Skip to main content
SpringerLink
Log in

Enabling the integration of clinical event and physiological data for real-time and retrospective analysis

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

There is a growing trend of developing advanced clinical decision support systems that analyze physiological data streams for early detection of a variety of clinical diagnoses. This paper presents prototype architecture for both a real-time mobile clinical event data capture application and an Artemis-based replay system for retrospective analysis and validation of physiological data analytics. These two components provide important information for improving the ability of clinical decision support systems and patient monitoring algorithms to detect and adjust for artifacts caused by clinical events. A description of the prototypes, as well as results from initial prototype testing is provided. Although the sample size is small for the initial testing, significant information with respect to design principles and infrastructure needs were uncovered. Future research directions are identified to improve the mobile application through increased security, robustness, further integration into data mining analysis, and future clinical decision support algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Bellazzi R, Ferrazzi F, Sacchi L (2011) Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Interdiscip Rev Data Min Knowl Discov 1:416–430. doi:10.1002/widm.23

    Article  Google Scholar 

  • Bjering H, McGregor C (2010) A multidimensional temporal abstractive data mining framework. In: Proceedings of the Fourth Australasian Workshop on health informatics and knowledge management—vol 108. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, pp 29–38

  • Blendon RJ, Schoen C, DesRoches C, Osborn R, Zapert K (2003) Common concerns amid diverse systems: health care experiences in five countries. Health Aff 22:106–121

    Article  Google Scholar 

  • Blount M, Ebling MR, Eklund JM, James AG, Mcgregor C, Percival N, Smith KP, Sow D (2010a) Real-time analysis for intensive care: development and deployment of the artemis analytic system. Eng Med Biol Mag IEEE 29:110–118. doi:10.1109/MEMB.2010.936454

    Article  Google Scholar 

  • Blount M, McGregor C, James AG, Sow D, Kamaleswaran R, Tuuha S, Percival J, Percival N (2010b) On the integration of an artifact system and a real-time healthcare analytics system. In: Proceedings of the 1st ACM international health informatics symposium. ACM, Arlington, Virginia, USA, pp 647–655

  • Bressan N, James A, McGregor C (2012a) Physiological data stream analytics to evaluate noxious stimuli in the newborn infant. In: 23rd Meeting of the European Society for Computing and Technology in Anaesthesia and Intensive Care (ESTAIC 2012). Timisoara, Romania, pp 17–18

  • Bressan N, McGregor C, Blount M, Sow D, James AG (2012b) identification of noxious events for newborn infants with a neural network. Archives of disease in childhood 97(supplement 2):abstract number 1618

    Google Scholar 

  • Chase C, Brady WJ (2000) Artifactual electrocardiographic change mimicking clinical abnormality on the ECG. Am J Emerg Med 18:312–316. doi:10.1016/S0735-6757(00)90126-8

    Article  Google Scholar 

  • Cios KJ, William Moore G (2002) Uniqueness of medical data mining. Artif Intell Med 26:1–24. doi:10.1016/S0933-3657(02)00049-0

    Article  Google Scholar 

  • Cunningham S, Symon A, McIntosh N (1994) The practical management of artifact in computerised physiological data. Int J Clin Monit Comput 11:211–216. doi:10.1007/BF01139872

    Article  Google Scholar 

  • Cvach M (2012) Monitor alarm fatigue: an integrative review. Biomed Instrum Technol 46:268–277. doi:10.2345/0899-8205-46.4.268

    Article  Google Scholar 

  • Eley R, Soar J, Buikstra E, Fallon T, Hegney D (2009) Attitudes of Australian nurses to information technology in the workplace: a national survey. Comput Inform Nurs 27:114–121. doi:10.1097/NCN.0b013e318197557e

    Article  Google Scholar 

  • Fabres J, Carlo WA, Phillips V, Howard G, Ambalavanan N (2007) Both extremes of arterial carbon dioxide pressure and the magnitude of fluctuations in arterial carbon dioxide pressure are associated with severe intraventricular hemorrhage in preterm infants. Pediatrics 119:299–305. doi:10.1542/peds.2006-2434

    Article  Google Scholar 

  • Foster D, McGregor C, El-Masri S (2005) A survey of agent-based intelligent decision support systems to support clinical management and research. In: Proceedings of the 2nd international workshop on multi-agent systems for medicine, computational biology, and bioinformatics. Citeseer, pp 16–34

  • Gillespie G (2000) There’s gold in them thar’databases. Health Data Manag 8:40

    Google Scholar 

  • Glover S, Rivers PA, Asoh DA, Piper CN, Murph K (2010) Data mining for health executive decision support: an imperative with a daunting future! Health Serv Manag Res 23:42–46. doi:10.1258/hsmr.2009.009029

    Article  Google Scholar 

  • Griffin MP, Moorman JR (2001) Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. Pediatrics 107:97–104

    Article  Google Scholar 

  • Griffin MP, Lake DE, Bissonette EA, Harrell FE, O’Shea TM, Moorman JR (2005) Heart rate characteristics: novel physiomarkers to predict neonatal infection and death. Pediatrics 116:1070–1074. doi:10.1542/peds.2004-2461

    Article  Google Scholar 

  • Heiat A (2005) Knowledge discovery and data mining in healthcare: challenges and issues. J AHIMA 11:35–55

    Google Scholar 

  • IBM (2012) IBM InfoSphere Streams Version 2.0 Infocenter—TCPSource. http://pic.dhe.ibm.com/infocenter/streams/v2r0/index.jsp?topic=/com.ibm.swg.im.infosphere.streams.spl-standard-toolkit-reference.doc/doc/tcpsource.html. Accessed 28 Apr 2013

  • IBM (2013) IBM—Stream Computing—InfoSphere Streams—Software. http://www-01.ibm.com/software/data/infosphere/streams/. Accessed 27 Apr 2013

  • Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13:395–405

    Article  Google Scholar 

  • Johnson SB, Bakken S, Dine D et al (2008) An electronic health record based on structured narrative. J Am Med Inform Assoc 15:54–64. doi:10.1197/jamia.M2131

    Article  Google Scholar 

  • Kamaleswaran R, McGregor C, Percival J (2009) Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. pp 1667–1670

  • Koh HC, Tan G (2011) Data mining applications in healthcare. J Healthc Inf Manag 19:65

    Google Scholar 

  • Kuperman GJ, Boyle D, Jha A, Rittenberg E, Ma’Luf N, Tanasijevic MJ, Teich JM, Winkelman J, Bates DW (1998) How promptly are inpatients treated for critical laboratory results? J Am Med Inform Assoc 5:112–119. doi:10.1136/jamia.1998.0050112

    Article  Google Scholar 

  • Márquez MF, Colín L, Guevara M, Iturralde P, Hermosillo AG (2002) Common electrocardiographic artifacts mimicking arrhythmias in ambulatory monitoring. Am Heart J 144:187–197

    Article  Google Scholar 

  • McGregor C, Catley C, James AG, Padbury J (2011) Next generation neonatal health informatics with Artemis. Stud Health Technol Inform 169:115

    Google Scholar 

  • McGregor C, Catley C, James AG (2012) Variability analysis with analytics applied to physiological data streams from the neonatal intensive care unit. 25th International Symposium on Computer-Based Medical Systems (CBMS), 2012. pp 1–5

  • McGregor C, Catley C, Padbury J, James A (2013) Late onset neonatal sepsis detection in newborn infants via multiple physiological streams. J Crit Care 28:e11–e12. doi:10.1016/j.jcrc.2012.10.037

    Article  Google Scholar 

  • McIntosh N, Becher J-C, Cunningham S, Stenson B, Laing IA, Lyon AJ, Badger P (2000) Clinical diagnosis of pneumothorax is late: use of trend data and decision support might allow preclinical detection. Pediatr Res 48:408–415

    Article  Google Scholar 

  • Mirvis DM, Berson AS, Goldberger AL et al (1989) Instrumentation and practice standards for electrocardiographic monitoring in special care units. A report for health professionals by a Task Force of the Council on Clinical Cardiology. Am Heart Assoc Circ 79:464–471. doi:10.1161/01.CIR.79.2.464

    Google Scholar 

  • Moorman JR, Carlo WA, Kattwinkel J et al (2011) Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial. J Pediatr 159(900–6):e1

    Google Scholar 

  • Nizami S, Green JR, McGregor C (2013) Implementation of artifact detection in critical care: a methodological review. IEEE Rev Biomed Eng 6:27–42

    Article  Google Scholar 

  • Ohlsson M, Peterson C, Dictor M (2001) Using hidden Markov models to characterize disease trajectories. In: Proceeding of the neural networks and expert systems in medicine and healthcare conference. pp 324–326

  • Percival J, Mcgregor C, Percival N, Kamaleswaran R, Tuuha S (2010) A framework for nursing documentation enabling integration with HER and real-time patient monitoring. Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on. pp 468–473

  • Prokosch HU, Ganslandt T (2009) Perspectives for medical informatics. Methods Inf Med 48:38–44. doi:10.3414/ME9132

    Google Scholar 

  • Raymer KE, Bergström J, Nyce JM (2012) Anaesthesia monitor alarms: a theory-driven approach. Ergonomics 55:1487–1501. doi:10.1080/00140139.2012.722695

    Article  Google Scholar 

  • Reuss E, Menozzi M, Büchi M, Koller J, Krueger H (2004) Information access at the point of care: what can we learn for designing a mobile CPR system? Int J Med Inform 73:363–369

    Article  Google Scholar 

  • Sarani B (2012) Accuracy of an expanded early warning score for patients in general and trauma surgery wards. Br J Surg 99:197–198. doi:10.1002/bjs.7787

    Article  Google Scholar 

  • Sarkar I (2010) Biomedical informatics and translational medicine. J Transl Med 8:1–12. doi:10.1186/1479-5876-8-22

    Article  Google Scholar 

  • Sawyer AM, Deal EN, Labelle AJ, Witt C, Thiel SW, Heard K, Reichley RM, Micek ST, Kollef MH (2011) Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Crit Care Med 39:469–473. doi:10.1097/CCM.0b013e318205df85

    Article  Google Scholar 

  • Schmid F, Goepfert MS, Kuhnt D, Eichhorn V, Diedrichs S, Reichenspurner H, Goetz AE, Reuter DA (2011) The wolf is crying in the operating room: patient monitor and anesthesia workstation alarming patterns during cardiac surgery. Anesth Analg 112:78–83. doi:10.1213/ANE.0b013e3181fcc504

    Article  Google Scholar 

  • Shankaran S, Langer JC, Kazzi SN, Laptook AR, Walsh M (2006) Cumulative index of exposure to hypocarbia and hyperoxia as risk factors for periventricular leukomalacia in low birth weight infants. Pediatrics 118:1654–1659. doi:10.1542/peds.2005-2463

    Article  Google Scholar 

  • Tuzcu V, Nas S, Ulusar U, Ugur A, Kaiser JR (2009) Altered heart rhythm dynamics in very low birth weight infants with impending intraventricular hemorrhage. Pediatrics 123:810–815. doi:10.1542/peds.2008-0253

    Article  Google Scholar 

  • Walls-Esquivel E, Vecchierini MF, Héberlé C, Wallois F (2007) Electroencephalography (EEG) recording techniques and artefact detection in early premature babies. Neurophysiol Clin Clin Neurophysiol 37:299–309. doi:10.1016/j.neucli.2007.09.001

    Article  Google Scholar 

  • Yen P-Y, Gorman PN (2005) Usability testing of a digital pen and paper system in nursing documentation. In: Proceedings of the AMIA Annual Symposium. p 844

  • Zhuang ZY, Churilov L, Burstein F, Sikaris K (2009) Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners. Eur J Oper Res 195:662–675. doi:10.1016/j.ejor.2007.11.003

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Business and Information Technology, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, Canada

    Jennifer Percival & Carolyn McGregor

  2. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, Canada

    Nathan Percival

  3. Division of Neonatology, The Hospital for Sick Children, Toronto, ON, Canada

    Andrew James

  4. Department of Paediatrics, University of Toronto, 555 University Avenue, Toronto, ON, Canada

    Andrew James

Authors
  1. Jennifer Percival
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolyn McGregor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nathan Percival
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew James
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer Percival.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Percival, J., McGregor, C., Percival, N. et al. Enabling the integration of clinical event and physiological data for real-time and retrospective analysis. Inf Syst E-Bus Manage 13, 693–711 (2015). https://doi.org/10.1007/s10257-014-0232-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-014-0232-9

Keywords

Search

Navigation