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Personalizing Healthcare in Smart Cities

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Smart Cities in Application

Abstract

Patient healthcare personalization provides new opportunities for health care systems in their pursuit of better patient outcomes and commitment to quality and safety. Much like the recent expansion of product customization, healthcare personalization has been expanding lately due to a variety of factors including technological advances that connect data, people, and systems. Much of the existing research has viewed service variability as something negative that must be controlled. However, customer variability in service needs provides an opportunity to deliver more value for patients through personalization of services. This chapter will examine patient healthcare personalization and the design of systems for service customization within smart cities. The chapter discusses patient healthcare personalization and service design, focusing on the concept of patient variability and the use of innovative computer and information science and engineering approaches to support the transformation of health and medicine.

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References

  1. Kannan P, Healey J (2011) Service customization research: a review and future directions. In: The science of service systems. Springer, Boston, pp 297–324

    Chapter  Google Scholar 

  2. Pérez E, Ambati R, Ruiz-Torres A (2018) Maximising the number of on-time jobs on parallel servers with sequence dependent deteriorating processing times and periodic maintenance. Int J Oper Res 32:267–289

    Article  MathSciNet  Google Scholar 

  3. Ruiz-Torres A, Alomoto N, Paletta G, Pérez E (2015) Scheduling to maximise worker satisfaction and on-time orders. Int J Prod Res 53:2836–2852

    Article  Google Scholar 

  4. Ruiz-Torres A, Paletta G, Perez-Roman E (2015) Maximizing the percentage of on-time jobs with sequence dependent deteriorating process times. Int J Oper Res Inform Syst 6:1–18

    Article  Google Scholar 

  5. Kannan P, Proenca J (2008) Design of service systems under variability: research issues. In: Proceedings of 41st Hawaii international conference on system sciences, pp 116–116

    Google Scholar 

  6. Frei F (2006) Customer-introduced variability in service operations. Harv Bus Rev 84:606–625

    Google Scholar 

  7. Pramanik M et al (2017) Smart health: big data enabled health paradigm within smart cities. Expert Syst Appl 87:370–383

    Article  Google Scholar 

  8. Kunene K, Weistroffer H (2008) An approach for predicting and describing patient outcome using multicriteria decision analysis and decision rules. Eur J Oper Res 185:984–997

    Article  Google Scholar 

  9. Liu Y, Kapur K (2008) New patient-centered models of quality-of-life measures for evaluation of interventions for multi-stage diseases. IIE Trans 40:870–879

    Article  Google Scholar 

  10. Shechter S, Bailey M, Schaefer A (2008) A modeling framework for replacing medical therapies. IIE Trans 40:861–869

    Article  Google Scholar 

  11. Preciado-Walters F et al (2004) A coupled column generation, mixed integer approach to optimal planning of intensity modulated radiation therapy for cancer. Math Program 101:319–338

    Article  MathSciNet  Google Scholar 

  12. Pérez E et al (2010) Modeling and simulation of nuclear medicine patient service management in DEVS. Simulation 86:481–501

    Article  Google Scholar 

  13. Green L, Savin S (2008) Reducing delays for medical appointments: a queueing approach. Oper Res 56:1526–1538

    Article  MathSciNet  Google Scholar 

  14. Suryadevara N, Mukhopadhyay S (2014) Determining wellness through an ambient assisted living environment. IEEE Intell Syst 29:30–37

    Article  Google Scholar 

  15. HealthIT.gov (2018) What is a patient portal? Available: https://www.healthit.gov/faq/what-patient-portal

  16. HIT PE (2018) Top hospital patient portal vendors by implementations. Available: https://patientengagementhit.com/news/top-hospital-patient-portal-vendors-by-implementations

  17. Kruse C, Bolton K, Freriks G (2015) The effect of patient portals on quality outcomes and its implications to meaningful use: a systematic review. J Med Internet Res 17:e22

    Article  Google Scholar 

  18. Cerrato P (2011) Hospital rooms get smart. Information Week Online. Available: www.informationweek.com/healthcare/clinical-information-systems/hospital-rooms-get-smart/d/d-id/1100822?

  19. Alvarado M, Ntaimo L (2018) Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming. Health Care Manag Sci 21:87–104

    Article  Google Scholar 

  20. Alvarado M et al (2018) Modeling and simulation of oncology clinic operations in discrete event system specification. Simulation 94:105–121

    Article  Google Scholar 

  21. Dzubay D, Pérez E (2016) The impact of system factors on patient perceptions of quality of care. In: Proceedings of the winter simulation conference, pp 2169–2179

    Google Scholar 

  22. Pérez E et al (2011) Patient and resource scheduling of multi-step medical procedures in nuclear medicine. IIE Trans Healthc Syst Eng 1:168–184

    Article  Google Scholar 

  23. Sowle T et al (2014) A simulation-IP based tool for patient admission services in a multi-specialty outpatient clinic. In: Proceedings of the winter simulation conference, pp 1186–1197

    Google Scholar 

  24. Walker D et al (2015) Towards a simulation based methodology for scheduling patient and providers at outpatient clinics. In: Proceedings of the winter simulation conference, pp 1515–1524

    Google Scholar 

  25. Pérez E et al (2013) Stochastic online appointment scheduling of multi-step sequential procedures in nuclear medicine. Health Care Manag Sci 16:281–299

    Article  Google Scholar 

  26. Reese H et al (2017) Improving patient waiting time at a pure walk-in clinic. In: Proceedings of the winter simulation conference, pp 2764–2773

    Google Scholar 

  27. Doll M et al (2015) Touchless technologies for decontamination in the hospital: a review of hydrogen peroxide and UV devices. Curr Infect Dis Rep 17:44

    Article  Google Scholar 

  28. Pérez E et al (2017) Assessing catheter associated urinary tract infections prevention interventions in intensive care units: a discrete event simulation study. IISE Trans Healthc Syst Eng 7:43–52

    Article  Google Scholar 

  29. Pérez E et al (2017) Catheter-associated urinary tract infections: challenges and opportunities for the application of systems engineering. Health Syst 6:68–76

    Article  Google Scholar 

  30. Alvarado M et al (2017) Barriers to remote health interventions for type 2 diabetes: a systematic review and proposed classification scheme. J Med Internet Res 19:e28

    Article  Google Scholar 

  31. Cook D et al (2018) Using smart city technology to make healthcare smarter. Proc IEEE 106:708–722

    Article  Google Scholar 

  32. Williams J, Cook D (2017) Forecasting behavior in smart homes based on sleep and wake patterns. Technol Health Care 25:89–110

    Article  Google Scholar 

  33. Kavakiotis I et al (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104–116

    Article  Google Scholar 

  34. Godman B et al (2013) Personalizing health care: feasibility and future implications. BMC Med 11:179

    Article  Google Scholar 

  35. Lee E et al (2018) Outcome-driven personalized treatment design for managing diabetes. Interfaces 48:422–435

    Article  Google Scholar 

  36. Asthma and Allergy Foundation of America (2018) The most challenging places to live with ASTHMA. https://www.aafa.org/allergy-capitals/

  37. Allen A (2017) How bourbon and big data are cleaning up Louisville. Politico. https://www.politico.com/magazine/story/2017/11/16/louisville-pollution-data-what-works-215836

  38. Barrett M, Combs V, Su J, Henderson K, Tuffli M, The AIR Louisville Collaborative (2018) AIR Louisville: addressing asthma with technology, crowdsourcing, cross-sector collaboration, and policy. Health Aff (Millwood) 37(4):525–534. https://doi.org/10.1377/hlthaff.2017.1315

    Article  Google Scholar 

  39. Merchant R et al (2018) Digital health intervention for asthma: patient-reported value and usability. JMIR Mhealth Uhealth 6(6):e133

    Article  Google Scholar 

  40. Merchant R, Inamdar R, Quade R (2016) Effectiveness of population health management using the propeller health asthma platform: a randomized clinical trial. J Allergy Clin Immunol Pract 4(3):455–463

    Article  Google Scholar 

  41. Merchant R et al (2018) Impact of a digital health intervention on asthma resource utilization. World Allergy Org J 11(1):28

    Article  Google Scholar 

  42. Propeller Health (2018) Air by propeller. API. https://www.propellerhealth.com/air-by-propeller/

  43. Kesselheim A, Avorn J (2016) Approving a problematic muscular dystrophy drug: implications for FDA policy. JAMA 316(22):2357–2358

    Article  Google Scholar 

  44. Chen J et al (2016) Racial and ethnic disparities in health care access and utilization under the affordable care act. Med Care 54(2):140

    Article  Google Scholar 

  45. Powers B, Rinefort S, Jain S (2016) Nonemergency medical transportation: delivering care in the era of Lyft and Uber. JAMA 316(9):921–922

    Article  Google Scholar 

  46. Algase DL et al (2007) Mapping the maze of terms and definitions in dementia-related wandering. Aging Ment Health 11:686–698

    Article  Google Scholar 

  47. Moore P et al (2013) Monitoring and detection of agitation in dementia: towards real-time and big-data solutions. In: Proceedings of the P2P, parallel, grid, cloud and internet computing (3PGCIC), pp 128–135

    Google Scholar 

  48. Dawadi PN, Cook DJ, Schmitter-Edgecombe M (2015). Automated cognitive health assessment from smart home-based behavior data IEEE journal of biomedical and health informatics, 20(4), 1188–1194

    Article  Google Scholar 

  49. Van Sickle D, Barrett M (2018) Transforming global public health using connected medicines. Respir Drug Deliv 1:61–70

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ingram School of Engineering, Texas State University, San Marcos, TX, USA

    Eduardo Pérez-Roman

  2. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Michelle Alvarado

  3. Science and Research, Propeller Health, Madison, WI, USA

    Meredith Barrett

Authors
  1. Eduardo Pérez-Roman
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  2. Michelle Alvarado
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  3. Meredith Barrett
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Corresponding author

Correspondence to Eduardo Pérez-Roman .

Editor information

Editors and Affiliations

  1. Ingram School of Engineering, Texas State University, San Marcos, TX, USA

    Stan McClellan

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Pérez-Roman, E., Alvarado, M., Barrett, M. (2020). Personalizing Healthcare in Smart Cities. In: McClellan, S. (eds) Smart Cities in Application. Springer, Cham. https://doi.org/10.1007/978-3-030-19396-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-19396-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19395-9

  • Online ISBN: 978-3-030-19396-6

  • eBook Packages: EngineeringEngineering (R0)

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