Association of admission date with cancer patient survival at a regional hospice: Utility of a statistical process control analysis

Abstract

Previously published multivariate analyses have not considered date of entry as a possible factor associated with length of stay (LOS), nor has the pattern of decreasing LOS been analyzed. We set out to assess mean LOS over time and to determine the factors, including date of death, which are independently associated with LOS. Cancer patients admitted to our hospice from 1996 through 2001 were assessed for dates of admission/discharge/death, age, gender, race, specific cancer diagnosis, referring physician characteristics, place of death, and heath insurance type. Statistical process control (SPC) charts and proportional hazard models were constructed for patients prioritized by date of admission, with active or discharged patients censored. A total of 2126 patients were analyzed. An abrupt and significant drop in mean LOS was seen for all cancer patients and for most cancer sites from April to December 1998 (temporally associated with a “Special Fraud Alert” issued by the Office of Inspector General) and again in the second and third quarter of 2000. A proportional hazards model revealed that LOS was associated with cancer site (p < 0.0001), quarter in which patient was admitted (p = 0.0020), and sex (women surviving longer, p=0.013), age (older patients surviving longer, p 0.0149), and insurance (p = 0.071). Mean LOS is associated with date of admission to hospice independent of other associated factors. LOS decreases do not occur in a gradual, continuous fashion but suddenly and intermittently, and they are not associated with changes in referral numbers or read-missions. SPC charts proved to be an effective method of tracking and evaluating hospice LOS on an ongoing basis.

Keywords

hospice length of stay (LOS)survival cancer

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References

Christakis NA , Iwashyna TJ: Impact of individual and market factors on the timing of initiation of hospice terminal care. Med Care. 2000; 38(5): 528-541.

Schonwetter RS: Care of the dying geriatric patient. Clin Geriatr Med. 1996; 12(2): 253-265.

Bloom BS , Amenta MO: Prospective payment and the Medicare hospice benefit. Am J Hospice Palliat Care. 1993; 10(2): 32-39.

Mor V , Kidder D: Cost savings in Hospice: Final results of the National Hospice Study. Health Service Res. 1985; 20(4): 407-422.

Kane RL , Wales J , Bernstein L , et al. : A randomised controlled trial of hospice care. Lancet. 1984; 1(8382): 890-894.

Christakis NA: Timing of referral of terminally ill patients to an outpatient hospice. J Gen Intern Med. 1994; 9(6): 314-320.

Christakis NA , Lamont EB: Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. Br Med J. 2000; 320(7233): 469-472.

Stillman MJ , Syrjala KL: Differences in physician access patterns to hospice care. J Pain Symptom Manage. 1999; 17(3): 157-163.

Schonwetter RS , Robinson BE , Ramirez G: Prognostic factors for survival in terminal lung cancer patients. J Gen Intern Med. 1994; 9(7): 366-371.

10.

Gibbs JG: Enhanced controls needed to assure validity of Medicare hospice enrollments. US Department of Health and Human Services, Office of Inspector General: November 1997.

11.

Office of Inspector General Special Fraud Alert: Fraud and abuse in nursing home arrangements with hospices. March 1998. Office of the Inspector General Web site. Available at www.oig.hhs.gov/fraud/docs/alertsandbulletins/hospice.pdf. Accessed on May 27, 2004.

12.

Frantz TT , Lawrence JC , Somov PG , et al. : Factors in hospice patients’ length of stay. Am J Hospice Palliat Care. 1999; 16(2): 449-454.

13.

Moinpour CM , Polissar L , Conrad DA: Factors associated with length of stay in hospice. Med Care. 1990; 28(4): 363-368.

14.

Doty LA : Statisical Process Control. 2nd Edition. New York: Industrial Press, 1996.

15.

Kelly DL: How to Use Control Charts in Health Care. Milwaukee: Quality Press, 1999.

16.

Mitra A: Fundamentals of Quality Control and Improvement. 2nd Edition, Saddle Ridge, NJ: Prentice Hall, 1993.

17.

Kaplan EL , Meier P: Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53: 457-481.

18.

Cox DR: Regression models and life tables (with discussion). J R Stat Soc. 1972; 34: 187-202.

19.

Christakis NA , Escarce JJ: Survival of Medicare patients after enrollment in hospice programs. N Engl J Med. 1996; 335: 172-178.

20.

Christakis NA: Predicting patient survival before and after hospice enrollment. Hospice J. 1998; 13(1-2): 71-87.

21.

Farnon C , Hofmann M: Factors contributing to late hospice admission and proposals for change. Am J Hospice Palliat Care. 1997; 14(5): 212-218.

22.

Miller RJ. Predicting survival in the advanced cancer patient. Henry Ford Hosp Med J. 1991; 39(2): 81-84.

23.

Schonwetter RS , Soendker S , Perron V , et al. : Review of Medicare’s proposed eligibility criteria for select noncancer patients. Am J Hospice Palliat Care. 1998; 15(3): 155-158.

24.

Schonwetter RS , Teasdale TA , Storey P , Luchi RJ. Estimation of survival time in termimal cancer patients: an impedance to hospice admission? Hospice J. 1990; 6(4): 65-79.