Value of Algorithm-Enabled Process Innovation: The Case of Sepsis

References

• Amarasingham R, Plantinga L, Diener-West M, Gaskin DJ, Powe NR (2009) Clinical information technologies and inpatient outcomes: A multiple hospital study. Arch. Intern. Med. 169(2):108–114.Crossref, Google Scholar
• Andritsos DA, Tang CS (2014) Linking process quality and resource usage: An empirical analysis. Production Oper. Management 23(12):2163–2177.Crossref, Google Scholar
• Angrist JD, Pischke J (2008) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Ash JS, Berg M, Coiera E (2004) Some unintended consequences of information technology in healthcare: The nature of patient care information system-related errors. J. Amer. Med. Inform. Assoc. 11(2):104–112.Crossref, Google Scholar
• Athey S, Imbens GW (2006) Identification and inference in nonlinear difference-in-differences models. Econometrica 74(2):431–497.Crossref, Google Scholar
• Athey S, Imbens GW (2022) Design-based analysis in difference-in-differences settings with staggered adoption. J. Econometrics 226(1):62–79.Crossref, Google Scholar
• Autor DH (2003) Outsourcing at will: The contribution of unjust dismissal doctrine to the growth of employment outsourcing. J. Labor Econom. 21(1):1–42.Crossref, Google Scholar
• Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. Quart. J. Econom. 118(4):1279–1333.Crossref, Google Scholar
• Ayer T, Ayvaci MUS, Karaca Z, Vlachy J (2019) The impact of health information exchanges on emergency department length of stay. Production Oper. Management 28(3):740–758.Crossref, Google Scholar
• Ayvaci MUS, Mobini Z, Özer Ö (2021) To catch a killer: A data-driven personalized and compliance-aware sepsis alert system. Preprint, submitted April 6, https://dx.doi.org/10.2139/ssrn.3805931.Google Scholar
• Bakshi N, Flynn SE, Gans N (2011) Estimating the operational impact of container inspections at international ports. Management Sci. 57(1):1–20.Link, Google Scholar
• Batt RJ, Terwiesch C (2016) Early task initiation and other load-adaptive mechanisms in the emergency department. Management Sci. 63(11):3531–3551.Link, Google Scholar
• Blumenthal-Barby JS, Krieger H (2015) Cognitive biases and heuristics in medical decision making: A critical review using a systematic search strategy. Med. Decision Making 35(4):539–557.Crossref, Google Scholar
• Blundell R, Dias MC (2009) Alternative approaches to evaluation in empirical microeconomics. J. Human Resources 44(3):565–640.Google Scholar
• Bolton GE, Katok E (2008) Learning by doing in the newsvendor problem: A laboratory investigation of the role of experience and feedback. Manufacturing Service Oper. Management 10(3):519–538.Link, Google Scholar
• Bornstein BH, Emler AC (2001) Rationality in medical decision making: A review of the literature on doctors’ decision-making biases. J. Eval. Clin. Pract. 7(2):97–107.Crossref, Google Scholar
• Breznitz S (2013) Cry Wolf: The Psychology of False Alarms (Psychology Press, New York).Crossref, Google Scholar
• Butterfield S (2019) Let the computer figure it out. Accessed June 19, 2019, https://acphospitalist.org/archives/2018/01/machine-learning-computer-figure-out.htm.Google Scholar
• Campbell EM, Guappone KP, Sittig DF, Dykstra RH, Ash JS (2009) Computerized provider order entry adoption: Implications for clinical workflow. J. Gen. Intern. Med. 24(1):21–26.Crossref, Google Scholar
• Cao S, Liu Y (2013) Effects of concurrent tasks on diagnostic decision making: An experimental investigation. IIE Trans. Healthcare Systems Engrg. 3(4):254–262.Crossref, Google Scholar
• Caro F, de Tejada Cuenca AS (2023) Believing in analytics: Managers’ adherence to price recommendations from a DSS. Manufacturing Service Oper. Management 25(2):524–542.Link, Google Scholar
• Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40(5):373–383.Crossref, Google Scholar
• Cowgill B (2020) Bias and productivity in humans and algorithms: Theory and evidence from resume screening. Working paper, Columbia University, New York.Google Scholar
• De-Arteaga M, Fogliato R, Chouldechova A (2020) A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores. CHI’20 Proc. 2020 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 1–12.Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. Gen. 144(1):114.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
• Donohue K, Katok E, Leider S (2018) The Handbook of Behavioral Operations (John Wiley and Sons, Hoboken, NJ).Crossref, Google Scholar
• ECRI Institute (2013) Top 10 health technology hazards for 2014. Health Devices 42(11):354–380.Google Scholar
• Forman C, King JL, Lyytinen K (2014) Special section introduction—Information, technology, and the changing nature of work. Inform. Systems Res. 25(4):789–795.Link, Google Scholar
• Freeman M, Savva N, Scholtes S (2016) Gatekeepers at work: An empirical analysis of a maternity unit. Management Sci. 63(10):3147–3167.Link, Google Scholar
• Gaieski DF, Edwards JM, Kallan MJ, Carr BG (2013) Benchmarking the incidence and mortality of severe sepsis in the United States. Crit. Care Med. 41(5):1167–1174.Crossref, Google Scholar
• Gardner J (2022) Two-stage differences in differences. Preprint, submitted July 13, https://arxiv.org/abs/2207.05943.Google Scholar
• Ginestra JC, Giannini HM, Schweickert WD, Meadows L, Lynch MJ, Pavan K, Chivers CJ, et al. (2019) Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Crit. Care Med. 47(11):1477–1484.Crossref, Google Scholar
• Greenhalgh T, Howick J, Maskrey N (2014) Evidence based medicine: A movement in crisis? BMJ 348:g3725.Crossref, Google Scholar
• Grove WM, Meehl PE (1996) Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psych. Public Policy Law 2:293–323.Crossref, Google Scholar
• Harrison AM, Herasevich V, Gajic O (2015) Automated sepsis detection, alert, and clinical decision support: Act on it or silence the alarm? Crit. Care Med. 43(8):1776–1777.Crossref, Google Scholar
• Holweg M, Staats BR, Upton DM (2018) Making process improvements stick. Preprint, submitted August 28, https://dx.doi.org/10.2139/ssrn.3240097.Google Scholar
• Howell MD, Davis AM (2017) Management of sepsis and septic shock. JAMA 317(8):847–848.Google Scholar
• Ibanez MR, Clark JR, Huckman RS, Staats BR (2018) Discretionary task ordering: Queue management in radiological services. Management Sci. 64(9):4389–4407.Link, Google Scholar
• International Sepsis Forum (2023) Sepsis: A guide to understanding sepsis. Accessed May 26, 2023, https://sepsisforum.org/brochure/.Google Scholar
• Kaukonen K, Bailey M, Pilcher D, Cooper DJ, Bellomo R (2015) Systemic inflammatory response syndrome criteria in defining severe sepsis. N. Engl. J. Med. 372(17):1629–1638.Crossref, Google Scholar
• KC D, Staats BR, Gino F (2013) Learning from my success and from others’ failure: Evidence from minimally invasive cardiac surgery. Management Sci. 59(11):2435–2449.Link, Google Scholar
• KC DS, Terwiesch C (2009) Impact of workload on service time and patient safety: An econometric analysis of hospital operations. Management Sci. 55(9):1486–1498.Link, Google Scholar
• KC DS, Scholtes S, Terwiesch C (2020) Empirical research in healthcare operations: Past research, present understanding, and future opportunities. Manufacturing Service Oper. Management 22(1):73–83.Link, Google Scholar
• Kesselheim AS, Cresswell K, Phansalkar S, Bates DW, Sheikh A (2011) Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Affairs 30(12):2310–2317.Crossref, Google Scholar
• Kim S, Chan CW, Olivares M, Escobar G (2014) ICU admission control: An empirical study of capacity allocation and its implication for patient outcomes. Management Sci. 61(1):19–38.Link, Google Scholar
• Korniewicz DM, Clark T, David Y (2008) A national online survey on the effectiveness of clinical alarms. Amer. J. Crit. Care 17(1):36–41.Crossref, Google Scholar
• Li L, Chang Q, Ni J (2009) Data driven bottleneck detection of manufacturing systems. Internat. J. Production Res. 47(18):5019–5036.Crossref, Google Scholar
• Liker JK, Morgan JM (2006) The Toyota way in services: The case of lean product development. Acad. Management Perspect. 20(2):5–20.Crossref, Google Scholar
• Lin W, Kim S, Tong J (2021) What drives algorithm use? An empirical analysis of algorithm use in type 1 diabetes self-management. Preprint, submitted August 25, https://dx.doi.org/10.2139/ssrn.3891832.Google Scholar
• Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, Iwashyna TJ (2014) Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 312(1):90–92.Crossref, Google Scholar
• Miller AR, Tucker C (2017) Privacy protection, personalized medicine, and genetic testing. Management Sci. 64(10):4648–4668.Link, Google Scholar
• Mills AF (2016) A simple yet effective decision support policy for mass-casualty triage. Eur. J. Oper. Res. 253(3):734–745.Crossref, Google Scholar
• Narayanan S, Balasubramanian S, Swaminathan JM (2009) A matter of balance: Specialization, task variety, and individual learning in a software maintenance environment. Management Sci. 55(11):1861–1876.Link, Google Scholar
• Obermeyer Z, Emanuel EJ (2016) Predicting the future—Big data, machine learning, and clinical medicine. N. Engl. J. Med. 375(13):1216–1219.Crossref, Google Scholar
• Obermeyer Z, Lee TH (2017) Lost in thought—The limits of the human mind and the future of medicine. N. Engl. J. Med. 377(13):1209–1211.Crossref, Google Scholar
• Özer Ö, Zheng Y, Chen K (2011) Trust in forecast information sharing. Management Sci. 57(6):1111–1137.Link, Google Scholar
• Pashler H (1989) Dissociations and dependencies between speed and accuracy: Evidence for a two-component theory of divided attention in simple tasks. Cognitive Psych. 21(4):469–514.Crossref, Google Scholar
• Powell A, Savin S, Savva N (2012) Physician workload and hospital reimbursement: Overworked physicians generate less revenue per patient. Manufacturing Service Oper. Management 14(4):512–528.Link, Google Scholar
• Puhani PA (2012) The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. Econom. Lett. 115(1):85–87.Crossref, Google Scholar
• Rangel-Frausto MS, Pittet D, Costigan M, Hwang T, Davis CS, Wenzel RP (1995) The natural history of the systemic inflammatory response syndrome (SIRS): A prospective study. JAMA 273(2):117–123.Crossref, Google Scholar
• Reyna MA, Josef CS, Jeter R, Shashikumar SP, Westover MB, Nemati S, Clifford GD, Sharma A (2020) Early prediction of sepsis from clinical data: The PhysioNet/Computing in Cardiology Challenge 2019. Crit. Care Med. 48(2):210–217.Google Scholar
• Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, et al. (2020) Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the global burden of disease study. Lancet 395(10219):200–211.Crossref, Google Scholar
• Senot C, Chandrasekaran A, Ward PT, Tucker AL, Moffatt-Bruce SD (2015) The impact of combining conformance and experiential quality on hospitals’ readmissions and cost performance. Management Sci. 62(3):829–848.Link, Google Scholar
• Song H, Tucker AL, Murrell KL (2015) The diseconomies of queue pooling: An empirical investigation of emergency department length of stay. Management Sci. 61(12):3032–3053.Link, Google Scholar
• Sun J, Zhang DJ, Hu H, Van Mieghem JA (2021) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.Link, Google Scholar
• Torio CM, Andrews RM (2013) National inpatient hospital costs: the most expensive conditions by payer, 2011. Healthcare Cost and Utilization Project (HCUP) Statistical Brief 160, Agency for Healthcare Policy and Research, Rockville, MD.Google Scholar
• Tucker AL (2015) The impact of workaround difficulty on frontline employees’ response to operational failures: A laboratory experiment on medication administration. Management Sci. 62(4):1124–1144.Link, Google Scholar
• Tucker AL, Spear SJ (2006) Operational failures and interruptions in hospital nursing. Health Services Res. 41(3p1):643–662.Crossref, Google Scholar
• Tversky A, Kahneman D (1973) Availability: A heuristic for judging frequency and probability. Cognitive Psychol. 5(2):207–232.Crossref, Google Scholar
• Uncapher MR, Thieu MK, Wagner AD (2016) Media multitasking and memory: Differences in working memory and long-term memory. Psychonomic Bull. Rev. 23(2):483–490.Crossref, Google Scholar
• Wears RL, Berg M (2005) Computer technology and clinical work: Still waiting for Godot. JAMA 293(10):1261–1263.Crossref, Google Scholar
• Wood W, Neal DT (2007) A new look at habits and the habit-goal interface. Psych. Rev. 114(4):843.Crossref, Google Scholar
• Yin J, Ngiam KY, Teo HH (2021) Role of artificial intelligence applications in real-life clinical practice: Systematic review. J. Med. Internet Res. 23(4):e25759.Crossref, Google Scholar