Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

References

• Achiume ET (2020) Racial discrimination and emerging digital technologies: A human rights analysis. Report A/HRC/44/57, United Nations 44th Human Rights Council, Geneva, Switzerland.Google Scholar
• Ahmadi-Javid A, Jalali Z, Klassen KJ (2017) Outpatient appointment systems in healthcare: A review of optimization studies. Eur. J. Oper. Res. 258(1):3–34.Crossref, Google Scholar
• Benjamin R (2016) Innovating inequity: If race is a technology, postracialism is the Genius Bar. Ethnic Racial Stud. 39(13):2227–2234.Crossref, Google Scholar
• Benjamin R (2019) Race After Technology: Abolitionist Tools for the New Jim Code (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Brier GW (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Rev. 78(1):1–3.Crossref, Google Scholar
• Campbell JD, Chez RA, Queen T, Barcelo A, Patron E (2000) The no-show rate in a high-risk obstetric clinic. J. Womens Health Gender Based Medicine 9(8):891–895.Crossref, Google Scholar
• Centers for Disease Control and Prevention (2013) Health disparities & inequalities report—United States, 2013. Morbidity and Mortality Weekly Rep. 62(suppl 3):1–187.Google Scholar
• Corfield L, Schizas A, Williams A, Noorani A (2008) Non-attendance at the colorectal clinic: A prospective audit. Ann. Roy. College Surgery England 90(5):377–380.Crossref, Google Scholar
• Cui R, Li J, Zhang DJ (2020) Reducing discrimination with reviews in the sharing economy: Evidence from field experiments on Airbnb. Management Sci. 66(3):1071–1094.Link, Google Scholar
• Dai T, Tayur SR (2020) Healthcare operations management: A snapshot of emerging research. Manufacturing Service Oper. Management 22(5):869–887.Link, Google Scholar
• Dantas LF, Fleck JL, Oliveira FLC, Hamacher S (2018) No-shows in appointment scheduling: A systematic literature review. Health Policy 122(4):412–421.Crossref, Google Scholar
• Ganju KK, Atasoy H, McCullough J, Greenwood B (2020) The role of decision support systems in attenuating racial biases in healthcare delivery. Management Sci. 66(11):5171–5181.Link, Google Scholar
• Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine 178(11):1544–1547.Crossref, Google Scholar
• Hamilton W, Round A, Sharp D (2002) Patient, hospital, and general practitioner characteristics associated with non-attendance: A cohort study. British J. General Practice 52(477):317–319.Google Scholar
• Han J, Pei J, Kamber M (2011) Data Mining: Concepts and Techniques (Elsevier, New York).Google Scholar
• Heckler MM (1985) Report of the Secretary’s Task Force Report on Black and Minority Health Volume I: Executive Summary (Government Printing Office, Washington, DC).Google Scholar
• Hersch J, Shinall JB (2015) Fifty years later: The legacy of the Civil Rights Act of 1964. J. Policy Anal. Management 34(2):424–456.Crossref, Google Scholar
• Hooper MW, Nápoles AM, Pérez-Stable EJ (2020) COVID-19 and racial/ethnic disparities. JAMA 323(24):2466–2467.Google Scholar
• Hostetter M, Klein S (2018) In focus: Reducing racial disparities in healthcare by confronting racism. Commonwealth Fund, Advancing Health Equity, September 27, 121-27. https://www.commonwealthfund.org/publications/2018/sep/focus-reducing-racial-disparities-health-care-confronting-racism.Google Scholar
• Huang Y, Hanauer DA (2014) Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl. Clinical Inform. 5(03):836–860.Crossref, Google Scholar
• Kaplan-Lewis E, Percac-Lima S (2013) No-show to primary care appointments: Why patients do not come. J. Primary Care Community Health 4(4):251–255.Crossref, Google Scholar
• Lacy NL, Paulman A, Reuter MD, Lovejoy B (2004) Why we don’t come: Patient perceptions on no-shows. Ann. Family Medicine 2(6):541–545.Crossref, Google Scholar
• LaGanga LR, Lawrence SR (2012) Appointment overbooking in healthcare clinics to improve patient service and clinic performance. Production Oper. Management 21(5):874–888.Crossref, Google Scholar
• Li Y, Tang SY, Johnson J, Lubarsky DA (2019) Individualized no‐show predictions: Effect on clinic overbooking and appointment reminders. Production Oper. Management 28(8):2068–2086.Crossref, Google Scholar
• Martin C, Perfect T, Mantle G (2005) Non-attendance in primary care: The views of patients and practices on its causes, impact and solutions. Family Practice 22(6):638–643.Crossref, Google Scholar
• Massey DS (2015) The legacy of the 1968 Fair Housing Act. Sociol. Forum 30:571–588.Crossref, Google Scholar
• Miller AJ, Chae E, Peterson E, Ko AB (2015) Predictors of repeated “no-showing” to clinic appointments. Amer. J. Otolaryngology 36(3):411–414.Crossref, Google Scholar
• Murray SG, Watcher RM, Cucina RJ (2020) Discrimination by artificial intelligence in a commercial electronic health record—A case study. Health Affairs Blog (January 31), https://www.healthaffairs.org/do/10.1377/hblog20200128.626576/full/.Google Scholar
• Neal RD, Hussain-Gambles M, Allgar VL, Lawlor DA, Dempsey O (2005) Reasons for and consequences of missed appointments in general practice in the UK: Questionnaire survey and prospective review of medical records. BMC Family Practice 6(1):47.Crossref, Google Scholar
• Nelson A (2002) Unequal treatment: confronting racial and ethnic disparities in healthcare. J. National Medical Assoc. 94(8):666.Google Scholar
• Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Sci. 366(6464):447–453.Crossref, Google Scholar
• Office of Disease Prevention and Health Promotion (2020) Social determinants of health. Accessed April 28, 2021, https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health.Google Scholar
• Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3):61–74.Google Scholar
• Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH (2018) Ensuring fairness in machine learning to advance health equity. Ann. Internal Medicine 169(12):866–872.Crossref, Google Scholar
• Rea D, Lozano L, Froehle C (2021a) Algorithmic justice: Fair compensation through principled compromises. Working paper., University of Cincinnati, Cincinnati, OH.Google Scholar
• Rea D, Froehle C, Masterson S, Stettler B, Fermann G, Pancioli A (2021b) Unequal but fair: Incorporating distributive justice in operational allocation models. Production Oper. Management Forthcoming.Crossref, Google Scholar
• Robinson LW, Chen RR (2010) A comparison of traditional and open-access policies for appointment scheduling. Manufacturing Service Oper. Management 12(2):330–346.Link, Google Scholar
• Robinson LW, Chen RR (2011) Estimating the implied value of the customer’s waiting time. Manufacturing Service Oper. Management 13(1):53–57.Link, Google Scholar
• Samorani M, Ganguly S (2016) Optimal sequencing of unpunctual patients in high‐service‐level clinics. Production Oper. Management 25(2):330–346.Crossref, Google Scholar
• Samorani M, Harris S (2019) The impact of probabilistic classifiers on appointment scheduling with no-shows. ICIS 2019 Proc. https://aisel.aisnet.org/icis2019/is_health/is_health/7/.Google Scholar
• Samorani M, LaGanga LR (2015) Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur. J. Oper. Res. 240(1):245–257.Crossref, Google Scholar
• Soltani M, Samorani M, Kolfal B (2019) Appointment scheduling with multiple providers and stochastic service times. Eur. J. Oper. Res. 277(2):667–683.Crossref, Google Scholar
• Srinivas S, Ravindran AR (2018) Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework. Expert Systems Appl. 102:245–261.Crossref, Google Scholar
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
• Williams DR, Mohammed SA, Leavell J, Collins C (2010) Race, socioeconomic status and health: Complexities, ongoing challenges and research opportunities. Ann. NY Acad. Sci. 1186:69–101.Crossref, Google Scholar
• Xu R, Wunsch D (2008) Clustering, vol. 10 (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Zacharias C, Pinedo M (2014) Appointment scheduling with no‐shows and overbooking. Production Oper. Management 23(5):788–801.Crossref, Google Scholar