Journal of General Internal Medicine

, Volume 18, Issue 8, pp 639–645 | Cite as

Is there a July Phenomenon?

The effect of July admission on intensive care mortality and length of stay in teaching hospitals
Original Articles

Abstract

BACKGROUND: It has been suggested that inexperience of new housestaff early in an academic year may worsen patient outcomes. Yet, few studies have evaluated the “July Phenomenon,” and no studies have investigated its effect in intensive care patients, a group that may be particularly susceptible to deficiencies in management stemming from housestaff inexperience.

OBJECTIVE: Compare hospital mortality and length of stay (LOS) in intensive care unit (ICU) admissions from July to September to admissions during other months, and compare that relationship in teaching and nonteaching hospitals, and in surgical and nonsurgical patients.

DESIGN, SETTING, AND PATIENTS: Retrospective cohort analysis of 156,136 consecutive eligible patients admitted to 38 ICUs in 28 hospitals in Northeast Ohio from 1991 to 1997.

RESULTS: Adjusting for admission severity of illness using the APACHE III methodology, the odds of death was similar for admissions from July through September, relative to the mean for all months, in major (odds ratio [OR], 0.96; 95% confidence interval [95% CI], 0.91 to 1.02; P=.18), minor (OR, 1.02; 95% CI, 0.93 to 1.10; P=.66), and nonteaching hospitals (OR, 0.96; 95% CI, 0.91 to 1.01; P=.09). The adjusted difference in ICU LOS was similar for admissions from July through September in major (0.3%; 95% CI, −0.7% to 1.2%; P=.61) and minor (0.2%; 95% CI, −0.9% to 1.4%; P=.69) teaching hospitals, but was somewhat shorter in nonteaching hospitals (−0.8%; 95% CI, −1.4 to −0.1; P=.03). Results were similar when individual months and academic years were examined separately, and in stratified analyses of surgical and nonsurgical patients.

CONCLUSIONS: We found no evidence to support the existence of a July phenomenon in ICU patients. Future studies should examine organizational factors that allow hospitals and residency programs to compensate for inexperience of new house-staff early in the academic year.

Key Words

hospitals, teaching outcome assessment (health care) intensive care units quality of health care severity of illness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical relation between surgical volume and mortality. N Engl J Med. 1979;301:1364–9.PubMedCrossRefGoogle Scholar
  2. 2.
    Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery. JAMA. 1998;280:1747–51.PubMedCrossRefGoogle Scholar
  3. 3.
    Birkmeyer JD, Warshaw AL, Finlayson SRG, Grove MR, Tosteson ANA. Relationship between hospital volume and late survival after pancreatoduodenectomy. Surgery. 1999;126:178–83.PubMedGoogle Scholar
  4. 4.
    Wennberg DE, Lucas FL, Birkmeyer JD, Bredenberg CE, Fisher ES. Variation in carotid endarterectomy mortality in the Medicare population: trial hospitals, volume, and patient characteristics. JAMA. 1998;279:1278–81.PubMedCrossRefGoogle Scholar
  5. 5.
    Hannan EL, Siu AL, Kumar D, Kilburn H, Chassin MR. The decline in coronary artery bypass graft surgery mortality in New York state: the role of surgeon volume. JAMA. 1995;273:209–13.PubMedCrossRefGoogle Scholar
  6. 6.
    Sanchez PL, Harrell LC, Salas RE, Palacios IF. Learning curve of the Inoue technique of percutaneous mitral balloon valvuloplasty. Am J Cardiol. 2001;88:662–7.PubMedCrossRefGoogle Scholar
  7. 7.
    Liberman L, Benton CL, Dershaw DD, Abramson AF, Latrenta LR, Morris EA. Learning curve for stereotactic breast biopsy: how many cases are enough? Am J Roentgenol. 2001;176:721–7.Google Scholar
  8. 8.
    Laffel GL, Barnett AI, Finklestein S, Kaye MP. The relationship between experience and outcome in heart transplantation. N Engl J Med. 1992;327:1220–5.PubMedCrossRefGoogle Scholar
  9. 9.
    Kitahata MM, Koepsell TD, Deyo RA, Maxwell CL, Dodge WT, Wagner EH. Physicians’ experience with the acquired immunodeficiency syndrome as a factor in patients’s survival. N Engl J Med. 1996;334:701–6.PubMedCrossRefGoogle Scholar
  10. 10.
    The Southern Surgeons Club. Moore MJ, Bennett CL. The learning curve for laparoscopic cholecystectomy. Am J Surg. 1995;170:55–9.PubMedCrossRefGoogle Scholar
  11. 11.
    Tassios PS, Ladas SD, Grammenos I, Demertzis K, Raptis SA. Acquisition of competence in colonoscopy: the learning curve of trainees. Endoscopy. 1999;31:702–6.PubMedCrossRefGoogle Scholar
  12. 12.
    Zuger A. Essay; it’s July, the greenest month in hospitals. No need to panic. New York Times. Science Desk, Section F4, July 7, 1998. Available at: www.nytimes.com. Accessibility verified May 21, 2002.Google Scholar
  13. 13.
    Buchwald D, Komaroff AL, Cook EF, Epstein AM. Indirect costs for Medical Education: is there a July Phenomenon? Arch Intern Med. 1989;149:765–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Rich EC, Gifford G, Luxenberg M, Dowd B. The relationship of house staff experience to the cost and quality of inpatient care. JAMA. 1990;263:953–8.PubMedCrossRefGoogle Scholar
  15. 15.
    Rich EC, Hillson SD, Dowd B, Morris N. Specialty differences in the July Phenomenon for Twin Cities teaching hospitals. Med Care. 1993;31:73–83.PubMedCrossRefGoogle Scholar
  16. 16.
    Shulkin DJ. The July Phenomenon revisited: are hospital complications associated with new house staff? Am J Med Qual. 1995;10:14–7.PubMedGoogle Scholar
  17. 17.
    Claridge JA, Schulman AM, Sawyer RG, Ghezel-Ayagh A, Young JS. The “July Phenomenon” and the care of the severely injured patient: fact or fiction? Surgery. 2001;130:346–53.PubMedCrossRefGoogle Scholar
  18. 18.
    Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100:1619–36.PubMedGoogle Scholar
  19. 19.
    Rosenthal GE, Harper DL. Cleveland Health Quality Choice: a model for collaborative community-based outcomes assessment. Jt Comm J Qual Improv. 1994;20:425–42.PubMedGoogle Scholar
  20. 20.
    Sirio CA, Angus DC, Rosenthal GE. Cleveland Health Quality Choice (CHQC)—an ongoing collaborative, community-based outcomes assessment program. New Horiz. 1994;2:321–5.PubMedGoogle Scholar
  21. 21.
    Sirio CA, Shepardson LB, Rotondi AJ, et al. Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: implications for critical care delivery from Cleveland Health Quality Choice. Chest. 1999;115:793–801.PubMedCrossRefGoogle Scholar
  22. 22.
    Ash AS, Shwartz M. Evaluating the performance of risk-adjustment methods: dichotomous outcomes. In: Iezzoni LI, ed. Risk Adjustment for Measuring Healthcare Outcomes. Chicago, Ill: Health Administration Press; 1997:427–70.Google Scholar
  23. 23.
    Daley J. Validity of risk-adjustment methods. In: Iezzoni LI, ed. Risk Adjustment for Measuring Healthcare Outcomes. Chicago, Ill: Health Administration Press; 1997:239–61.Google Scholar
  24. 24.
    The LOGISTIC Procedure. In: SAS/STAT Users Guide, Version 8. Cary, NC: SAS Institute, Inc.; 1999:1:1914–5.Google Scholar
  25. 25.
    Vella K, Goldfrad C, Rowan K, Bion J, Black N. Use of consensus development to establish national research priorities in critical care. BMJ. 2000;320:976–80.PubMedCrossRefGoogle Scholar
  26. 26.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–29.PubMedCrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2003

Authors and Affiliations

  1. 1.Received from the Division of General Internal Medicine, Department of MedicineIowa City VA Medical Center and The University of Iowa Carver College of MedicineIowa City

Personalised recommendations