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Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

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Machine Learning for Health Informatics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain.

This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

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References

  1. Mannu, G.S., Kyu, M.M., Bettencourt-Silva, J.H., Loke, Y.K., Clark, A.B., Metcalf, A.K., Potter, J.F., Myint, P.K.: Age but not abcd2 score predicts any level of carotid stenosis in either symptomatic or asymptomatic side in transient ischaemic attack. Int. J. Clin. Prac. 69(9), 948–956 (2015)

    Article  Google Scholar 

  2. Bettencourt-Silva, J., De La Iglesia, B., Donell, S., Rayward-Smith, V.: On creating a patient-centric database from multiple hospital information systems. Methods Inf. Med. 51(3), 210–220 (2012)

    Article  Google Scholar 

  3. Bettencourt-Silva, J.H., Clark, J., Cooper, C.S., Mills, R., Rayward-Smith, V.J., de la Iglesia, B.: Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer. JMIR Med. Inform. 3(3), e26 (2015)

    Article  Google Scholar 

  4. Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35–50 (2012)

    Article  Google Scholar 

  5. Zhang, Y., Padman, R., Patel, N.: Paving the cowpath: learning and visualizing clinical pathways from electronic health record data. J. Biomed. Inform. 58, 186–197 (2015)

    Article  Google Scholar 

  6. Shneiderman, B., Plaisant, C., Hesse, B.W.: Improving healthcare with interactive visualization. Computer 46(5), 58–66 (2013)

    Article  Google Scholar 

  7. Potamias, G.: State of the art on systems for data analysis, information retrieval and decision support. INFOBIOMED project (Deliverable D1) (2006)

    Google Scholar 

  8. HiMSS: Healthcare information and management systems society (HiMSS). http://www.himss.org/clinical-informatics/medical-informatics. Accessed 30 Dec 2015

  9. Dell Ltd: data mining techniques. http://documents.software.dell.com/statistics/textbook/data-mining-techniques. Accessed 30 Dec 2015

  10. Perer, A., Wang, F.: Frequence: interactive mining and visualization of temporal frequent event sequences. In: Proceedings of the 19th International Conference on Intelligent User Interfaces, IUI 2014, pp. 153–162. ACM, New York (2014)

    Google Scholar 

  11. Thomas, J.J., Cook, K.A.: Illuminating the path: the research and development agenda for visual analytics. National Visualization and Analytics Ctr (2005)

    Google Scholar 

  12. Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  13. Hunter, B., Segrott, J.: Re-mapping client journeys and professional identities: a review of the literature on clinical pathways. Int. J. Nurs. Stud. 45(4), 608–625 (2008)

    Article  Google Scholar 

  14. Shahar, Y., Goren-Bar, D., Boaz, D., Tahan, G.: Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artif. Intell. Med. 38(2), 115–135 (2006)

    Article  Google Scholar 

  15. Field, M.J., Lohr, K.N. (eds.): Guidelines for Clinical Practice. Institute of Medicine, National Academy Press, Washington, D.C. (1992). An optional note

    Google Scholar 

  16. Turkay, C., Jeanquartier, F., Holzinger, A., Hauser, H.: On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 117–140. Springer, Heidelberg (2014)

    Google Scholar 

  17. Rind, A., Wang, T.D., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., Shneiderman, B.: Interactive information visualization to explore and query electronic health records. Found. Trends Hum. Comput. Interact. 5(3), 207–298 (2011)

    Article  Google Scholar 

  18. Roque, F.S., Slaughter, L., Tkatšenko, A.: A comparison of several key information visualization systems for secondary use of electronic health record content. In: Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents, Louhi 2010, Stroudsburg, PA, USA, pp. 76–83. Association for Computational Linguistics (2010)

    Google Scholar 

  19. West, V.L., Borland, D., Hammond, W.E.: Innovative information visualization of electronic health record data: a systematic review. J. Am. Med. Inform. Assoc. 22(2), 330–339 (2014)

    Google Scholar 

  20. Caban, J.J., Gotz, D.: Visual analytics in healthcare – opportunities and research challenges. J. Am. Med. Inform. Assoc. 22(2), 260–262 (2015)

    Article  Google Scholar 

  21. Lesselroth, B.J., Pieczkiewicz, D.S.: Data visualization strategies for the electronic health record. In: Berhardt, L.V. (ed.) Advances in Medicine and Biology, vol. 16, pp. 107–140. Nova Science Publisher Inc. (2012)

    Google Scholar 

  22. Aigner, W., Miksch, S., Schuman, H., Tominski, C.: Visualization of Time-Oriented Data. HCI, 1st edn. Springer, London (2011)

    Book  Google Scholar 

  23. Plaisant, C., Milash, B., Rose, A., Widoff, S., Shneiderman, B.: Lifelines: visualizing personal histories. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1996, pp. 221–227. ACM, New York (1996)

    Google Scholar 

  24. Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., Shneiderman, B.: Lifelines: using visualization to enhance navigation and analysis of patient records. In: Proceedings of the AMIA Symposium, pp. 76–80 (1998)

    Google Scholar 

  25. Wang, T.D., Plaisant, C., Quinn, A.J., Stanchak, R., Murphy, S., Shneiderman, B.: Aligning temporal data by sentinel events: discovering patterns in electronic health records. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 457–466. ACM, New York (2008)

    Google Scholar 

  26. Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: Lifeflow: visualizing an overview of event sequences. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 1747–1756. ACM, New York (2011)

    Google Scholar 

  27. Shahar, Y., Cheng, C.: Intelligent visualization and exploration of time-oriented clinical data. In: Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 1999, HICSS-32, vol. Track4, 12 pages, January 1999

    Google Scholar 

  28. Klimov, D., Shahar, Y., Taieb-Maimon, M.: Intelligent visualization and exploration of time-oriented data of multiple patients. Artif. Intell. Med. 49(1), 11–31 (2010)

    Article  Google Scholar 

  29. Duke, J.D., Bolchini, D.: A successful model and visual design for creating context-aware drug-drug interaction alerts. In: AMIA Annual Symposium Proceedings 2011, pp. 339–348 (2011)

    Google Scholar 

  30. Huang, C.W., Lu, R., Iqbal, U., Lin, S.H., Nguyen, P.A.A., Yang, H.C., Wang, C.F., Li, J., Ma, K.L., Li, Y.C.J., Jian, W.S.: A richly interactive exploratory data analysis and visualization tool using electronic medical records. BMC Med. Inform. Decis. Making 15(1), 1–14 (2015)

    Article  Google Scholar 

  31. Riehmann, P., Hanfler, M., Froehlich, B.: Interactive sankey diagrams. In: IEEE Symposium on Information Visualization, INFOVIS 2005, pp. 233–240, October 2005

    Google Scholar 

  32. Wong, B.L.W., Xu, K., Holzinger, A.: Interactive visualization for information analysis in medical diagnosis. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 109–120. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25364-5_11

    Chapter  Google Scholar 

  33. Aigner, W., Miksch, S.: Carevis: integrated visualization of computerized protocols and temporal patient data. Artif. Intell. Med. 37(3), 203–218 (2006). Knowledge-Based Data Analysis in Medicine

    Article  Google Scholar 

  34. Aigner, W., Miksch, S.: Supporting protocol-based care in medicine via multiple coordinated views. In: Second International Conference on Coordinated and Multiple Views in Exploratory Visualization, Proceedings, pp. 118–129, July 2004

    Google Scholar 

  35. Bodesinsky, P., Federico, P., Miksch, S.: Visual analysis of compliance with clinical guidelines. In: Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies, i-Know 2013, pp. 12: 1–12: 8. ACM, New York (2013)

    Google Scholar 

  36. Krause, J., Perer, A., Stavropoulos, H.: Supporting iterative cohort construction with visual temporal queries. IEEE Trans. Vis. Comput. Graph. 22(1), 91–100 (2016)

    Article  Google Scholar 

  37. Gotz, D., Stavropoulos, H.: Decisionflow: visual analytics for high-dimensional temporal event sequence data. IEEE Trans. Vis. Comput. Graph. 20(12), 1783–1792 (2014)

    Article  Google Scholar 

  38. Kamsu-Foguem, B., Tchuent-Foguem, G., Allart, L., Zennir, Y., Vilhelm, C., Mehdaoui, H., Zitouni, D., Hubert, H., Lemdani, M., Ravaux, P.: User-centered visual analysis using a hybrid reasoning architecture for intensive care units. Decis. Support Syst. 54(1), 496–509 (2012)

    Article  Google Scholar 

  39. Stolper, C., Perer, A., Gotz, D.: Progressive visual analytics: User-driven visual exploration of in-progress analytics. IEEE Trans. Vis. Comput. Graph. 20(12), 1653–1662 (2014)

    Article  Google Scholar 

  40. Sturm, W., Schreck, T., Holzinger, A., Ullrich, T.: Discovering medical knowledge using visual analytics. In: Buhler, K., Linsen, L., John, N.W. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association (2015)

    Google Scholar 

  41. Otasek, D., Pastrello, C., Holzinger, A., Jurisica, I.: Visual data mining: effective exploration of the biological universe. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 19–33. Springer, Heidelberg (2014)

    Google Scholar 

  42. Lavrac, N., Bohanec, M., Pur, A., Cestnik, B., Debeljak, M., Kobler, A.: Data mining and visualization for decision support and modeling of public health-care resources. J. Biomed. Inform. 40(4), 438–447 (2007). Public Health Informatics

    Article  Google Scholar 

  43. Perer, A., Wang, F., Hu, J.: Mining and exploring care pathways from electronic medical records with visual analytics. J. Biomed. Inform. 56, 369–378 (2015)

    Article  Google Scholar 

  44. Tate, A., Beloff, N., Al-Radwan, B., Wickson, J., Puri, S., Williams, T., van Staa, T., Bleach, A.: Exploiting the potential of large databases of electronic health records for research using rapid search algorithms and an intuitive query interface. J. Am. Med. Inform. Assoc. 21(2), 292–298 (2014)

    Article  Google Scholar 

  45. Jeanquartier, F., Jean-Quartier, C., Holzinger, A.: Integrated web visualizations for protein-protein interaction databases. BMC Bioinform. 16(1), 1–16 (2015)

    Article  Google Scholar 

  46. Müller, H., Reihs, R., Zatloukal, K., Holzinger, A.: Analysis of biomedical data with multilevel glyphs. BMC Bioinform. 15(6), 1–12 (2014)

    Google Scholar 

  47. Shneiderman, B., Plaisant, C.: Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In: Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, BELIV 2006, pp. 1–7. ACM, New York (2006)

    Google Scholar 

  48. Pickering, B.W., Dong, Y., Ahmed, A., Giri, J., Kilickaya, O., Gupta, A., Gajic, O., Herasevich, V.: The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: a pilot step-wedge cluster randomized trial. Int. J. Med. Inform. 84(5), 299–307 (2015)

    Article  Google Scholar 

  49. Buschmann, F., Henney, K., Schimdt, D.: Pattern-Oriented Software Architecture: On Patterns and Pattern Language, vol. 5. Wiley, New York (2007)

    Google Scholar 

  50. Berner, E.S.: Clinical Decision Support Systems: Theory and Practice, 2nd edn. Springer, New York (2010)

    Google Scholar 

  51. Nakashima, J., Ozu, C., Nishiyama, T., Oya, M., Ohigashi, T., Asakura, H., Tachibana, M., Murai, M.: Prognostic value of alkaline phosphatase flare in patients with metastatic prostate cancer treated with endocrine therapy. Urology 56(5), 843–847 (2000)

    Article  Google Scholar 

  52. Weinstein, S.J., Mackrain, K., Stolzenberg-Solomon, R.Z., Selhub, J., Virtamo, J., Albanes, D.: Serum creatinine and prostate cancer risk in a prospective study. Cancer Epidemiol. Biomark. Prev. 18(10), 2643–2649 (2009)

    Article  Google Scholar 

  53. Hill, A.M., Philpott, N., Kay, J., Smith, J., Fellows, G., Sacks, S.: Prevalence and outcome of renal impairment at prostatectomy. Br. J. Urol. 71(4), 464–468 (1993)

    Article  Google Scholar 

  54. Weiskopf, N.G., Weng, C.: Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J. Am. Med. Inform. Assoc. 20(1), 144–151 (2013)

    Article  Google Scholar 

  55. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1721–1730. ACM, New York (2015)

    Google Scholar 

  56. Van Der Aalst, W., Adriansyah, A., de Medeiros, A.K.A., Arcieri, F., Baier, T., Blickle, T., Bose, J.C., van den Brand, P., Brandtjen, R., Buijs, J., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  57. Mojahed, A., Bettencourt-Silva, J.H., Wang, W., Iglesia, B.: Applying clustering analysis to heterogeneous data using similarity matrix fusion (smf). In: Perner, P. (ed.) MLDM 2015. LNCS, vol. 9166, pp. 251–265. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21024-7_17

    Chapter  Google Scholar 

  58. Aigner, W., Federico, P., Gschwandtner, T., Miksch, S., Rind, A.: Challenges of time-oriented data in visual analytics for healthcare. In: Caban, J.J., Gotz, D. (eds.) IEEE VisWeek Workshop on Visual Analytics in Healthcare, p. 4. IEEE (2012)

    Google Scholar 

  59. Gschwandtner, T., Gärtner, J., Aigner, W., Miksch, S.: A taxonomy of dirty time-oriented data. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds.) CD-ARES 2012. LNCS, vol. 7465, pp. 58–72. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32498-7_5

    Chapter  Google Scholar 

  60. Kopanitsa, G., Hildebrand, C., Stausberg, J., Englmeier, K., et al.: Visualization of medical data based on ehr standards. Methods Inf. Med. 52(1), 43–50 (2013)

    Article  Google Scholar 

  61. Tang, P.C., Patel, V.L.: Major issues in user interface design for health professional workstations: summary and recommendations. Int. J. Bio-Med. Comput. 34(14), 139–148 (1994). The Health Care Professional Workstation

    Article  Google Scholar 

  62. Thyvalikakath, T.P., Dziabiak, M.P., Johnson, R., Torres-Urquidy, M.H., Acharya, A., Yabes, J., Schleyer, T.K.: Advancing cognitive engineering methods to support user interface design for electronic health records. Int. J. Med. Inform. 83(4), 292–302 (2014)

    Article  Google Scholar 

  63. Kay, J.D.: Communicating with clinicians. Ann. Clin. Biochem. 38, 103 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Medicine, Clinical Informatics, University of Cambridge, Cambridge, CB2 0QQ, UK

    Joao H. Bettencourt-Silva

  2. Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK

    Gurdeep S. Mannu

  3. School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

    Beatriz de la Iglesia

Authors
  1. Joao H. Bettencourt-Silva
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  2. Gurdeep S. Mannu
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  3. Beatriz de la Iglesia
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Corresponding author

Correspondence to Joao H. Bettencourt-Silva .

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Editors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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Bettencourt-Silva, J.H., Mannu, G.S., de la Iglesia, B. (2016). Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_5

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