{
  "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": 4,
  "pageCount": 26,
  "prevPage": 3,
  "nextPage": 5,
  "slideType": "diagnosis",
  "function": "diagnose",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "Uses a heatmap visualization to contrast model fitting approaches.",
  "elementsJson": [
    "headline_text",
    "heatmap",
    "paragraph"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b638-e91d3b8478cc/4",
  "deckHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b638-e91d3b8478cc#slide-4",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "While ML algorithms are adapting themselves by spotting & recording patterns without clinging to any predetermined corset",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-d3bc-73c4-a2bb-ba31adf49ad0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.28,
        "x": 0.36,
        "y": 0.22
      },
      "kind": "chart",
      "text": "How Traditional stats sees it",
      "attrs": null,
      "subkind": "heatmap",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5184fbf9-6cf4-4586-90df-b6463b4b8107",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.28,
        "x": 0.69,
        "y": 0.22
      },
      "kind": "chart",
      "text": "How Machine Learning sees it",
      "attrs": null,
      "subkind": "heatmap",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "84529178-9f11-4b35-8634-ec26a829bb58",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.28,
        "x": 0.03,
        "y": 0.22
      },
      "kind": "chart",
      "text": "The actual phenomenon (real historical data)",
      "attrs": null,
      "subkind": "heatmap",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "922f8737-9dfa-486b-887c-966e0eed2a5b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.28,
        "x": 0.36,
        "y": 0.6
      },
      "kind": "paragraph",
      "text": "Traditional stats will fit a predetermined “shape” into the phenomenon (e.g. linear, quadratic, logarithmic models) – the square peg into the round hole!",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "39f34fb7-5fcd-4467-a3fb-777645940dc6",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.28,
        "x": 0.03,
        "y": 0.6
      },
      "kind": "paragraph",
      "text": "Real life phenomenon come in “all shapes and flavors” – showing patterns that are usually complex, non-linear and apparently disorganized",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "46d2c618-eb15-4faf-b417-be370ae9013e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.28,
        "x": 0.69,
        "y": 0.6
      },
      "kind": "paragraph",
      "text": "While ML algorithms are adapting themselves by spotting & recording patterns without clinging to any predetermined corset",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6360e8bd-8f72-48e5-89d1-ab5302d80931",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.45,
        "x": 0.01,
        "y": 0.03
      },
      "kind": "title",
      "text": "Why is machine learning different?",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e0e52d5e-bccd-4d78-bd8d-0ef3d0599fa5",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Audience Definition",
      "slug": "audience-definition",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "75cc0503-d101-4446-ae67-aa3945702269",
      "evidence": "The slide is targeted at explaining why machine learning is different, indicating an audience with some statistical background.",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 4,
      "from": 1,
      "beatId": "408b877d-e516-4bcc-a74e-b47c048efe99",
      "arcName": "Problem-Agitate-Solution",
      "arcSlug": "problem-agitate-solution",
      "beatName": "Problem (Identify pain)",
      "beatSlug": "problem-agitate-solution-problem-identify-pain",
      "evidence": "The deck starts by highlighting the problem of healthcare errors and their consequences.",
      "position": 0,
      "confidence": 0.8,
      "parentBeatName": "Complication",
      "parentBeatSlug": "complication"
    }
  ],
  "loops": [
    {
      "to": 14,
      "from": 4,
      "name": "Golden Circle",
      "slug": "11-golden-circle",
      "bestFor": "Visionary leadership, brand positioning, mission statements",
      "matchId": "1b36e03c-871f-4ba9-977e-2461066df8bf",
      "evidence": "The deck presents various case studies and examples of AI applications in healthcare, using the golden circle framework.",
      "position": 1,
      "objective": "Explain why AI is different and how it can be applied in healthcare",
      "structure": "The Why (Belief) -> The How (Process) -> The What (Result)",
      "confidence": 0.6,
      "description": "Invert the typical pitch by starting with why you exist, rather than what you do"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}