Appl Clin Inform 2010; 01(01): 32-37
DOI: 10.4338/ACI-2009-12-RA-0023
Research Article
Schattauer GmbH

Distribution of Problems, Medications and Lab Results in Electronic Health Records: The Pareto Principle at Work

Adam Wright PhD
1   Brigham and Women’s Hospital, Boston, MA, USA
2   Harvard Medical School, Boston, MA, USA
3   Partners HealthCare, Boston, MA, USA
,
David W. Bates MD, MSc
1   Brigham and Women’s Hospital, Boston, MA, USA
2   Harvard Medical School, Boston, MA, USA
3   Partners HealthCare, Boston, MA, USA
› Institutsangaben
Weitere Informationen

Correspondence to:

Adam Wright, Ph.D.
Division of General Medicine and Primary Care
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02120
Telefon: (781) 416-8764   
Fax: (617) 732-7072   

Publikationsverlauf

received: 15. Dezember 2009

accepted: 10. März 2010

Publikationsdatum:
16. Dezember 2017 (online)

 

Summary

Background: Many natural phenomena demonstrate power-law distributions, where very common items predominate. Problems, medications and lab results represent some of the most important data elements in medicine, but their overall distribution has not been reported.

Objective: Our objective is to determine whether problems, medications and lab results demonstrate a power law distribution.

Methods: Retrospective review of electronic medical record data for 100,000 randomly selected patients seen at least twice in 2006 and 2007 at the Brigham and Women’s Hospital in Boston and its affiliated medical practices.

Results: All three data types exhibited a power law distribution. The 12.5% most frequently used problems account for 80% of all patient problems, the top 11.8% of medications account for 80% of all medication orders and the top 4.5% of lab result types account for all lab results.

Conclusion: These three data elements exhibited power law distributions with a small number of common items representing a substantial proportion of all orders and observations, which has implications for electronic health record design.


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Conflict of Interest

Neither Dr. Wright nor Dr. Bates have any conflicts to report. Dr. Wright had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

  • References

  • 1 Anderson C. The long tail.. New York: Hyperion; 2008
  • 2 Trueswell RL. Some Behavioral Patterns of Library Users: The 80/20 Rule.. Wilson Libr Bull. 1969; 43 (Suppl. 05) 458-61 69.
  • 3 Bookstein A. Informetric distributions, part I: Unified overview.. J Am Soc Inform Sci. 1990; 41 (05) 368-75.
  • 4 Reed WJ. The Pareto, Zipf and other power laws.. Economics Letters. 2001; 74 (01) 15-9.
  • 5 Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the internet topology.. Proc Conf on Applications, Technologies, Architectures, and Protocols for Computer Communication. 1999: 251-62.
  • 6 Kucera H, Francis WN. Computational analysis of present-day American English.. Providence: Brown University Press; 1967
  • 7 Zipf GK. The psycho-biology of language; an introduction to dynamic philology.. Boston: Houghton Mifflin Company; 1935
  • 8 Gabaix X. Zipf’s Law For Cities: An Explanation.. Quarterly Journal of Economics. 1999; 114 (03) 739-67.
  • 9 Pareto V. Cours d‘économie politique.. Geneva: Librairie Droz; 1964
  • 10 DesRoches CM, Campbell EG, Rao SR. et al. Electronic health records in ambulatory care--a national survey of physicians.. N Engl J Med. 2008; Jul 3 359 (01) 50-60.
  • 11 Bates DW. Physicians and ambulatory electronic health records.. Health Aff (Millwood). 2005; Sep-Oct 24 (Suppl. 05) 1180-9.
  • 12 Adler-Milstein J, McAfee AP, Bates DW, Jha AK. The state of regional health information organizations: current activities and financing.. Health Aff (Millwood) 2008; Jan-Feb 27 (Suppl. 01) w60-9.
  • 13 Evert S, Baroni M. zipfR: Word frequency distributions in R.. Proc 45th Ann Meeting of the Association for Computational Linguistics. 2007: 29-32.
  • 14 Baayen RH. Word frequency distributions.. Dordrecht; Boston: Kluwer Academic; 2001
  • 15 Johnston D, Pan E, Walker J, Bates DW, Middleton B. Patient Safety in the Physician’s Office: Assessing the Value of Ambulatory CPOE. Oakland, CA: California Healthcare Foundation; 2004
  • 16 Lovis C, Chapko MK, Martin DP. et al. Evaluation of a command-line parser-based order entry pathway for the Department of Veterans Affairs electronic patient record.. J Am Med Inform Assoc 2001; Sep-Oct 8 (Suppl. 05) 486-98.
  • 17 Wright A, Sittig DF. Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system.. AMIA Annu Symp Proc. 2006: 819-23.
  • 18 Dart RC, Borron SW, Caravati EM. et al. Expert consensus guidelines for stocking of antidotes in hospitals that provide emergency care.. Ann Emerg Med. 2009; Sep 54 (Suppl. 03) 386-94e1.
  • 19 Bartholdi JJ, Hackman ST. Allocating space in a forward pick area of a distribution center for small parts.. Institute of Industrial Engineers Transactions. 2008; 40 (11) 1046-53.

Correspondence to:

Adam Wright, Ph.D.
Division of General Medicine and Primary Care
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02120
Telefon: (781) 416-8764   
Fax: (617) 732-7072   

  • References

  • 1 Anderson C. The long tail.. New York: Hyperion; 2008
  • 2 Trueswell RL. Some Behavioral Patterns of Library Users: The 80/20 Rule.. Wilson Libr Bull. 1969; 43 (Suppl. 05) 458-61 69.
  • 3 Bookstein A. Informetric distributions, part I: Unified overview.. J Am Soc Inform Sci. 1990; 41 (05) 368-75.
  • 4 Reed WJ. The Pareto, Zipf and other power laws.. Economics Letters. 2001; 74 (01) 15-9.
  • 5 Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the internet topology.. Proc Conf on Applications, Technologies, Architectures, and Protocols for Computer Communication. 1999: 251-62.
  • 6 Kucera H, Francis WN. Computational analysis of present-day American English.. Providence: Brown University Press; 1967
  • 7 Zipf GK. The psycho-biology of language; an introduction to dynamic philology.. Boston: Houghton Mifflin Company; 1935
  • 8 Gabaix X. Zipf’s Law For Cities: An Explanation.. Quarterly Journal of Economics. 1999; 114 (03) 739-67.
  • 9 Pareto V. Cours d‘économie politique.. Geneva: Librairie Droz; 1964
  • 10 DesRoches CM, Campbell EG, Rao SR. et al. Electronic health records in ambulatory care--a national survey of physicians.. N Engl J Med. 2008; Jul 3 359 (01) 50-60.
  • 11 Bates DW. Physicians and ambulatory electronic health records.. Health Aff (Millwood). 2005; Sep-Oct 24 (Suppl. 05) 1180-9.
  • 12 Adler-Milstein J, McAfee AP, Bates DW, Jha AK. The state of regional health information organizations: current activities and financing.. Health Aff (Millwood) 2008; Jan-Feb 27 (Suppl. 01) w60-9.
  • 13 Evert S, Baroni M. zipfR: Word frequency distributions in R.. Proc 45th Ann Meeting of the Association for Computational Linguistics. 2007: 29-32.
  • 14 Baayen RH. Word frequency distributions.. Dordrecht; Boston: Kluwer Academic; 2001
  • 15 Johnston D, Pan E, Walker J, Bates DW, Middleton B. Patient Safety in the Physician’s Office: Assessing the Value of Ambulatory CPOE. Oakland, CA: California Healthcare Foundation; 2004
  • 16 Lovis C, Chapko MK, Martin DP. et al. Evaluation of a command-line parser-based order entry pathway for the Department of Veterans Affairs electronic patient record.. J Am Med Inform Assoc 2001; Sep-Oct 8 (Suppl. 05) 486-98.
  • 17 Wright A, Sittig DF. Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system.. AMIA Annu Symp Proc. 2006: 819-23.
  • 18 Dart RC, Borron SW, Caravati EM. et al. Expert consensus guidelines for stocking of antidotes in hospitals that provide emergency care.. Ann Emerg Med. 2009; Sep 54 (Suppl. 03) 386-94e1.
  • 19 Bartholdi JJ, Hackman ST. Allocating space in a forward pick area of a distribution center for small parts.. Institute of Industrial Engineers Transactions. 2008; 40 (11) 1046-53.