Leveraging Patient Data to Support Clinical Practice

  • Craig S. Ledbetter
Part of the Health Informatics Series book series (HI)

Abstract

To manage appropriately the clinical care of their patients, clinicians must simultaneously consider information from a number of sources:
  1. a.

    The health history of the patient and the patient’s family.

     
  2. b.

    The current health problems of the patient.

     
  3. c.

    The clinical status of the patient including laboratory and other physiological results.

     
  4. d.

    The current treatment regimen and the patient’s individual response to selected treatment options.

     
  5. e.

    A body of knowledge concerning diagnostic possibilities and their treatment recommendations.

     
  6. f.

    Evidence about the efficacy and risks of the various treatment options.

     
  7. g.

    The expected outcomes and prognosis of patients with similar health problems, clinical status, and treatment regimen.

     
  8. h.

    A large body of general scientific and medical knowledge that facilitates the understanding and interpretation of all this information.

     

Keywords

Clinical Decision Support Clinical Decision Support System Clinical Process Order Entry Aggregate Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Trowbridge R, Weingarten S. Clinical decision support systems. In: Shojania KG, Duncan B W, McDonald KM, et al., editors. Making health care safer: a critical analysis of patient safety practices. Evidence Report/Technology Assessment No. 43 (prepared by the University of California at San Francisco–Stanford Evidence–based Practice Center under contract no. 290–97–0013); AHRQ Publication No. 01–E058. Rockville, MD: Agency for Healthcare Research and Quality; 2001. p. 589 – 594.Google Scholar
  2. 2.
    Committee on Quality of Health Care in America, Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001.Google Scholar
  3. 3.
    Committee on Quality of Health Care in America, Institute of Medicine. Kohn LT, Corrigan JM, Donaldson MS, editors. To err is human: building a safer health system. Washington, DC: National Academy Press; 2000.Google Scholar
  4. 4.
    Medical errors—congressional and federal agency activity on errors. JHITA Advocacy Paper. Joint Healthcare Information Technology Alliance. November 2000. Available on-line: http://www.jhita.org/mederrors.htm.
  5. 5.
    Booth M, Riley T, Rosenthal J. Medical errors and adverse events: a report of a 50-state survey. NASHP Publication No. GNL31. Portland, ME: National Academy for State Health Policy; 2000.Google Scholar
  6. 6.
    Forum for State Health Policy Leadership. Frequently asked questions… access and the uninsured. Washington DC: National Conference of State Legislatures. 2002. Available online: http://www.ncsl.org/programs/health/forum/faqaccess.htm.
  7. 7.
    Kaushal R, Bates D. Computerized physician order entry (CPOE) with clinical decision support systems (CDSS). In: Shojania KG, Duncan B W, McDonald KM, et al., editors. Making health care safer: a critical analysis of patient safety practices. Evidence Report/Technology Assessment No. 43 (prepared by the University of California at San Francisco–Stanford Evidence–based Practice Center under contract no. 290–97–0013); AHRQ Publication No. 01–E058. Rockville, MD: Agency for Healthcare Research and Quality; 2001. p. 589 – 594.Google Scholar
  8. 8.
    Perreault LE, Metzger JB. A pragmatic framework for understanding clinical decision support. J HIMSS 1999; 13 (2).Google Scholar
  9. 9.
    Wu R, Peters W, Morgan MW. The next generation of clinical decision support: linking evidence to best practice. J HIMSS 2002; 16 (4): 50–55.Google Scholar
  10. 10.
    Metzger J, Stablein D, Turisco F. Clinical decision support: finding the right path. First reports series. Long Beach, CA: First Consulting Group; 2002.Google Scholar
  11. 11.
    Raiford R. The power and value of embedded clinical decision support. CARING Newsletter. Columbia, MD: The Capital Area Roundtable on Informatics in Nursing; 2002.Google Scholar
  12. 12.
    Elstein AS, Schwarz A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 2002; 324: 729–732.PubMedCrossRefGoogle Scholar
  13. 13.
    Elstein AS. Clinical problem solving and decision psychology: comment on “the epistemology of clinical reasoning.” Acad Med 2000; 75: S134–S136.Google Scholar
  14. 14.
    Norman GR. The epistemology of clinical reasoning: perspectives from philosophy, psychology, and neuroscience. Acad Med 2000; 75 (suppl 10): S127–S133.PubMedCrossRefGoogle Scholar
  15. 15.
    Graber M, Gordon R, Franklin N. Reducing diagnostic errors in medicine: what’s the goal? Acad Med 2002; 77: 981–992.PubMedCrossRefGoogle Scholar
  16. 16.
    Redelmeier DA, Ferris LE, Tu JV, et al. Problems for clinical judgement: introducing cognitive psychology as one more basic science. CMAJ (Can Med Assoc J) 2001; 164 (3): 358–360.Google Scholar
  17. 17.
    Framework for improving performance. Oakbrook Terrace, IL: Joint Commission on Accreditation of Healthcare Organizations; 1994.Google Scholar
  18. 18.
    Nussbaum GM, Ault SP. The best little data warehouse. J HIMSS 1998; 12 (4): 79–93.Google Scholar
  19. 19.
    Amatayakul M. The state of the computer-based patient record. J AHIMA 1998; 69 (9): 34–40.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Craig S. Ledbetter

There are no affiliations available

Personalised recommendations