Artificial Intelligence for Healthcare Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
Part II - Optimizing Healthcare Systems
Published online by Cambridge University Press: 21 April 2022
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Artificial Intelligence for HealthcareInterdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World, pp. 81 - 82Publisher: Cambridge University PressPrint publication year: 2022
References
References
Arria, A. M., Winick, E. R., Garnier-Dykstra, L. M., et al. (2011). Help seeking and mental health service utilization among college students with a history of suicide ideation. Psychiatric Services, 62(12), 1510–1513. https://doi.org/10.1176/appi.ps.005562010Google Scholar
Becker, M. A. S., & Drum, D. J. (2015). The influence of suicide prevention gatekeeper training on resident assistants’ mental health. Journal of Student Affairs Research and Practice, 52(1), 76–88. https://doi.org/10.1080/19496591.2015.996055Google Scholar
Becker, S. P., Holdaway, A. S., & Luebbe, A. M. (2018). Suicidal behaviors in college students: frequency, sex differences, and mental health correlates including sluggish cognitive tempo. Journal of Adolescent Health, 63(2), 181–188.CrossRefGoogle ScholarPubMed
Benz, M. B., DiBello, A. M., Balestrieri, S. G., et al. (2017). Off-campus residence as a risk factor for heavy drinking among college students. Substance Use & Misuse, 52(9), 1236–1241. https://doi.org/10.1080/10826084.2017.1298620CrossRefGoogle ScholarPubMed
Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational & Mathematical Organization Theory, 12(1), 21–34. https://doi.org/10.1007/s10588–006-7084-xCrossRefGoogle Scholar
Boster, F. J., Kotowski, M. R., Andrews, K. R., & Serota, K. (2011). Identifying influence: development and validation of the Connectivity, Persuasiveness, and Maven scales. Journal of Communication, 61(1), 178–196. https://doi.org/10.1111/j.1460-2466.2010.01531.xGoogle Scholar
Bruffaerts, R., Mortier, P., Auerbach, R. P., et al. (2019). Lifetime and 12-month treatment for mental disorders and suicidal thoughts and behaviors among first year college students. International Journal of Methods in Psychiatric Research, 28(2), e1764. https://doi.org/10.1002/mpr.1764CrossRefGoogle ScholarPubMed
Burke, T. A., Ammerman, B. A., & Jacobucci, R. (2019). The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: a systematic review. Journal of Affective Disorders, 245, 869–884. https://doi.org/10.1016/j.jad.2018.11.073Google Scholar
Burnette, C., Ramchand, R., & Ayer, L. (2015). Gatekeeper training for suicide prevention. Rand Health Quarterly, 5(1). Retrieved from www.ncbi.nlm.nih.gov/pmc/articles/PMC5158249/Google Scholar
Busby, D. R., Zheng, K., Eisenberg, D., et al. (2021). Black college students at elevated risk for suicide: barriers to mental health service utilization. Journal of American College Health, 69(3), 308–314. https://doi.org/10.1080/07448481.2019.1674316CrossRefGoogle ScholarPubMed
Center for Collegiate Mental Health. (2009). 2009 Annual Report. https://ccmh.psu.edu/assets/docs/2009_CCMH_Report-zwcspm.pdfGoogle Scholar
Center for Collegiate Mental Health. (2019). 2019 Annual Report. https://ccmh.psu.edu/assets/docs/2019-CCMH-Annual-Report_3.17.20.pdfGoogle Scholar
Centers for Disease Control and Prevention (CDC). (2019, September 11). Preventing suicide |violence prevention|injury center|CDC. www.cdc.gov/violenceprevention/suicide/fastfact.htmlGoogle Scholar
Coleman, D., Black, N., Ng, J., & Blumenthal, E. (2019). Kognito’s avatar-based suicide prevention training for college students: results of a randomized controlled trial and a naturalistic evaluation. Suicide and Life-Threatening Behavior, 49(2). https://doi.org/10.1111/sltb.12550Google Scholar
Cross, W., Matthieu, M. M., Lezine, D., & Knox, K. L. (2010). Does a brief suicide prevention gatekeeper training program enhance observed skills? Crisis, 31(3), 149–159. https://doi.org/10.1027/0227-5910/a000014Google Scholar
Deane, F. P., Wilson, C. J., & Ciarrochi, J. (2001). Suicidal ideation and help‐negation: not just hopelessness or prior help. Journal of Clinical Psychology, 57(7), 901–914.Google Scholar
Dennis, M. J. (2017). Bumpy road ahead for U.S. higher education. Enrollment Management Report, 21(9), 1–3. https://doi.org/10.1002/emt.30362Google Scholar
Downs, M. F., & Eisenberg, D. E. (2012). Help seeking and treatment use among suicidal college students. Journal of American College Health, 60(2), 104–114. https://doi.org/10.1080/07448481.2011.619611CrossRefGoogle ScholarPubMed
Drum, D. J., Brownson, C., Burton Denmark, A., & Smith, S. E. (2009). New data on the nature of suicidal crises in college students: shifting the paradigm. Professional Psychology: Research and Practice, 40(3), 213–222. https://doi.org/10.1037/a0014465Google Scholar
Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., & Vanderford, N. L. (2018). Evidence for a mental health crisis in graduate education. Nature Biotechnology, 36(3), 282–284. https://doi.org/10.1038/nbt.4089CrossRefGoogle ScholarPubMed
Everett, D. D., & Loftus, Z. V. (2011). Resident assistants as rule enforcers versus friends: an exploratory study of role conflict. Journal of College and University Student Housing, 37(2), 72–89.Google Scholar
Ford-Paz, R. E., Kuebbeler, A., Contreras, R., Garduño, M., & Sánchez, B. (2015). Training community opinion leaders to raise awareness and promote early intervention for depressed Latino adolescents. Progress in Community Health Partnerships: Research, Education, and Action, 9(2), 191–201. https://doi.org/10.1353/cpr.2015.0039CrossRefGoogle ScholarPubMed
Franklin, J., Ribeiro, J., Fox, K., et al. (2017). Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187–232. https://doi.org/10.1037/bul0000084Google Scholar
Fulginiti, A., Pahwa, R., Frey, L. M., Rice, E., & Brekke, J. S. (2016). What factors influence the decision to share suicidal thoughts? a multilevel social network analysis of disclosure among individuals with serious mental illness. Suicide and Life-Threatening Behavior, 46(4), 398–412. https://doi.org/10.1111/sltb.12224Google Scholar
Fulginiti, A., Rice, E., Hsu, H.-T., Rhoades, H., & Winetrobe, H. (2016). Risky integration. Crisis, 37(3), 184–193. https://doi.org/10.1027/0227-5910/a000374CrossRefGoogle ScholarPubMed
Goldsmith, S. K., Pellmar, T. C., Kleinman, A. M., & Bunney, W. E. (Series Eds.). (2002). Reducing Suicide: A National Imperative. Washington, DC: National Academies Press.Google Scholar
Goodwill, J. R., & Zhou, S. (2020). Association between perceived public stigma and suicidal behaviors among college students of color in the U.S. Journal of Affective Disorders, 262, 1–7. https://doi.org/10.1016/j.jad.2019.10.019Google Scholar
Han, B., Compton, W. M., Eisenberg, D., et al. (2016). Prevalence and mental health treatment of suicidal ideation and behavior among college students aged 18–25 years and their non-college-attending peers in the United States. Journal of Clinical Psychiatry, 77(6), 815–824. https://doi.org/10.4088/JCP.15m09929Google Scholar
Isaac, M., Elias, B., Katz, L. Y., et al. (2009). Gatekeeper training as a preventative intervention for suicide: a systematic review. The Canadian Journal of Psychiatry, 54(4), 260–268. https://doi.org/10.1177/070674370905400407Google Scholar
Kisch, J., Leino, E. V., & Silverman, M. M. (2005). Aspects of suicidal behavior, depression, and treatment in college students: results from the Spring 2000 National College Health Assessment Survey. Suicide and Life-Threatening Behavior, 35(1), 3–13.Google Scholar
Li, W., Dorstyn, D. S., & Jarmon, E. (2020). Identifying suicide risk among college students: a systematic review. Death Studies, 44(7), 450–458. https://doi.org/10.1080/07481187.2019.1578305CrossRefGoogle ScholarPubMed
Lipson, S. K., Kern, A., Eisenberg, D., & Breland-Noble, A. M. (2018). Mental health disparities among college students of color. Journal of Adolescent Health, 63(3), 348–356. https://doi.org/10.1016/j.jadohealth.2018.04.014Google Scholar
Litteken, C., & Sale, E. (2018). Long-term effectiveness of the Question, Persuade, Refer (QPR) Suicide Prevention Gatekeeper Training Program: lessons from Missouri. Community Mental Health Journal, 54(3), 282–292. https://doi.org/10.1007/s10597-017-0158-zCrossRefGoogle ScholarPubMed
Marin, A., & Hampton, K. N. (2007). Simplifying the personal network name generator: alternatives to traditional multiple and single name generators. Field Methods, 19(2), 163–193. https://doi.org/10.1177/1525822X06298588Google Scholar
McCarthy, K. (2020). Resident assistant secondary trauma and burnout associated with student nonsuicidal self-injury. Journal of American College Health, 68(7), 673–677. https://doi.org/10.1080/07448481.2019.1590374Google Scholar
McLaughlin, W. G. (2018). Overloaded and overlooked: improving resident advisors’ self-care. Journal of American College Health, 66(8), 831–833. https://doi.org/10.1080/07448481.2018.1440573CrossRefGoogle ScholarPubMed
Michelmore, L., & Hindley, P. (2012). Help-seeking for suicidal thoughts and self-harm in young people: a systematic review. Suicide and Life-Threatening Behavior, 42(5), 507–524. https://doi.org/10.1111/j.1943-278X.2012.00108.xGoogle Scholar
Midorikawa, H., Tachikawa, H., Nemoto, K., et al. (2020). Mental health of gatekeepers may influence their own attitudes toward suicide: a questionnaire survey from a suicide-prevention gatekeeper training program. Asian Journal of Psychiatry, 47, 101842. https://doi.org/10.1016/j.ajp.2019.101842Google Scholar
Mo, P. K. H., Ko, T. T., & Xin, M. Q. (2018). School-based gatekeeper training programmes in enhancing gatekeepers’ cognitions and behaviours for adolescent suicide prevention: a systematic review. Child and Adolescent Psychiatry and Mental Health, 12(1), 29. https://doi.org/10.1186/s13034-018-0233-4Google Scholar
Moore, J. T., Cigularov, K. P., Chen, P. Y., Martinez, J. M., & Hindman, J. (2011). The effects of situational obstacles and social support on suicide-prevention gatekeeper behaviors. Crisis, 32(5), 264–271. https://doi.org/10.1027/0227-5910/a000090Google Scholar
Mortier, P., Auerbach, R. P., Alonso, J., et al. (2018). Suicidal thoughts and behaviors among college students and same-aged peers: results from the World Health Organization World Mental Health Surveys. Social Psychiatry and Psychiatric Epidemiology, 53(3), 279–288.CrossRefGoogle ScholarPubMed
Mortier, P., Cuijpers, P., Kiekens, G., et al. (2018). The prevalence of suicidal thoughts and behaviours among college students: a meta-analysis. Psychological Medicine, 48(4), 554–565.Google Scholar
Paladino, D. A., Murray, T., Newgent, R. A., & Goiin, L. A. (2005). Resident assistant burnout: factors impacting depersonalization, emotional exhaustion, and personal accomplishment. Journal of College and University Student Housing, 33, 18–27.Google Scholar
Paul, E., Tsypes, A., Eidlitz, L., Ernhout, C., & Whitlock, J. (2015). Frequency and functions of non-suicidal self-injury: associations with suicidal thoughts and behaviors. Psychiatry Research, 225(3), 276–282. https://doi.org/10.1016/j.psychres.2014.12.026Google Scholar
Pickering, T. A., Wyman, P. A., Schmeelk-Cone, K., et al. (2018). Diffusion of a peer-led suicide preventive intervention through school-based student peer and adult networks. Frontiers in Psychiatry, 9. https://doi.org/10.3389/fpsyt.2018.00598CrossRefGoogle ScholarPubMed
Pisani, A. R., Schmeelk-Cone, K., Gunzler, D., et al. (2012). Associations between suicidal high school students’ help-seeking and their attitudes and perceptions of social environment. Journal of Youth and Adolescence, 41(10), 1312–1324. https://doi.org/10.1007/s10964-012-9766-7CrossRefGoogle ScholarPubMed
Porter, S. C., & Newman, L. (2016). A brief measure of attitudes toward resident advisors. College Student Journal, 50(1), 107–111.Google Scholar
Rahmattalabi, A., Vayanos, P., Fulginiti, A., Rice, E., et al (2019). Exploring algorithmic fairness in robust graph covering problems. In H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32, 15750–15761. http://papers.nips.cc/paper/9707-exploring-algorithmic-fairness-in-robust-graph-covering-problems.pdf.Google Scholar
Rahmattalabi, A., Vayanos, P., Fulginiti, A., & Tambe, M. (2019). Robust peer-monitoring on graphs with an application to suicide prevention in social networks. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2168–2170. http://dl.acm.org/citation.cfm?id=3306127.3332046Google Scholar
Reetz, D. R., Barr, V., & Krylowicz, B. (2014). The Association of University and College Counseling Center Directors Annual Survey. www.aucccd.org/assets/documents/2014%20aucccd%20monograph%20-%20public%20pdf.pdfGoogle Scholar
Reingle, J., Thombs, D., Osborn, C., Saffian, S., & Oltersdorf, D. (2010). Mental health and substance use: a qualitative study of resident assistants’ attitudes and referral practices. Journal of Student Affairs Research and Practice, 47(3), 325–342. https://doi.org/10.2202/1949-6605.6016CrossRefGoogle Scholar
Rice, E., Yoshioka-Maxwell, A., Petering, R., et al. (2018). Piloting the use of artificial intelligence to enhance HIV prevention interventions for youth experiencing homelessness. Journal of the Society for Social Work and Research, 9(4), 551–573. https://doi.org/10.1086/701439Google Scholar
Rickwood, D., Deane, F. P., Wilson, C. J., & Ciarrochi, J. (2005). Young people’s help-seeking for mental health problems. Australian E-Journal for the Advancement of Mental Health, 4(3), 218–251. https://doi.org/10.5172/jamh.4.3.218CrossRefGoogle Scholar
Robinson-Link, N., Hoover, S., Bernstein, L., et al. (2019). Is gatekeeper training enough for suicide prevention? School Mental Health 12(2): 239–249. https://doi.org/10.1007/s12310-019-09345-xGoogle Scholar
Rudd, M. D., Joiner, T. E., & Rajab, M. H. (1995). Help negation after acute suicidal crisis. Journal of Consulting and Clinical Psychology, 63(3), 499–503.Google Scholar
Schneider, J. A., Zhou, A. N., & Laumann, E. O. (2015). A new HIV prevention network approach: sociometric peer change agent selection. Social Science & Medicine, 125, 192–202. https://doi.org/10.1016/j.socscimed.2013.12.034Google Scholar
Shtivelband, A., Aloise-Young, P. A., & Chen, P. Y. (2015). Sustaining the effects of gatekeeper suicide prevention training. Crisis, 36(2), 102–109. https://doi.org/10.1027/0227-5910/a000304CrossRefGoogle ScholarPubMed
Taub, D. J., Servaty‐Seib, H. L., Miles, N., et al. (2013). The impact of gatekeeper training for suicide prevention on university resident assistants. Journal of College Counseling, 16(1), 64–78. https://doi.org/10.1002/j.2161-1882.2013.00027.xGoogle Scholar
Tompkins, T. L., & Witt, J. (2009). The short-term effectiveness of a suicide prevention gatekeeper training program in a college setting with residence life advisers. The Journal of Primary Prevention, 30(2), 131–149. https://doi.org/10.1007/s10935-009-0171-2CrossRefGoogle Scholar
Turner, J. C., Leno, E. V., & Keller, A. (2013). Causes of mortality among American college students: a pilot study. Journal of College Student Psychotherapy, 27(1), 31–42.Google Scholar
Twale, D. J., & Burrell, L. F. (1994). Resident assistants on Black and White campuses assess resident student problems. Journal of College Student Development, 35(1), 29–34.Google Scholar
U.S. Census Bureau. (2010). Group quarters population in college/ university housing. 2010 Census Congressional District Summary File (113th Congress). http://factfinder2.census.gov.Google Scholar
U.S. Department of Health and Human Services (HHS) Office of the Surgeon General and National Action Alliance for Suicide Prevention. (2012, September). 2012 National Strategy for Suicide Prevention: Goals and Objectives for Action. Washington, DC: HHS. www.ncbi.nlm.nih.gov/books/NBK109917/Google Scholar
Valente, T. W. (2010). Social Networks and Health: Models, Methods, and Applications. Oxford University Press.Google Scholar
Valente, T. W. (2012). Network interventions. Science, 337(6090), 49–53. https://doi.org/10.1126/science.1217330Google Scholar
Valente, T. W. (2017). Putting the network in network interventions. Proceedings of the National Academy of Sciences of the United States of America, 114(36), 9500–9501. https://doi.org/10.1073/pnas.1712473114Google Scholar
Valente, T. W., & Pumpuang, P. (2007). Identifying opinion leaders to promote behavior change. Health Education & Behavior, 34(6), 881–896. https://doi.org/10.1177/1090198106297855Google Scholar
Van Orden, K. A., Witte, T. K., Cukrowicz, K. C., et al. (2010). The interpersonal theory of suicide. Psychological Review, 117(2), 575–600. https://doi.org/10.1037/a0018697CrossRefGoogle ScholarPubMed
Walrath, C., Garraza, L. G., Reid, H., Goldston, D. B., & McKeon, R. (2015). Impact of the Garrett Lee Smith Youth Suicide Prevention Program on suicide mortality. American Journal of Public Health, 105(5), 986–993. https://doi.org/10.2105/AJPH.2014.302496Google Scholar
Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3), 457–469. https://doi.org/10.1177/2167702617691560Google Scholar
Wolitzky-Taylor, K., LeBeau, R. T., Perez, M., Gong-Guy, E., & Fong, T. (2020). Suicide prevention on college campuses: what works and what are the existing gaps? A systematic review and meta-analysis. Journal of American College Health, 68(4), 419–429. https://doi.org/10.1080/07448481.2019.1577861CrossRefGoogle ScholarPubMed
Wyman, P., Brown, C. H., Inman, J., et al. (2008). Randomized trial of a gatekeeper program for suicide prevention: 1-year impact on secondary school staff. Journal of Consulting and Clinical Psychology, 76(1), 104–115.CrossRefGoogle ScholarPubMed
Wyman, P. A., Brown, C. H., LoMurray, M., et al. (2010). An outcome evaluation of the sources of strength suicide prevention program delivered by adolescent peer leaders in high schools. American Journal of Public Health, 100(9), 1653–1661. https://doi.org/10.2105/AJPH.2009.190025Google Scholar
Yonemoto, N., Kawashima, Y., Endo, K., & Yamada, M. (2019). Gatekeeper training for suicidal behaviors: a systematic review. Journal of Affective Disorders, 246, 506–514. https://doi.org/10.1016/j.jad.2018.12.052Google Scholar
Zinzow, H. M., Thompson, M. P., Fulmer, C. B., Goree, J., & Evinger, L. (2020). Evaluation of a brief suicide prevention training program for college campuses. Archives of Suicide Research, 24(1), 82–95. https://doi.org/10.1080/13811118.2018.1509749Google Scholar
References
Aufderheide, T., Hazinski, M. F., Nichol, G., et al. (2006). Community lay rescuer automated external defibrillation programs: key state legislative components and implementation strategies: a summary of a decade of experience for healthcare providers, policymakers, legislators, employers, and community leaders from the American Heart Association Emergency Cardiovascular Care Committee, Council on Clinical Cardiology, and Office of State Advocacy. Circulation 113(9): 1260–1270.Google Scholar
Baldi, E., Sechi, G. M., Mare, C., et al. (2020). Out-of-hospital cardiac arrest during the Covid-19 outbreak in Italy. New England Journal of Medicine 383(5): 496–498.CrossRefGoogle ScholarPubMed
Becker, L. B., Han, B. H., Meyer, P. M., et al. (1993). Racial differences in the incidence of cardiac arrest and subsequent survival. The CPR Chicago Project. New England Journal of Medicine 329(9): 600–606.Google Scholar
Benjamin, E. J., Virani, S. S., Callaway, C. W., et al. (2018). Heart disease and stroke statistics – 2018 update: a report from the American Heart Association. Circulation 137(12): e67–e492.Google Scholar
Berdowski, J., Blom, M. T., Bardai, A., et al. (2011). Impact of onsite or dispatched automated external defibrillator use on survival after out-of-hospital cardiac arrest. Circulation 124(20): 2225–2232.Google Scholar
Boutilier, J. J., Brooks, S. C., Janmohamed, A., et al. (2017). Optimizing a drone network to deliver automated external defibrillators. Circulation 135(25): 2454–2465.Google Scholar
Brooks, S. C., Hsu, J. H., Tang, S. K., Jeyakumar, R. and Chan, T. C. (2013). Determining risk for out-of-hospital cardiac arrest by location type in a Canadian urban setting to guide future public access defibrillator placement. Annals of Emergency Medicine 61(5): 530–538e532.Google Scholar
Brooks, S. C., Simmons, G., Worthington, H., Bobrow, B. J. and Morrison, L. J. (2016). The PulsePoint Respond mobile device application to crowdsource basic life support for patients with out-of-hospital cardiac arrest: challenges for optimal implementation. Resuscitation 98: 20–26.Google Scholar
Caffrey, S. L., Willoughby, P. J., Pepe, P. E. and Becker, L. B. (2002). Public use of automated external defibrillators. New England Journal of Medicine 347(16): 1242–1247.Google Scholar
Chan, T. C., Li, H., Lebovic, G., et al. (2013). Identifying locations for public access defibrillators using mathematical optimization. Circulation 127(17): 1801–1809.Google Scholar
Chan, T. C. Y. (2017). Rise and shock: optimal defibrillator placement in a high-rise building. Prehospital Emergency Care 21(3): 309–314.CrossRefGoogle Scholar
Chan, T. C. Y., Demirtas, D. and Kwon, R. H. (2016). Optimizing the deployment of public access defibrillators. Management Science 62(12): 3617–3635.Google Scholar
Chan, T. C. Y., Shen, Z.-J. M. and Siddiq, A. (2018). Robust defibrillator deployment under cardiac arrest location uncertainty via row-and-column generation. Operations Research 66(2): 358–379.Google Scholar
Cheskes, S., Snobelen, P., McLeod, S., et al. (2019). AED on the fly: a drone delivery feasibility study for rural and remote out-of-hospital cardiac arrest. Circulation 140(Suppl_2): A147–A147.Google Scholar
Church, R. and Revelle, C. (1974). The maximal covering location problem. Papers of the Regional Science Association 32: 101–120.Google Scholar
Claesson, A., Backman, A., Ringh, M., et al. (2017). Time to delivery of an automated external defibrillator using a drone for simulated out-of-hospital cardiac arrests vs emergency medical services. JAMA 317(22): 2332–2334.Google Scholar
Cummins, R. O., Ornato, J. P., Thies, W. H. and Pepe, P. E. (1991). Improving survival from sudden cardiac arrest: the “chain of survival” concept. A statement for health professionals from the Advanced Cardiac Life Support Subcommittee and the Emergency Cardiac Care Committee, American Heart Association. Circulation 83(5): 1832–1847.Google Scholar
Davies, C. S., Colquhoun, M. C., Boyle, R. and Chamberlain, D. A. (2005). A national programme for on-site defibrillation by lay people in selected high risk areas: initial results. Heart 91(10): 1299–1302.Google Scholar
Daya, M. R., Schmicker, R. H., Zive, D. M., et al. (2015). Out-of-hospital cardiac arrest survival improving over time: results from the Resuscitation Outcomes Consortium (ROC). Resuscitation 91: 108–115.Google Scholar
Delage, E. and Ye, Y. (2010). Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research 58(3): 595–612.Google Scholar
Drennan, I. R., Strum, R. P., Byers, A., et al. (2016). Out-of-hospital cardiac arrest in high-rise buildings: delays to patient care and effect on survival. CMAJ 188(6): 413–419.Google Scholar
Edelson, D. P., Sasson, C., Chan, P. S., et al. (2020). Interim guidance for basic and advanced life support in adults, children, and neonates with suspected or confirmed COVID-19: from the emergency cardiovascular care committee and get with the guidelines-resuscitation adult and pediatric task forces of the American Heart Association. Circulation 141(25): e933–e943.CrossRefGoogle ScholarPubMed
Fedoruk, J. C., Currie, W. L., Gobet, M., Currie, W. L. and Gobet, M. (2002). Locations of cardiac arrest: affirmation for community Public Access Defibrillation (PAD) Program. Prehospital & Disaster Medicine 17(4): 202–205.Google Scholar
Folke, F., Lippert, F. K., Nielsen, S. L., et al. (2009). Location of cardiac arrest in a city center: Strategic placement of automated external defibrillators in public locations. Circulation 120(6): 510–517.CrossRefGoogle Scholar
Grasner, J. T., Lefering, R., Koster, R. W., et al. (2016). Corrigendum to “EuReCa ONE-27 Nations, ONE Europe, ONE Registry: a prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe” [Resuscitation 105 (2016) 188-195]. Resuscitation 109: 145–146.Google Scholar
Gratton, M., Lindholm, D. J. and Campbell, J. P. (1999). Public-access defibrillation: where do we place the AEDs? Prehospital Emergency Care 3(4): 303–305.Google Scholar
Gundry, J. W., Comess, K. A., DeRook, F. A., Jorgenson, D. and Bardy, G. H. (1999). Comparison of naive sixth-grade children with trained professionals in the use of an automated external defibrillator. Circulation 100(16): 1703–1707.Google Scholar
Hakimi, S. L. (1964). Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research 12(3): 450–459.Google Scholar
Hallstrom, A. P., Ornato, J. P., Weisfeldt, M., et al. (2004). Public-access defibrillation and survival after out-of-hospital cardiac arrest. New England Journal of Medicine 351(7): 637–646.Google Scholar
Hansen, C. M., Wissenberg, M., Weeke, P., et al. (2013). Automated external defibrillators inaccessible to more than half of nearby cardiac arrests in public locations during evening, nighttime, and weekends. Circulation 128(20): 2224–2231.Google Scholar
Hansen, C. M., Lippert, F. K., Wissenberg, M., et al. (2014). Temporal trends in coverage of historical cardiac arrests using a volunteer-based network of automated external defibrillators accessible to laypersons and emergency dispatch centers. Circulation 130(21): 1859–1867.Google Scholar
Hansen, S. M., Hansen, C. M., Folke, F., et al. (2017). Bystander defibrillation for out-of-hospital cardiac arrest in public vs residential locations.” JAMA Cardiology 2(5): 507–514.Google Scholar
Holmberg, M. J., Vognsen, M., Andersen, M. S., Donnino, M. W. and Andersen, L. W. (2017). Bystander automated external defibrillator use and clinical outcomes after out-of-hospital cardiac arrest: A systematic review and meta-analysis. Resuscitation 120: 77–87.Google Scholar
Homier, V., de Champlain, F., Nolan, M. and Fleet, R. (2019). Identification of swimmers in distress using unmanned aerial vehicles: experience at the Mont-Tremblant IRONMAN triathlon. Prehospital Emergency Care 24(3): 1–8.Google Scholar
Huang, C. Y. and Wen, T. H. (2014). Optimal installation locations for automated external defibrillators in Taipei 7-Eleven Stores: using GIS and a genetic algorithm with a new stirring operator. Computational and Mathematical Methods in Medicine 2014: 241435.Google Scholar
Iwami, T., Hiraide, A., Nakanishi, N., et al. (2006). Outcome and characteristics of out-of-hospital cardiac arrest according to location of arrest: a report from a large-scale, population-based study in Osaka, Japan. Resuscitation 69(2): 221–228.Google Scholar
Karlsson, L., Sondergaard, K., Malta Hansen, C., et al. (2017). Straight line versus route distance to nearest automated external defibrillator-implications for cardiac arrest coverage. European Heart Journal 38(Suppl 1).CrossRefGoogle Scholar
Karlsson, L., Hansen, C. M., Vourakis, C., et al. (2020). Improving bystander defibrillation in out-of-hospital cardiac arrests at home. European Heart Journal: Acute Cardiovascular Care 9(Suppl 4): S74–S81. 2048872619891675.Google Scholar
Kitamura, T., Iwami, T., Kawamura, T., et al. (2010). Nationwide public-access defibrillation in Japan. New England Journal of Medicine 362(11): 994–1004.Google Scholar
Kronick, S. L., Kurz, M. C., Lin, S., et al. (2015). Part 4: systems of care and continuous quality improvement: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 132(18 Suppl. 2): S397–413.Google Scholar
Lai, P. H., Lancet, E. A., Weiden, M. D., et al. (2020). Characteristics associated with out-of-hospital cardiac arrests and resuscitations during the novel coronavirus disease 2019 pandemic in New York City. JAMA Cardiology 5(10): 1154–1163.Google Scholar
Larsen, M., Eisenberg, M., Cummins, R. O. and Hallstrom, A. P. (1993). Predicting survival from out-of-hospital cardiac arrest: a graphic model. Annals of Emergency Medicine 22: 1652–1658.Google Scholar
Lateef, F. and Anantharaman, V. (2000). Delays in the EMS response to and the evacuation of patients in high-rise buildings in Singapore. Prehospital Emergency Care 4(4): 327–332.Google Scholar
Lee, M., Demirtas, D., Buick, J. E., et al. (2017). Increased cardiac arrest survival and bystander intervention in enclosed pedestrian walkway systems. Resuscitation 118: 1–7.Google Scholar
Lee, C.-T., Lee, Y.-C. and Chen, A. Y. (2019). In-building automated external defibrillator location planning and assessment through building information models. Automation in Construction 106: 102883.Google Scholar
Leung, K. B., Sun, C. L., Yang, M., et al. (2020). Optimal in-hospital defibrillator placement. Resuscitation 151: 91–98.CrossRefGoogle ScholarPubMed
Leung, K. H. B., Alam, R., Brooks, S. C. and Chan, T. C. Y. (2021). Public defibrillator accessibility and mobility trends during the COVID-19 pandemic in Canada. Resuscitation 162: 329–333.Google Scholar
Marenco, J. P., Wang, P. J., Link, M. S., Homoud, M. K. and Estes, N. A., 3rd (2001). Improving survival from sudden cardiac arrest: the role of the automated external defibrillator. JAMA 285(9): 1193–1200.Google Scholar
Mateen, F. J., Leung, K. B., Vogel, A. C., Cissé, A. F. and Chan, T. C. (2020). A drone delivery network for antiepileptic drugs: a framework and modelling case study in a low-income country. Transactions of The Royal Society of Tropical Medicine and Hygiene 114(4): 308–314.Google Scholar
Morrison, L. J., Angelini, M. P., Vermeulen, M. J. and Schwartz, B. (2005). Measuring the EMS patient access time interval and the impact of responding to high-rise buildings. Prehospital Emergency Care 9(1): 14–18.Google Scholar
Murakami, Y., Iwami, T., Kitamura, T., et al. (2014). Outcomes of out-of-hospital cardiac arrest by public location in the public-access defibrillation era. Journal of the American Heart Association 3(2): e000533.Google Scholar
Nichol, G., Thomas, E. and Callaway, C. W. (2008). Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA 300(12): 1423–1431.Google Scholar
Page, R. L., Joglar, J. A., Kowal, R. C., et al. (2000). Use of automated external defibrillators by a U.S. airline. New England Journal of Medicine 343(17): 1210–1216.Google Scholar
Perkins, G. D., Handley, A. J., Koster, R. W., et al. (2015). European Resuscitation Council Guidelines for Resuscitation 2015: Section 2. Adult basic life support and automated external defibrillation. Resuscitation 95: 81–99.Google Scholar
Pollack, R. A., Brown, S. P., Rea, T., et al. (2018). Impact of bystander automated external defibrillator use on survival and functional outcomes in shockable observed public cardiac arrests. Circulation 137(20): 2104–2113.Google Scholar
Pulver, A., Wei, R. and Mann, C. (2016). Locating AED enabled medical drones to enhance cardiac arrest response times. Prehospital Emergency Care 20(3): 378–389.Google Scholar
Reed, D. B., Birnbaum, A., Brown, L. H., et al. (2006). Location of cardiac arrests in the Public Access Defibrillation Trial. Prehospital Emergency Care 10(1): 61–67.Google Scholar
Reinier, K., Thomas, E., Andrusiek, D. L., et al. (2011). Socioeconomic status and incidence of sudden cardiac arrest. CMAJ 183(15): 1705–1712.Google Scholar
Ringh, M., Rosenqvist, M., Hollenberg, J., et al. (2015). Mobile-phone dispatch of laypersons for CPR in out-of-hospital cardiac arrest. New England Journal of Medicine 372(24): 2316–2325.Google Scholar
Robertson, R. M. (2000). Sudden death from cardiac arrest – improving the odds. New England Journal of Medicine 343(17): 1259–1260.Google Scholar
Sasson, C., Keirns, C. C., Smith, D., et al. (2010). Small area variations in out-of-hospital cardiac arrest: does the neighborhood matter? Annals of Internal Medicine 153(1): 19–22.Google Scholar
Sasson, C., Rogers, M. A., Dahl, J. and Kellermann, A. L. (2010). Predictors of survival from out-of-hospital cardiac arrest: a systematic review and meta-analysis. Circulation: Cardiovascular Quality and Outcomes 3(1): 63–81.Google Scholar
Siddiq, A. A., Brooks, S. C. and Chan, T. C. (2013). Modeling the impact of public access defibrillator range on public location cardiac arrest coverage. Resuscitation 84(7): 904–909.Google Scholar
Silverman, R. A., Galea, S., Blaney, S., et al. (2007). The “vertical response time”: barriers to ambulance response in an urban area. Academic Emergency Medicine 14(9): 772–778.Google Scholar
Smith, C. M., Wilson, M. H., Ghorbangholi, A., et al. (2017). The use of trained volunteers in the response to out-of-hospital cardiac arrest – the GoodSAM experience. Resuscitation 121: 123–126.Google Scholar
Stoesser, C., Boutilier, J., Sun, C. L., et al. (2018). The effect of response time on out-of-hospital cardiac arrest survival varies by patient subpopulation. Circulation 138(Suppl. 2): A249–A249.Google Scholar
Sun, C. L. F., Demirtas, D., Brooks, S. C., Morrison, L. J. and Chan, T. C. Y. (2016).Overcoming spatial and temporal barriers to public access defibrillators via optimization. Journal of the American College of Cardiology 68(8): 836–845.Google Scholar
Sun, C. L., Brooks, S. C., Morrison, L. J. and Chan, T. C. (2017). Ranking businesses and municipal locations by spatiotemporal cardiac arrest risk to guide public defibrillator placement. Circulation 135(12): 1104–1119.Google Scholar
Sun, C. L. F., Karlsson, L., Torp-Pedersen, C., et al. (2018). Spatiotemporal AED optimization is generalizable. Resuscitation 131: 101–107.Google Scholar
Sun, C. L., Karlsson, L., Torp-Pedersen, C., et al. (2019). In silico trial of optimized versus actual public defibrillator locations. Journal of the American College of Cardiology 74(12): 1557–1567.CrossRefGoogle ScholarPubMed
Sun, C. L., Karlsson, L., Morrison, L. J., et al. (2020). Effect of optimized versus guidelines‐based automated external defibrillator placement on out‐of‐hospital cardiac arrest coverage: an in silico trial. Journal of the American Heart Association 9: e016701.Google Scholar
Sun, C., Dyer, S., Salvia, J., Segal, L. and Levi, R. (2021). Worse cardiac arrest outcomes during the COVID-19 pandemic in Boston can be attributed to patient reluctance to seek care. Health Affairs (Millwood) 40(6): 101377hlthaff202100250.Google Scholar
Tierney, N. J., Reinhold, H. J., Mira, A., et al. (2018). Novel relocation methods for automatic external defibrillator improve out-of-hospital cardiac arrest coverage under limited resources. Resuscitation 125: 83–89.Google Scholar
Tsai, Y. S., Ko, P. C. I., Huang, C. Y. and Wen, T. H. (2012). Optimizing locations for the installation of automated external defibrillators (AEDs) in urban public streets through the use of spatial and temporal weighting schemes. Applied Geography 35(1-2): 394–404.Google Scholar
Valenzuela, T. D., Roe, D. J., Cretin, S., Spaite, D. W. and Larsen, M. P. (1997). Estimating effectiveness of cardiac arrest interventions – a logistic regression survival model. Circulation 96(10): 3308–3313.Google Scholar
Valenzuela, T. D., Roe, D. J., Nichol, G., et al. (2000). Outcomes of rapid defibrillation by security officers after cardiac arrest in casinos. New England Journal of Medicine 343(17): 1206–1209.Google Scholar
Weisfeldt, M. L., Sitlani, C. M., Ornato, J. P., et al. (2010). Survival after application of automatic external defibrillators before arrival of the emergency medical system: evaluation in the Resuscitation Outcomes Consortium population of 21 million. Journal of the American College of Cardiology 55(16): 1713–1720.Google Scholar
Wissenberg, M., Lippert, F. K., Folke, F., et al (2013). Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac arrest. JAMA 310(13): 1377–1384.Google Scholar
References
Andriole, GL, Crawford, ED, Grubb, RL III, et al. Mortality results from a randomized prostate-cancer screening trial. New England Journal of Medicine. 2009 Mar 26;360(13):1310–1319.Google Scholar
Andriole, GL, Crawford, ED, Grubb, RL III, et al. Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. Journal of the National Cancer Institute. 2012 Jan 6;104(2):125–132.Google Scholar
Arias, E. United States life tables 2006. National Vital Statistics Reports. 2010;58(21):1–40.Google Scholar
Ayer, T, Alagz, O, Stout, NK. A POMDP approach to personalize mammography screening decisions. Operations Research. 2012; 60(5):1019–1034.Google Scholar
Barnett, CL, Tomlins, SA, Underwood, DJ, et al. Two-stage biomarker protocols for improving the precision of early detection of prostate cancer. Medical Decision Making. 2017 Oct;37(7):815–826.Google Scholar
Carter, HB, Albertsen, PC, Barry, MJ, et al. Early detection of prostate cancer: AUA Guideline. The Journal of Urology. 2013 Aug;190(2):419–426.Google Scholar
Cassandra, AR, Kaelbling, LP, Littman, ML. Acting optimally in partially observable stochastic domains. Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, 1994: 1023–1028.Google Scholar
Draisma, G, Boer, R, Otto, SJ, et al. Lead times and overdetection due to prostate-specific antigen screening: estimates from the European Randomized Study of Screening for Prostate Cancer. Journal of the National Cancer Institute. 2003 Jun 18;95(12):868–878.Google Scholar
Erenay, FS, Alagoz, O, Said, A. Optimizing colonoscopy screening for colorectal cancer prevention and surveillance. Manufacturing & Service Operations Management. 2014; 16(3):381–400.Google Scholar
Haas, GP, Delongchamps, NB, Jones, RF, et al. Needle biopsies on autopsy prostates: sensitivity of cancer detection based on true prevalence. Journal of the National Cancer Institute. 2007 Oct 3;99(19):1484–1489.Google Scholar
Hamdy, FC, Donovan, JL, Lane, JA, et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. New England Journal of Medicine. 2016 Oct 13;375(15):1415–1424.Google Scholar
Hartigan, JA, Wong, MA. Algorithm AS 136: a k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics). 1979 Jan 1;28(1):100–108.Google Scholar
Heijnsdijk, EA, Wever, EM, Auvinen, A, et al. Quality-of-life effects of prostate-specific antigen screening. New England Journal of Medicine. 2012 Aug 16;367(7):595–605.Google Scholar
Liu, J, Womble, PR, Merdan, S, et al Factors influencing selection of active surveillance for localized prostate cancer. Urology. 2015 Nov 1;86(5):901–905.Google Scholar
Monahan, G. A survey of partially observable Markov decision processes: theory, models, and algorithms. Management Science. 1982; 28(1):1–16.Google Scholar
Moyer, VA. Screening for prostate cancer: US Preventive Services Task Force recommendation statement. Annals of Internal Medicine. 2012; 157(2):120–134.Google Scholar
Nguyen, CT, Yu, C, Moussa, A, Kattan, MW, Jones, JS. Performance of prostate cancer prevention trial risk calculator in a contemporary cohort screened for prostate cancer and diagnosed by extended prostate biopsy. The Journal of Urology. 2010 Feb;183(2):529–533.Google Scholar
Ries, LA, Young, JL Jr, Keel, GE, et al. Cancer survival among adults: US SEER program, 1988–2001. Patient and Tumor Characteristics SEER Survival Monograph Publication. 2007:07-6215.Google Scholar
Roehl, KA, Han, M, Ramos, CG, Antenor, JA, Catalona, WJ. Cancer progression and survival rates following anatomical radical retropubic prostatectomy in 3,478 consecutive patients: long-term results. The Journal of Urology. 2004 Sep;172(3):910–914.Google Scholar
Ross, KS, Carter, HB, Pearson, JD, Guess, HA. Comparative efficiency of prostate-specific antigen screening strategies for prostate cancer detection. JAMA. 2000 Sep 20;284(11):1399–1405.Google Scholar
Sanders, GD, Neumann, PJ, Basu, A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016 Sep 13;316(10):1093–1103.CrossRefGoogle ScholarPubMed
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Screening and prostate-cancer mortality in a randomized European study. New England Journal of Medicine. 2009 Mar 26;360(13):1320–1328.Google Scholar
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Prostate-cancer mortality at 11 years of follow-up. New England Journal of Medicine. 2012 Mar 15;366(11):981–990.Google Scholar
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. The Lancet. 2014 Dec 6;384(9959):2027–2035.Google Scholar
Simmons Ivy, J, Black Nembhard, H, Baran, K. Quantifying the impact of variability and noise on patient outcomes in breast cancer decision making. Quality Engineering. 2009; 21(3):319–334.Google Scholar
Smallwood, RD, Sondik, EJ. The optimal control of partially observable Markov processes over a finite horizon. Operations Research. 1973 Oct;21(5):1071–1088.CrossRefGoogle Scholar
Thompson, IM, Ankerst, DP, Chi, C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. Journal of the National Cancer Institute. 2006 Apr 19;98(8):529–534.Google Scholar
Tomlins, SA, Day, JR, Lonigro, RJ, et al. Urine TMPRSS2:ERG plus PCA3 for individualized prostate cancer risk assessment. European Urology. 2016; 70(1):45–53.Google Scholar
Zhang, J, Denton, BT, Balasubramanian, H, Shah, ND, Inman, BA. Optimization of prostate biopsy referral decisions. Manufacturing & Service Operations Management. 2012; 14(4):529–547.Google Scholar
References
Alberta Health Services. Canadian Triage and Acuity Scale (CTAS) National Guidelines, 2016. www.calgaryhealthregion.ca/policy/docs/1451/Admission_over-capacity_AppendixA.pdf.Google Scholar
Birge, J and Louveaux, F. Introduction to Stochastic Programming. Springer, New York, 2nd ed., 2011.Google Scholar
Brandeau, ML and Hopkins, DSP. A patient mix model for hospital financial planning. Inquiry, 21 (1):32–44, 1984.Google Scholar
Childers, P and Maggard-Gibbons, M. Understanding costs of care in the operating room. JAMA Surgery, 153 (4):e176233, 2018.Google Scholar
Fairley, M, Scheinker, D, and Brandeau, M L. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Management Science, 22 (4): 756–767, 2019.Google Scholar
Hillier, FS and Lieberman, GJ. Introduction to Operations Research. McGraw Hill, New York, 8th ed., 2004.Google Scholar
National Conference of State Legislators. CON – Certificate of Need State Laws, 2016. www.ncsl.org/research/health/con-certificate-of-need-state-laws.aspx.Google Scholar
Ozcan, YA. Quantitative Methods in Health Care Management. Jossey-Bass, San Francisco, 2nd ed., 2009.Google Scholar
Robinson, JC, Brown, T, and Whaley, C. Reference-based benefit design changes consumers’ choices and employers’ payments for ambulatory surgery. Health Affairs (Millwood), 34 (3): 415–422, 2015.Google Scholar
Scheinker, D and Brandeau, M L. Implementing analytics projects in a hospital: successes, failures, and opportunities. INFORMS Journal on Applied Analytics, 50 (3):176–189, 2020.Google Scholar
The Advisory Board Company. National Ablation Volume Estimates, 2013. www.advisory.com.Google Scholar
Yeh, RW, Mauri, L, Wolf, R, et al. Population trends in rates of coronary revascularization. JAMA Internal Medicine, 175 (3): 454–456, 2015.Google Scholar
Zhou, Z, Miller, D, Master, N, et al. Detecting inaccurate predictions of pediatric surgical durations. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 452–457. IEEE, 2016.Google Scholar
References
Rodriguez, F, Scheinker, D and Harrington, RA. Promise and perils of big data and artificial intelligence in clinical medicine and biomedical research. Circulation Research. 2018;123(12):1282–1284.Google Scholar
Scheinker, D and Brandeau, ML. implementing analytics projects in a hospital: successes, failures, and opportunities. INFORMS Journal on Applied Analytics. 2020;50(3):176–189.Google Scholar
Esteva, A, Kuprel, B, Novoa, RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118.Google Scholar
Monsalve-Torra, A, Ruiz-Fernandez, D, Marin-Alonso, O, et al. Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm. Journal of Biomedical Informatics. 2016;62:195–201.Google Scholar
Vashisht, R, Jung, K and Shah, N. Learning effective treatment pathways for type-2 diabetes from a clinical data warehouse. AMIA Annual Symposium Proceedings. 2016;2016:2036–2042.Google Scholar
Sotiriou, C, Neo, SY, McShane, LM, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proceedings of the National Academy of Sciences U S A. 2003;100:10393–10398.Google Scholar
Ryan Poplin, AVV, Blumer, K, Liu, Y. Webster prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018;2:158–164.Google Scholar
Yu, KH, Zhang, C, Berry, GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications. 2016;7:12474.Google Scholar
Al’Aref, SJ, Anchouche, K, Singh, G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal. 2018;40(24)1975–1986.Google Scholar
Yeung, S, Downing, NL, Fei-Fei, L and Milstein, A. Bedside computer vision - moving artificial intelligence from driver assistance to patient safety. New England Journal of Medicine. 2018;378:1271–1273.Google Scholar
Emanuel, EJ, Wachter, RM. Artificial intelligence in health care: will the value match the hype?. JAMA. 2019 Jun 18;321(23):2281–2282.Google Scholar
Shah, NH, Milstein, A, Bagley, SC. Making machine learning models clinically useful. JAMA. 2019 Oct 8;322(14):1351–1352.Google Scholar
Russell, A, Sutherland, S, and Scheinker, D. Operationalizing Risk Prediction Algorithms in Healthcare Settings. INFORMS Healthcare, Cambridge, MA. 2019.Google Scholar
Reddy, A and Scheinker, D., 2020. The case for mathematical optimization in health care: building a strong foundation for artificial intelligence. Health Affairs blog.Google Scholar
Lin, SY, Shanafelt, TD and Asch, SM. Reimagining clinical documentation with artificial intelligence. Mayo Clinic Proceedings. 2018;93:563–565.Google Scholar
Longhurst, CA, Harrington, RA and Shah, NH. A “green button” for using aggregate patient data at the point of care. Health Affairs (Millwood). 2014;33:1229–1235.Google Scholar
Scheinker, D, Ward, A, Shin, AY, et al. Differences in central line–associated bloodstream infection rates based on the criteria used to count central line days. JAMA. 2020;323(2):183–185.Google Scholar
Zou, J and Schiebinger, L. AI can be sexist and racist – it’s time to make it fair. Nature; 2018;559(7714):324–326.CrossRefGoogle Scholar
Obermeyer, Z, Powers, B, Vogeli, C and Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453.Google Scholar