We could not find published studies of the relationship between LOS and ACH in home health patients. Concerning LOS, Peters (1999)
found important regional differences and noted that chain-associated agencies had shorter ALOS, controlling for patient characteristics. Our results showed that proprietary agencies had longer ALOS; whether this agrees with Peters' findings depends on the relationship of “chain” and “proprietary” ownership. Peters did not report linkage between ALOS and ACH. Murkofsky et al. (2003)
demonstrated that ALOS in home health declined sharply in 1,053 HHAs after the adoption of the 1997 Balanced Budget Act amendments, which implemented prospective payment for home health services. For-profit agencies experienced a greater reduction in ALOS than not for-profit, but they continued to have longer ALOS than other HHAs, consistent with our more recent findings. However, these investigators did not study association between ALOS and ACH.
Wray et al. (1988)
noted that living in the South is a risk factor for recurrent hospitalization among male veterans. Philbin and DiSalvo (1999)
found that rural hospital location and discharge to skilled nursing facilities predicted reduced readmission rates in New York State heart failure patients, but their study did not report impacts of home health care. In our model, region was correlated strongly with both ALOS and ACH; omitting it from the model reduced the r2
by 0.0042, suggesting a small independent effect on ACH.
Studies of impact of home health care on hospitalization are not common. An older systematic review showed limited evidence of impact of substituting home care services on hospitalization costs, but they focused on early hospital discharge, rather than rehospitalization (Soderstrom, Tousignant, and Kaufman 1999
). Shipton (1996)
found that length of hospital
stay was a risk factor for readmission. Using hospital discharge data, Rich and Freedland (1988)
observed that decreased hospital LOS associated with adoption of diagnosis-related groups (DRGs) had not resulted in increased rehospitalization among congestive heart failure patients in a single hospital.
In the face of increasing hospital discharge rates to home health care, these authors speculated that such care might have reduced readmission rates; they did not study utilization of home health itself. Li, Morrow-Howell, and Proctor (2004)
studied postacute care for heart failure (CHF) patients and found no evidence of service impact on ACH. In contrast, a review by Konetzka, Spector, and Limcangco (2008)
found weak evidence that home health services substituted for hospital care might reduce readmissions in CHF patients. Neither Li's nor Konetzka's studies addressed ALOS.
As recently as 2001 one researcher, noting the overall poor quality of evidence, had this to say about using readmissions as a quality indicator: “While some studies have demonstrated a process-outcome link between substandard care and the likelihood of readmission, the association is not strong enough to be a valid and useful quality indicator” (Hasan 2001
). Nonetheless, a measure of rehospitalization may well be appropriate for home health outcome, provided that the measure tracks desirable results of home health interventions in reducing unnecessary hospital care and does not merely reflect the cumulative probability of hospital readmission related to patient age, sex, and health status.
The current measure, risk-adjusted ACH, does not meet this requirement. ALOS bias makes it unsuitable for fair comparison of agency performance. The easiest way for a provider to reduce ACH is to reduce LOS, as illustrated in the fourth simulation scenario. In the post-Balanced Budget Act environment, where the financial incentive might be to reduce LOS to increase profit, this may be a perverse incentive. We have no reason to believe agencies are generally using this as a strategy; recently, ALOS has increased substantially nationwide along with a slight reduction in ACH. Our simulation suggests this could be due to a combination of better care and patients requiring longer LOS for reasons that might include a sicker population.
Could the apparent bias be due to inadequate risk adjustment? Risk adjustment of ACH rates is intended to compensate for variation in patient condition, as documented in OASIS, allowing fair comparison of agencies with dissimilar populations. Iezzoni and colleagues have documented the imperfect ability of risk adjustment methods to separate patient-level risk from institutional sources of variation, at least in the hospital setting (Iezzoni et al. 1996
; Iezzoni 1997
;). There is no reason to expect the home care environment to be different. The effect of LOS was recognized in the risk-adjustment methods used for the national quality measure during the period of this study (Shaughnessy and Hittle 2002
). In fact, the dichotomous variable “length of stay less than 31 days” used in the risk-adjustment model was the strongest protective factor against hospital readmission among the covariates included in the published model.
In our preliminary studies, we found that correlating log(ALOS) with the square root of risk-adjusted ACH instead of unadjusted ACH resulted in a change in correlation coefficient from 0.495 to 0.472 (transformations were performed so that the variables of interest had distributions that were approximately normal). The risk-adjustment model currently in use makes no reference to LOS and would therefore not correct for ALOS bias at all (Hittle, Goodrich, and Nuccio 2008
The risk adjustment model in effect at the time of our work did not include adjustment for age. Because of widely differing hospital readmission rates among Medicare beneficiaries discharged to home health, we tested the simulation using age stratification and saw little difference in results. This is most likely due to the age distribution of beneficiaries receiving home health care, which is dominated those age 85 and older. The interaction between ALOS and ACH, in which increased ALOS “causes” increased ACH, whereas hospital admissions during home care act to reduce ALOS, makes it doubtful that a logistic regression model would fully adjust for ALOS' effect on the current measure.
A fixed-interval hospital readmission rate avoids this problem. Moreover, it is not subject to reporting errors to the same extent as OASIS-derived measures, as it only depends on accurate reporting dates of admission on home health and hospital claims, which are audited by the fiscal intermediaries who pay Medicare claims. Longer fixed intervals have the additional theoretical advantage of capturing postdischarge impact of home health services, for example, improved self-management or better hand-offs to community care, leading to an overall suppression of hospital readmissions. However, comparing the change in readmission rates after across-the-board increases in t1/2 with those following increases that dissipate after the end of service in the simulation suggests such effects would be small. The 30-day readmission rate is currently used to monitor quality during care transitions, and the other two fixed-interval rates are consistent with it.
A disadvantage of the alternative measures is the required linking of two data streams (hospital discharges and home health claims). This is not difficult in Medicare fee-for-service cases, but it would be arduous if an all-payer measure were desired. However, an offsetting advantage would be the availability of hospital discharge information to improve the precision of risk adjustment. Linking home health to hospital data could verify that a fixed-interval measure is feasible in practice, and it could test whether improved comparability of the measure across HHAs was worth the additional time and expense to produce it.