Analysis of the rationale for, and consequences of, nonprofit and for-profit ownership conversions by Tami Mark Health Services Research, April 1999 Presentation by Maia Platt

Objective In accordance to Medicare Reports, from 1989 through 1992: 4 33 private nonprofit hospitals converted to for- profit status; 4 50 for-profit hospitals converted to nonprofit status. Research goal: How did those conversions affect hospitals’ financial and operating performance?

Data 4 Data sources: HCFA’s Medicare Cost Reports and American Hospitals Association (AHA)’s Annual Survey of Hospitals 4 Data was obtained not only for (years when conversions took place), but also 2 years prior to, and 2 years following, conversion. 4 Study group included not only those hospitals, which converted, but also a comparison group of approx. 3,800 acute care private hospitals that did not convert over the same period. 4 Total of 32,000 observations

Model 1: logistic regression R i,t = g 0 + g 1 f i,t-1,2 + g 2 P i,t-1,2 + g 3 Y i,t-1,2 + g 4 M i,t-1,2 +  i,t (1) R indicates whether hospital i converted (R=2) in year t or not (R=1); f i,t-1,2 is hospital i’s profit margin in the 2 years prior to converting [(TR-TC)/operating revenues ]; P i,t-1,2 is managed care penetration (% of population enrolled in HMOs); Y i,t-1,2 is hospital size (# of impatient discharges); M i,t-1,2 is are other hospital market characteristics: Herfindahl index (sum of hospital’s market shares) # of acute care beds per capita Per capita income Urban status (=2 if urban; =1 if suburban)

Model 1: logistic regression This regression was run separately for both types of conversions: 4 Nonprofit to For-Profit conversion; 4 For-Profit to Nonprofit conversion. The goal is to find out what causes those conversions in either case, and whether the determinants will be the same for both types of conversions.

Logistic regression results: financial status prior to conversion 4 Respectively 80% and 50% of converted hospitals showed negative profit margins in the 2 years prior to conversion (compared to 33% in average for all hospitals)  hope to increase profits appears to be the major reason for converting. 4 Urban nonprofit hospitals are more likely to convert. 4 Per capita income doesn’t play significant role for for- profit hospitals decision to convert; but it is significant and negatively related to the probability of conversion for nonprofit hospitals. 4 Conversion activity is more likely when hospital markets are more competitive (negative Herfindahl index). 4 Managed care plays only weak role in hospitals conversions.

Table 2: Logistic regression results: nonprofit to for-profit conversions Intercept Total profit margin -7.55* * Log (Discharges) Urban 3.15*2.25*2.88* Log (beds / capita)3.27* * Log (HMO enrollment)-0.34* Log (Herfindahl index) -1.64* Log (per capita income) * Table 3: Logistic regression results: for-profit to nonprofit conversions Intercept Total profit margin -3.25* Log (Discharges) *-0.25 Urban * Log (beds / capita) * Log (HMO enrollment) Log (Herfindahl index) * Log (per capita income)

Model 2: effects of hospital conversion on the acquired hospitals’ performance F i,t =  0 +  1 W i,t +  2 M i,t +  3 B i,t +  4 I i,t +  5 T i,t +e i +  i,t (2) F indicates hospital i’s financial performance in year t W i,t are input prices (=Area Wage Indexes, since capital costs are not expected to vary substantially from year to year) M i,t are hospital market characteristics B i,t is a dummy: =2 if hospital is for-profit; =1 if nonprofit T i,t are particular year’s dummy variables e i is a hospital-specific error term  i,t is a random disturbance

Model 2: fixed-effects regression This regression in its log-log form is used to test the effect of conversion on a set financial variables (i.e. regression was run separately on each of those variables, taking values of F): 4 total profit margin (%); 4 average inpatient Medicare costs per Medicare discharge; 4 average operating expenses per discharge; 4 average revenues per discharge. Also, same set of independent variables was regressed on staffing ratios per patient day (adjusted for the case mix): 4 total full time equivalents (FTE); 4 all nurses 4 registered nurses; 4 administrators.

Table 4: Fixed-effects model results - effects of conversion on financial performance and stuffing ratios Independent Profit Average Average Revenues FTEs Registered All Administr- Variable margins Operating Medicare per case Nurses Nurses ators costs costs Nonprofit-for-profit Conversions 0.026* 0.050* * 284*-0.21* Interaction 0.008* * 485* 0.15* 0.15* * Wage Index 0.084* 0.024* 0.18* -842* Beds per capita *-0.071*-0.077*-2,833*-1.82*-0.451*-0.383*-0.034* Herfindahl Index-0.033* 0.142* 0.053* 845* * Per Capita Income 0.055*1.44* 1.1* 12,610* 0.803* 0.156*-0.641* 0.096* HMO penetration * * For-Profit-Nonprofit Conversions 0.034* 0.053* * 769* * * Rsquared

Fixed effects model results:profit margins 4 Profit margins increased after conversion for either type of hospital; the effect is slightly greater in for-profit  nonprofit cases; 4 negative coefficient on “beds” indicates that as competition   # of beds   profit  4 HMOs penetration is significant and has negative coefficient: as % of HMO-covered population  profit  4 positive effect of per capita income on profit; 4 the only rather unexpected result is negative Herfindahl index: as hospital’s market share  profit 

Fixed effects model results:costs 4 Operating expenditures increased after conversion for either type of hospital, since usually investment into converted hospital increases; 4 but Medicare costs decreased; and the effect is greater for non-profit to for-profit conversions. 4 Intuitively, as wages  or per capita income   operating expenses  & Medicare costs  (positive association) 4 But as beds per capita  or competition   operating expenses  & Medicare costs  (negative association) 4 Average revenues increased after conversion for either type of hospital; 4 as beds per capita  or competition   average revenues  4 while per capita income is positively associated with average revenues

Fixed effects model results: staff-to-patient ratios 4 Nonprofit to for-profit conversion was associated with a decrease in FTE ratio, while effect on other 3 ratios was insignificant  this suggests, that new for-profit owners reduce staff-to-patient ratio by eliminating some other professions, not measured here (e.g. technicians, custodial stuff); 4 For-profit to nonprofit conversion didn’t cause significant effect on total staff-to-patient ratios, even though it caused increase in registered nurses- and administrators-to patient ratios  this suggests, that this increase must have been balanced by reduction on other staff members, not measured here.

Fixed effects model results: staff-to-patient ratios 4 As beds per capita   stuffing ratios  4 as income per capita   stuffing ratios  (except for total nurses); 4 as HMO penetration   administrators-to-patient ratios  (all the rest are insignificant)

Probability of closure and ownership effects 4 Non-profit to for-profit conversions: 3 out of 33 converted hospitals closed by 1995 (11%); 4 For-profit to nonprofit conversions: 3 out of 50 converted hospitals closed by 1995 (6%). 4 Also the study found out, that actually there was little difference in performance of nonprofit  for- profit-converted hospitals, and for- profit  nonprofit-converted hospitals: type of ownership doesn’t matter. This was done by comparing average characteristics of all hospitals by ownership and by year - see Table 6.

Table 6: Average of hospital operating characteristics by ownership status and year Av.operating costs Nonprofit 4,799 5,335 5,365 6,019 6,529 8,367 7,406 For profit 5,868 6,076 7,319 7,365 7,996 8,272 7,367 Av.Medicare costs Nonprofit 4,238 4,564 4,820 4,961 5,059 5,054 5,027 For profit 4,298 4,640 4,826 4,998 4,947 4,734 4,703 Av.revenues per discharge Nonprofit For profit Av.profit margins Nonprofit 5,713 6,349 7,086 7,723 8,291 8,826 9,306 For profit 6,154 6,636 7,456 8,163 8,671 8,820 9,556 FTEs per 1000 adjusted patient days Nonprofit For profit Registered nurses per 1000 adjusted patient days Nonprofit For profit Total nurses per 1000 adjusted patient days Nonprofit For profit

Selection effects Trying to further distinguish, whether the changes in converted hospitals stem from differences between for- profit and nonprofit hospitals (i.e. selection effect), or from something else, - the operating performance of all converted as well as non-converted hospitals was examined during 2 years prior to conversion. The only significant difference, influenced by selection effects, was the effects of nonprofit to for-profit conversion on total stuff-to-patient ratios: converted nonprofit hospitals had higher ratios before the conversion, than did nonconverted hospitals.

Limitations Four serious limitations of this study: 4 postconversion time period was not long enough: only 3-6 years; 4 study examined only those hospitals, that converted during 1989 to some period-specific things might be present, but they are not accounted for; 4 it didn’t examine the links between characteristics of hospital conversion and type of acquiring company. This suggests the possibility of further studies, improving the reliability of the results.

Conclusions 1. Hospital conversions seem to be the response to poor financial health. 2. Hospital conversion, on average, resulted in improvement of a hospital’s financial performance. 3. Improvement in converted hospitals’ financial performance stems more from the acquiring hospital's better management and / or enhanced resources (e.g. improved collection policies, better investments), than from characteristics inherent in different ownership types. 4. The main difference between for-profit to nonprofit conversions vs. nonprofit to for-profit conversions seems to be the effect on staffing: for-profit management tends to reduce total staff-to-patient ratios. While nonprofit management tends to increase registered-nurse-to-patient and administrator-to-patient ratios after conversion.