As previously shown, AB 394 generated a substantial shock to the operations of California hospitals both through its input requirement for nurses and the resulting wage increase for nurses. This shock was felt to varying degrees by hospitals across California based on their pre-passage nurse-staffing levels. As hospitals adjusted by reorienting their production processes and service mix, patients faced an altered hospital landscape.
This chapter examines the demand response to this altered hospital landscape in the greater Los Angeles region for traditional Medicare patients with Part A coverage over 1999–2002 and 2003– 2006. In crafting this analysis, a number of assumptions were employed related to patient demand. The first was the standard assumption that patients choose hospitals that maximize their utility. This forms the basis of the empirical approach using McFadden’s discrete choice models, which have been applied in numerous studies examining hospital choice [Capps et al., 2003, Gaynor and Vogt, 2003, Geweke et al., 2003, Gowrisankaran and Town, 1999, Ho, 2006, Kessler and McClellan, 2000, 2002, Luft et al., 1990, Tay, 2003, Town and Vistnes, 2001]. Second, I postulated that patients face a basic trade-off in their hospital choice decisions between travel time (i.e., time-to-treatment) and hospital “quality,” broadly defined as non-proximity attributes. Faced with this trade-off, I posited that patients seeking non-urgent discretionary services would place greater weight on the latter than patients with sudden, urgent health needs. As a result, these discretionary service patients would exhibit more elastic demand responses to the changes induced by AB 394 than their counterparts with urgent, unplanned health needs. This variation was used to look for differential demand responses across patients and as a falsification test for the results. The third assumption was that demand responses to changes precipitated by AB 394 would be more pronounced in the later period of 2003– 2006 than the earlier period of 1999–2002. This was based on the observed trends and activities surrounding the development and implementation of the law as discussed in Chapters 2, 5, and 6. More specifically, the early period spanned the 4-year stretch from one year before the law’s passage to two years after its passage when draft regulations were starting to be introduced. This comprised a time when nurse staffing and wages had just begun to increase as shown in Figures 5.1 and 5.3 and significant effects on operating margins and closure were not yet observed (Tables 6.6 and 6.11). The later period, by contrast, spanned the 4-year stretch from the year before the law’s implementation when draft regulations were finally passed to two years after the law’s initial implementation. This period was characterized by much more dramatic increases in nurse staffing and wages as hospitals
sought to comply with the law’s staffing ratios by its implementation deadline. These trends were also accompanied by corresponding decreases in hospital operating margins and increased closures for facilities that had had low versus not-low nurse staffing levels prior to the law. Based on these trends, if the law had an impact on patient choice, it should have occurred more noticeably in the later period when greater changes occurred rather than in the earlier one. Comparing results for these two time periods therefore provided a further check of the results.
Identification of the demand response to changes precipitated by AB 394 came from exploiting variation across hospitals related to their nurse-staffing level pre-passage of the law. More specifically, hospitals with low versus moderate or high nurse-staffing levels pre-passage of the law were expected to have experienced greater disruptions to their operations as a result of the law. These differential effects related to hospitals’ nurse-staffing levels pre-passage of AB 394 were captured in the analysis via two hospital-level variables—1) the low nurse-staffing indicator described in Section 6.3.245 and 2) the hospital closure propensity over the law’s post-passage period, as predicted by the hospital’s pre-passage nurse-staffing level and other characteristics (see Section 6.4.3).46 Combined use of these two variables teased apart separate after-effects of the law to allow for different demand responses to them. The closure propensity captured the financial strain the law imposed that increased hospital closure probability, while the low nurse-staffing indicator captured all other effects. Separate use of just the latter identified the net demand response to the law.
7.2.1
Hypotheses
The above assumptions formed the basis of the following hypotheses that were tested.
Hypothesis 1 Discretionary service patients will exhibit greater demand responses than
urgent service patients to changes across the hospital landscape from AB 394.
45As described in Section 6.3.2, the low nurse-staffing indicator was based on data from AB 394’s pre-passage period
and accounted for a hospital’s nurse-staffing levels across the five largest and most common units that were affected by the law. In addition, the indicator adjusted for the relative size of these units within the given hospital (i.e., the hospital’s composition). In this manner, it identified those hospitals that likely sustained the greatest changes from the law at the facility-level. For hospitals that had received waivers for the law and were therefore not subject to its staffing mandate, the indicator was set to 0.
46Hospital closure propensity in the six years following passage of AB 394 (i.e., 2000–2006) was estimated using
a variation of the logistic regression models presented in Section 6.4. Predictors consisted of hospital characteristics from the law’s pre-passage period, including the aforementioned low nurse-staffing indicator, operating margin, size as captured by logged available beds, and hospital ownership. In contrast to the models in Section 6.4, hospitals that had received waivers for the law were also included with the low nurse-staffing indicator modified accordingly.
Hypothesis 2 The demand response to changes related to AB 394 will be more pronounced
in the later period of 2003–2006 than in the earlier period of 1999–2002, consistent with the greater changes observed in the later period.
Hypothesis 3 Different demand responses may be observed for the after-effects of AB 394.
In particular, those associated with law’s adverse effects on hospital closure and hospitals’ inability to successfully adapt will be negatively received.