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To Be Healthy, Wealthy, and Wise: Using Decision Modeling to Personalize Policy in Health, Hunger Relief, and Education

  • Julie Simmons IvyEmail author
  • Muge Capan
  • Karen Hicklin
  • Nisha Nataraj
  • Irem Sengul Orgut
  • Amy Craig Reamer
  • Anita Vila-Parrish
Chapter
Part of the Women in Engineering and Science book series (WES)

Abstract

Decision-making to satisfy the basic human needs of health, food, and education is complex, accomplishing this in such a way that solutions are personalized to the needs of the individual has been the focus of my research. This chapter will present an overview of my research with several of my former students in the area of decision-making under conditions of uncertainty with a focus on human-centric decision problems in three primary areas: health care, humanitarian logistics, and education. This research seeks to inform decision-making with the goal of improving decision quality. To this end, this research utilizes and develops theory in the areas of Markov decision processes (MDPs), semi-Markov decision processes (SMDPs), partially observable Markov decision processes (POMDPs), optimization, simulation, and Bayesian decision analysis to address these real-world problems. This research has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making.

References

  1. American Diabetes Association (2017) Pharmacologic approaches to glycemic treatment. In: Standards of medical care in diabetes-2017. pp S64–S74Google Scholar
  2. Anderson G, Horvath J (2004) The growing burden of chronic disease in America. Public Health Rep (Washington, DC: 1974) 119(3):263–270CrossRefGoogle Scholar
  3. Badrick E, Renehan AG (2014) Diabetes and cancer: 5 years into the recent controversy. Eur J Cancer 50(12):2119–2125CrossRefGoogle Scholar
  4. Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53MathSciNetCrossRefGoogle Scholar
  5. Boyd CM, Fortin M (2010) Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Public Health Rev 32(2):451–474CrossRefGoogle Scholar
  6. Brailsford SC (2007) Proceedings of the 2007 winter simulation conference. In: Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR (eds). pp 1436–1448Google Scholar
  7. Bristow PJ, Hillman KM, Chey T et al (2000) Rates of in-hospital arrests, deaths and intensive care admissions: the effect of a medical emergency team. Med J Aust 173:236–240Google Scholar
  8. Buist MD, Moore GE, Bernard SA et al (2002) Effects of a medical emergency team on reduction of incidence of and mortality from unexpected cardiac arrests in hospital: preliminary study. Br Med J 324(7334):387–390CrossRefGoogle Scholar
  9. Bynd S, Sen A, Subbe C, Gemmell L (2004) Modified early warning score: one for all and A&E? Br J Anaesth 92(4):611P–612PGoogle Scholar
  10. Capan M, Ivy JS, Rohleder T, Hickman J, Huddleston JM (2015a) Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients. Resuscitation 93:107–112.  https://doi.org/10.1016/j.resuscitation.2014.12.032 CrossRefGoogle Scholar
  11. Capan M, Ivy JS, Wilson JR, Huddleston JM (2015b) A stochastic model of acute-care decisions based on patient and provider heterogeneity. Health Care Manag Sci 20(2):187–206.  https://doi.org/10.1007/s10729-015-9347-x CrossRefGoogle Scholar
  12. Capan M, Wu P, Campbell M, Mascioli S, Jackson EV (2017) Using electronic health records and nursing assessment to redesign clinical early recognition systems. Health Syst 6(2):112–121CrossRefGoogle Scholar
  13. Caughey G, Roughead E, Roughead EE (2011) Multimorbidity research challenges: where to go from here? J Comorb 1(1):8–10CrossRefGoogle Scholar
  14. CDC/NCHS (2010) National hospital discharge survey, 2000–2010. http://www.cdc.gov/nchs/data/databriefs/db118.htm
  15. Chan PS, Jain R, Nallmothu BK et al (2010) Rapid response teams: a systematic review and meta-analysis. Arch Intern Med 170:18–26CrossRefGoogle Scholar
  16. Chang C-H, Lin J-W, Wu L-C, Lai M-S, Chuang L-M (2012) Oral insulin secretagogues, insulin, and cancer risk in type 2 diabetes mellitus. J Clin Endocrinol Metab 97(7):E1170–E1175CrossRefGoogle Scholar
  17. Committee on Quality of Health Care in America, Institute of Medicine (2001) Crossing the quality chasm: a new health system for the 21st century. National Academies Press, Washington, DCGoogle Scholar
  18. Confrey J, Maloney A (2010) The construction, refinement, and early validation of the equipartitioning learning trajectory. In: Proceedings of the 9th international conference of the learning sciences, Chicago, vol 1, pp 968–975Google Scholar
  19. Cox CE, Wysham NG (2015) Untangling health trajectories among patients with sepsis. Ann Am Thorac Soc 12(6):796–797CrossRefGoogle Scholar
  20. Czura C (2011) Merinoff symposium 2010: sepsis—speaking with one voice. Mol Med 17(1–2):2–3CrossRefGoogle Scholar
  21. DeVita MA, Bellomo R, Hillman K, Kellum J, Rotondi A, Teres D, Auerbach A, Chen W, Duncan K, Kenward G, Bell M, Buist M, Chen J, Bion J, Kirby A, Lighthall G, Ovreveit J, Braithwaite RS, Gosbee J, Milbrandt E, Peberdy M, Savitz L, Young L, Harvey M, Galhotra S (2006) Findings of the first consensus conference on medical emergency teams. Crit Care Med 34:2463–2478CrossRefGoogle Scholar
  22. Esper AM, Moss M, Lewis CA, Nisbet R, Mannino DM, Martin GS (2006) The role of infection and comorbidity: factors that influence disparities in sepsis. Crit Care Med 34(10):2576–2582CrossRefGoogle Scholar
  23. FBCENC (2018) Food Bank of Central & Eastern North Carolina. http://www.foodbankcenc.org/site/PageServer?pagename=FBCENCHome. Accessed 2018
  24. Feeding America (2018). https://hungerandhealth.feedingamerica.org/. Accessed 2018
  25. Food and Agriculture Organization of the United Nations (2018). http://www.fao.org/state-of-food-security-nutrition/en/
  26. Franciosi M, Lucisano G, Lapice E, Strippoli GFM, Pellegrini F, Nicolucci A (2013) Metformin therapy and risk of cancer in patients with type 2 diabetes: systematic review. PLoS One 8(8):1–12CrossRefGoogle Scholar
  27. Geraci JM, Escalante CP, Freeman JL, Goodwin JS (2005) Comorbid disease and cancer: the need for more relevant conceptual models in health services research. J Clin Oncol 23(30):7399–7404CrossRefGoogle Scholar
  28. Gerteis J, Izrael D, Deitz D, LeRoy L, Ricciardi R, Miller T et al (2014) Multiple chronic conditions chartbook. Agency for Healthcare Research and Quality, RockvilleGoogle Scholar
  29. Giovannetti ER, Wolff JL, Xue Q-L, Weiss CO, Leff B, Boult C et al (2012) Difficulty assisting with health care tasks among caregivers of multimorbid older adults. J Gen Intern Med 27(1):37–44CrossRefGoogle Scholar
  30. Gray L, Smyth K, Palmer R, Zhu X, Callahan J (2002) Heterogeneity in older people: examining physiologic failure, age, and comorbidity. J Am Geriatr Soc 50(12):1955–1961CrossRefGoogle Scholar
  31. Hamilton BE, Martin JA, Osterman MJK, Driscoll AK, Rossen LM (2017) Births: provisional data for 2016. NVSS Vital Statistics Rapid Release 2(2):1–21. https://www.cdc.gov/nchs/data/vsrr/report002.pdf
  32. Harper LM, Caughey AB, Odibo AO, Roehl KA, Zhao Q, Cahill AG (2012) Normal progress of induced labor. Obstet Gynecol 119(6):1113–1118. https://doi.org/10.1097/AOG.0b013e318253d7aa CrossRefGoogle Scholar
  33. Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM (2013) UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia 56(9):1925–1933CrossRefGoogle Scholar
  34. Hicklin K (2016) Decision models for mode of delivery combining patient and clinician risk perceptions and preferences. North Carolina State UniversityGoogle Scholar
  35. Hicklin K, Ivy J, Cobb Payton F, Viswanathan M, Myers E (2018) Exploring the value of waiting during labor. Serv Sci 10(3):334–353.  https://doi.org/10.1287/serv.2018.0205 CrossRefGoogle Scholar
  36. Hillman K (2008) Rapid response systems. Indian J Crit Care 12(2):77–81CrossRefGoogle Scholar
  37. Howard R (1960) Dynamic programming and Markov processes. M.I.T. Press, CambridgezbMATHGoogle Scholar
  38. Institute of Safe Medication Practices (2010) National survey on drug shortages reveals serious impact of patient safety [WWW document]. http://www.ismp.org/pressroom/PR20100923.pdf. Accessed 19 Feb 2012
  39. Iskander KN, Osuchowski MF, Stearns-Kurosawa DJ, Kurosawa S, Stepien D, Valentine C et al (2013) Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding. Physiol Rev 93(3):1247–1288CrossRefGoogle Scholar
  40. Karlstad O, Starup-Linde J, Vestergaard P, Hjellvik V, Bazelier MT, Schmidt MK et al (2013) Use of insulin and insulin analogs and risk of cancer—systematic review and meta-analysis of observational studies. Curr Drug Saf 8(5):333–348CrossRefGoogle Scholar
  41. Kerr EA, Heisler M, Krein SL, Kabeto M, Langa KM, Weir D et al (2007) Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients’ treatment priorities and self-management? J Gen Intern Med 22(12):1635–1640CrossRefGoogle Scholar
  42. Kohn LT, Corrigan JM, Donaldson MS (1999) To err is human: building a safer health system. National Academy Press: Institute of Medicine Report, WashingtonGoogle Scholar
  43. Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC et al (2014) Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 312(1):90CrossRefGoogle Scholar
  44. Loeb DF, Binswanger IA, Candrian C, Bayliss EA (2015) Primary care physician insights into a typology of the complex patient in primary care. Ann Fam Med 13(5):451–455CrossRefGoogle Scholar
  45. Ludikhuize J, Smorenburg SM, de Rooij SE et al (2012) Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the modified early warning score. J Crit Care 27(4):424.e7–424.13CrossRefGoogle Scholar
  46. Maddigan SL, Feeny DH, Johnson JA (2005) Health-related quality of life deficits associated with diabetes and comorbidities in a Canadian National Population Health Survey. Qual Life Res 14(5):1311–1320CrossRefGoogle Scholar
  47. Mannucci PM, Nobili A, REPOSI Investigators (2014) Multimorbidity and polypharmacy in the elderly: lessons from REPOSI. Intern Emerg Med 9(7):723–734CrossRefGoogle Scholar
  48. Marengoni A, Onder G (2015) Guidelines, polypharmacy, and drug-drug interactions in patients with multimorbidity. BMJ (Clinical Research Ed) 350(4):h1059Google Scholar
  49. Mazze RS, Strock ES, Bergenstal RM, Criego A, Cuddihy R, Langer O et al (2011) Staged diabetes management. Wiley-Blackwell, OxfordCrossRefGoogle Scholar
  50. McGloin H, Adam SK, Singer M (1999) Unexpected deaths and referrals to intensive care of patients on general wards. are some cases potentially avoidable? J R Coll Physicians Lond 33:255–259Google Scholar
  51. Nataraj N (2017) Modeling for the care of complex patients. North Carolina State University, RaleighGoogle Scholar
  52. Nataraj N, Ivy JS, Payton FC, Norman J (2018) Diabetes and the hospitalized patient: a cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011. Health Care Manag Sci 21(4):534–553CrossRefGoogle Scholar
  53. Nation’s Report Card (2015) The nation’s report card: mathematics. https://www.nations reportcard.gov/reading_math_2015/#?grade=4. Acceseed 2.1.2018
  54. National Academy of Engineering (2018) NAE grand challenges for engineering in the 21st century: advance personalized learning. http://www.engineeringchallenges.org/challenges/learning.aspx. Accessed 2.1.2018
  55. National Academy of Sciences, National Academy of Engineering & Institute of Medicine (2007) Rising above the gathering storm: energizing and employing America for a brighter economic future. The National Academies Press, Washington, DCGoogle Scholar
  56. National Center for Education Statistics (2006) The nation’s report card: mathematics 2005. http://nces.ed.gov/nationsreportcard/pdf/main2005/2006453.pdf. Accessed 2.1.2018
  57. National Research Council Center for Education: Mathematics Learning Study Committee (2001) Adding it up: helping children learn mathematics. The National Academies Press, Washington, DCGoogle Scholar
  58. National Science Board (2007) National action plan for addressing the critical needs of U.S. science, technology, engineering, and mathematics education system. http://www.nsf.gov/nsb/documents/2007/stem_action.pdf. Accessed 2.1.2018
  59. North Carolina Education Research Data Center (2018) Homepage. Duke University Center for Child and Family Policy. https://childandfamilypolicy.duke.edu/research/nc-education-data-center/. Accessed 2.1.2018
  60. Nunes BP, Flores TR, Mielke GI, Thumé E, Facchini LA (2016) Multimorbidity and mortality in older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr 67:130–138CrossRefGoogle Scholar
  61. Piette J, Kerr E (2006) The impact of comorbid chronic conditions on diabetes care. Diabetes Care 29(3):725–731CrossRefGoogle Scholar
  62. Prytherch DR, Smith GB, Schmidt PE, Featherstone PI (2010) ViEWS—towards a national early warning score for detecting adult inpatient deterioration. Resuscitation 81(8):932–937CrossRefGoogle Scholar
  63. Public Schools of North Carolina (2018a) North Carolina end-of-grade tests at grades 3-8. http://www.ncpublicschools.org/accountability/testing/eog/. Accessed 2.1.2018
  64. Public Schools of North Carolina (2018b) ABC program information. http://www.dpi.state.nc.us/accountability/reporting/abc/2000-01/history. Accessed 2.1.2018
  65. Public Schools of North Carolina (2018c) Educator effectiveness model: EVAAS. http://www.ncpublicschools.org/effectiveness-model/evaas/. Accessed 2.1.2018
  66. Puterman ML (1994) Markov decision processes: discrete stochastic dynamic programming. Wiley-Interscience, New YorkCrossRefGoogle Scholar
  67. Reamer A (2012) Characterizing the progression of performance in mathematics over time: the application of Markovian models to an education system. Doctoral Dissertation. Retrieved from North Carolina State University Libraries: http://www.lib.ncsu.edu/resolver/1840.16/8728. Accessed 2.1.2018
  68. Reamer A, Ivy J, Vila-Parrish A, Young R (2015) Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: a Markovian approach. Comput Hum Behav 47:4–17CrossRefGoogle Scholar
  69. Royal College of Physicians (2012) National early warning score (NEWS): standardizing the assessment of acute illness severity in the NHS. Report of a working party. RCP, LondonGoogle Scholar
  70. Safford MM, Allison JJ, Kiefe CI (2007) Patient complexity: more than comorbidity. The vector model of complexity. J Gen Intern Med 22(Suppl 3):382–390CrossRefGoogle Scholar
  71. Schaink A, Kuluski K, Lyons R, Fortin M (2012) A scoping review and thematic classification of patient complexity: offering a unifying framework. J Comorb 2(1):1–9CrossRefGoogle Scholar
  72. Sengul Orgut I (2015) Modeling for the equitable and effective distribution of food donations under capacity constraints. Doctoral dissertation. https://repository.lib.ncsu.edu/bitstream/handle/1840.16/10469/etd.pdf?sequence=2
  73. Sengul Orgut I, Ivy JS, Uzsoy R, Wilson JR (2015) Modeling for the equitable and effective distribution of donated food under capacity constraints. IIE Trans 48(3):252–266CrossRefGoogle Scholar
  74. Sengul Orgut I, Ivy JS, Uzsoy R (2017) Modeling for the equitable and effective distribution of food donations under stochastic receiving capacities. IISE Trans 49(6):567–578CrossRefGoogle Scholar
  75. Sengul Orgut I, Ivy JS, Uzsoy R, Hale C (2018) Robust optimization approaches for the equitable and effective distribution of donated food. Eur J Oper Res 269(2):516–531CrossRefGoogle Scholar
  76. Simon M, Tzur R (2004) Explicating the role of mathematical tasks in conceptual learning: an elaboration of the hypothetical learning trajectory. Math Think Learn 6(2):91–104CrossRefGoogle Scholar
  77. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M et al (2016) The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315(8):801–810CrossRefGoogle Scholar
  78. Smallwood R (1971) The analysis of economic teaching strategies for a simple learning model. J Math Psychol 8:285–301CrossRefGoogle Scholar
  79. Smith AF, Wood J (1998) Can some in-hospital cardio-respiratory arrests be prevented? A prospective survey. Resuscitation 37:133–137CrossRefGoogle Scholar
  80. Smith GB, Prytherch DR, Schmidt P et al (2006) Hospital-wide physiological surveillance—a new approach to the early identification and management of the sickpatient. Resuscitation 71:19–28CrossRefGoogle Scholar
  81. Smith KN, Vila-Parrish AR, Ivy JS, Abel SR (2016) A simulation approach for evaluating medication supply chain structures. Int J Syst Sci Oper Logist 4(1):13–26Google Scholar
  82. Subbe C, Kruger M, Rutherford P, Gemmel L (2001) Validation of a modified early warning score in medical admissions. QJM 94(10):521–526CrossRefGoogle Scholar
  83. Taffel SM, Placek PJ, Liss T (1987) Trends in the United States cesarean section rate and reasons for the 1980-85 rise. Am J Public Health 77(8):955–959CrossRefGoogle Scholar
  84. Tejada JJ, Diehl K, Ivy JS, Wilson JR, King RE, Ballan MJ et al (2013) Combined DES/SD simulaton model of breast cancer screening for older women: an overview. In: 2013 winter simulations conference (WSC). IEEE, pp 41–53Google Scholar
  85. Tejada JJ, Ivy JS, King RE, Wilson JR, Ballan MJ, Kay MG et al (2014) Combined DES/SD model of breast cancer screening for older women, II: screening-and-treatment simulation. IIE Trans 46(7):707–727CrossRefGoogle Scholar
  86. Tsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA (2015) Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. BMJ 350:g7607CrossRefGoogle Scholar
  87. United States Department of Education (2008) Foundations for success: the final report of the national mathematics advisory panel. http://www2.ed.gov/about/bdscomm/list/mathpanel/report/final-report.pdf. Accessed 2.1.2018
  88. Vila-Parrish AR, Ivy JS, King RE (2008) A simulation based approach for inventory modeling of perishable pharmaceuticals. In: Proceedings of the winter simulation conference, pp 1532–1538Google Scholar
  89. Vila-Parrish AR, Ivy JS, King R, Abel SR (2012) Patient-based pharmaceutical inventory management: a two-stage inventory and production model for perishable products with Markovian demand. Health Syst 1(1):69–83CrossRefGoogle Scholar
  90. Vila-Parrish AR, Ivy JS, He B (2015) Impact of the influenza season on a hospital from a pharmaceutical inventory management perspective. Health Syst 4(1):12–28. Special Issue: Public Health Preparedness; AbingdonCrossRefGoogle Scholar
  91. Vincent JL, Opal SM, Marshall JC, Tracey KJ (2013) Sepsis definitions: time for change. Lancet 381(9868):774–775CrossRefGoogle Scholar
  92. Vogeli C, Shields AE, Lee TA, Gibson TB, Marder WD, Weiss KB et al (2007) Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med 22(S3):391–395CrossRefGoogle Scholar
  93. Wachter RM, Pronovost PJ (2006) The 100,000 lives campaign: a scientific and policy review. Jt Comm J Qual Patient Saf 32(11):621–627CrossRefGoogle Scholar
  94. Wang HE, Shapiro NI, Griffin R, Safford MM, Judd S, Howard G et al (2012) Chronic medical conditions and risk of sepsis. PLoS One 7(10):e48307. Gold JA (ed)CrossRefGoogle Scholar
  95. Ward BW, Schiller JS, Goodman RA (2014) Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis 11:E62Google Scholar
  96. Wolff JL, Starfield B, Anderson G et al (2002) Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med 162(20):2269CrossRefGoogle Scholar
  97. World Health Organization Human Reproduction Programme, 10 April 2015 (2015) WHO statement on caesarean section rates. Reprod Health Matters 23(45):149–150.  https://doi.org/10.1016/j.rhm.2015.07.007
  98. Zhang J, Landy HJ, Branch DW, Burkman R, Haberman S, Gregory KD, Hatjis CG et al (2010a) Contemporary patterns of spontaneous labor with normal neonatal outcomes. Obstet Gynecol 116(6):1281–1287CrossRefGoogle Scholar
  99. Zhang J, Troendle J, Reddy UM, Laughon SK, Branch DW, Burkman R, Landy HJ et al (2010b) Contemporary cesarean delivery practice in the United States. Am J Obstet Gynecol 203(4):326.e1–326.e10.  https://doi.org/10.1016/j.ajog.2010.06.058. Elsevier Inc.CrossRefGoogle Scholar
  100. Zhang S, Payton FC, Ivy JS (2013) Characterizing the impact of mental disorders on HIV patient length of stay and total charges. IIE Trans Healthcare Syst Eng 3(3):139–146CrossRefGoogle Scholar
  101. Zimmerman E, Woolf S, Haley A (2015) Understanding the relationship between education and health. U.S. Department of Health and Human Services Agency for Healthcare Research and Quality. https://www.ahrq.gov/professionals/education/curriculum-tools/population-health/zimmerman.html. Accessed 2.1.2018

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Authors and Affiliations

  • Julie Simmons Ivy
    • 1
    Email author
  • Muge Capan
    • 2
  • Karen Hicklin
    • 3
  • Nisha Nataraj
    • 1
  • Irem Sengul Orgut
    • 4
  • Amy Craig Reamer
    • 1
    • 5
  • Anita Vila-Parrish
    • 6
  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Drexel UniversityPhiladelphiaUSA
  3. 3.University of North Carolina—Chapel HillChapel HillUSA
  4. 4.University of AlabamaTuscaloosaUSA
  5. 5.University of North Carolina-WilmingtonWilmingtonUSA
  6. 6.GartnerStamfordUSA

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