The Effects of Critical Access Hospital Conversion on Patient Safety

1
The Effects of Critical Access Hospital
Conversion on Patient Safety

• Pengxiang Li, PhD, University of Pennsylvania
• John E. Schneider, PhD, University of Iowa
• Marcia M. Ward, PhD, University of Iowa

Support for this work was funded by the Agency
for Healthcare Research and Quality through grant
HS015009
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Background

• Many of the smallest rural hospitals were not
  able to recover their Medicare costs under the
  prospective payment system (PPS) rates (Dalton,
  et al, 2005 Stensland, et al, 2004)
• Medicare Rural Hospital Flexibility Program of
  the 1997 Balanced Budget Act
• To protect small, financially vulnerable rural
  hospitals
• Allowed hospitals meeting certain criteria to
  convert to critical access hospitals (CAH)
• Changed Medicare reimbursement mechanism from
  prospective (PPS) to cost-based.
• One of the objectives of the policy was to
  increase the quality of care in these hospitals

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Patient Safety

• Definition
• freedom from accidental injury due to medical
  care, or medical errors. (IOM, 1999)
• the failure of a planned action to be completed
  as intended or the use of a wrong plan to achieve
  an aimincluding problems in practice,
  products, procedures, and systems. (Quality
  Interagency Coordination Task Force, 2000)
• Each year, more than 44,000 Americans die in
  hospitals due to preventable medical errors (AHA,
  1999).

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Objective

• To examine the impact of critical access hospital
  (CAH) conversion on hospital patient safety in
  rural hospitals

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Study Sample and Unit of Analysis

• Eight year panel data for 89 Iowa rural hospitals
  (rural PPS hospitals and CAHs)
• Unit of analysis is hospital-year
• Figure 1 Time Frame of Iowa Rural Hospitals CAH
  Conversion

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Outcome Measurements AHRQ Patient
Safety Indicators

• Created by AHRQ PSI SAS macro V3.0
• Each observed rate of patient safety indicator
  can be defined as the outcome of interest in the
  population at risk
• The risk-adjusted rate is the rate the provider
  would have if it had the same case-mix as the
  reference population (2003 state inpatient
  datasets from 38 states) given the providers
  actual performance.
• AHRQ recommends suppressing the estimates if
  fewer than 30 cases are in the denominator
• Only five patient safety indicators are able to
  provide PSI measures for all rural Iowa hospitals
  
• PSI-5 foreign body left during procedure
• PSI-6 iatrogenic pneumothorax
• PSI-7 selected infections due to medical care
• PSI-15 accidental puncture or laceration
• PSI-16 transfusion reaction
• Too rare to provide variability to differentiate
  hospitals in Iowa

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Outcome Measurements AHRQ Patient
Safety Indicators

• Four patient safety indicators (PSI-5, PSI-6,
  PSI-7 and PSI-15) are selected as the main
  measures for patient safety performance in Iowa
  rural hospitals.
• A composite patient safety indicator variable is
  the weighted average of each indicator
• The weights are based on the frequency of the
  numerator of each PSI in our sample (8-year
  inpatient discharges among 89 Iowa rural
  hospitals).
• Binary PSIs
• If the value of a PSI is higher than the median
  of the PSI in our sample, the binary variable for
  the PSI (poor performance) is equal to one
  otherwise, it is equal to zero

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CAH Variables

• CAH CAH binary indicator
• CAHit1, if hospital i is in CAH status in year t
  
• CAHit0, if hospital i is in PPS status in year t
• CAHmv moving average CAH variable
• To examine the relatively long-term effects of
  CAH conversion
• CAHmvit(CAHitCAHi(t-1)CAHi(t-2) )/3
• e.g. CAHmv0.33 if the hospital is in the first
  year of conversion

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Models

• Panel data tobit model
• The distributions of PSIs are skewed
• The value of PSIs is non-negative with a mass at
  0
• Tobit models have a conceptual advantage in
  analyzing data where the distribution of the
  dependent variable is normal above a limiting
  value
• PSIit ß0 ß1CAHit ?ß2Xit ?ß3Zt eit
• PSIit max (0, PSIit)

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Control variables

• Xit a vector of other explanatory variables for
  hospital i in year t
• Medicare patient days
• Medicaid days
• The hospital mean of the Charlson comorbidity
  score
• surgical discharges
• market concentration (HHI)
• county-level per capita income
• county-level population density
• Zt is a vector of the year dummy variables which
  will adjust the effects of unmeasured,
  time-specific factors

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Models

• Problem with tobit model
• Assumption truncated normal distribution
• A deviation to the assumption will lead to
  significant bias
• Using binary PSIs as dependent variables
• Generalized Estimating Equations (GEE) Logit
  Model (predict odds of bad performance)
• log(pit/1-pit) b0 b1CAHit ?bnXit ?bmZt
  eit
• The variance function is pit(1-pit)
• We assume the within-subject association among
  repeated measures is a first-order autoregressive
  correlation pattern

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Data Sources

• Iowa state inpatient datasets (SID) used to
  calculate PSIs for each hospital each year.
• Hospital case mix and HHI were calculated using
  Iowa SIDs.
• Other county variables were retrieved from Bureau
  of Health Professions Area Resource File (ARF)

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Table 1 Means, Standard Deviation and Sources of
Variables, 1997 and 2004
Variable Data sources 1997 1997 2004 2004
Variable Data sources Mean Std. Dev. Mean Std. Dev.
PSI-5 (per 1000 discharges) Iowa SID, AHRQ 0.04 0.22 0.07 0.33
PSI-6 (per 1000 discharges) Iowa SID, AHRQ 0.47 1.37 0.18 0.81
PSI-7 (per 1000 discharges) Iowa SID, AHRQ 0.83 1.81 0.27 0.90
PSI-15 (per 1000 discharges) Iowa SID, AHRQ 2.44 3.22 1.78 3.24
Composite PSI (per 1000 discharges) Iowa SID, AHRQ 3.78 4.46 2.30 3.65
CAH IHA 0 0 0.74 0.44
CAHmv IHA 0 0 0.62 0.43
Medicare days Iowa SID 66.68 11.33 65.71 14.88
Medicaid days Iowa SID 6.72 4.49 7.20 4.72
Hospital casemix CMS 1.06 0.09 0.99 0.12
Market concentration (HHI) Iowa SID 8,936 1,969 8,925 2,008
County per capita income ARF 21,180 1,846 26,401 4,338
County population density ARF 31.80 18.51 31.80 19.34
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Table 2. Cross-sectional Comparison of Means of
PSIs between Rural PPS and CAHs, 1997 to 2004
Year Hospital categories Number of Hospitals PSI-5 PSI-6 PSI-7 PSI-15 Composite score of 4PSIs
1997 Rural PPS 89 0.04 0.47 0.83 2.44 1.93
1998 Rural PPS 89 0.05 0.27 0.36 2.72 2.04
1999 Rural PPS 88 0.02 0.3 0.69 2.99 2.29
  CAH 1 0 0 0 0 0
2000 Rural PPS 78 0.06 0.36 0.6 3.09 2.35
  CAH 11 0 0 0.41 2.12 1.58
2001 Rural PPS 57 0.07 0.21 0.69 2.65 2.03
  CAH 32 0 0.07 1.17 2.24 1.8
2002 Rural PPS 45 0.04 0.23 0.64 2.00 1.56
  CAH 44 0.21 0.34 0.14 1.89 1.41
2003 Rural PPS 34 0.01 0.46 0.88 2.38 1.89
  CAH 55 0.06 0.26 0.41 1.89 1.45
2004 Rural PPS 23 0.12 0.29 0.54 2.68 2.04
  CAH 66 0.06 0.14 0.17 1.46 1.09
Significant difference in PSIs between rural
PPS and CAH at p 0.10 (Wilcoxon rank sum test)
Significant difference in PSIs between rural
PPS and CAH at p 0.05 (Wilcoxon rank sum test)
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Figure 2. Changes in Patient Safety Indicators
after Conversion
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Table 3. GEE Logit Models of Binary PSIs (1poor
performance, 0good performance)
  PSI-5 PSI-6 PSI-7 PSI-15 Composite score of 4PSIs
CAH -0.80 -1.19 -1.26 -0.92 -0.70
Medicare days -0.01 -0.01 -0.01 -0.01 -0.01
Medicaid days -0.01 0.02 0.03 0.02 0.03
of surgical discharges 0.09 0.05 0.01 0.18 0.16
Charlson Index 2.01 0.69 1.05 1.51 1.37
Market concentration (HHI) -2.11 0.37 0.24 -0.21 -0.15
Per capita income (1,000) 0 -0.03 0.09 0.06 0.07
Population density -0.01 0.02 0.01 0 0
Intercept -3.23 -2.67 -3.73 -3.87 -3.54
observations 712 712 712 712 712
Note Due to space limit, year dummy variables
were omitted in this table. Statistically
significant at 0.10 level. Statistically
significant at 0.05 level.
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Table 4. Sensitivity analysis GEE models
Models   PSI-5 PSI-6 PSI-7 PSI-15 Composite score of 4PSIs
Models in Table 3 (89 hospitals, 1997 to 2004) CAH -0.8 -1.19 -1.26 -0.92 -0.70
Models in Table 3 (89 hospitals, 1997 to 2004) CAHmv -0.67 -1.57 -2.05 -1.37 -1.03
Models adding proxy variable (the lag 1 year dependent variable) as covariates CAH -1.08 -0.90 -1.13 -0.83 -0.64
Models adding proxy variable (the lag 1 year dependent variable) as covariates CAHmv -1.06 -1.08 -1.67 -1.08 -0.80
Models using DRG-weight as risk adjustment (89 hospitals, 1997 to 2004) CAH -0.78 -1.19 -1.25 -0.92 -0.70
Models using DRG-weight as risk adjustment (89 hospitals, 1997 to 2004) CAHmv -0.63 -1.56 -2.07 -1.36 -1.03
Models excluding 8 hospitals which is in rural PPS in 2006 (81 hospitals, 1997 to 2004) CAH -0.94 -1.11 -1.41 -0.85 -0.63
Models excluding 8 hospitals which is in rural PPS in 2006 (81 hospitals, 1997 to 2004) CAHmv -0.79 -1.45 -2.28 -1.27 -0.94
Models adjusting for hospital transfer behavior CAH -0.84 -1.24 -1.28 -0.93 -0.72
Models adjusting for hospital transfer behavior CAHmv -0.76 -1.68 -2.12 -1.43 -1.12
Statistically significant at 0.1 level.
Statistically significant at 0.05 level. The
same as the model in Table 3. Adding two
variables (percentage of acute inpatient
admission were transferred from other short-term
hospital and percentage of acute inpatient
patients were transferred to other short-term
hospitals) into the models in Table II-6
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Table 5. Sensitivity Analyses Tobit Models of
Continuous PSIs
Statistically significant at 0.1 level.
Statistically significant at 0.05 level.
Convergence was not achieved. Add hospital
dummy variables in cross-sectional Tobit model
The results should be interpreted with caution,
given that estimations for some coefficients were
not stable under quadchk ? PSI-5 are observed
rate.
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Results

• Cross-section and pre-post conversion comparisons
  showed that CAH hospitals had better performance
  of patient safety than rural PPS hospitals.
• The odds ratios of poor performance in CAH
  hospitals compared to rural PPS hospitals are
  0.30 (CI 0.14-0.64) for PSI-6, 0.29 (CI
  0.15-0.56) for PSI-7, 0.40 (CI 0.24-0.67) for
  PSI-15, and 0.49 (CI 0.31-0.80) for composite
  score of 4PSIs. CAH conversion had no significant
  impact on the observed rates of foreign body left
  during procedure.
• Moving average CAH indicator had larger effects
  than binary CAH scale.
• Sensitivity analyses using tobit models
  consistent results
• Findings were robust among sensitivity analyses
  using different samples and different methods

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Better PSI Performance Associated CAH Conversion
Alternative Explanations

• Changes in coding behaviors
• Did not find any significant change in
  hospital-level mean number of DX, mean Charlson
  score, mean number of Elixhauser comorbidities
• Changes in referral patterns (get less severe
  patients)
• No significant change in
• percentage of patients transferred from other
  hospitals
• percentage of patients transferred to other
  hospitals
• Models adjusting for referral behaviors show
  consistent results
• It reflects a trend toward improvement in patient
  safety for all hospitals
• None-converted hospitals are comparison group
• Add year dummy variables
• Difference in difference

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Conclusion and implication

• CAH conversion in rural hospitals resulted in
  enhanced performance of patient safety.
• After conversion, CAHs have better perform in
  patient safety in the long run.
• We speculate that the likely mechanism involved
  an increase in financial resources following CAH
  conversion to cost-based reimbursement for
  Medicare patients
• Limitations of the study
• Administrative databases (missing codes, coding
  errors)
• We are not able to rule out the possibility of
  coding behavior change
• The five indicators may not reflect the whole
  picture of patient safety in rural hospitals
• CAH conversion variable is endogeneous.