Skip to main content

Advertisement

Log in

A Review of Analytics and Clinical Informatics in Health Care

  • Education & Training
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. “Big Data”). Health care systems have leveraged Big Data for quality and performance improvements using analytics—the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Stark, P., Congressional intent for the HITECH Act. Am. J. Manag. Care 16:24–28, 2010.

    Google Scholar 

  2. Jamoom, E., Beatty, P., Bercovitz, A., Woodwell, D., Palso, K., and Rechsteiner, E., Physician adoption of electronic health record systems: United States, 2011. NCHS Data Brief 98:1–8, 2012.

    Google Scholar 

  3. Horner, P., and Basu, A., Analytics and the future of health care. Analytics 1:1–7, 2012.

    Google Scholar 

  4. Glaser, J., HITECH lays the foundation for more ambitious outcomes-based reimbursement. Am. J. Manag. Care 16:19–23, 2010.

    Google Scholar 

  5. Diamond, G. A., and Kaul, S., Evidence-based financial incentives for healthcare reform. Circ: Cardiovasc. Qual. Outcomes. 2:134–140, 2009.

    Google Scholar 

  6. Jacobs, A., The pathologies of big data. Commun. ACM 52:36–44, 2009.

    Article  Google Scholar 

  7. Wolfe, P., Making sense of big data. Proc. Natl. Acad. Sci. U. S. A. 110:18031–18032, 2013.

    Article  MathSciNet  Google Scholar 

  8. Costa, F. F., Big data in biomedicine. Drug Discov. Today. 2013.

  9. Stead, W. W., Searle, J. R., Fessler, H. E., Smith, J. W., and Shortliffe, E. H., Biomedical informatics: changing what physicians need to know and how they learn. Acad. Med. 86:429–434, 2011.

    Article  Google Scholar 

  10. Davenport, T. H., Harris, J., and Shapiro, J., Competing on talent analytics. Harv. Bus. Rev. 88:52–8, 150, 2010.

    Google Scholar 

  11. Kudyba, S., Healthcare informatics: increasing efficiency and productivity. Taylor Francis, New York, 2010.

    Book  Google Scholar 

  12. Erhardt, R. A., Schneider, R., and Blaschke, C., Status of text-mining techniques applied to biomedical text. Drug Discov. Today 11:315–325, 2006.

    Article  Google Scholar 

  13. Michelson, J. D., Pariseau, J. S., and Paganelli, W. C., Assessing surgical site infection risk factors using electronic medical records and text mining. Am. J. Infect. Control 42:333–336, 2014.

    Article  Google Scholar 

  14. Gotz, D., Stavropoulos, H., Sun, J., and Wang, F., ICDA: a platform for intelligent care delivery analytics. AMIA Annu. Symp. Proc. 2012:264–273, 2012.

    Google Scholar 

  15. Miriovsky, B. J., Shulman, L. N., and Abernethy, A. P., Importance of health information technology, electronic health records, and continuously aggregating data to comparative effectiveness research and learning health care. J. Clin. Oncol. 30:4243–4248, 2012.

    Article  Google Scholar 

  16. Wharam, J. F., and Weiner, J. P., The promise and peril of healthcare forecasting. Am. J. Manag. Care 18:82–85, 2012.

    Google Scholar 

  17. Rojas, C. C., Patton, R. M., and Beckerman, B. G., Characterizing mammography reports for health analytics. J. Med. Syst. 35:1197–1210, 2011.

    Article  Google Scholar 

  18. Blount, M., Ebling, M. R., Eklund, J. M., James, A. G., McGregor, C., Percival, N., Smith, K. P., and Sow, D., Real-time analysis for intensive care: development and deployment of the artemis analytic system. IEEE Eng. Med. Biol. Mag. 29:110–118, 2010.

    Article  Google Scholar 

  19. Holdsworth, M. T., Fichtl, R. E., Raisch, D. W., Hewryk, A., Behta, M., Mendez-Rico, E., Wong, C. L., Cohen, J., Bostwick, S., and Greenwald, B. M., Impact of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 120:1058–1066, 2007.

    Article  Google Scholar 

  20. van Rosse, F., Maat, B., Rademaker, C. M., van Vught, A. J., Egberts, A. C., and Bollen, C. W., The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: a systematic review. Pediatrics 123:1184–1190, 2009.

    Article  Google Scholar 

  21. Chau, A., and Ehrenfeld, J. M., Using real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol. Clin. 29:57–69, 2011.

    Article  Google Scholar 

  22. Resetar, E., Reichley, R. M., Noirot, L. A., Dunagan, W. C., and Bailey, T. C., Customizing a commercial rule base for detecting drug-drug interactions. AMIA Annu. Symp. Proc. 1094, 2005.

  23. Guzek, M., Zorina, O. I., Semmler, A., Gonzenbach, R. R., Huber, M., Kullak-Ublick, G. A., Weller, M., and Russmann, S., Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System). Pharmacoepidemiol. Drug Saf. 20:930–938, 2011.

    Google Scholar 

  24. Slonimc, N., Carmeli, B., Goldsteen, A., Keller, O., Kent, C., and Rinott, R., Knowledge-analytics synergy in clinical decision support. Stud. Health Technol. Inform. 180:703–707, 2012.

    Google Scholar 

  25. Chan, M., Estève, D., Fourniols, J. Y., Escriba, C., and Campo, E., Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56:137–156, 2012.

    Article  Google Scholar 

  26. Banaee, H., Ahmed, M. U., and Loutfi, A., Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13:17472–17500, 2013.

    Article  Google Scholar 

  27. Tseng, K. C., Hsu, C. L., and Chuang, Y. H., Designing an intelligent health monitoring system and exploring user acceptance for the elderly. J. Med. Syst. 37:9967, 2013.

    Article  Google Scholar 

  28. Baig, M. M., and Gholamhosseini, H., Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37:9898, 2013.

    Article  Google Scholar 

  29. Schouten, P., Big data in health care: solving provider revenue leakage with advanced analytics. Healthc. Finan. Manag. 67:40–42, 2013.

    Google Scholar 

  30. Kudyba, S., and Gregorio, T., Identifying factors that impact patient length of stay metrics for healthcare providers with advanced analytics. Health Inf. J. 16:235–245, 2010.

    Article  Google Scholar 

  31. Bradley, P., and Kaplan, J., Turning hospital data into dollars. Healthc. Finan. Manage 64:64–68, 2010.

    Google Scholar 

  32. Costantino, M. E., Frey, B., Hall, B., and Painter, P., The influence of a postdischarge intervention on reducing hospital readmissions in a medicare population. Popul. Health Manag. 16:310–316, 2013.

    Article  Google Scholar 

  33. Buell, D., Leveraging data and analytics to generate new revenue. Healthc. Financ. Manage 67:40–2, 44, 2013.

    Google Scholar 

  34. Tufte, E. R., The visual display of quantitative information, 2nd edition. Graphics Press, Cheshire, 2001.

    Google Scholar 

  35. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., and Becker, B. (Eds.), The data warehouse lifecycle toolkit, 2nd edition. Wiley, Hoboken, 2008.

    Google Scholar 

  36. Barton, D., and Court, D., Making advanced analytics work for you. Harv. Bus. Rev. 90:78–83, 128, 2012.

    Google Scholar 

  37. Thomas, J. J., and Cook, K. A., A visual analytics agenda. IEEE Comput. Graph. Appl. 26:10–13, 2006.

    Article  Google Scholar 

  38. Kumasaka, N., Nakamura, Y., and Kamatani, N., The textile plot: a new linkage disequilibrium display of multiple-single nucleotide polymorphism genotype data. PLoS One 5:e10207, 2010.

    Article  Google Scholar 

  39. Naumova, E. N., Visual analytics for immunologists: data compression and fractal distributions. Self Nonself 1:241–249, 2010.

    Article  Google Scholar 

  40. Chui, K. K., Wenger, J. B., Cohen, S. A., and Naumova, E. N., Visual analytics for epidemiologists: understanding the interactions between age, time, and disease with multi-panel graphs. PLoS One 6:e14683, 2011.

    Article  Google Scholar 

  41. Wang, T. D., Wongsuphasawat, K., Plaisant, C., and Shneiderman, B., Extracting insights from electronic health records: case studies, a visual analytics process model, and design recommendations. J. Med. Syst. 35:1135–1152, 2011.

    Article  Google Scholar 

  42. Mane, K. K., Bizon, C., Owen, P., Gersing, K., Mostafa, J., and Schmitt, C., Patient electronic health data-driven approach to clinical decision support. Clin. Transl. Sci. 4:369–371, 2011.

    Article  Google Scholar 

  43. Mane, K. K., Bizon, C., Schmitt, C., Owen, P., Burchett, B., Pietrobon, R., and Gersing, K., VisualDecisionLinc: a visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry. J. Biomed. Inform. 45:101–106, 2012.

    Article  Google Scholar 

  44. Gálvez, J. A., Ahumada, L., Simpao, A. F., Lin, E. E., Bonafide, C. P., Choudhry, D., England, W. R., Jawad, A. F., Friedman, D., Sesok-Pizzini, D. A., and Rehman, M. A., Visual analytical tool for evaluation of 10-year perioperative transfusion practice at a children’s hospital. J. Am. Med. Inform. Assoc. 2013.

  45. Kang, Y. A., Görg, C., and Stasko, J., How can visual analytics assist investigative analysis? Design implications from an evaluation. IEEE Trans. Vis. Comput. Graph 17:570–583, 2011.

    Article  Google Scholar 

  46. Goldsmith, M. R., Transue, T. R., Chang, D. T., Tornero-Velez, R., Breen, M. S., and Dary, C. C., PAVA: physiological and anatomical visual analytics for mapping of tissue-specific concentration and time-course data. J. Pharmacokinet. Pharmacodyn. 37:277–287, 2010.

    Article  Google Scholar 

  47. Perer, A., and Sun, J., MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. AMIA Annu. Symp. Proc. 2012:716–725, 2012.

    Google Scholar 

  48. Lo, Y. S., Lee, W. S., and Liu, C. T., Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections. J. Med. Syst. 37:9923, 2013.

    Article  Google Scholar 

  49. Rajwan, Y. G., Barclay, P. W., Lee, T., Sun, I. F., Passaretti, C., and Lehmann, H., Visualizing central line-associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners-an evaluation study. Online J. Public Health Inform. 5:218, 2013.

    Google Scholar 

  50. Fouzas, S., Priftis, K. N., and Anthracopoulos, M. B., Pulse oximetry in pediatric practice. Pediatrics 128:740–752, 2011.

    Article  Google Scholar 

  51. Edelstein, P., Emerging directions in analytics. Predictive analytics will play an indispensable role in healthcare transformation and reform. Health Manag. Technol. 34:16–17, 2013.

    Google Scholar 

  52. Detmer, D. E., Munger, B. S., and Lehmann, C. U., Clinical informatics board certification: history, current status, and predicted impact on the clinical informatics workforce. Appl. Clin. Inform. 1:11–18, 2010.

    Article  Google Scholar 

  53. Lehmann, C. U., Shorte, V., and Gundlapalli, A. V., Clinical informatics sub-specialty board certification. Pediatr. Rev. 34:525–530, 2013.

    Article  Google Scholar 

  54. Galvez, J. A., Simpao, A. F., Utidjian, L. H., and Rehman, M. A., Clinical informatics: bridging the gap between physician and information technology. AAP News 11:14, 2013.

    Google Scholar 

  55. Kerr, W. T., Lau, E. P., Owens, G. E., and Trefler, A., The future of medical diagnostics: large digitized databases. Yale J. Biol. Med. 85:363–377, 2012.

    Google Scholar 

  56. Chawla, N. V., and Davis, D. A., Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28(Suppl 3):S660–S665, 2013.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Allan F. Simpao.

Additional information

This article is part of the Topical Collection on Education & Training

Rights and permissions

Reprints and permissions

About this article

Cite this article

Simpao, A.F., Ahumada, L.M., Gálvez, J.A. et al. A Review of Analytics and Clinical Informatics in Health Care. J Med Syst 38, 45 (2014). https://doi.org/10.1007/s10916-014-0045-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-014-0045-x

Keywords

Navigation