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Big Data for the Greater Good pp 51–100Cite as

Big Data for Predictive Analytics in High Acuity Health Settings

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Part of the book series: Studies in Big Data ((SBD,volume 42))

Abstract

Automated data capture is more prevalent than ever in healthcare today. Electronic health record systems (EHRs) and real-time data from medical devices and laboratory equipment, imaging, and patient demographics have greatly increased the capability to closely monitor, diagnose, and administer therapies to patients. This chapter focuses on the use of data for in-patient care management in high-acuity spaces, such as operating rooms (ORs), intensive care units (ICUs) and emergency departments (EDs). In addition, a discussion of various types of mathematical techniques and approaches for identifying patients at risk will be discussed as well as the identification and challenges associated with issuing of alarm signals on monitored patients.

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References

  1. The Joint Commission, Sentinel Event Alert, Issue 50, April 8, 2013, The Joint Commission, (2013)

    Google Scholar 

  2. AAMI, Clinical alarms 2011 summit, Association for the Advancement of Medical Instrumentation, (2011)

    Google Scholar 

  3. ECRI Institute, Executive Brief: Top 10 Health Technology Hazards for 2017, ECRI (2016)

    Google Scholar 

  4. The Joint Commission, R3 Report I: Alarm system safety; Issue 5, December 11, 2013, The Joint Commission, (2013)

    Google Scholar 

  5. Gartner, Gartner IT Glossary Definition for Big Data, 18 Oct 2016. (Online). Available: http://www.gartner.com/it-glossary/big-data

  6. S.E. White, A review of big data in health care: challenges and opportunities, Open Access Bioinformatics, 31 Oct 2014

    Google Scholar 

  7. M. McNickle, 5 reasons medical device data is vital to the success of EHRs, Healthcare IT News, 5 Jan 2012

    Google Scholar 

  8. Bernoulli Enterprises, Inc., Bernoulli Health, (Online). Available: http://bernoullihealth.com/solutions/medical-device-integration/. Accessed Nov 21 2016

  9. Qualcomm, (Online). Available: https://www.qualcomm.com/solutions/health-care/hospital-connectivity-and-integration. Accessed Nov 21 2016

  10. J.R. Zaleski, Semantic data alignment of medical devices supports improved interoperability. Med. Res. Arch. 4(4) (2016)

    Google Scholar 

  11. J.R. Zaleski, Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems (Health Information Management Systems Society, Chicago, 2015), pp. 7–56

    Google Scholar 

  12. J.R. Zaleski, Integrating Device Data into the Electronic Medical Record: A Developer’s Guide to Design and a Practitioner’s Guide to Application (Publicis Publishing, Erlangen, 2009), pp. 39–69

    Google Scholar 

  13. Z. Obermeyer, E.J. Emanuel, Predicting the future-big data, machine learning, and clinical medicine. N. Engl. J. Med. 375(13), 29 (2016)

    Article  Google Scholar 

  14. J.R. Zaleski, Medical Device Data and Modeling for Clinical Decision Making (Artech House, Boston, 2011), pp. 173–178

    Google Scholar 

  15. M. Field, K. Lohr, Guidelines for Clinical Practice: From Development to Use (Institute of Medicine, National Academy Press, Washington, 1992)

    Google Scholar 

  16. National Guidelines Clearinghouse, Inclusion Criteria, 1 June 2014. (Online). Available: https://www.guideline.gov/help-and-about/summaries/inclusion-criteria. Accessed Nov 17 2016

  17. Institute for Clinical Systems Improvement, Health Care Protocol: Rapid Response Team, 4th edn. 7 2011. (Online). Available: https://www.icsi.org/_asset/8snj28/RRT.pdf. Accessed Nov 23 2016

  18. J. Li, University of Florida Course Notes: EEL 6537—Introduction to Spectral Analysis—Spring 2010, 2010. (Online). Available: http://www.sal.ufl.edu/eel6537_2010/LSP.pdf. Accessed Nov 21 2016

  19. J. Long, Recovering Signals from Unevenly Sampled Data, 24 11 2014. (Online). Available: http://joseph-long.com/writing/recovering-signals-from-unevenly-sampled-data/. Accessed Nov 21 2016

  20. A. Mathias, F. Grond, R. Guardans, D. Seese, M. Canela, H.H. Diebner, Algorithms for spectral analysis of irregularly sampled time series. J. Stat. Softw. 11(2), 5 (2004)

    Article  Google Scholar 

  21. T. Ruf, The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biol. Rhythm Res. 30(2), 178–201 (1999)

    Article  Google Scholar 

  22. J. Pucik, Heart Rate Variability Spectrum: Physiologic Aliasing and Non-Stationarity Considerations, Bratislava (2009)

    Google Scholar 

  23. “Lomb Scargle periodogram for unevenly sampled time series,” 10 Jan 2013. (Online). Available: https://www.r-bloggers.com/lomb-scargle-periodogram-for-unevenly-sampled-time-series-2/. Accessed 15 Nov 2016

  24. G.L. Brethorst, Frequency Estimation and Generalized Lomb-Scargle Periodograms, 20 Apr 2015. (Online). Available: http://bayes.wustl.edu/glb/Lomb1.pdf

  25. J.R. Zaleski, Investigating the Use of the Lomb-Scargle Periodogram for Heart Rate Variability Quantification, April 2015. (Online). Available: http://www.medicinfotech.com/2015/04/lombscargle-periodogram-measure-heart-rate-variability

  26. M. Malik, Heart rate variability: standards of measurement, physiologic interpretation, and clinical use, in Task Force of the European Society of Cardiology: the North American Society of Pacing Electrophysiology (1996)

    Article  Google Scholar 

  27. M.M. Corrales, B. de la Cruz Torres, A.G. Esquival, M.A.G. Salazar, J.N. Orellana, Normal values of heart rate variability at rest in a young, healthy and active Mexican population. SciRes 4(7), 377–385 (2012)

    Google Scholar 

  28. M. Saeed, M. Villarroel, A. Reisner, G. Clifford, L. Lehman, G. Moody, T. Heldt, T. Kyaw, B. Moody, R. Mark, Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public access ICU database. Crit. Care Med. 39(5):952–960 (2011)

    Google Scholar 

  29. Polar, Heart Rate Variability, 23 Apr 2015. (Online). Available: http://support.polar.com/us-en/support/Heart_Rate_Variability__HRV_. Accessed 15 Nov 2016

  30. K.C. Bilchick, R.D. Berger, Heart rate variability. J. Cardiovasc. Electrophysiol. 17(6), 691–694 (2006)

    Article  Google Scholar 

  31. “Physiotools,” 23 Apr 2015. (Online). Available: http://physionet.org/physiotools/ecgsyn/. Accessed 23-Apr 2015

  32. P. Stoica, J. Li, H. He, Spectral analysis of nonuniformly sampled data: a new approach versus the periodogram. IEEE Trans. Sig. Process. 57(3), 843–858 (2009)

    Article  MathSciNet  Google Scholar 

  33. M. Zechmeister, M. Kuerster, The generalised Lomb-Scargle periodogram—A new formalism for the floating-mean and Keplerian periodograms. Astron. Astrophys. 20, 1 (2009)

    Google Scholar 

  34. S. Ahmad, T. Ramsay, L. Huebsch, S. Flanagan, S. McDiarmid, I. Batkin, L. McIntyre, S.R. Sundaresan, D.E. Maziak, F.M. Shamji, P. Hebert, D. Fergusson, A. Tinmouth, A.J. Seely, Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS ONE 4(8) (2009)

    Article  Google Scholar 

  35. N. Stevens, A.R. Giannareas, V. Kern, A.V. Trevino, M. Fortino-Mullen, Smart alarms: multivariate medical alarm integration for post CABG surgery, in ACM SIGHIT International Health Informatics Symposium (IHI 2012), Miami, FL (2012)

    Google Scholar 

  36. V. Herasevich, S. Chandra, D. Agarwal, A. Hanson, J.C. Farmer, B.W. Pickering, O. Gajic, V. Herasevich, The use of an electronic medical record based automatic claculation tool to quantify risk of unplanned readmission to the intensive care unit: a validation study. J. Crit. Care 26 (2011)

    Google Scholar 

  37. O. Gajic, M. Malinchoc, T.B. Comfere, M.R. Harris, A. Achouiti, M. Yilmaz, M.J. Schultz, R.D. Hubmayr, B. Afessa, J.C. Farmer, The stability and workload index for transfer score predicts unplanned intensive care unit patient readmission: Initial development and validation. Crit. Care Med. 36(3) (2008)

    Article  Google Scholar 

  38. U.R. Ofoma, S. Chandra, R. Kashyap, V. Herasevich, A. Ahmed, O. Gajic, B.W. Pickering, C.J. Farmer, Findings from the implementation of a validated readmission predictive tool in the discharge workflow of a medical intensive care unit. AnnalsATS 11(5) (2014)

    Article  Google Scholar 

  39. V.M. Boniatti, M.M. Boniatti, C.F. Andrade, C.C. Zigiotto, P. Kaminski, S.P. Gomes, R. Lippert, M.C. Diego, E.A. Felix, The modified integrative weaning index as a predictor of extubation failure. Respir. Care 59(7) (2014)

    Article  Google Scholar 

  40. J. Garah, O.E. Adiv, I. Rosen, R. Shaoul, The value of Integrated Pulmonary Index (IPI) during endoscopies in children. J. Clin. Monit. Comput. 29, 773–778 (2015)

    Article  Google Scholar 

  41. M.B. Weinger, L.A. Lee, No patient shall be harmed by opioid-induced respiratory depression. APSF Newslett. 26(2), 21–40 (2011)

    Google Scholar 

  42. J.F. Mudge, L.F. Baker, C.B. Edge, J.E. Houlahan, Setting an optimal alpha that minimizes errors in null hypothesis significance tests. PLoS ONE 7(2) (2012)

    Article  Google Scholar 

  43. M.A. Cretikos, R. Bellomo, K. Hillman, J. Chen, S. Finfer, A. Flabouris, Respiratory rate: the neglected vital sign. Med. J. Aust. 188(11), 657–659 (2008)

    Google Scholar 

  44. B. Page, “Capnography helps conscious patients too. J. Emerg. Med. Serv. 29 (2010)

    Google Scholar 

  45. J.R. Zaleski, Mathematical Techniques for Mitigating Alarm Fatigue, 14 Oct 2014. (Online). Available: http://www.medicinfotech.com/2014/10/mathematical-techniques-mitigating-alarm-fatigue. Accessed 15 Nov 2016

  46. I. MacDonald, Hospitals rank alarm fatigue as top patient safety concern. Fierce Healthcare, 22 Jan 2014

    Google Scholar 

  47. G. Gachco, J. Perez-Calle, A. Barbado, J. Lledo, R. Ojea, V. Fernandez-Rodriguez, Capnography is superior to pulse oximetry for the detection of respiratory depression during colonoscopy. Rev. Esp. Enferm. Dig. 102(2), 86–89 (2010)

    Google Scholar 

  48. R.G. Soto, E.S. Fu, J.H. Vila, R.V. Miquel, Capnography accurately detects apnea during monitoried anesthesia care. Anesth. Analg. 99, 379–382 (2004)

    Article  Google Scholar 

  49. D. Carr, A. Cartwright, Rationales and applications for capnography monitoring during sedation. Clinical Foundations (2011)

    Google Scholar 

  50. B.S. Kodali, Capnography outside the operating rooms. Anesthesiology 118(1), 192–201 (2013)

    Article  Google Scholar 

  51. M. Wong, A. Mabuyi, B. Gonzalez, First national survey of patient-controlled analgesia practices, A Promise to Amanda Foundation and the Physician-Patient Alliance for Health & Safety, March-April 2013

    Google Scholar 

  52. B. Sullivan, 5 things to know about capnography and respiratory distress, EMS1 News, 15 Oct 2015

    Google Scholar 

  53. G. Welch, G. Bishop, An Introduction to the Kalman Filter, (Chapel Hill, NC, 27599-3175: ACM, Inc., pp. 24–25) (2001)

    Google Scholar 

  54. L. Kleeman, Understanding and Applying Kalman Filtering, (Online). Available: http://biorobotics.ri.cmu.edu/papers/sbp_papers/integrated3/kleeman_kalman_basics.pdf. Accessed 15 Nov 2016

  55. R. Sameni, M. Shamsollahi, C. Jutten, Filtering electrocardiogram signals using the extended Kalman Filter, in 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Sep 2005, Shanghai, China, Shanghai, China, 2005

    Google Scholar 

  56. R.F. Suoto, J.Y. Ishihara, A robust extended Kalman Filter for discrete-time systems with uncertain dynamics, measurements and correlated noise, in American Control Conference, St. Louis, MO, 2009

    Google Scholar 

  57. R. Gao, R. Yan, Chapter 2: from fourier transform to wavelet transform: a historical perspective, Wavelets: Theory and Applicationf for Manufacturing (LLC, Springer Science + Business Media, 2011), p. 18

    Chapter  Google Scholar 

  58. B.D. Patil, Introduction to Wavelet, Spring 2014. (Online). Available: https://inst.eecs.berkeley.edu/~ee225b/sp14/lectures/shorterm.pdf. Accessed 13 Dec 2016

  59. G. Dallas, Wavelets 4 Dummies: Signal Processing, Fourier Transforms and Heisenberg, 14 5 2014. (Online). Available: https://georgemdallas.wordpress.com/2014/05/14/wavelets-4-dummies-signal-processing-fourier-transforms-and-heisenberg/. Accessed 13 Dec 2016

  60. C.S. Burrus, R.A. Gopinath, H. Gao, Introduction to Wavelets and Wavelet Transforms: A Primer (Simon & Schuster, Upper Saddle River, 1998)

    Google Scholar 

  61. G. Strang, Wavelet transforms versus fourier transforms. Bull. Am. Math. Soc. 28(2), 288–305 (1993)

    Article  MathSciNet  Google Scholar 

  62. A.C. Bridi, T.Q. Louro, R.C. Lyra da Silva, Clinical Alarm in inensive care: implications of alarm fatigue for the safety of patients, Rev. Lat. Am. Enfermagem 22(6), 1034–1040, Nov–Dec (2014)

    Google Scholar 

  63. CMS, HCAHPS: Patient’s Perspectives of Care Survey, (Online). Available: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/HospitalHCAHPS.html. Accessed 31 August 2017

  64. CMS, (Online). Available: www.hospitalcompare.hhs.gov

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Authors and Affiliations

  1. Bernoulli, Enterprise, Inc., Milford, CT, USA

    John Zaleski

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  1. John Zaleski
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Correspondence to John Zaleski .

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Editors and Affiliations

  1. Operations and Information Management, Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

  2. Buckingham Business School, University of Buckingham, Buckingham, United Kingdom

    Vincent Charles

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Zaleski, J. (2019). Big Data for Predictive Analytics in High Acuity Health Settings. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_4

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