Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities

References

• Ahire SL, Jensen JB (2017) Snider Tire optimizes its customers-stores-plants transportation network. Interfaces 47(2):150–162.Link, Google Scholar
• Ahmadi E, Masel DT, Metcalf AY, Schuller K (2019) Inventory management of surgical supplies and sterile instruments in hospitals: A literature review. Health Systems (Basingstoke) 8(2):134–151.Google Scholar
• Alden JM, Burns LD, Costy T, Hutton RD, Jackson CA, Kim DS, Kohls KA, Owen JH, Turnquist MA, Vander Veen DJ (2006) General Motors increases its production throughput. Interfaces 36(1):6–25.Link, Google Scholar
• Arthur J (2016) Lean Six Sigma for Hospitals, 2nd ed. (McGraw Hill, New York).Google Scholar
• Bauer MS, Damschroder L, Hagedorn H, Smith J, Kilbourne AM (2015) An introduction to implementation science for the non-specialist. BMC Psych. 3(1):32.Google Scholar
• Benchoff B, Yano CA, Newman A (2017) Kaiser Permanente Oakland Medical Center optimizes operating room block schedule for new hospital. Interfaces 47(3):214–229.Link, Google Scholar
• Bokhorst JAC, Slomp J (2010) Lean production control at a high-variety, low-volume parts manufacturer. Interfaces 40(4):303–312.Link, Google Scholar
• Bowers MR, Noon CE, Wu W, Bass JK (2016) Neonatal physician scheduling at the University of Tennessee Medical Center. Interfaces 46(2):168–182.Link, Google Scholar
• Brandeau ML, Sainfort F, Pierskalla WP (2004) Operations Research and Healthcare: A Handbook of Methods and Applications (Kluwer Academic Publishers, Norwell, MA).Google Scholar
• Bravo F, Levi R, Ferrari LR, McManus ML (2015) The nature and sources of variability in pediatric surgical case duration. Pediatric Anesthesia 25(10):999–1006.Google Scholar
• Brown ML, Kros JF (2003) Data mining and the impact of missing data. Indust. Management Data Systems 103(8):611–621.Google Scholar
• Chien LC, Lu KJ, Wo CC, Shoemaker WC (2007) Hemodynamic patterns preceding circulatory deterioration and death after trauma. J. Trauma 62(4):928–932.Google Scholar
• Childers CP, Showen A, Nuckols T, Maggard-Gibbons M (2018) Interventions to reduce intraoperative costs: A systematic review. Ann. Surgery 268(1):48–57.Google Scholar
• Cohn A, Root S, Kymissis C, Esses J, Westmoreland N (2009) Scheduling medical residents at Boston University School of Medicine. Interfaces 39(3):186–195.Link, Google Scholar
• Denizel M, Ekinci U, O¨zyurt G, Turhan D (2007) Ford-Otosan optimizes its stocks using a Six-Sigma framework. Interfaces 37(2):97–107.Link, Google Scholar
• Eccles MP, Mittman BS (2006) Welcome to Implementation Science. Implementation Sci. 1(1).Google Scholar
• Emanuel EJ, Wachter RM (2019) Artificial intelligence in healthcare: Will the value match the hype? JAMA 321(23):2281–2282.Google Scholar
• Fairley M, Scheinker D, Brandeau ML (2019) Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Management Sci. 22(4):756–767.Google Scholar
• Ferrand Y, Magazine M, Rao US, Glass TF (2011) Building cyclic schedules for emergency department physicians. Interfaces 41(6):521–533.Link, Google Scholar
• Hagtvedt R, Griffin P, Keskinocak P, Roberts R (2009) A simulation model to compare strategies for the reduction of health-care–associated infections. Interfaces 39(3):256–270.Link, Google Scholar
• Hanne T, Melo T, Nickel S (2009) Bringing robustness to patient flow management through optimized patient transports in hospitals. Interfaces 39(3):241–255.Link, Google Scholar
• Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R (2005) Can electronic medical record systems transform healthcare? Potential health benefits, savings, and costs. Health Affairs (Millwood) 24(5):1103–1117.Google Scholar
• Ista E, van der Hoven B, Kornelisse RF, van der Starre C, Vos MC, Boersma E, Helder OK (2016) Effectiveness of insertion and maintenance bundles to prevent central line-associated bloodstream infections in critically ill patients of all ages: A systematic review and meta-analysis. Lancet Infectious Diseases 16(6):724–734.Google Scholar
• Kellermann AL, Jones SS (2013) What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Affairs (Millwood) 32(1):63–68.Google Scholar
• Kellogg DL, Walczak S (2007) Nurse scheduling: From academia to implementation or not? Interfaces 37(4):355–369.Link, Google Scholar
• LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.Google Scholar
• Lee EK, Wei X, Baker-Witt F, Wright M, Quarshie A (2018) Outcome-driven personalized treatment design for managing diabetes. Interfaces 48(5):422–435.Link, Google Scholar
• Lee EK, Yuan F, Templeton A, Yao R, Kiel K, Chu JCC (2013) Biological planning for high-dose-rate brachytherapy: Application to cervical cancer treatment. Interfaces 43(5):462–476.Link, Google Scholar
• Lee EK, Atallah HY, Wright MD, Post ET, Thomas C IV, Wu DT, Haley LL Jr (2015) Transforming hospital emergency department workflow and patient care. Interfaces 45(1):58–82.Link, Google Scholar
• Liberati EG, Ruggiero F, Galuppo L, Gorli M, Gonz’alez-Lorenzo M, Maraldi M, Ruggieri P, et al.. (2017) What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implementation Sci. 12:113.Google Scholar
• Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sanchez CI (2017) A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88.Google Scholar
• Master N, Zhou Z, Miller D, Scheinker D, Bambos N, Glynn P (2017) Improving predictions of pediatric surgical durations with supervised learning. Internat. J. Data Sci. Anal. 4(1):35–52.Google Scholar
• Mathew R, Simms A, Taylor K, Ferrari S, Bain L, Valencia A, Bargmann-Losche J, Donnelly L, Lee G (2020) Reduction of central line-associated bloodstream infection through focus on key drivers: Standardization, data, and accountability. Pediatric Quality Safety. Forthcoming.Google Scholar
• Miller D, Scheinker D, Bambos N (2017) A practical approach to machine learning for clinical decision support. Cappanera P, Li J, Matta A, Sahin E, Vandaele N, Visintin F, eds. Healthcare Systems Engineering, Springer Proceedings in Mathematics and Statistics, vol. 10 (Springer, Cham, Switzerland), 111–120.Google Scholar
• Miller D, Ward A, Maahs D, Scheinker D (2019a) Personalized diabetes management using data from continuous glucose monitors. Diabetes 68(Suppl. 1):960-P.Google Scholar
• Miller D, Ward A, Bambos N, Shin A, Scheinker D (2018) Physiological waveform imputation of missing data using convolutional autoencoders. 2018 IEEE 20th Internat. Conf. e-Health Networking, Appl. Services (Healthcom) (IEEE, Piscataway, NJ), 1–6.Google Scholar
• Miller D, Ward A, Bambos N, Shin A, Scheinker D (2019b) Noninvasive identification of hypotension using densely connected convolutional networks. 2019 IEEE 20th Internat. Conf. e-Health Networking, Appl. Services (Healthcom) (IEEE, Piscataway, NJ), 1–6.Google Scholar
• Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2017) Deep learning for healthcare: Review, opportunities and challenges. Briefing Bioinformatics 19(6):1236–1246.Google Scholar
• National Healthcare Safety Network. (2019) Surveillance for bloodstream infections. Accessed August 30, 2019, https://www.cdc.gov/nhsn/acute-care-hospital/clabsi/index.html.Google Scholar
• Our World in Data. (2017) Link between health spending and life expectancy: US is an outlier. Accessed August 30, 2019, https://ourworldindata.org/the-link-between-life-expectancy-and-health-spending-us-focus.Google Scholar
• Patrick J, Montazeri A, Michalowski W, Banerjee D (2019) Automated pathologist scheduling at the Ottawa Hospital. INFORMS J. Appl. Analytics 49(2):93–103.Link, Google Scholar
• PhysioNet (2019) PhysioNet: The research resource for complex physiologic signals. Accessed August 30, 2019, https://physionet.org/.Google Scholar
• Pitt AL, Scheinker D (2020) Scheduling algorithm improves system utilization at Lucile Packard Children’s Hospital Stanford infusion center. Working paper, Stanford University, Stanford, CA.Google Scholar
• Prahalad P, Addala A, Scheinker D, Hood K, Maahs DM (2020) CGM initiation soon after type 1 diabetes diagnosis results in sustained CGM use and wear time. Diabetes Care 43(1):e3–e4.Google Scholar
• Prahalad P, Yang J, Scheinker D, Desai M, Hood K, Maahs DM (2019) Hemoglobin A1c trajectory in pediatric patients with newly diagnosed type 1 diabetes. Diabetes Tech. Therapeutics 21(8):456–461.Google Scholar
• Rais S, Viana A (2011) Operations research in healthcare: A survey. Internat. Trans. Oper. Res. 18(1):1–31.Google Scholar
• Rieb W (2015) Increasing patient throughput in the MGH Cancer Center infusion unit. Unpublished PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
• Rodriguez F, Scheinker D, Harrington RA (2018) Promise and perils of big data and artificial intelligence in clinical medicine and biomedical research. Circulation Res. 123(12):1282–1284.Google Scholar
• Russell WA, Sutherland S, Brown C, Morse K, Scheinker D (2020) Predicting chronic kidney disease in pediatric acute kidney injury survivors. Working paper, Department of Management Science and Engineering, Stanford University, Stanford, CA.Google Scholar
• Scheinker D, Petersen K, Claure R (2020a) Use of data from the electronic medical record to increase surgical volume at a pediatric academic medical center. Working paper, Stanford University, Stanford, CA.Google Scholar
• Scheinker D, Valencia A, Rodriguez F (2019) Identification of factors associated with variation in US county-level obesity prevalence rates using epidemiologic vs. machine learning models. JAMA Network Open 2(4):e192884.Google Scholar
• Scheinker D, Ward A, Shin A, Lee G, Mathew R, Donnelly L (2020b) Differences in central line-associated bloodstream infection rates based on the criteria used to count central line days. JAMA 323(2):183–185.Google Scholar
• Scheinker D, Hollingsworth M, Chukaeva A, Phelps C, Bryant W, Pei F, Petersen K, Reddy A, Wall J (2020c) The use of electronic health record data to optimize surgical preference cards. Working paper, Stanford University, Stanford, CA.Google Scholar
• Schneider AJT, Besselink PL, Zonderland ME, Boucherie RJ, van den Hout WB, Kievit J, Bilars P, Fogteloo AJ, Rabelink TJ (2018) Allocating emergency beds improves the emergency admission flow. Interfaces 48(4):384–394.Link, Google Scholar
• Seneviratne M, Ward A, Mirchandani S, Li R, Champarathy A, Mathew R, Wood M, et al.. (2020) Development and implementation of a bundle compliance dashboard for central line-associated bloodstream infections. Working paper, Stanford University, Stanford, CA.Google Scholar
• Shah NH, Milstein A, Bagley SC (2019) Making machine learning models clinically useful. JAMA 322(14):1351–1352.Google Scholar
• Shanafelt TD, Dyrbye LN, Sinsky C, Hasan O, Satele D, Sloan J, West CP (2016) Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clinic Proc. 91(7):836–848.Google Scholar
• Simpao AF, Ahumada LM, G’alvez JA, Rehman MA (2014) A review of analytics and clinical informatics in healthcare. J. Medical Systems 38(4):45.Google Scholar
• Sir MY, Nestler D, Hellmich T, Das D, Laughlin MJ Jr, Dohlman MC, Pasupathy K (2017) Optimization of multidisciplinary staffing improves patient experiences at the Mayo Clinic. Interfaces 47(5):425–441.Link, Google Scholar
• Smalley HK, Keskinocak P, Vats A (2015) Physician scheduling for continuity: An application in pediatric intensive care. Interfaces 45(2):133–148.Link, Google Scholar
• Smallman B, Dexter F (2010) Optimizing the arrival, waiting, and NPO times of children on the day of pediatric endoscopy procedures. Anesthesia Analgesia 110(3):879–887.Google Scholar
• Sokovic M, Pavletic D, Kern Pipan K (2010) Quality improvement methodologies – PDCA cycle, RADAR matrix, DMAIC and DFSS. J. Achievements Materials Manufacturing Engrg. 23(10):476–483.Google Scholar
• SURF Stanford Medicine (2019) Systems Utilization Research for Stanford Medicine. Accessed March 11, 2020, https://surf.stanford.edu.Google Scholar
• Taylor R, Bower A, Girosi F, Bigelow J, Fonkych K, Hillestad R (2005) Promoting health information technology: Is there a case for more-aggressive government action? Health Affairs (Millwood) 24(5):1234–1245.Google Scholar
• Thomas BG, Bollapragada S, Akbay K, Toledano D, Katlic P, Dulgeroglu O, Yang D (2013) Automated bed assignments in a complex and dynamic hospital environment. Interfaces 43(5):435–448.Link, Google Scholar
• Trusko BE, Pexton C, Harrington HJ, Gupta P (2007) Improving Healthcare Quality and Cost with Six Sigma (Pearson Education, Upper Saddle River, NJ).Google Scholar
• Venkatesh V, Davis FD (2000) Theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Sci. 46(2):186–204.Link, Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Quart. 27(3):425–478.Google Scholar
• Ward A, Scheinker D, Mathew R, Donnelly L, Lee G, Shin A (2020) Development and implementation of data analytics for reducing central line-associated bloodstream infections in pediatric care. Working paper, Stanford University, Stanford, CA.Google Scholar
• Woodall JC, Gosselin T, Boswell A, Murr M, Denton BT (2013) Improving patient access to chemotherapy treatment at Duke Cancer Institute. Interfaces 43(5):449–461.Link, Google Scholar
• Zhou Z, Miller D, Master N, Scheinker D, Bambos N, Glynn P (2016) Detecting inaccurate predictions of pediatric surgical durations. 2016 IEEE Internat. Conf. Data Sci. Advanced Analytics (DSAA) (IEEE, Piscataway, NJ), 452–457.Google Scholar
• Zidel T (2006) A Lean Guide to Transforming Healthcare: How to Implement Lean Principles in Hospitals, Medical Offices, Clinics, and Other Healthcare Organizations (Quality Press, Milwaukee).Google Scholar