e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.6,
        "x": 0.05,
        "y": 0.045
      },
      "kind": "image",
      "text": "Analytic and Executive Dashboards showing absenteeism metrics",
      "attrs": null,
      "subkind": "screenshot",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6e2d79bf-ce33-4522-a88a-55c625862d24",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.3,
        "w": 0.25,
        "x": 0.7,
        "y": 0.048
      },
      "kind": "list",
      "text": "€15-20M saved in absenteeism costs; Root cause drivers of attrition by cohort/cluster; Tailored interventions based on individual risk lists",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "67fa9563-3409-4ffb-bfc5-3d633f518c86",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.9,
        "x": 0.05,
        "y": 0.25
      },
      "kind": "list",
      "text": "Leading European financial institution was facing above-industry employee absenteeism; Significant loss of value estimated at 20m USD yearly for 2k employees; Strict regulatory environment prohibiting sanctions around absenteeism",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ca1b4731-09fe-4e08-b6e0-136b8423afee",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Absenteeism costs: €15-20M",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-d3bc-73c4-a2ba-397f38560a86",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.9,
        "x": 0.05,
        "y": 0.05
      },
      "kind": "title",
      "text": "Data-driven employee management can allow companies to identify the root causes of absenteeism and reduce related costs",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "549fbf83-640b-40cd-949a-6553e3fe9daa",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Absenteeism costs",
      "numberRaw": "€15-20M",
      "numberKind": "money",
      "actionTitle": "Data-driven employee management can allow companies to identify the root causes of absenteeism and reduce related costs",
      "calloutText": "€15-20M saved in absenteeism costs",
      "numberScale": null,
      "numberValue": 15,
      "metricFamily": "cost_savings",
      "numberCurrency": "€"
    }
  ],
  "tools": [
    {
      "name": "Data Story Arc",
      "slug": "data-story-arc",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "54e94461-f998-4eba-81f2-2b04137ec928",
      "evidence": "€15-20M saved in absenteeism costs; Root cause drivers of attrition by cohort/cluster; Tailored interventions based on individual risk lists",
      "confidence": 0.6
    }
  ],
  "frameworks": [
    {
      "name": "before-after-framing",
      "slug": null,
      "matchId": "c16444f5-f918-49c0-a7ac-aa75dbd49751",
      "evidence": "Contrasts the initial problem state with the post-intervention impact results.",
      "confidence": 0.85
    }
  ],
  "arcBeats": [
    {
      "to": 26,
      "from": 10,
      "beatId": "9b6e4ffe-95ca-40d4-92d5-70f2cd8f60f8",
      "arcName": "Problem-Agitate-Solution",
      "arcSlug": "problem-agitate-solution",
      "beatName": "Solution (Provide relief)",
      "beatSlug": "problem-agitate-solution-solution-provide-relief",
      "evidence": "The deck offers AI as a solution to prevent healthcare errors and improve patient outcomes.",
      "position": 2,
      "confidence": 0.8,
      "parentBeatName": "Resolution",
      "parentBeatSlug": "resolution"
    }
  ],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}