{
 "name": "Kent Applied facts",
 "url": "https://www.kentapplied.com/facts/",
 "license": "https://creativecommons.org/licenses/by/4.0/",
 "count": 50,
 "topics": [
  {
   "name": "AI adoption",
   "slug": "ai-adoption",
   "about": "Where AI tools help healthcare organizations, the evidence behind them, and what they cost.",
   "count": 16
  },
  {
   "name": "Data and analytics",
   "slug": "data-and-analytics",
   "about": "Data warehouses, reporting, dashboards and the people who build them.",
   "count": 3
  },
  {
   "name": "Hospital operations",
   "slug": "hospital-operations",
   "about": "Patient flow, length of stay, ED boarding, discharge delays and hospital costs.",
   "count": 15
  },
  {
   "name": "Health centers",
   "slug": "health-centers",
   "about": "UDS reporting, HRSA rules and the patients that community health centers serve.",
   "count": 6
  },
  {
   "name": "Payment and policy",
   "slug": "payment-and-policy",
   "about": "Medicare and payer rules, payment models such as TEAM, claim denials, hospital margins and federal fines.",
   "count": 10
  }
 ],
 "facts": [
  {
   "slug": "dentists-using-ai-43-percent-2026",
   "title": "ADA: 43.3% of dentists in private practice use AI (2026)",
   "value": 0.433,
   "display": "43.3%",
   "unit": "of US dentists in private practice said they use AI for at least one task, second quarter of 2026",
   "statement": "43.3% of US dentists in private practice said they currently use AI for at least one task in their practice, in the ADA Health Policy Institute's poll for the second quarter of 2026. Another 26.4% said they do not use AI but plan to.",
   "context": "The poll went out on June 15, 2026, and drew 552 responses from dentists in private practice, including large group practices and dental service organizations.",
   "source": {
    "name": "The State of the US Dental Economy, 2nd Quarter 2026 Update",
    "url": "https://www.ada.org/-/media/project/ada-organization/ada/ada-org/files/resources/research/hpi/state_us_dental_economy_q22026.pdf",
    "publisher": "American Dental Association Health Policy Institute",
    "published": "2026-08",
    "locator": "Slide 26, Two out of Five Dentists Currently Using AI; data notes on slides 48 and 50"
   },
   "kind": "survey",
   "tags": [
    "Dentists",
    "Practices",
    "AI adoption",
    "ADA"
   ],
   "topic": "AI adoption",
   "charts": [
    "dentist-ai-use-by-task-2026"
   ],
   "verified": "2026-10-14",
   "published": "2026-10-20",
   "home": {
    "url": "/blog/practice-ai-without-patient-data/",
    "title": "What a medical practice can hand to AI without patient data",
    "kind": "Blog"
   },
   "url": "https://www.kentapplied.com/facts/dentists-using-ai-43-percent-2026/"
  },
  {
   "slug": "health-centers-served-32-7-million-patients-2025",
   "title": "HRSA health centers served 32.7 million patients in 2025",
   "value": 32746392,
   "display": "32.7M",
   "unit": "patients served by HRSA-funded health centers in 2025",
   "statement": "Health centers funded by HRSA served 32,746,392 patients in 2025, across 1,356 Health Center Program awardees, in HRSA's 2025 Uniform Data System.",
   "source": {
    "name": "HRSA Uniform Data System, national data, 2025",
    "url": "https://data.hrsa.gov/topics/healthcenters/uds/overview/national",
    "publisher": "Health Resources and Services Administration (HRSA)",
    "locator": "National overview, reporting period 2025: Total Patients Served; Total Number of Reporting Program Awardees"
   },
   "kind": "measured",
   "tags": [
    "Health centers",
    "UDS",
    "HRSA",
    "Patients"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/blog/uds-reporting-is-a-pipeline/",
    "title": "Running UDS reporting as a year-round data pipeline",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-19",
   "published": "2026-10-19",
   "url": "https://www.kentapplied.com/facts/health-centers-served-32-7-million-patients-2025/"
  },
  {
   "slug": "us-community-hospitals-3567-in-a-system",
   "title": "AHA: 3,567 of 5,121 US community hospitals are in a system",
   "value": 3567,
   "display": "3,567",
   "unit": "of 5,121 US community hospitals belong to a health system of any size",
   "statement": "3,567 of the 5,121 US community hospitals belonged to a health system of any size in the 2024 AHA Annual Survey, and 1,797 were rural, AHA's 2026 Fast Facts reports. The two counts overlap, because some rural hospitals also belong to a system.",
   "source": {
    "name": "Fast Facts on U.S. Hospitals, 2026",
    "url": "https://www.aha.org/statistics/fast-facts-us-hospitals",
    "publisher": "American Hospital Association",
    "published": "2026-02",
    "locator": "U.S. Hospital Statistics table: Number of Community Hospitals in a System"
   },
   "kind": "reported",
   "tags": [
    "Hospitals",
    "Health systems",
    "Rural hospitals",
    "AHA"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "types"
   },
   "charts": [
    "healthcare-organization-problems-by-area"
   ],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/us-community-hospitals-3567-in-a-system/"
  },
  {
   "slug": "snf-nursing-staff-turnover-46-percent-2024",
   "title": "MedPAC: nursing staff turnover of 46% at the median SNF",
   "value": 0.46,
   "display": "46%",
   "unit": "of nursing staff turned over in 12 months at the median skilled nursing facility",
   "statement": "The 12-month rate of nursing staff turnover as of 2024 was 46% for the median skilled nursing facility, MedPAC reported in March 2026.",
   "context": "One quarter of facilities had turnover above 56%. MedPAC notes that a change in how CMS calculates turnover makes 2024 not directly comparable with earlier years.",
   "source": {
    "name": "March 2026 Report to the Congress, Chapter 7: Skilled nursing facility services",
    "url": "https://www.medpac.gov/wp-content/uploads/2026/03/Mar26_Ch7_MedPAC_Report_To_Congress_SEC.pdf",
    "publisher": "Medicare Payment Advisory Commission (MedPAC)",
    "published": "2026-03",
    "locator": "Chapter 7, quality of care section, Figure 7-4"
   },
   "kind": "measured",
   "tags": [
    "Skilled nursing",
    "Staffing",
    "Turnover",
    "MedPAC"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/snf-nursing-staff-turnover-46-percent-2024/"
  },
  {
   "slug": "rural-hospitals-41-percent-in-the-red-2026",
   "title": "Chartis: 41.2% of rural hospitals operate in the red (2026)",
   "value": 0.412,
   "display": "41.2%",
   "unit": "of US rural hospitals operate in the red",
   "statement": "41.2% of US rural hospitals operate in the red, and the median rural hospital's operating margin is 2.0%, Chartis reported in its 2026 study of rural hospitals.",
   "context": "Chartis reports an improvement from 46% in 2025, mostly from rural hospitals in Medicaid expansion states.",
   "source": {
    "name": "2026 Rural Health State of the State",
    "url": "https://www.chartis.com/insights/2026-rural-health-state-state",
    "publisher": "Chartis",
    "published": "2026",
    "locator": "How many rural hospitals are operating in the red?"
   },
   "kind": "measured",
   "tags": [
    "Rural hospitals",
    "Operating margin",
    "Hospital finance"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/rural-hospitals-41-percent-in-the-red-2026/"
  },
  {
   "slug": "practice-firms-92-percent-fewer-than-20-employees",
   "title": "92.3% of practice firms had fewer than 20 employees in 2022, by our sum of Census data",
   "value": 0.923,
   "display": "92.3%",
   "unit": "of US practice firms with employees have fewer than 20",
   "statement": "Of 440,667 US physician, dentist, other health practitioner and outpatient mental health firms with employees in 2022, 92.3% had fewer than 20 employees, by Kent Applied's sum of Census Bureau figures.",
   "context": "406,615 of 440,667 firms, summed from the US rows for NAICS 6211, 6212, 6213 and 621420 in the 2022 Statistics of US Businesses.",
   "source": {
    "name": "Statistics of US Businesses 2022, US and state totals by 6-digit NAICS",
    "url": "https://www2.census.gov/programs-surveys/susb/tables/2022/us_state_6digitnaics_2022.txt",
    "publisher": "US Census Bureau",
    "published": "2025-04",
    "locator": "US rows, NAICS 6211, 6212, 6213 and 621420, enterprise sizes Total and under 20"
   },
   "kind": "derived",
   "tags": [
    "Practices",
    "Small business",
    "Census"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "types"
   },
   "charts": [
    "healthcare-organization-problems-by-area"
   ],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/practice-firms-92-percent-fewer-than-20-employees/"
  },
  {
   "slug": "physicians-40-prior-authorizations-a-week-2025",
   "title": "AMA: physicians complete 40 prior authorizations a week",
   "value": 40,
   "display": "40",
   "unit": "prior authorizations a week, on average, per physician",
   "statement": "Physicians complete an average of 40 prior authorizations a week, which take 13 hours of physician and staff time, the AMA's 2025 survey of 1,000 physicians found.",
   "source": {
    "name": "AMA survey: Prior authorization reform pledge falls short with physicians",
    "url": "https://www.ama-assn.org/press-center/ama-press-releases/ama-survey-prior-authorization-reform-pledge-falls-short-physicians",
    "publisher": "American Medical Association",
    "published": "2026-05-13",
    "locator": "Findings of the 2025 AMA Prior Authorization Physician Survey: Added Burden; Diverted Time and Resources"
   },
   "kind": "survey",
   "tags": [
    "Prior authorization",
    "Practices",
    "Administrative burden",
    "AMA"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [],
   "verified": "2026-10-14",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/physicians-40-prior-authorizations-a-week-2025/"
  },
  {
   "slug": "hospital-drug-expenses-up-13-6-percent-2025",
   "title": "AHA: hospital drug expenses grew 13.6% in 2025",
   "value": 0.136,
   "display": "13.6%",
   "shape": "change",
   "unit": "growth in US hospital drug expenses in 2025",
   "statement": "Hospital drug expenses grew 13.6% in 2025, against 7.5% for total hospital expenses, AHA reports from Strata Decision Technology benchmark data.",
   "compare": [
    {
     "label": "Drug expenses",
     "value": 13.6,
     "display": "13.6%"
    },
    {
     "label": "Total expenses",
     "value": 7.5,
     "display": "7.5%"
    }
   ],
   "source": {
    "name": "Costs of Caring 2026",
    "url": "https://www.aha.org/system/files/media/file/2026/03/Costs-of-Caring-2026.pdf",
    "publisher": "American Hospital Association",
    "published": "2026-03",
    "locator": "Page 6, High Cost of Medicines; page 2, What's Driving Hospital Spending in 2025"
   },
   "kind": "measured",
   "tags": [
    "Hospital costs",
    "Drug spending",
    "AHA"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [
    "hospital-expense-growth-by-category-2025"
   ],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/hospital-drug-expenses-up-13-6-percent-2025/"
  },
  {
   "slug": "healthcare-problems-needing-no-patient-data",
   "title": "30 of the 44 problems we mapped need no patient data",
   "value": 30,
   "display": "30",
   "unit": "of the 44 healthcare problems Kent Applied mapped need no patient data",
   "statement": "For 30 of the 44 problems Kent Applied mapped across six kinds of healthcare organizations, the AI tools advised can help without ever reading a patient record.",
   "context": "Ten more have a first step that needs no patient data and a later step that does, three need patient data from the start, and for one, hiring at small practices, no AI tool is advised. The 44 problems and how each is classified are Kent Applied's own; the method is on the research page.",
   "source": {
    "name": "Where AI tools fit in healthcare, by kind of organization",
    "url": "https://www.kentapplied.com/research/ai-tools-by-organization/",
    "publisher": "Kent Applied",
    "published": "2026-10-09",
    "locator": "Part four and Fig. 4"
   },
   "kind": "analysis",
   "tags": [
    "Patient data",
    "AI tools",
    "HIPAA",
    "Kent Applied analysis"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "fig-4"
   },
   "charts": [
    "healthcare-problems-patient-data-by-type"
   ],
   "verified": "2026-10-14",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/healthcare-problems-needing-no-patient-data/"
  },
  {
   "slug": "health-center-patients-27-percent-other-language-2025",
   "title": "KFF: 27% of health center patients are best served in another language",
   "value": 0.27,
   "display": "27%",
   "unit": "of health center patients are best served in a language other than English",
   "statement": "27% of HRSA-funded health center patients, 8.8 million people, were best served in a language other than English in 2025, KFF reports from HRSA data.",
   "source": {
    "name": "Community Health Center Patients, Financing, and Services",
    "url": "https://www.kff.org/medicaid/community-health-center-patients-financing-and-services/",
    "publisher": "KFF",
    "published": "2026-10-05",
    "locator": "Health Center Patients"
   },
   "kind": "reported",
   "tags": [
    "Health centers",
    "Language access",
    "HRSA",
    "KFF"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [],
   "verified": "2026-10-09",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/health-center-patients-27-percent-other-language-2025/"
  },
  {
   "slug": "health-care-businesses-ai-use-24-8-percent-2026",
   "title": "Census: 24.8% of health care and social assistance firms used AI",
   "value": 0.248,
   "display": "24.8%",
   "unit": "of US health care and social assistance businesses said they used AI between September 7 and 20, 2026",
   "statement": "In a Census Bureau survey covering September 7 to 20, 2026, 24.8% of US health care and social assistance businesses said they had used AI somewhere in their business. 23.8% of all US businesses did.",
   "compare": [
    {
     "label": "Health care and social assistance",
     "value": 24.8,
     "display": "24.8%"
    },
    {
     "label": "All US businesses",
     "value": 23.8,
     "display": "23.8%"
    }
   ],
   "source": {
    "name": "Business Trends and Outlook Survey, sector estimates",
    "url": "https://www.census.gov/hfp/btos/downloads/Sector.xlsx",
    "publisher": "US Census Bureau",
    "published": "2026-10-08",
    "locator": "Response Estimates sheet, question 7, answer Yes, period 202620, sector 62"
   },
   "also": [
    {
     "name": "Business Trends and Outlook Survey, national estimates",
     "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx",
     "locator": "Response Estimates sheet, question 7, answer Yes, period 202620 (all businesses)"
    }
   ],
   "kind": "survey",
   "tags": [
    "AI adoption",
    "Census",
    "Business survey",
    "Health care"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "fig-3"
   },
   "charts": [
    "ai-use-by-sector-health-care-2026"
   ],
   "verified": "2026-10-14",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/health-care-businesses-ai-use-24-8-percent-2026/"
  },
  {
   "slug": "digital-health-purchasers-38-percent-cannot-verify",
   "title": "PHTI: 38% of buyers with performance-based contracts can't verify outcomes",
   "value": 0.38,
   "display": "38%",
   "unit": "of digital health purchasers with performance-based contracts can't independently verify vendors' reported outcomes",
   "statement": "38% of digital health purchasers that use performance-based contracts say they cannot independently verify the outcomes vendors report, in PHTI's 2026 survey of health plans, employers and health systems.",
   "context": "PHTI asked this of the 182 respondents with performance-based contracts, out of 321 in all. In the same survey, 68% of employers and 55% of health plans said they use performance-based contracts for digital health.",
   "source": {
    "name": "Peterson Health Technology Institute, 2026 State of Digital Health Purchasing",
    "url": "https://www.phti.org/wp-content/uploads/2026/09/PHTI-2026-State-of-Digital-Health-Purchasing.pdf",
    "publisher": "Peterson Health Technology Institute (PHTI)",
    "published": "2026-09-29",
    "locator": "Page 22, challenges with vendor-provided data in performance-based contracts (n=182); page 18, use of performance-based contracts (n=321)"
   },
   "kind": "survey",
   "tags": [
    "Digital health",
    "Outcomes",
    "Purchasing",
    "PHTI"
   ],
   "topic": "Data and analytics",
   "home": {
    "url": "/research/ai-tools-by-organization/",
    "title": "Where AI tools fit in healthcare, by kind of organization",
    "kind": "Research",
    "anchor": "explore"
   },
   "charts": [],
   "verified": "2026-10-16",
   "published": "2026-10-09",
   "url": "https://www.kentapplied.com/facts/digital-health-purchasers-38-percent-cannot-verify/"
  },
  {
   "slug": "team-stay-over-target-4-of-1539-pairs",
   "title": "In 4 of 1,539 TEAM hospital and surgery pairs, the hospital stay alone costs more than the target",
   "value": 4,
   "display": "4 of 1,539",
   "unit": "hospital and surgery pairs where the stay alone exceeds the target",
   "statement": "In 4 of 1,539 TEAM hospital and surgery pairs, the hospital stay alone costs more than the 2026 target price, according to Kent Applied's analysis of CMS files.",
   "context": "Each pair is one hospital's 2024 Medicare stays in one surgery, counted the way TEAM counts them and divided by CMS's 2026 target for its region and mix of cases. The other 1,535 leave part of the target for doctors and the 30 days after discharge.",
   "source": {
    "name": "TEAM position report: where each hospital stands in CMS's TEAM model",
    "url": "https://www.kentapplied.com/team-report/",
    "publisher": "Kent Applied",
    "published": "2026-10",
    "locator": "Fig. 1 table, Over 100% column"
   },
   "also": [
    {
     "name": "CMS, TEAM model page",
     "url": "https://www.cms.gov/priorities/innovation/innovation-models/team-model",
     "locator": "Model overview: what target prices cover and when an episode ends"
    }
   ],
   "kind": "analysis",
   "tags": [
    "TEAM",
    "Bundled payment",
    "Medicare"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/team-report/",
    "anchor": "figure",
    "title": "Where your hospital stands in CMS's TEAM model",
    "kind": "Research"
   },
   "charts": [
    "team-hospital-stay-share-by-hospital"
   ],
   "verified": "2026-10-08",
   "published": "2026-10-07",
   "url": "https://www.kentapplied.com/facts/team-stay-over-target-4-of-1539-pairs/"
  },
  {
   "slug": "team-hip-fracture-stay-share-38-percent",
   "title": "At the median TEAM hospital, the hip fracture stay uses 38% of the target price",
   "value": 0.384,
   "display": "38%",
   "unit": "of the 2026 target used by the stay",
   "statement": "At the median TEAM hospital, the hip or femur fracture stay uses 38% of the 2026 target price, according to Kent Applied's analysis of CMS files.",
   "context": "For coronary bypass the stay uses 70%. The rest of each target is left for doctors and the 30 days after discharge.",
   "source": {
    "name": "TEAM position report: where each hospital stands in CMS's TEAM model",
    "url": "https://www.kentapplied.com/team-report/",
    "publisher": "Kent Applied",
    "published": "2026-10",
    "locator": "Fig. 1 table, Hip or femur fracture, 447 hospitals"
   },
   "also": [
    {
     "name": "CMS, TEAM model page",
     "url": "https://www.cms.gov/priorities/innovation/innovation-models/team-model",
     "locator": "Model overview: what target prices cover and when an episode ends"
    }
   ],
   "kind": "analysis",
   "tags": [
    "TEAM",
    "Post-acute care",
    "Medicare"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/team-report/",
    "title": "Where your hospital stands in CMS's TEAM model",
    "kind": "Research"
   },
   "charts": [
    "team-hospital-stay-share-of-target"
   ],
   "verified": "2026-10-08",
   "published": "2026-10-07",
   "url": "https://www.kentapplied.com/facts/team-hip-fracture-stay-share-38-percent/"
  },
  {
   "slug": "team-track-3-repayment-limit-20-percent",
   "title": "TEAM caps Track 3 repayment at 20% of the target price",
   "value": 0.2,
   "display": "20%",
   "unit": "of its total target price, the most a Track 3 hospital repays",
   "statement": "Under TEAM, a Track 3 hospital's repayment to CMS for a year is capped at 20% of its total target price, per 42 CFR 512.550.",
   "context": "Track 2 hospitals are capped at 5%, and Track 1 hospitals repay nothing. CMS can also take back part of unusually high post-episode spending, outside the cap.",
   "source": {
    "name": "42 CFR 512.550, Reconciliation process and determination of the reconciliation payment or repayment amount",
    "url": "https://www.ecfr.gov/current/title-42/section-512.550",
    "publisher": "Electronic Code of Federal Regulations",
    "published": "2026-08-04",
    "locator": "512.550(e)(1) and (e)(3); post-episode spending, 512.550(f)"
   },
   "kind": "rule",
   "tags": [
    "TEAM",
    "Bundled payment",
    "CMS"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/blog/team-episode-baseline/",
    "title": "TEAM started the 30-day clock for about 700 hospitals. Here is the baseline a hospital needs for each TEAM surgery.",
    "kind": "Blog"
   },
   "charts": [
    "team-repayment-limit-by-track"
   ],
   "verified": "2026-10-08",
   "published": "2026-10-06",
   "url": "https://www.kentapplied.com/facts/team-track-3-repayment-limit-20-percent/"
  },
  {
   "slug": "team-low-volume-threshold-31-episodes",
   "title": "TEAM's low-volume threshold is 31 baseline episodes",
   "value": 31,
   "display": "31",
   "unit": "baseline episodes: TEAM's low-volume threshold",
   "statement": "CMS holds a TEAM hospital to account for spending over target in a surgery only if it had at least 31 episodes in the three-year baseline.",
   "context": "Below that threshold, CMS still runs its year-end comparison of those episodes against the target, but it does not make the hospital repay spending above the target.",
   "source": {
    "name": "TEAM Model Overview slides (updated September 2026)",
    "url": "https://www.cms.gov/files/document/team-overview-updated.pdf",
    "publisher": "Centers for Medicare & Medicaid Services",
    "published": "2026-09",
    "locator": "Slide 32, Reconciliation Process"
   },
   "kind": "rule",
   "tags": [
    "TEAM",
    "Medicare",
    "CMS"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/blog/team-episode-baseline/",
    "title": "TEAM started the 30-day clock for about 700 hospitals. Here is the baseline a hospital needs for each TEAM surgery.",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-10-06",
   "url": "https://www.kentapplied.com/facts/team-low-volume-threshold-31-episodes/"
  },
  {
   "slug": "team-716-participating-hospitals",
   "title": "CMS lists 716 hospitals in the TEAM model",
   "value": 716,
   "display": "716",
   "unit": "hospitals in TEAM, on CMS's model page",
   "statement": "CMS lists 716 hospitals as participants in TEAM, Medicare's mandatory episode payment model for five surgeries.",
   "context": "TEAM runs five performance years, from January 1, 2026, to December 31, 2030. CMS's participant list, linked from the same model page, names 721 hospitals as of August 18, 2026. Three of them have participation end dates in early 2026 and two start in January 2027; leaving those five out gives the 716 on the model page. The TEAM explorer on this site uses CMS's June 2026 list, which named 720.",
   "source": {
    "name": "Transforming Episode Accountability Model (TEAM) model page",
    "url": "https://www.cms.gov/priorities/innovation/innovation-models/team-model",
    "publisher": "Centers for Medicare & Medicaid Services",
    "published": "2026-09-23",
    "locator": "Model Summary, Number of Participants"
   },
   "also": [
    {
     "name": "CMS, TEAM participant list (updated September 2026)",
     "url": "https://www.cms.gov/team-model-participant-list",
     "locator": "TEAM Participant List sheet, participation start and end dates"
    }
   ],
   "kind": "reported",
   "tags": [
    "TEAM",
    "Medicare",
    "CMS"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/blog/team-episode-baseline/",
    "title": "TEAM started the 30-day clock for about 700 hospitals. Here is the baseline a hospital needs for each TEAM surgery.",
    "kind": "Blog"
   },
   "charts": [
    "team-repayment-limit-by-track"
   ],
   "verified": "2026-10-14",
   "published": "2026-10-06",
   "url": "https://www.kentapplied.com/facts/team-716-participating-hospitals/"
  },
  {
   "slug": "genai-tools-5-percent-reach-production",
   "title": "MIT NANDA: 5% reached production with GenAI tools",
   "value": 0.05,
   "display": "5%",
   "unit": "of organizations reached production with a generative AI tool",
   "statement": "MIT NANDA's 2025 research found that 60% of organizations evaluated custom or vendor-sold generative AI tools, 20% reached a pilot and 5% reached production.",
   "context": "MIT counted a tool as in production once users or executives reported a marked, lasting effect on productivity or profit. The report calls its figures directionally accurate.",
   "source": {
    "name": "The GenAI Divide: State of AI in Business 2025",
    "url": "https://web.archive.org/web/20250818145714/https://nanda.media.mit.edu/ai_report_2025.pdf",
    "publisher": "MIT NANDA, MIT Media Lab",
    "published": "2025-07",
    "locator": "Executive summary, p. 3; section 3.2, p. 6-7"
   },
   "kind": "survey",
   "tags": [
    "Generative AI",
    "AI pilots",
    "MIT NANDA"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/ai-foundation-compounds/",
    "title": "Building AI so each project makes the next one cheaper",
    "kind": "Blog"
   },
   "charts": [
    "enterprise-genai-pilot-to-production"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-22",
   "url": "https://www.kentapplied.com/facts/genai-tools-5-percent-reach-production/"
  },
  {
   "slug": "genai-95-percent-organizations-zero-return",
   "title": "MIT NANDA: 95% of organizations got zero return on GenAI",
   "value": 0.95,
   "display": "95%",
   "unit": "of organizations getting zero return from generative AI",
   "statement": "MIT NANDA's 2025 report found that 95% of organizations were getting zero return from generative AI, despite $30 billion to $40 billion in enterprise investment.",
   "context": "The report presents its results as preliminary findings. It draws on interviews with 52 organizations, a survey of 153 senior leaders and a review of more than 300 public AI initiatives.",
   "source": {
    "name": "The GenAI Divide: State of AI in Business 2025",
    "url": "https://web.archive.org/web/20250818145714/https://nanda.media.mit.edu/ai_report_2025.pdf",
    "publisher": "MIT NANDA, MIT Media Lab",
    "published": "2025-07",
    "locator": "Executive summary, p. 3"
   },
   "kind": "survey",
   "tags": [
    "Generative AI",
    "ROI",
    "MIT NANDA"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/ai-foundation-compounds/",
    "title": "Building AI so each project makes the next one cheaper",
    "kind": "Blog"
   },
   "charts": [
    "enterprise-genai-pilot-to-production"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-22",
   "url": "https://www.kentapplied.com/facts/genai-95-percent-organizations-zero-return/"
  },
  {
   "slug": "gartner-30-percent-genai-projects-abandoned",
   "title": "Gartner: 30% of generative AI projects would be abandoned",
   "value": 0.3,
   "display": "30%",
   "unit": "of generative AI projects to be abandoned, Gartner predicted",
   "statement": "Gartner predicted in July 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025.",
   "context": "Gartner named poor data quality, inadequate risk controls, escalating costs and unclear business value as the causes.",
   "source": {
    "name": "Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025",
    "url": "https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025",
    "publisher": "Gartner",
    "published": "2024-07-29",
    "locator": "First paragraph"
   },
   "kind": "estimate",
   "tags": [
    "Generative AI",
    "AI pilots",
    "Gartner"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/ai-foundation-compounds/",
    "title": "Building AI so each project makes the next one cheaper",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-09-22",
   "url": "https://www.kentapplied.com/facts/gartner-30-percent-genai-projects-abandoned/"
  },
  {
   "slug": "llms-repeat-planted-clinical-error-up-to-83-percent",
   "title": "LLMs repeated a planted clinical error in up to 83% of cases",
   "value": 0.83,
   "display": "83%",
   "unit": "of cases, at worst, models repeated a planted error",
   "statement": "Six large language models repeated or built on a fabricated lab value, sign or condition planted in a clinical case in up to 83% of cases, a 2025 study in Communications Medicine found.",
   "context": "The study used 300 physician-validated vignettes. A prompt written to prevent the errors lowered the average across the six models from 66% to 44%.",
   "source": {
    "name": "Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support",
    "url": "https://www.nature.com/articles/s43856-025-01021-3",
    "publisher": "Communications Medicine (Nature Portfolio)",
    "published": "2025-08-02",
    "locator": "Plain language summary; Abstract, Results"
   },
   "kind": "measured",
   "tags": [
    "Generative AI",
    "Accuracy",
    "Clinical decision support",
    "Patient safety"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/research/where-ai-has-proof/",
    "title": "Where healthcare AI has proof, and where it does not",
    "kind": "Research",
    "anchor": "caveat-sec"
   },
   "charts": [
    "healthcare-ai-evidence-map-seven-areas"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-15",
   "url": "https://www.kentapplied.com/facts/llms-repeat-planted-clinical-error-up-to-83-percent/"
  },
  {
   "slug": "hrsa-uds-report-238-staff-hours-2025",
   "title": "HRSA: one UDS report takes about 238 staff hours (2025)",
   "value": 238,
   "display": "238",
   "unit": "staff hours per annual UDS report, 2025",
   "statement": "HRSA estimates that a health center spends an average of 238 staff hours preparing its 2025 UDS report, the yearly report of patient and service data that health centers send HRSA.",
   "context": "The estimate covers the Universal Report, the main UDS submission, for 1,538 respondents. HRSA set the 238-hour figure in December 2019, up from 223 hours, and still used it in the June 2025 notice it sent to OMB.",
   "source": {
    "name": "HRSA Uniform Data System information collection notice, Federal Register, June 23, 2025",
    "url": "https://www.govinfo.gov/content/pkg/FR-2025-06-23/html/2025-11444.htm",
    "publisher": "Health Resources and Services Administration (HRSA)",
    "published": "2025-06-23",
    "locator": "Burden statement table, Universal Report row, 90 FR 26594"
   },
   "also": [
    {
     "name": "HRSA Uniform Data System information collection notice, Federal Register, December 13, 2019",
     "url": "https://www.govinfo.gov/content/pkg/FR-2019-12-13/html/2019-26876.htm",
     "locator": "Burden statement and its table, 84 FR 68177"
    }
   ],
   "kind": "estimate",
   "tags": [
    "UDS",
    "Health centers",
    "Reporting burden",
    "HRSA"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/blog/uds-reporting-is-a-pipeline/",
    "title": "Running UDS reporting as a year-round data pipeline",
    "kind": "Blog"
   },
   "charts": [
    "uds-report-staff-hours-2025-vs-2026"
   ],
   "verified": "2026-10-14",
   "published": "2026-09-15",
   "url": "https://www.kentapplied.com/facts/hrsa-uds-report-238-staff-hours-2025/"
  },
  {
   "slug": "hrsa-uds-report-184-staff-hours-2026",
   "title": "HRSA: a UDS report takes 184.2 staff hours in 2026",
   "value": 184.2,
   "display": "184.2",
   "unit": "staff hours per annual UDS report, 2026 estimate",
   "statement": "For 2026, HRSA estimates that a health center spends an average of 184.2 staff hours on its UDS report, the yearly report of patient and service data that health centers send HRSA. That is down from 238 hours in HRSA's estimate for 2025, mostly because HRSA removed some lines from the report's tables and merged others.",
   "context": "The figure covers 1,596 respondents and comes from the notice HRSA sent to OMB for approval in June 2026.",
   "source": {
    "name": "HRSA Uniform Data System information collection notice, Federal Register, June 16, 2026",
    "url": "https://www.govinfo.gov/content/pkg/FR-2026-06-16/html/2026-12046.htm",
    "publisher": "Health Resources and Services Administration (HRSA)",
    "published": "2026-06-16",
    "locator": "Burden statement table, UDS Universal Report row, 91 FR 36150"
   },
   "also": [
    {
     "name": "HRSA Uniform Data System information collection notice, Federal Register, June 23, 2025",
     "url": "https://www.govinfo.gov/content/pkg/FR-2025-06-23/html/2025-11444.htm",
     "locator": "Burden statement table, Universal Report row, 90 FR 26594"
    }
   ],
   "kind": "estimate",
   "tags": [
    "UDS",
    "Health centers",
    "Reporting burden",
    "HRSA"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/blog/uds-reporting-is-a-pipeline/",
    "title": "Running UDS reporting as a year-round data pipeline",
    "kind": "Blog"
   },
   "charts": [
    "uds-report-staff-hours-2025-vs-2026"
   ],
   "verified": "2026-10-14",
   "published": "2026-09-15",
   "compare": [
    {
     "label": "2026",
     "value": 184.2,
     "display": "184.2 hours"
    },
    {
     "label": "2025",
     "value": 238,
     "display": "238 hours"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hrsa-uds-report-184-staff-hours-2026/"
  },
  {
   "slug": "hrsa-uds-2026-changes-cut-72701-hours",
   "title": "HRSA: 2026 UDS changes cut 72,701 reporting hours",
   "value": 72701,
   "display": "72,701",
   "unit": "fewer UDS reporting hours a year, nationally",
   "statement": "HRSA estimates that total UDS reporting time drops by about 72,701 hours a year for 2026, mostly because it removed some lines from the report's tables and merged others.",
   "context": "Total estimated burden falls from 377,317 hours for 2025 to about 304,616 hours for 2026, counting the Universal Report and grant reports.",
   "source": {
    "name": "HRSA Uniform Data System information collection notice, Federal Register, June 16, 2026",
    "url": "https://www.govinfo.gov/content/pkg/FR-2026-06-16/html/2026-12046.htm",
    "publisher": "Health Resources and Services Administration (HRSA)",
    "published": "2026-06-16",
    "locator": "Burden statement, 91 FR 36150"
   },
   "kind": "estimate",
   "tags": [
    "UDS",
    "Health centers",
    "Reporting burden",
    "HRSA"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/blog/uds-reporting-is-a-pipeline/",
    "title": "Running UDS reporting as a year-round data pipeline",
    "kind": "Blog"
   },
   "charts": [
    "uds-report-staff-hours-2025-vs-2026"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-15",
   "compare": [
    {
     "label": "2026",
     "value": 304616,
     "display": "304,616 hours"
    },
    {
     "label": "2025",
     "value": 377317,
     "display": "377,317 hours"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hrsa-uds-2026-changes-cut-72701-hours/"
  },
  {
   "slug": "health-centers-served-32-4-million-patients-2024",
   "title": "HRSA health centers served 32.4 million patients in 2024",
   "value": 32400000,
   "display": "32.4M",
   "unit": "patients served by HRSA-funded health centers in 2024",
   "statement": "Health centers funded by HRSA served a record 32.4 million patients in 2024, HRSA reports, 1.1 million more than the year before.",
   "source": {
    "name": "HRSA Health Center Program bulletin: HRSA Released New Health Center Data",
    "url": "https://content.govdelivery.com/accounts/USHHSHRSA/bulletins/3ed916e",
    "publisher": "Health Resources and Services Administration (HRSA)",
    "published": "2025-08-12",
    "locator": "Top Stories, HRSA Released New Health Center Data"
   },
   "kind": "measured",
   "tags": [
    "Health centers",
    "UDS",
    "HRSA",
    "Patients"
   ],
   "topic": "Health centers",
   "home": {
    "url": "/blog/uds-reporting-is-a-pipeline/",
    "title": "Running UDS reporting as a year-round data pipeline",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-09-15",
   "url": "https://www.kentapplied.com/facts/health-centers-served-32-4-million-patients-2024/"
  },
  {
   "slug": "clinical-documentation-ai-plans-42-percent-2025-survey",
   "title": "Sponsored survey: 42% of health executives planned AI documentation by 2026",
   "value": 0.42,
   "display": "42%",
   "unit": "of executives planned AI documentation by 2026",
   "statement": "In a Healthcare Dive and Microsoft survey of 130 healthcare executives, published in March 2025, 42% planned to use an AI tool for clinical documentation by 2026, against 10% using one at the time.",
   "context": "The article reported the gap as 320% growth. It compares plans with current use, so it does not measure growth in use.",
   "source": {
    "name": "This healthcare AI use case will grow 320% by 2026, survey finds",
    "url": "https://www.healthcaredive.com/spons/this-healthcare-ai-use-case-will-grow-320-by-2026-survey-finds/741469/",
    "publisher": "Healthcare Dive, sponsored content by Microsoft",
    "published": "2025-03-10",
    "locator": "Clinical documentation paragraph"
   },
   "compare": [
    {
     "label": "Planned to use one by 2026",
     "value": 0.42,
     "display": "42%"
    },
    {
     "label": "Using one at the time",
     "value": 0.1,
     "display": "10%"
    }
   ],
   "kind": "survey",
   "tags": [
    "Clinical documentation",
    "Survey",
    "AI vendors",
    "Market claims"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/research/where-ai-has-proof/",
    "title": "Where healthcare AI has proof, and where it does not",
    "kind": "Research",
    "anchor": "fig-2"
   },
   "charts": [
    "clinical-documentation-ai-320-percent-plans-vs-use"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-15",
   "url": "https://www.kentapplied.com/facts/clinical-documentation-ai-plans-42-percent-2025-survey/"
  },
  {
   "slug": "ai-use-cases-2-of-6-pass-three-outcome-test",
   "title": "2 of 6 healthcare AI use cases pass the three-outcome test",
   "value": 2,
   "display": "2 of 6",
   "unit": "AI use cases have at least moderate evidence on all three outcomes",
   "statement": "Two of the six healthcare AI use cases Kent Applied scored have at least moderate published evidence on clinical, operational and financial outcomes at once.",
   "context": "Tyler Kent scored each use case from Weak to Strong on each outcome in September 2026, with the sources listed in the analysis. Discharge and transition optimization and dementia care coordination pass; ambient clinical documentation does not yet.",
   "source": {
    "name": "Where healthcare AI has proof, and where it does not",
    "url": "https://www.kentapplied.com/research/where-ai-has-proof/#test",
    "publisher": "Kent Applied",
    "published": "2026-09-15",
    "locator": "Fig. 5 and the three-outcome test explorer"
   },
   "kind": "analysis",
   "tags": [
    "Healthcare AI",
    "ROI",
    "Measurement"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/research/where-ai-has-proof/",
    "title": "Where healthcare AI has proof, and where it does not",
    "kind": "Research",
    "anchor": "fig-5"
   },
   "charts": [
    "ai-use-cases-three-outcome-scorecard",
    "three-outcome-test-explorer"
   ],
   "verified": "2026-10-14",
   "published": "2026-09-15",
   "url": "https://www.kentapplied.com/facts/ai-use-cases-2-of-6-pass-three-outcome-test/"
  },
  {
   "slug": "kore1-genai-data-scientist-search-14-to-20-weeks",
   "title": "KORE1: 14 to 20 weeks to hire a GenAI data scientist",
   "value": 14,
   "display": "14 to 20",
   "unit": "weeks or more to hire a generative AI data scientist through an internal search",
   "statement": "Staffing firm KORE1 estimates that a company recruiting on its own takes 14 to 20 weeks or more to hire a generative AI data scientist.",
   "context": "Its estimate for a senior data scientist is 9 to 13 weeks. KORE1 sells recruiting services.",
   "source": {
    "name": "Time to fill a data scientist role in 2026",
    "url": "https://www.kore1.com/time-to-fill-data-scientist-2026/",
    "publisher": "KORE1",
    "published": "2026-07-06",
    "locator": "Table \"How long a data scientist search runs in 2026, by level\", Internal Search column"
   },
   "kind": "estimate",
   "tags": [
    "Hiring",
    "Data science",
    "Workforce"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/fractional-ai-lead-or-first-hire/",
    "title": "Choosing between a fractional AI lead and a first AI hire",
    "kind": "Blog"
   },
   "charts": [
    "data-scientist-time-to-fill-by-level-2026"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-08",
   "url": "https://www.kentapplied.com/facts/kore1-genai-data-scientist-search-14-to-20-weeks/"
  },
  {
   "slug": "hfma-cfos-chief-ai-innovation-officer-66-percent",
   "title": "HFMA: 66.4% of CFOs expect a chief AI innovation officer",
   "value": 0.664,
   "display": "66.4%",
   "unit": "of CFOs expect a chief AI innovation officer role",
   "statement": "In a 2025 HFMA survey, 66.4% of healthcare CFOs said a chief AI innovation officer will become an important C-suite role in the next three years.",
   "context": "Among the other C-suite leaders surveyed, 59.6% said the same. Respondents could pick more than one role, and this one was picked most often by both groups.",
   "source": {
    "name": "The healthcare C-suite of the future: From AI to ROI",
    "url": "https://www.hfma.org/finance-and-business-strategy/the-healthcare-c-suite-of-the-future-from-ai-to-roiemerging-needs-drive-demand-for-new-skillsets/",
    "publisher": "Healthcare Financial Management Association (HFMA)",
    "published": "2025-11-06",
    "locator": "Exhibit \"Healthcare's C-suite envisions new roles\""
   },
   "kind": "survey",
   "tags": [
    "AI leadership",
    "CFOs",
    "Health systems",
    "Survey"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/fractional-ai-lead-or-first-hire/",
    "title": "Choosing between a fractional AI lead and a first AI hire",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-09-08",
   "compare": [
    {
     "label": "CFOs",
     "value": 0.664,
     "display": "66.4%"
    },
    {
     "label": "Other C-suite leaders",
     "value": 0.596,
     "display": "59.6%"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hfma-cfos-chief-ai-innovation-officer-66-percent/"
  },
  {
   "slug": "hfma-cfo-ai-deployment-readiness-2-82",
   "title": "CFOs rate their AI deployment readiness 2.82 out of 5",
   "value": 2.82,
   "display": "2.82",
   "unit": "out of 5, CFOs' self-rated preparedness for AI deployment",
   "statement": "Healthcare CFOs rated their preparedness for AI deployment at 2.82 out of 5 in a 2025 HFMA survey, the lowest score among the eight named pressures.",
   "context": "Other C-suite leaders rated it 3.15. HFMA surveyed 125 CFOs and 57 other C-suite leaders.",
   "source": {
    "name": "The healthcare C-suite of the future: From AI to ROI",
    "url": "https://www.hfma.org/finance-and-business-strategy/the-healthcare-c-suite-of-the-future-from-ai-to-roiemerging-needs-drive-demand-for-new-skillsets/",
    "publisher": "Healthcare Financial Management Association (HFMA)",
    "published": "2025-11-06",
    "locator": "Text, and exhibit \"C-suite leaders don't feel well-prepared to navigate top healthcare pressures\""
   },
   "kind": "survey",
   "tags": [
    "AI readiness",
    "CFOs",
    "Health systems",
    "Survey"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/fractional-ai-lead-or-first-hire/",
    "title": "Choosing between a fractional AI lead and a first AI hire",
    "kind": "Blog"
   },
   "charts": [
    "cfo-ai-deployment-readiness-hfma-2025"
   ],
   "verified": "2026-10-08",
   "published": "2026-09-08",
   "compare": [
    {
     "label": "CFOs",
     "value": 2.82,
     "display": "2.82"
    },
    {
     "label": "Other C-suite leaders",
     "value": 3.15,
     "display": "3.15"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hfma-cfo-ai-deployment-readiness-2-82/"
  },
  {
   "slug": "bls-data-scientist-median-wage-120230-2025",
   "title": "BLS: data scientists' median wage was $120,230 in 2025",
   "value": 120230,
   "display": "$120,230",
   "unit": "median annual wage, data scientists, May 2025",
   "statement": "The US Bureau of Labor Statistics puts the median annual wage for data scientists at $120,230 in May 2025.",
   "source": {
    "name": "Occupational Outlook Handbook: Data Scientists",
    "url": "https://www.bls.gov/ooh/math/data-scientists.htm",
    "publisher": "US Bureau of Labor Statistics",
    "published": "2026-08-27",
    "locator": "Summary and Pay tabs"
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   "kind": "measured",
   "tags": [
    "Data science",
    "Workforce",
    "Pay",
    "BLS"
   ],
   "topic": "Data and analytics",
   "home": {
    "url": "/blog/fractional-ai-lead-or-first-hire/",
    "title": "Choosing between a fractional AI lead and a first AI hire",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-09-08",
   "url": "https://www.kentapplied.com/facts/bls-data-scientist-median-wage-120230-2025/"
  },
  {
   "slug": "bls-data-scientist-jobs-35-percent-growth-2025-2035",
   "title": "BLS: data scientist jobs to grow 35% from 2025 to 2035",
   "value": 0.35,
   "display": "35%",
   "unit": "projected job growth for data scientists, 2025 to 2035",
   "statement": "The US Bureau of Labor Statistics projects employment of data scientists to grow 35% from 2025 to 2035, much faster than the average for all occupations.",
   "source": {
    "name": "Occupational Outlook Handbook: Data Scientists",
    "url": "https://www.bls.gov/ooh/math/data-scientists.htm",
    "publisher": "US Bureau of Labor Statistics",
    "published": "2026-08-27",
    "locator": "Summary and Job Outlook tabs"
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   "kind": "estimate",
   "tags": [
    "Data science",
    "Workforce",
    "BLS"
   ],
   "topic": "Data and analytics",
   "home": {
    "url": "/blog/fractional-ai-lead-or-first-hire/",
    "title": "Choosing between a fractional AI lead and a first AI hire",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-09-08",
   "url": "https://www.kentapplied.com/facts/bls-data-scientist-jobs-35-percent-growth-2025-2035/"
  },
  {
   "slug": "olive-ai-4-billion-valuation-shutdown",
   "title": "Olive, once valued at $4 billion, announced its shutdown in 2023",
   "value": 4000000000,
   "display": "$4B",
   "unit": "Olive's former valuation",
   "statement": "Healthcare Dive reported in November 2023 that Olive, a healthcare AI startup once valued at $4 billion, would shut down.",
   "context": "Olive said it would sell its clearinghouse and patient access businesses to Waystar and Humata Health and wind down the rest.",
   "source": {
    "name": "Healthcare Dive, report on the Olive shutdown (November 1, 2023)",
    "url": "https://www.healthcaredive.com/news/olive-ai-shuts-down/698455/",
    "publisher": "Healthcare Dive",
    "published": "2023-11-01",
    "locator": "First paragraph"
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   "kind": "reported",
   "tags": [
    "AI vendors",
    "Digital health",
    "Startups"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/score-an-ai-vendor-before-the-demo/",
    "title": "How to score an AI vendor before the demo",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-16",
   "published": "2026-09-03",
   "url": "https://www.kentapplied.com/facts/olive-ai-4-billion-valuation-shutdown/"
  },
  {
   "slug": "initial-claim-denials-11-8-percent-2024",
   "title": "Payers initially denied 11.8% of claims in Kodiak's 2024 data",
   "value": 0.1181,
   "display": "11.8%",
   "unit": "of claims initially denied in 2024, in Kodiak Solutions' hospital and physician data",
   "statement": "Data from the more than 2,100 hospitals and 300,000 physicians that use Kodiak Solutions' revenue cycle software show that payers initially denied 11.81% of claims in 2024.",
   "context": "The rate was 2.4% higher than in 2023. Denials over medical necessity and requests for more information grew, while denials over authorization fell 7.7%.",
   "source": {
    "name": "Rate of initial denials of medical insurance claims continued to rise in 2024, Kodiak Solutions proprietary data show",
    "url": "https://www.businesswire.com/news/home/20250521892947/en/Rate-of-initial-denials-of-medical-insurance-claims-continued-to-rise-in-2024-Kodiak-Solutions-proprietary-data-show",
    "publisher": "Kodiak Solutions (Business Wire)",
    "published": "2025-05-21",
    "locator": "Paragraph beginning \"The initial denial rate on claims in 2024\""
   },
   "kind": "measured",
   "tags": [
    "Claim denials",
    "Revenue cycle",
    "Payers"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/blog/score-an-ai-vendor-before-the-demo/",
    "title": "How to score an AI vendor before the demo",
    "kind": "Blog"
   },
   "charts": [
    "initial-claim-denial-rate-2024"
   ],
   "verified": "2026-10-14",
   "published": "2026-09-03",
   "url": "https://www.kentapplied.com/facts/initial-claim-denials-11-8-percent-2024/"
  },
  {
   "slug": "johns-hopkins-command-center-16-beds",
   "title": "Johns Hopkins command center freed 16 beds of capacity",
   "value": 16,
   "display": "16",
   "unit": "beds of capacity a day",
   "statement": "Five years after it opened, Johns Hopkins said its capacity command center had freed the equivalent of 16 beds a day without building or hiring.",
   "context": "The figure comes from Jim Scheulen, Johns Hopkins Medicine's chief administrative officer for emergency medicine and capacity management. He also said bed use rose from 85% to about 94%.",
   "source": {
    "name": "Capacity Command Center Celebrates 5 Years of Improving Patient Safety, Access",
    "url": "https://www.hopkinsmedicine.org/news/articles/2021/03/capacity-command-center-celebrates-5-years-of-improving-patient-safety-access",
    "publisher": "Johns Hopkins Medicine",
    "published": "2021-03-16",
    "locator": "Dome, March/April 2021"
   },
   "kind": "estimate",
   "tags": [
    "Command centers",
    "Capacity",
    "Johns Hopkins"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/ed-boarding/",
    "title": "ED boarding starts with inpatient capacity",
    "kind": "Blog"
   },
   "charts": [
    "johns-hopkins-command-center-early-results"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-30",
   "url": "https://www.kentapplied.com/facts/johns-hopkins-command-center-16-beds/"
  },
  {
   "slug": "johns-hopkins-bed-assignment-30-percent-faster",
   "title": "Johns Hopkins assigned ED patients beds 30% faster",
   "value": 0.3,
   "display": "30%",
   "unit": "faster bed assignment for admitted ED patients",
   "statement": "Johns Hopkins reported in 2016 that its new capacity command center assigned beds to admitted ED patients 30% faster.",
   "context": "The same release reported 21% more patients discharged before noon.",
   "source": {
    "name": "The Johns Hopkins Hospital Launches Capacity Command Center to Enhance Hospital Operations (news release)",
    "url": "https://www.newswise.com/articles/the-johns-hopkins-hospital-launches-capacity-command-center-to-enhance-hospital-operations",
    "publisher": "Johns Hopkins Medicine",
    "published": "2016-10-26",
    "locator": "Early results, Emergency Department"
   },
   "kind": "measured",
   "tags": [
    "ED boarding",
    "Command centers",
    "Johns Hopkins"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/ed-boarding/",
    "title": "ED boarding starts with inpatient capacity",
    "kind": "Blog"
   },
   "charts": [
    "johns-hopkins-command-center-early-results"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-30",
   "url": "https://www.kentapplied.com/facts/johns-hopkins-bed-assignment-30-percent-faster/"
  },
  {
   "slug": "ed-boarding-violence-54-percent-emergency-physicians",
   "title": "Survey: 54% of emergency doctors report boarding violence",
   "value": 0.541,
   "display": "54%",
   "unit": "of 195 emergency physicians surveyed reported violence tied to boarding",
   "statement": "In a 2025 survey of 195 emergency physicians, published in Health Affairs Scholar, 54% reported experiencing violence related to emergency department boarding.",
   "context": "The respondents were members of the American Academy of Emergency Medicine, surveyed over four weeks in early 2025. In the paper's results, 105 of the 195 (53.8%) said they had personally experienced violence because of boarding or crowding, and 157 (80.5%) had seen violence toward another member of the ED staff.",
   "source": {
    "name": "Norton, Schreyer and Kuhn, Workforce impact of emergency department boarding, Health Affairs Scholar 3(8)",
    "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12342924/",
    "publisher": "Health Affairs Scholar",
    "published": "2025-07-04",
    "locator": "Abstract, Results; Results section"
   },
   "kind": "survey",
   "tags": [
    "ED boarding",
    "Workforce",
    "Workplace violence",
    "Survey"
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   "topic": "Hospital operations",
   "home": {
    "url": "/blog/ed-boarding/",
    "anchor": "fact-1",
    "title": "ED boarding starts with inpatient capacity",
    "kind": "Blog"
   },
   "charts": [
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   ],
   "verified": "2026-10-09",
   "published": "2026-08-30",
   "url": "https://www.kentapplied.com/facts/ed-boarding-violence-54-percent-emergency-physicians/"
  },
  {
   "slug": "ed-boarding-stroke-patient-daily-cost",
   "title": "Boarding a med-surg stroke patient in the ED cost $1,856 a day at one hospital",
   "value": 1856,
   "display": "$1,856",
   "unit": "a day per boarded med-surg stroke patient",
   "statement": "A 2024 Annals of Emergency Medicine study estimated that boarding a med-surg stroke patient in the ED cost $1,856 a day, against $993 for the same patient on a ward.",
   "context": "The study covered patients with acute stroke at one large academic stroke center. For ICU-level patients, boarding cost $2,267 a day against $2,165 in the ICU.",
   "compare": [
    {
     "label": "Boarded in the ED",
     "value": 1856,
     "display": "$1,856"
    },
    {
     "label": "On a ward",
     "value": 993,
     "display": "$993"
    }
   ],
   "source": {
    "name": "Canellas MM et al., Measurement of Cost of Boarding in the Emergency Department Using Time-Driven Activity-Based Costing, Annals of Emergency Medicine 84(4)",
    "url": "https://pubmed.ncbi.nlm.nih.gov/38795079/",
    "publisher": "Annals of Emergency Medicine",
    "published": "2024-10",
    "locator": "Abstract, Results"
   },
   "kind": "estimate",
   "tags": [
    "ED boarding",
    "Hospital finance"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/ed-boarding/",
    "title": "ED boarding starts with inpatient capacity",
    "kind": "Blog"
   },
   "charts": [
    "ed-boarding-daily-cost-stroke-patients"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-30",
   "url": "https://www.kentapplied.com/facts/ed-boarding-stroke-patient-daily-cost/"
  },
  {
   "slug": "cms-ed-measure-boarding-over-4-hours",
   "title": "CMS's new ED measure counts boarding over 4 hours",
   "value": 4,
   "display": "4 hours",
   "unit": "of ED boarding before CMS's measure counts it",
   "statement": "CMS's Emergency Care Access & Timeliness measure counts admitted patients who board in the ED for more than 4 hours.",
   "context": "It also counts ED stays over 8 hours, waits of more than 1 hour for a treatment room and patients who leave before evaluation. Hospital reporting is voluntary in 2027 and required from 2028.",
   "source": {
    "name": "CY 2026 Hospital Outpatient Prospective Payment System and Ambulatory Surgical Center final rule, 90 FR 53448",
    "url": "https://www.federalregister.gov/documents/2025/11/25/2025-20907/medicare-program-hospital-outpatient-prospective-payment-and-ambulatory-surgical-center-payment",
    "publisher": "Centers for Medicare & Medicaid Services",
    "published": "2025-11-25",
    "locator": "Section XV.B.1.b, Measure Overview, and XV.B.1.c, Measure Calculation"
   },
   "kind": "rule",
   "tags": [
    "ED boarding",
    "Quality measures",
    "CMS"
   ],
   "topic": "Payment and policy",
   "home": {
    "url": "/blog/ed-boarding/",
    "title": "ED boarding starts with inpatient capacity",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-08-30",
   "url": "https://www.kentapplied.com/facts/cms-ed-measure-boarding-over-4-hours/"
  },
  {
   "slug": "hospitals-predictive-ai-ehr-71-percent-2024",
   "title": "71% of non-federal acute care hospitals used predictive AI",
   "value": 0.71,
   "display": "71%",
   "unit": "of non-federal acute care hospitals used predictive AI in the EHR",
   "statement": "ASTP reports that 71% of non-federal acute care hospitals used predictive AI integrated with their EHR in 2024, up from 66% in 2023.",
   "context": "The data come from the American Hospital Association's IT Supplement survey. Small, rural, independent and critical access hospitals adopted it at lower rates.",
   "source": {
    "name": "ASTP Data Brief No. 80: Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023-2024",
    "url": "https://www.healthit.gov/sites/default/files/2025-09/hospital-trends-use-evaluation-and-governance-predictive-ai-2023-2024.pdf",
    "publisher": "Assistant Secretary for Technology Policy (ASTP/ONC), US Department of Health and Human Services",
    "published": "2025-09",
    "locator": "Highlights, p. 1; Figure 1, p. 2"
   },
   "kind": "survey",
   "tags": [
    "Predictive AI",
    "Hospitals",
    "EHR",
    "ASTP"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/where-ai-pays/",
    "title": "Where healthcare AI pays off in 2026 budgets",
    "kind": "Blog"
   },
   "charts": [
    "hospital-predictive-ai-uses-2023-2024"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "compare": [
    {
     "label": "2024",
     "value": 0.71,
     "display": "71%"
    },
    {
     "label": "2023",
     "value": 0.66,
     "display": "66%"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hospitals-predictive-ai-ehr-71-percent-2024/"
  },
  {
   "slug": "hospitals-predictive-ai-billing-61-percent-2024",
   "title": "Hospital predictive AI for billing rose to 61% in 2024",
   "value": 0.61,
   "display": "61%",
   "unit": "of hospitals using predictive AI apply it to billing",
   "statement": "ASTP reports that among hospitals using predictive AI, the share using it to simplify or automate billing rose from 36% in 2023 to 61% in 2024.",
   "context": "Billing was the fastest-growing use in the brief. Scheduling came next, rising from 51% to 67%.",
   "source": {
    "name": "ASTP Data Brief No. 80: Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023-2024",
    "url": "https://www.healthit.gov/sites/default/files/2025-09/hospital-trends-use-evaluation-and-governance-predictive-ai-2023-2024.pdf",
    "publisher": "Assistant Secretary for Technology Policy (ASTP/ONC), US Department of Health and Human Services",
    "published": "2025-09",
    "locator": "Figure 2, p. 4; Summary, p. 8"
   },
   "kind": "survey",
   "tags": [
    "Predictive AI",
    "Hospitals",
    "Revenue cycle",
    "ASTP"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/where-ai-pays/",
    "title": "Where healthcare AI pays off in 2026 budgets",
    "kind": "Blog"
   },
   "charts": [
    "hospital-predictive-ai-uses-2023-2024"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "compare": [
    {
     "label": "2024",
     "value": 0.61,
     "display": "61%"
    },
    {
     "label": "2023",
     "value": 0.36,
     "display": "36%"
    }
   ],
   "url": "https://www.kentapplied.com/facts/hospitals-predictive-ai-billing-61-percent-2024/"
  },
  {
   "slug": "healthcare-ai-return-3-20-per-dollar",
   "title": "Microsoft-sponsored survey: $3.20 back per AI dollar",
   "value": 3.2,
   "display": "$3.20",
   "unit": "self-reported return per $1 invested in AI",
   "statement": "Microsoft reported that healthcare organizations in an IDC survey it sponsored averaged a return of $3.20 for every $1 invested in AI, and saw a return within 14 months.",
   "context": "The figures are survey answers, not audited results. IDC dated the survey document November 2023, and Microsoft published the healthcare figures in March 2024.",
   "source": {
    "name": "Microsoft, reporting the IDC Business Value of AI Survey (IDC #US51331223, November 2023)",
    "url": "https://blogs.microsoft.com/blog/2024/03/11/microsoft-makes-the-promise-of-ai-in-healthcare-real-through-new-collaborations-with-healthcare-organizations-and-partners/",
    "publisher": "Microsoft",
    "published": "2024-03-11",
    "locator": "First paragraph and endnote ii"
   },
   "kind": "survey",
   "tags": [
    "ROI",
    "AI investment",
    "IDC"
   ],
   "topic": "AI adoption",
   "home": {
    "url": "/blog/where-ai-pays/",
    "anchor": "fact-1",
    "title": "Where healthcare AI pays off in 2026 budgets",
    "kind": "Blog"
   },
   "charts": [],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/healthcare-ai-return-3-20-per-dollar/"
  },
  {
   "slug": "hanys-placement-setting-delay-days",
   "title": "Finding somewhere for the patient to go next was a cause in 35,625 discharge delay days",
   "value": 35625,
   "display": "35,625",
   "unit": "inpatient delay days where finding the patient's next place of care was a cause",
   "statement": "In HANYS's 2022 pilot, finding a placement setting, somewhere for the patient to go after the hospital, was a cause in 35,625 inpatient discharge delay days, more than any other cause.",
   "context": "A patient could count under more than one cause. The most common placement problem was a facility unable to accept the patient.",
   "source": {
    "name": "The scope of complex case discharge delays in New York state",
    "url": "https://www.hanys.org/communications/publications/scope_of_complex_case/docs/complex_case_discharge_delays_survey.pdf",
    "publisher": "Healthcare Association of New York State (HANYS)",
    "published": "2023-02",
    "locator": "What is causing the delays?, p. 18"
   },
   "kind": "measured",
   "tags": [
    "Discharge delays",
    "Post-acute care",
    "New York"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/discharge-delays/",
    "title": "Discharge delays cost 52 hospitals an estimated $169 million in one quarter",
    "kind": "Blog"
   },
   "charts": [
    "new-york-discharge-delay-days-by-cause"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/hanys-placement-setting-delay-days/"
  },
  {
   "slug": "hanys-discharge-delays-169-million-one-quarter",
   "title": "Discharge delays: $169M at 52 New York hospitals in 3 months",
   "value": 169000000,
   "display": "$169M",
   "unit": "estimated cost of discharge delays at 52 hospitals in one quarter",
   "statement": "HANYS found that 52 New York hospitals reported about 60,000 discharge delay days and $169 million in estimated costs from April to June 2022.",
   "context": "The pilot counted 1,115 patients who waited more than 4 days in the ED or more than 14 days on an inpatient unit after they were ready to leave.",
   "source": {
    "name": "The scope of complex case discharge delays in New York state",
    "url": "https://www.hanys.org/communications/publications/scope_of_complex_case/docs/complex_case_discharge_delays_survey.pdf",
    "publisher": "Healthcare Association of New York State (HANYS)",
    "published": "2023-02",
    "locator": "Executive summary, p. 1"
   },
   "kind": "estimate",
   "tags": [
    "Discharge delays",
    "New York",
    "Hospital finance"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/discharge-delays/",
    "title": "Discharge delays cost 52 hospitals an estimated $169 million in one quarter",
    "kind": "Blog"
   },
   "charts": [
    "new-york-discharge-delay-days-by-cause"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/hanys-discharge-delays-169-million-one-quarter/"
  },
  {
   "slug": "hanys-cost-per-inpatient-delay-day",
   "title": "HANYS's 2022 cost estimate works out to about $2,800 per inpatient delay day",
   "value": 2800,
   "display": "$2,800",
   "unit": "estimated cost per inpatient delay day",
   "statement": "Kent Applied divided the $167 million in estimated inpatient costs by the 58,710 inpatient delay days in the Healthcare Association of New York State's 2022 pilot survey, which puts one inpatient discharge delay day at about $2,800.",
   "context": "HANYS reports $167 million in estimated inpatient costs over 58,710 inpatient delay days at 50 hospitals; $167 million divided by 58,710 is $2,844. HANYS does not state a cost per day, and hospitals used different methods to estimate their costs.",
   "source": {
    "name": "The scope of complex case discharge delays in New York state",
    "url": "https://www.hanys.org/communications/publications/scope_of_complex_case/docs/complex_case_discharge_delays_survey.pdf",
    "publisher": "Healthcare Association of New York State (HANYS)",
    "published": "2023-02",
    "locator": "Inpatient table, p. 9; What are the costs?, p. 16; Discussion, p. 23"
   },
   "kind": "derived",
   "tags": [
    "Discharge delays",
    "Hospital finance",
    "New York"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/discharge-delays/",
    "anchor": "fact-1",
    "title": "Discharge delays cost 52 hospitals an estimated $169 million in one quarter",
    "kind": "Blog"
   },
   "charts": [
    "new-york-discharge-delay-days-by-cause"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/hanys-cost-per-inpatient-delay-day/"
  },
  {
   "slug": "cincinnati-childrens-surgery-volume-7-percent",
   "title": "Cincinnati Children's surgical volume grew 7% a year",
   "value": 0.07,
   "display": "7%",
   "unit": "yearly surgical volume growth at one children's hospital",
   "statement": "Surgical volume at Cincinnati Children's grew 7% a year for at least two years after it smoothed its elective schedule, Eugene Litvak reported.",
   "context": "The same account says operating-room overtime fell 57%.",
   "source": {
    "name": "Eugene Litvak, Bridging the Gap: Operations Management Science, Patient Safety and Health Care Cost (presentation)",
    "url": "https://nam.edu/wp-content/uploads/2017/12/Eugene-Litvak.pdf",
    "publisher": "National Academy of Medicine",
    "published": "2012-12-14",
    "locator": "Slides 21 and 22, Managing variability in patient flow: success stories; source on slide: Frederic Ryckman, MD, Cincinnati Children's Hospital Medical Center"
   },
   "kind": "measured",
   "tags": [
    "Surgical scheduling",
    "Smoothing",
    "Operating room"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/proven-and-ignored/",
    "title": "Surgical schedule smoothing works, and few hospitals use it",
    "kind": "Blog"
   },
   "charts": [
    "cincinnati-childrens-occupancy-after-smoothing"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/cincinnati-childrens-surgery-volume-7-percent/"
  },
  {
   "slug": "cincinnati-childrens-smoothing-137-million-a-year",
   "title": "One hospital reported $137M a year from smoothing its elective surgery schedule",
   "value": 137000000,
   "display": "$137M",
   "unit": "a year, in one 2012 account",
   "statement": "In Eugene Litvak's 2012 account, the occupancy gain at Cincinnati Children's resulted in $137 million a year, plus more than $100 million in avoided capital cost for 75 beds.",
   "context": "The slide does not say whether the $137 million is revenue or margin. The figures are the hospital's, as Litvak presented them.",
   "source": {
    "name": "Eugene Litvak, Bridging the Gap: Operations Management Science, Patient Safety and Health Care Cost (presentation)",
    "url": "https://nam.edu/wp-content/uploads/2017/12/Eugene-Litvak.pdf",
    "publisher": "National Academy of Medicine",
    "published": "2012-12-14",
    "locator": "Slides 21 and 22, Managing variability in patient flow: success stories; source on slide: Frederic Ryckman, MD, Cincinnati Children's Hospital Medical Center"
   },
   "kind": "estimate",
   "tags": [
    "Surgical scheduling",
    "Smoothing",
    "Hospital finance"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/proven-and-ignored/",
    "title": "Surgical schedule smoothing works, and few hospitals use it",
    "kind": "Blog"
   },
   "charts": [
    "cincinnati-childrens-occupancy-after-smoothing"
   ],
   "verified": "2026-10-08",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/cincinnati-childrens-smoothing-137-million-a-year/"
  },
  {
   "slug": "cincinnati-childrens-occupancy-76-to-91-percent",
   "title": "Cincinnati Children's occupancy rose from 76% to 91%",
   "value": 0.91,
   "display": "91%",
   "unit": "inpatient occupancy at one children's hospital after it smoothed its elective surgery schedule",
   "statement": "Inpatient occupancy at Cincinnati Children's rose from 76% to 91% after it smoothed its elective surgery schedule, Eugene Litvak reported in 2012.",
   "context": "Cincinnati Children's built a scheduling system for elective surgery to cut swings in daily demand, the Institute for Healthcare Improvement reports. The slide credits the figures to Dr. Frederic Ryckman of the hospital.",
   "compare": [
    {
     "label": "After smoothing",
     "value": 0.91,
     "display": "91%"
    },
    {
     "label": "Before smoothing",
     "value": 0.76,
     "display": "76%"
    }
   ],
   "source": {
    "name": "Eugene Litvak, Bridging the Gap: Operations Management Science, Patient Safety and Health Care Cost (presentation)",
    "url": "https://nam.edu/wp-content/uploads/2017/12/Eugene-Litvak.pdf",
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    "locator": "Slides 21 and 22, Managing variability in patient flow: success stories; source on slide: Frederic Ryckman, MD, Cincinnati Children's Hospital Medical Center"
   },
   "also": [
    {
     "name": "Achieving Hospital-wide Patient Flow (Second Edition), IHI White Paper",
     "url": "https://www.ihi.org/sites/default/files/IHIAchievingHospitalWidePatientFlowWhitePaper.pdf",
     "publisher": "Institute for Healthcare Improvement",
     "published": "2020",
     "locator": "Page 13, Cincinnati Children's elective surgery scheduling"
    }
   ],
   "kind": "measured",
   "tags": [
    "Surgical scheduling",
    "Smoothing",
    "Capacity"
   ],
   "topic": "Hospital operations",
   "home": {
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   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/cincinnati-childrens-occupancy-76-to-91-percent/"
  },
  {
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   "title": "Hospital stays before skilled nursing got 20.2% longer, 2019 to 2022",
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   "unit": "rise in average length of stay for patients going to skilled nursing, 2019 to 2022, case-mix adjusted",
   "statement": "Adjusted for case mix, the average hospital stay for patients discharged to skilled nursing facilities got 20.2% longer from 2019 to 2022, the American Hospital Association reported.",
   "context": "The brief uses Strata Decision Technology data. On the same basis, the average stay before a transfer to a psychiatric hospital got 28.9% longer.",
   "source": {
    "name": "Patients and Providers Faced with Increasing Delays in Timely Discharges (issue brief)",
    "url": "https://www.aha.org/system/files/media/file/2022/12/Issue-Brief-Patients-and-Providers-Faced-with-Increasing-Delays-in-Timely-Discharges.pdf",
    "publisher": "American Hospital Association",
    "published": "2022-12",
    "locator": "Figure 1, p. 1"
   },
   "kind": "measured",
   "tags": [
    "Length of stay",
    "Post-acute care",
    "Skilled nursing"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/discharge-delays/",
    "title": "Discharge delays cost 52 hospitals an estimated $169 million in one quarter",
    "kind": "Blog"
   },
   "charts": [
    "length-of-stay-growth-by-discharge-destination"
   ],
   "verified": "2026-10-14",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/aha-snf-discharge-stays-up-20-percent/"
  },
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   "slug": "aha-length-of-stay-up-19-percent-2019-2022",
   "title": "Hospital stays got 19.2% longer from 2019 to 2022 (AHA)",
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   "display": "19.2%",
   "unit": "rise in average length of stay, 2019 to 2022",
   "statement": "Average hospital length of stay rose 19.2% from 2019 to 2022, according to Strata Decision Technology data published by the American Hospital Association.",
   "context": "For patients discharged to post-acute care, the rise was nearly 24%.",
   "source": {
    "name": "Patients and Providers Faced with Increasing Delays in Timely Discharges (issue brief)",
    "url": "https://www.aha.org/system/files/media/file/2022/12/Issue-Brief-Patients-and-Providers-Faced-with-Increasing-Delays-in-Timely-Discharges.pdf",
    "publisher": "American Hospital Association",
    "published": "2022-12",
    "locator": "p. 1"
   },
   "kind": "measured",
   "tags": [
    "Length of stay",
    "Discharge delays",
    "Post-acute care"
   ],
   "topic": "Hospital operations",
   "home": {
    "url": "/blog/discharge-delays/",
    "title": "Discharge delays cost 52 hospitals an estimated $169 million in one quarter",
    "kind": "Blog"
   },
   "charts": [
    "length-of-stay-growth-by-discharge-destination"
   ],
   "verified": "2026-10-14",
   "published": "2026-08-26",
   "url": "https://www.kentapplied.com/facts/aha-length-of-stay-up-19-percent-2019-2022/"
  }
 ]
}