1 See Fast Facts on U.S. Hospitals, Am. Hosp. Ass'n. (2020), https://www.aha.org/statistics/fast-facts-us-hospitals [https://perma.cc/2TMT-6LE9].

2 See Agency for Healthcare Research and Quality, Snapshot of U.S. Health Systems, 2016 Data Highlight 1 (2017), https://www.ahrq.gov/sites/default/files/wysiwyg/snapshot-ofus-health-systems-2016v2.pdf [https://perma.cc/KA4T-6LJY].

3 Robert E. Suter, Emergency Medicine in the United States: A Systematic Review, 3 World J. Emerging Med. 5, 9–10 (2012).

4 See Furrow, Barry R., Adverse Events and Patient Injury: Coupling Detection, Disclosure, and Compensation, 46 New Eng. L. Rev. 437, 439 (2012)Google Scholar.

5 Id.

6 Barry R. Furrow, The Limits of Current AI in Health Care: Patient Safety Policy in Hospitals, 12 Ne. U. L. Rev. 1, 3–4 (2020).

7 Charles N. Kahn III et al., Assessing Medicare's Hospital Pay-For-Performance Programs and Whether They Are Achieving Their Goals, 34 Health Affairs 1281 (2015).

8 Lewis Thomas, The Lives of a Cell: Notes of a Biology Watcher, 17–19 (1974).

9 Id.

10 Id. (recalling “the early 1950s, just before the emergence of the basic research that made the [polio] vaccine possible … the cost of those institutes for rehabilitation, with all those ceremonially applied hot fomentations, and the debates about whether the affected limbs should be totally immobilized or kept in passive motion as frequently as possible, and the masses of statistically tormented data mobilized to support one view or the other?”).

11 Id. at 20.

12 Id. at 18–19.

13 Id. at 20.

14 Cheryl L. Wagonhurst and M. Leeann Habte, Health Care Boards of Directors' Legal Responsibilities for Quality, Health Care Compliance Association (Dec. 2008), https://www.foley.com/-/media/files/insights/publications/2008/12/health-care-boards-of-directors-legal-responsibili/files/health-care-boards-of-directors-legal-responsibili/fileattachment/ct1208_wagonhursthabte.pdf [https://perma.cc/7W49-PUY4].

15 Agency for Healthcare Research and Quality, Glossary, PSNet, https://psnet.ahrq.gov/glossary [https://perma.cc/Z3NU-99KX] [hereinafter AHRQ Glossary].

16 AHRQ Glossary, supra note 15 (click “Adverse Event”).

17 Newman-Toker, David E. et al., Serious Misdiagnosis-Related Harms in Malpractice Claims: The “Big Three” – Vascular Events, Infections, and Cancers, 6 Diagnosis 228, 233 (2019)CrossRefGoogle ScholarPubMed.

18 Schwendimann, René, et al., The Occurrence, Types, Consequences and Preventability of Inhospital Adverse Events – a Systematic Scoping Review, 18 BMC Health Servs. Res. 521, 521 (2018)CrossRefGoogle Scholar. For a broader review of the literature on hospital adverse events, see generally Furrow, supra note 4.

19 Off. of the Inspector Gen., Dep't of Health and Human Servs., OEI-06-09-00090, Adverse Events in Hospitals: Medicare's Responses to Alleged Serious Events i-ii (Oct. 2011).

20 Furrow, supra note 4 at 451–53.

21 See Eric Nalder & Cathleen F. Crowley, Patients Beware: Hospital Safety's a Wilderness of Data, Hous. Chron. (Mar. 22, 2010), http://www.chron.com/news/article/Patients-beware-Hospital-safetys-a-wilderness-1702575.php [https://perma.cc/N8U3-D9CV] (illustrating that hospitals often underreport adverse events and showing that, in some instances, hospitals have missed cases where patients were killed).

22 Classen, David C. et al., ‘Global Trigger Tool’ Shows that Adverse Events in Hospitals May Be Ten Times Greater than Previously Measured, 30 Health Aff. 581, 581 (2011)CrossRefGoogle ScholarPubMed.

23 Matt Austin & Jordan Derk, Lives Lost, Lives Saved: An Updated Comparative Analysis of Avoidable Deaths at Hospitals Graded by The Leapfrog Group 5 (2019).

24 Id. at 6.

25 Makary, Martin A. & Daniel, Michael, Medical Error—The Third Leading Cause of Death in the US, 353 BMJ 2139, 2140 (2016)Google ScholarPubMed.

26 Inst. of Med., To Err Is Human: Building a Safer Health System 26 (Linda T. Kohn et al. eds., 2000) [hereinafter IOM Report]. The IOM Report suggested a range from 44,000 to 98,000 individuals. Id.

27 Inst. of Med., Crossing The Quality Chasm: A Health System for the 21^st Century 3 (2001).

28 Id. at 25.

29 Off. of the Inspector Gen., Dep't of Health and Human Servs., OEI-06-09-00090, Adverse Events in Hospitals: National Incidence Among Medicare Beneficiaries i (Nov. 2010) [hereinafter OIG Report].

30 Id; Spotlight On … Adverse Events, Off. of the Inspector Gen., https://oig.hhs.gov/newsroom/spotlight/2012/adverse.asp [https://perma.cc/AKJ7-GBGU].

31 See Furrow, supra note 6, at 6.

32 Id. at 7–9.

33 Philip Aspden et al., Patient Safety: Achieving a New Standard for Care 178 (2004).

34 Id. at 7.

35 Id. at 5.

36 Adler-Milstein, Julia & Jha, Ashish K., HITECH Act Drove Large Gains in Hospital Electronic Health Record Adoption, 36 Health Affairs 1416, 1420 (2017)CrossRefGoogle ScholarPubMed.

37 Brian Schilling, The Federal Government Has Put Billions into Promoting Electronic Health Record Use: How Is It Going? Commonwealth Fund, https://www.commonwealthfund.org/publications/newsletter-article/federal-government-has-put-billions-promoting-electronic-health [https://perma.cc/Q7QJ-9XAS].

38 OIG Report, supra note 29, at iii.

39 Id. at 32 (emphasis added).

40 Id. at 40.

41 See Furrow, supra note 4, at 448.

42 Freedman, Seth et al., Information Technology and Patient Health: Analyzing Outcomes, Populations, and Mechanisms, 4 Am. J. Health Econ. 51, 75 (2017)Google Scholar.

43 Id. at 54.

44 See Fred Schulte & Erika Fry, Death By 1,000 Clicks: Where Electronic Health Records Went Wrong, Kaiser Health News (Mar. 18, 2019), https://khn.org/news/death-by-a-thousand-clicks/ [https://perma.cc/2FMN-W9CG].

45 Ratwani, Raj. M. et al., Identifying Electronic Health Record Usability and Safety Challenges in Pediatric Settings, 37 Health Aff. 1752, 1754 (2018)CrossRefGoogle ScholarPubMed (examination of 9,000 patient safety reports led authors to conclude that over a third had an EHR usability issue that contributed to a medication error).

46 See generally Ross Koppel, Uses of the Legal System That Attenuate Patient Safety, 68 DePaul L. Rev. 273 (2019) (discussing the range of problems caused by the rapid diffusion of EHRs, and the limits placed by vendors on their usability and ease of error correction among others).

47 Internet of Things, Aruba, https://www.arubanetworks.com/solutions/internet-of-things [https://perma.cc/DP6R-J8PQ].

48 Artificial Intelligence: Healthcare's New Nervous System, Accenture 2 (2017), https://www.accenture.com/_acnmedia/PDF-49/Accenture-Health-Artificial-Intelligence.pdf#zoom=50 [https://perma.cc/WTZ5-3JRH].

49 Id. at 1.

50 I focus on patient safety here, but AI has many other potential benefits in health care. See, e.g., id.

51 For a good discussion of AI history and definitions, see Anthony Chang, AIMed, Analytics and Algorithms, Big Data, Cognitive Computing, and Deep Learning in Healthcare and Medicine (2017).

52 See, e.g., Furrow, Barry R., Searching for Adverse Events: Big Data and Beyond, 27 Annals Health L. 149, 160, 178-79 (2018)Google Scholar.

53 See id. at 167-68.

54 Froomkin, A. Michael et al., When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning, 61 Ariz. L. Rev. 33, 36 (2019)Google Scholar.

55 Data Mining: What it is and Why it Matters, SAS, https://www.sas.com/en_us/insights/analytics/data-mining.html [https://perma.cc/VY4L-RYKB].

56 Nicholson, W. Price, Artificial Intelligence in the Medical System: Four Roles for Potential Transformation, 18 Yale J. Health Pol'y. L. & Ethics 122, 128 (2019)Google Scholar.

57 Karen Weintraub, The Power of AI, WebMD Mag. Jan.–Feb. 2020, at 34.

58 See Laura Lovett, Organizing Messy Data, a Google Developer's View, MobiHealthNews (June 22, 2018), https://www.mobihealthnews.com/content/organizing-messy-data-google-developers-view [https://perma.cc/BB7T-KYQV].

59 Sweet, Lauren E. & Moulaison, Heather L., Electronic Health Records Data and Metadata, 1 Big Data 245, 247 (2013)CrossRefGoogle ScholarPubMed.

60 Artificial Intelligence: What it is and Why it Matters, SAS, https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html [https://perma.cc/H3ZV-2BJW].

61 One such tool is the use of automated electronic search strategies. Automated extraction of data from electronic health records (EHRs) conducts high-quality retrospective analysis of large patient cohorts; these automated techniques can predict with high accuracy preoperative predictors and identify postoperative complications, such as postoperative myocardial infarction in large cohorts of surgical patients. Oludare O. Olatoye et al., Derivation and Validation of an Automated Electronic Search Algorithm to Identify Patients at Risk for Obstructive Sleep Apnea, 13 Signa Vitae 96 (2017).

62 See Steele, Andrew J. et al., Machine Learning Models in Electronic Health Records Can Outperform Conventional Survival Models for Predicting Patient Mortality in Coronary Artery Disease, 13 PLOS One 1, 19 (2018)CrossRefGoogle ScholarPubMed.

63 See, e.g., Michael Matheny et al., Nat'l Acad. of Med., Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril 15 (2019) (hereinafter Artificial Intelligence in Health Care).

64 Stephan Fihn et al., Chapter 6 Deploying AI in Clinical Settings, in Artificial Intelligence in Health Care 145, 146 (Michael Matheny et al., forthcoming).

65 See Hoffman, Sharona & Podgurski, Andy, E-Health Hazards: Provider Liability and Electronic Health Record Systems, 24 Berkeley Tech. L. J. 1523, 1531 (2009)Google Scholar (citing Biomedical Informatics: Computer Applications in Health Care and Biomedicine 952 (Edward H. Shortliffe & James J. Cimino eds., 2006)) (“Interoperability’ means the ability of two or more systems to exchange data and to operate in a coordinated fashion.”).

66 See Sharona Hoffman, Electronic Health Records And Medical Big Data: Law and Policy 18 (2016); Janet Marchibroda, Health Policy Brief: Interoperability, Health Aff. 1, 2 (2014).

67 Sharona Hoffman, Electronic Health Records And Medical Big Data: Law and Policy 18 (2016).

68 See Konnoth, Craig & Scheffler, Gabriel, Can Electronic Health Records Be Saved?, 46 Am. J. L. & Med. 7, 9 (2020)Google ScholarPubMed.

69 See Sabyasachi Dash et al., Big Data in Healthcare: Management, Analysis and Future Prospects, 6 J. Big Data, 2019, at 1, 3.

70 Ricardo Alonso-Zaldivar, Government headed for close to half of nation's health tab, AP (Feb. 20, 2019), https://apnews.com/5dc460ae8d8b4c8a93c6c3108fd71e9c [https://perma.cc/GT9YDFL8?type=image].

71 See Eleanor D. Kinney, Medicare Payment to Hospitals for a Return on Capital: The Influence of Federal Budget Policy on Judicial Decision-Making, 11 J. Contemp. L. 453, 454–61 (1985).

72 Judith Mistichelli, Diagnosis Related Groups (DRGs) and the Prospective Payment System: Forecasting Social Implications, 4 Bioethical Issues: Scope Notes, June 1984, at 1, 1.

73 Id.

74 Kinney, supra note 71, at 455-57.

75 Id. at 456-57.

76 Rick Mayes, The Origins, Development, and Passage of Medicare's Revolutionary Prospective Payment System, 62 J. Hist. Med. & Allied Sci. 21, 21 (2007).

77 Natasa Mihailovic et al., Review of Diagnosis-Related Group-Based Financing of Hospital Care, 3 Health Servs. Res. & Managerial Epidemiology, 2016, at 1, 4.

78 Inst. of Med., Crossing The Quality Chasm, supra note 27.

79 See Kinney, Eleanor D., The Accidental Administrative Law of the Medicare Program, 15 Yale J. Health Pol'y, L. & Ethics 111, 134 (2015)Google ScholarPubMed.

80 Inst. of Med., Crossing The Quality Chasm, supra note 27, at 135–37.

81 “The Hospital VBP Program rewards acute care hospitals with incentive payments for the quality of care provided in the inpatient hospital setting. This program adjusts payments to hospitals under the Inpatient Prospective Payment System (IPPS) based on the quality of care they deliver, paying based on the quality of care provided to Medicare patients.” The Hospital Value-Based Purchasing (VBP) Program, Ctrs for Medicare & Medicaid Servs., https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/HVBP/Hospital-Value-Based-Purchasing [https://perma.cc/5YNW-H522]. The Hospital VBP Program “withholds participating hospitals' Medicare payments by a percentage specified by law (2%); uses these reductions to fund incentive payments to hospitals based on their performance; applies the net result of the reduction and the incentive as a claim-by-claim adjustment factor to the base operating Medicare severity diagnosis-related group (MS-DRG) payment amount for Medicare fee-for-service claims in the fiscal year associated with the performance period.” Id. The measures in the VBP Program include “mortality and complications; healthcare-associated infections; patient safety; patient experience; process; and efficiency and cost reduction.” Id.

82 Inst. of Med., Crossing The Quality Chasm, supra note 27, at 33–35.

83 Hospital-Acquired Condition Reduction Program (HACRP), Ctrs. for Medicare & Medicaid Servs. (Jul. 16, 2019), https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/HAC-Reduction-Program.html [https://perma.cc/82Y8-3AC8].

84 Fact Sheet on Patient Safety Indicators, Agency Healthcare Research & Quality, https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/systems/hospital/qitoolkit/combined/a1b_combo_psifactsheet.pdf [https://perma.cc/RY8M-4LBV] [hereinafter Fact Sheet on Patient Safety].

85 Hospital-Acquired Condition Reduction Program Fiscal Year 2020 Fact Sheet, Ctrs. for Medicare & Medicaid Servs., (Jul. 2019), https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Downloads/HAC-Reduction-Program-Fact-Sheet.pdf [https://perma.cc/Z75UJXNR] [hereinafter HAC Reduction Program Fact Sheet].

86 See Jennifer Bresnick, Using Big Data Analytics for Patient Safety, Hospital Acquired Conditions, Health IT Analytics, (Aug. 6, 2018), https://healthitanalytics.com/features/using-big-data-analytics-for-patient-safety-hospital-acquired-conditions [https://perma.cc/MUK5-W4JR?type=image].

87 See, e.g., Jensen, Kasper et al., Analysis of Free Text in Electronic Health Records for Identification of Cancer Patient Trajectories, 7 Sci. Reps. 1, 7 (2017)Google ScholarPubMed, https://www.nature.com/articles/srep46226 [https://perma.cc/T3ML-QDBD] (noting that data-driven decision-making may decrease adverse events and readmissions, as well as provide higher quality care).

88 Id. (“By using these disease trajectories, we predict 80% of patient events ahead in time. … We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.”).

89 See Medicare “Pay for Performance (P4P)” Initiatives, Ctrs. for Medicare & Medicaid Servs. (Jan. 31, 2005), https://www.cms.gov/newsroom/fact-sheets/medicare-pay-performance-p4p-initiatives [https://perma.cc/8L7X-JQR7].

90 Hospital-Acquired Condition (HAC) Reduction Program, Ctrs. for Medicare & Medicaid Servs. (Jan. 6, 2020), https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/HAC/Hospital-Acquired-Conditions [https://perma.cc/26ZH-UX4G].

91 See Jessica Martin, Substantial Economic Burden Attributed to Recurrent Clostridium Difficile, Infectious Disease Advisor (Feb. 21, 2018), https://www.infectiousdiseaseadvisor.com/home/topics/gi-illness/clostridioidesdifficile/substantial-economic-burden-attributed-to-recurrent-clostridium-difficile/ [https://perma.cc/23SM-N37C] (explaining the money lost by hospitals for treatment of C. diff infections, i.e. the money lost by hospitals as a result of CMS' non-payment policy for HACs).

92 See Elizabeth A. Fehlberg et al., Impact of the CMS No-Pay Policy on Hospital-Acquired Fall Prevention Related Practice Patterns, 00 Innovation in Aging 1, 5–6 (2018), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6002153/pdf/igx036.pdf [https://perma.cc/JK84-SRBK] (“The CMS no-pay policy increased utilization of fall prevention strategies despite little evidence that these measures prevent falls.”).

93 Gandhi, Tejal K. et al., Patient Safety at the Crossroads, 315 JAMA 1829, 1829 (2016)CrossRefGoogle ScholarPubMed.

94 See Gerard Anderson et al., Medicare Payment Reform: Aligning Incentives for Better Care, Commonwealth Fund, (June 29, 2015), https://www.commonwealthfund.org/publications/issue-briefs/2015/jun/medicare-payment-reform-aligning-incentives-better-care [https://perma.cc/2S2M-JJ3K].

95 See id. (discussing penalties for hospitals with poor performance or higher-than-expected rates of hospital-acquired conditions).

96 See Jessica Kent, Medicare ACOs Use Analytics for Care Coordination, Population Health, Health IT Analytics (May 29, 2019), https://healthitanalytics.com/news/medicare-acos-use-analytics-for-care-coordination-population-health [https://perma.cc/D6V5-EJ9W].

97 See generally Ctrs. for Medicaid & Medicare Servs., Roadmap for Implementing Value Driven Healthcare in The Traditional Medicare Fee-for-Service Program, https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/downloads/VBPRoadmap_OEA_1-16_508.pdf [https://perma.cc/LJ27-7J6M].

98 See Raghupathi, Wullianallur & Raghupathi, Viju, Big Data Analytics in Healthcare: Promise and Potential, 2 Health Info. Sci. & Sys. 1, 9 (2014)Google ScholarPubMed.

99 Maddox, Thomas et al., Questions for Artificial Intelligence in Health Care, 321 JAMA 31, 31 (2019)CrossRefGoogle ScholarPubMed.

100 Id. at 31.

101 Schulte & Fry, supra note 44.

102 See Palabindala, Venkataraman et al., Adoption of Electronic Health Records and Barriers, 6 J. Community Hosp. Internal Med. Persp. 1, 1 (2016)Google ScholarPubMed.

103 Fact Sheet on Patient Safety, supra note 84.

104 Id.

105 HAC Reduction Program Fact Sheet, supra note 85.

106 See Zohrabian, Armineh et al., The Economic Case for U.S. Hospitals to Revise Their Approach to Heart Failure Readmission Reduction, 6 Annals Translational Med. 298, 299 (2018)CrossRefGoogle Scholar.

107 Winters, Bradford D. et al., Validity of the Agency for Health Care Research and Quality Patient Safety Indicators and the Centers for Medicare and Medicaid Services Hospital-Acquired Conditions, 54 Med. Care 1105, 1107 (2016)CrossRefGoogle Scholar (finding that though only one Patient Safety Indicator met the study's proposed threshold for being a valid test, the results were limited because a meta-analysis was only able to be performed for five of the indicators due to a lack of data).

108 See id. at 1106; see also Austin, supra note 106, at 429.

109 See Asta Sorensen et al., HAC-POA Policy Effects on Hospitals, Other Payers, and Patients, 4 Medicare & Medicaid Res. Rev. E1, E1 (2014).

110 See id. at E11.

111 Id. at E7.

112 Sheetz, Kyle H. et al., Hospital-Acquired Condition Reduction Program Is Not Associated With Additional Patient Safety Improvement, 38 Health Aff. 1858, 1858 (2019)CrossRefGoogle Scholar (examining rates of hospital-acquired conditions from 2010 to 2018 from a large surgical collaborative in Michigan, and concluding, “the program did not improve patient safety in Michigan beyond existing trends” and “[t]hese findings raise questions about whether the program will lead to improvements in patient safety as intended”); see also Mendelson, Aaron et al., The Effects of Pay-for-Performance Programs on Health, Health Care Use, and Processes of Care: A Systematic Review, 166 Annals Internal Med. 341, 341 (2017)CrossRefGoogle ScholarPubMed (“In the hospital setting, there was low-strength evidence that P4P had little or no effect on patient health outcomes and a positive effect on reducing hospital readmissions …. [P4P] programs may be associated with improved processes of care in ambulatory settings, but consistently positive associations with improved health outcomes have not been demonstrated in any setting.”).

113 Sorensen et al., supra note 109, at 6–7.

114 Common Hospital Safety Measures Are Often Misleading to Public, Johns Hopkins Univ. (May 16, 2016), https://hub.jhu.edu/2016/05/16/hospital-safety-measures-accuracy [https://perma.cc/2S5U-2UM5].

115 Winters, supra note 107, at 1105.

116 See Roshun Sankaran et al., Changes in Hospital Safety Following Penalties in the U.S. Hospital Acquired Condition Reduction Program, 366 BMJ l4109 (2019).

117 Maddox et al., supra note 99, at 31.

118 Id.

119 Id.

120 For a more thorough treatment of EHRs and data problems, see Furrow, supra note 4.

121 Id.

122 Pronovost recently outlined additional fixes that could be implemented by the rating community in a commentary published in the April 2016 issue of JAMA. See Jha, Ashish & Pronovost, Peter, Toward a Safer Health Care System, 315 JAMA 1831, 1831 (2016)CrossRefGoogle Scholar (designating a separate reporting entity to establish standards for data collection and making funds available for systems engineering research were listed as possible starting points).

123 Wadhera, Rishi K. et al., The Hospital Readmissions Reduction Program—Time for a Reboot, 380 New Eng. J. Med. 2289, 2291 (2019)CrossRefGoogle ScholarPubMed.

124 See Tucker, Anita L. & Edmondson, Amy C., Why Hospitals Don't Learn from Failures: Organizational and Psychological Dynamics that Inhibit System Change, 45 Cal. Mgmt. Rev. 55, 68 (2003)Google Scholar.

125 Jake Miller, Pay-for-Performance Fails to Perform, Harvard Medical School (Nov. 27, 2017), https://hms.harvard.edu/news/pay-performance-fails-perform [https://perma.cc/PR5K-R4SX].

126 Thomas, Eric J. & Brennan, Troyen A., Incidence and Types Of Preventable Adverse Events In Elderly Patients: Population Based Review Of Medical Records, 320 BMJ 741, 743 (2000)CrossRefGoogle ScholarPubMed.

127 Id.

128 Id.; see also Michelle M. Mello & Allen Kachalia, Medical Malpractice: Evidence on Reform Alternatives and Claims Involving Elderly Patients, A Report to the Medicare Payment Advisory Commission, MedPAC 1, 31 (2016).

129 Id. at 1, 23-26.

130 Id. at 22, 25.

131 Id. at 32, 37.

132 Myungho Paik et al., How Do the Elderly Fare in Medical Malpractice Litigation, Before and After Tort Reform? Evidence from Texas, 14 Am. L. & Econ. Rev. 561, 597–98 (2012).

133 See Furrow, supra note 6 at 16–18.

134 Id.

135 See, e.g., Watson, Kenneth & Kottenhagen, Rob, Patients' Rights, Medical Error and Harmonisation of Compensation Mechanisms in Europe, 25 Eur. J. Health L. 1, 10 (2018)Google Scholar.

136 See generally Eleanor D. Kinney & William M. Sage, Dances with Elephants: Administrative Resolution of Medical Injury Claims by Medicare Beneficiaries, 5 Ind. Health L. Rev. 1 (2008).

137 Id. at 7.

138 See Ctr. for Medicare & Medicaid Serv., Report to Congress The Administration, Cost, and Impact of the Quality Improvement Organization Program for Medicare Beneficiaries for Fiscal Year 2018 1 (2018).

139 See, e.g., Allen Kachalia et al., Effects Of A Communication-And-Resolution Program On Hospitals' Malpractice Claims And Costs, 37 Health Aff. 1836 (2018) (describing CRP programs, in which hospitals communicate transparently with patients after adverse events, investigate what happened and offer an explanation, and, when warranted, apologize, take responsibility, and proactively offer compensation, and finding that CRP programs improve care while not increasing hospital liability costs).

140 Randall R. Bovbjerg & Laurence R. Tancredi, Liability Reform Should Make Patients Safer: “Avoidable Classes of Events” are a Key Improvement, 33 J. L. Med. & Ethics 478,479 (2005).

141 Randall R. Bovbjerg, Reform of Medical Liability and Patient Safety: Are Health Courts and Medicare the Keys to Effective Change?, 9 J. Health Care L. & Pol'y 252 (2006) (considering the viability of changing Medicare benefit structure “… to provide more assistance through ordinary program operations, not through injury adjudication”).

142 William M. Sage & Eleanor D. Kinney, Medicare-Led Malpractice Reform, in Medical Malpractice and the U.S. Health Care System 318 (William M. Sage & Rogan Kersh eds., 2006).

143 See Mello, Michelle et al., “Health Courts” and Accountability for Patient Safety, 84 Milbank Q. 459, 462 (2006)CrossRefGoogle ScholarPubMed (states would have to pass enabling legislation to create such health courts).

144 See Barry R. Furrow, Searching for Adverse Events: Big Data and Beyond, 27 Annals Health L. 149 (2018).

145 Sage, William M., The Role of Medicare in Medical Malpractice Reform, 9 J. Health Care L. & Pol'y 217, 221 (2006)Google Scholar.

146 See Ziad Obermeyer & Ezekiel J. Emanuel, Predicting the Future – Big Data, Machine Learning, and Clinical Medicine, 375 New Eng. J. Med. 1216 (2016).

147 Crigger, Elliott & Khoury, Christopher, Making Policy on Augmented Intelligence in Health Care, 21 AMA J. Ethics 188, 190 (2019)Google ScholarPubMed.

148 42 C.F.R. § 482.12(a)(5) (2019).

149 See Vogus, Timothy J. et al., Doing No Harm: Enabling, Enacting, and Elaborating a Culture of Safety in Health Care, 24 Acad. Mgmt. Persp. 60, 71 (2010)Google Scholar.

150 Jiang, H. Joanna et al., Board Oversight of Quality: Any Differences in Process of Care and Mortality?, 54 J. Healthcare Mgmt. 15, 15 (2009)CrossRefGoogle ScholarPubMed.

151 Id. at 15.

152 Furrow, Barry R., Patient Safety and The Fiduciary Hospital: Sharpening Judicial Remedies, 1 Drexel L. Rev. 439, 469 (2009)Google Scholar.

153 See, e.g., Faunce, T.A. & Bolsin, S.N., Fiduciary Disclosure of Medical Mistakes: The Duty to Promptly Notify Patients of Adverse Health Events, 12 J. L. & Med. 478, 479 (2005)Google Scholar.

154 Furrow, supra note 152.

155 Cheryl L. Wagonhurst & M. Leeann Habte, Health Care Boards of Directors' Legal Responsibilities for Quality, Health Care Compliance Ass'n, (December 2008), https://www.foley.com/-/media/files/insights/publications/2008/12/health-care-boards-of-directors-legal-responsibili/files/health-care-boards-of-directors-legal-responsibili/fileattachment/ct1208_wagonhursthabte.pdf.

156 Thomas C. Tsai et al, Hospital Board and Management Practices Are Strongly Related to Hospital Performance on Clinical Quality Metrics, 34 Health Affairs 1304 (2015).

157 Chervenak, Frank A. et al, Physicians and Hospital Managers as Cofiduciaries of Patients: Rhetoric or Reality, 48 J. Healthcare Mgmt. 172, 173 (2003)CrossRefGoogle ScholarPubMed.

158 Thomas, supra note 8, at 17–19.