Proposed Changes to Existing Measure for HEDIS® 2015: Plan All

Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
1
Proposed Changes to Existing Measure for HEDIS® 2015:
Plan All-Cause Readmissions (PCR)
NCQA seeks comments on proposed modifications to the Plan All-Cause Readmissions measure. This
measure assesses the number of hospital discharges that were followed by a readmission for any diagnosis
within 30 days, and the predicted probability of an acute readmission.
We propose the following changes:
1. Eliminate the exclusion that removes readmissions from the denominator, so readmissions can serve
as a potential index admission.
2. Add an exclusion for planned readmissions.
The proposed changes will improve the measure’s validity and align it with the CMS Hospital Wide
Readmission measure, which assesses hospital-level performance at reducing readmissions for Medicare
fee-for-service beneficiaries. Eliminating the exclusion for readmissions from the denominator ensures that
the measure captures every readmission, even if there are multiple readmissions. The exclusion for planned
readmissions ensures that the measure captures only unplanned readmissions.
We tested these proposed changes in a research database of two years of Medicare Advantage health
plan claims data with more than 1 million hospital admissions, provided by Inovalon, and two years of
commercial claims data with more than 1 million hospital admissions, provided by OptumInsight. Data from
one year are presented below for brevity, although results were similar in both years of data.
Excluding planned readmissions from the numerator reduced the rate of readmission, while allowing
readmissions to count as index admissions increased the rate of readmission. Allowing readmissions to count
as index admissions had a greater effect, leading to an overall increase in the observed rate of readmission
(an average 12 percent increase for commercial beneficiaries; a 5 percent increase for Medicare beneficiaries
≥65 [Table 1]).
We estimated a new risk adjustment model for expected readmissions using the revised approach to defining
hospital stays and readmissions. On average, plans show almost no change in their observed to expected
ratio (O/E) with the proposed approach. A simulation of results in a sample of plans showed that some plans
may see their O/E increase slightly, while others will see their O/E rate decrease slightly. Results were similar
for 2011 and 2010.
Table 1: 2011 Change in Observed Readmission Rate and O/E Ratio With Proposed
Approach
Commercial 18–64
Observed
Avg. O/E (n=72
Readmission Rate
plans)
Medicare ≥65
Observed
Avg. O/E
Readmission Rate
(n=47 plans)
Current PCR Approach: Readmission not
as an index admission and planned
admissions as readmissions
10.60%
1.11
13.50%
0.96
Revised Approach: Readmission as an
index admission and planned admissions
not as readmissions
11.92%
1.10
14.19%
0.96
Avg O/E: Average observed rate of readmission/expected rate of readmission.
Supporting documents for the proposed measure include the draft measure specification, evidence work-up,
and performance data.
NCQA acknowledges the contributions of the Geriatric Measurement Advisory Panel.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
2
Plan All-Cause Readmissions (PCR)
SUMMARY OF CHANGES TO HEDIS 2015
 Removed the former step 4 to exclude acute inpatient stays with a discharge date in the 30 days prior to
the Index Admission Date.
 Added step 5 (required exclusions) to exclude acute inpatient discharges followed by a planned
readmission within 30 days.
 Removed gender strata from reporting table PCR-A-2/3 and PCR-B-3. Rates will be reported for both
genders combined by age strata.
Description
For members 18 years of age and older, the number of acute inpatient stays during the measurement year
that were followed by an unplanned acute readmission for any diagnosis within 30 days and the predicted
probability of an acute readmission. Data are reported in the following categories:
1. Count of Index Hospital Stays (IHS) (denominator).
2. Count of 30-Day Readmissions (numerator).
3. Average Adjusted Probability of Readmission.
Note: For commercial, only members 18–64 years of age are reported.
Definitions
HIS
Index hospital stay. An acute inpatient stay with a discharge on or between
January 1 and December 1 of the measurement year. Exclude stays that meet
the exclusion criteria in the denominator section.
Index Admission Date
The IHS admission date.
Index Discharge Date
The IHS discharge date. The index discharge date must occur on or between
January 1 and December 1 of the measurement year.
Index Readmission
Stay
An acute inpatient stay for any diagnosis with an admission date within 30
days of a previous Index Discharge Date.
Index Readmission
Date
The admission date associated with the Index Readmission Stay.
Planned hospital stay
A hospital stay is considered planned if it meets criteria as described in step 5
(required exclusions) of the Eligible Population.
Classification period
365 days prior to and including an Index Discharge Date.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
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Risk Adjustment Tables
Table
HCC-Surg
PCR-DischCC
CC-Comorbid
HCC –Rank
HCC-Comb
PCR-MA-DischCC-WeightUnder65
PCR-MA-DischCC-Weight-65plus
PCR-Comm-DischCC-Weight
PCR-MA-ComorbHCC-WeightUnder65
PCR-MA-ComorbHCC-Weight65plus
PCR-Comm-ComorbHCC-Weight
PCR-MA-OtherWeights-Under65
PCR-MA-OtherWeights-65plus
PCR-Comm-OtherWeights
Table Description
Surgery codes for Risk Adjustment Determination
Discharge Clinical Condition category codes for Risk Adjustment Determination
Comorbid Clinical Condition category codes for Risk Adjustment Determination step 2
HCC rankings for Risk Adjustment Determination step 3
Combination HCCs for Risk Adjustment Determination step 5
MA and SNP primary discharge weights for Risk Adjustment Weighting step 2 for ages
under 65
MA and SNP primary discharge weights for Risk Adjustment Weighting step 2 for ages 65
and older
Commercial primary discharge weights for Risk Adjustment Weighting step 2
MA and SNP comorbidity weights for Risk Adjustment Weighting step 3 for ages under 65
MA and SNP comorbidity weights for Risk Adjustment Weighting step 3 for ages 65 and
older
Commercial comorbidity weights for Risk Adjustment Weighting step 3
MA and SNP base risk, surgery, age and gender weights for Risk Adjustment Weighting
steps 1, 4, 5 for ages under 65
MA and SNP base risk, surgery, age and gender weights for Risk Adjustment Weighting
steps 1, 4, 5 for ages 65 and older
Commercial base risk, surgery, age and gender weights for Risk Adjustment Weighting
steps 1, 4, 5
Note: The risk adjustment tables will be released on November 1, 2013, and posted to www.ncqa.org.
Eligible Population
Product line
Commercial, Medicare (report each product line separately).
Ages
For commercial, ages 18-64 as of the Index Discharge Date.
For Medicare, ages 18 and older as of the Index Discharge Date.
Continuous
enrollment
365 days prior to the Index Discharge Date through 30 days after the Index Discharge
Date.
Allowable gap
No more than one gap in enrollment of up to 45 days during the 365 days prior to the
Index Discharge Date and no gap during the 30 days following the Index Discharge
date.
Anchor date
Index Discharge Date.
Benefit
Medical.
Event/
diagnosis
An acute inpatient discharge on or between January 1 and December 1 of the
measurement year.
The denominator for this measure is based on discharges, not members. Include all
acute inpatient discharges for members who had one or more discharges on or
between January 1 and December 1 of the measurement year.
The organization should follow the steps below to identify acute inpatient stays.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
4
Administrative Specification
Denominator
Step 1
The eligible population.
Identify all acute inpatient stays with a discharge date on or between January 1 and
December 1 of the measurement year.
Include acute admissions to behavioral healthcare facilities. Exclude nonacute inpatient
rehabilitation services, including nonacute inpatient stays at rehabilitation facilities.
Step 2
Acute-to-acute transfers: Keep the original admission date as the Index Admission
Date, but use the transfer’s discharge date as the Index Discharge Date.
Step 3
Exclude hospital stays where the Index Admission Date is the same as the Index
Discharge Date.
Step 4
Exclude any acute inpatient stay with a discharge date in the 30 days prior to the Index
Admission Date.
Step 45
Exclude stays for the following reasons:
 Acute inpatient discharges for death.
 Acute inpatient discharge with a principal diagnosis of pregnancy (Pregnancy
Value Set).
 Acute inpatient discharge with a principal diagnosis of a condition originating in the
perinatal period (Perinatal Conditions Value Set).
Step 5
For all acute inpatient discharges identified using steps 1–4, determine if there was a
planned readmission within 30 days using all acute inpatient stays. Exclude any acute
inpatient discharge as an Index Hospital Stay if the admission date of the first
readmission is within 30 days and includes any of the following.
 A principal diagnosis of maintenance chemotherapy (Chemotherapy Value Set).
 A principal diagnosis of rehabilitation (Rehabilitation Value Set).
 An organ transplant (Kidney Transplant Value Set, Bone Marrow Transplant Value
Set; Organ Transplant Other Than Kidney Value Set).
 A potentially planned procedure (Potentially Planned Procedure Value Set) without
a principal acute diagnosis (Acute Condition Value Set).
Example 1
For a member with the following acute inpatient stays, exclude stay 1.
 Stay 1 (January 30–February 1 of the measurement year): Acute inpatient
discharge with a principal diagnosis of COPD.
 Stay 2 (February 5–7 of the measurement year): Acute inpatient discharge with a
principal diagnosis of chemotherapy.
Example 2
For a member with the following acute inpatient stays, exclude stays 2 and 3 in the
following scenario.
 Stay 1 (January 15–17 of the measurement year): Acute inpatient discharge with a
principal diagnosis of diabetes
 Stay 2 (January 30–February 1 of the measurement year): Acute inpatient
discharge with a principal diagnosis of COPD.
 Stay 3 (February 5–7 of the measurement year): Acute inpatient discharge with an
organ transplant.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
5
 Stay 4 (February 10–15 of the measurement year): Acute inpatient discharge
with a principal diagnosis of rehabilitation.
Step 56
Calculate continuous enrollment.
Step 67
Assign each acute inpatient stay to one age and gender category. Refer to Tables
PCR-A-2/3 and Table PCR-B-3.
Risk Adjustment Determination
For each IHS, use the following steps to identify risk adjustment categories based on presence of surgeries,
discharge condition, comorbidity, age and gender.
Surgeries
Determine if the member underwent surgery during the inpatient stay. Download the list
of codes from the NCQA Web site (Table HCC-Surg) and use it to identify surgeries.
Consider an IHS to include a surgery if at least one procedure code in Table HCC-Surg
is present from any provider between the admission and discharge dates.
Discharge
Condition
Assign a discharge Clinical Condition (CC) category code to the IHS based on its
primary discharge diagnosis, using Table PCR-DischCC. For acute-to-acute transfers,
use the transfer’s primary discharge diagnosis.
Exclude diagnoses that cannot be mapped to Table PCR-DischCC.
Comorbidities
Step 1
Identify all diagnoses for encounters during the classification period. Include the
following when identifying encounters:
 Outpatient visits (Outpatient Value Set).
 Observation visits (Observation Value Set).
 Nonacute inpatient encounters (Nonacute Inpatient Value Set).
 Acute inpatient encounters (Acute Inpatient Value Set).
 ED visits (ED Value Set).
 Exclude the primary discharge diagnosis on the IHS.
Step 2
Assign each diagnosis to one comorbid Clinical Condition (CC) category using Table
CC—Comorbid.
Exclude all diagnoses that cannot be assigned to a comorbid CC category. For
members with no qualifying diagnoses from face-to-face encounters, skip to the Risk
Adjustment Weighting section.
All digits must match exactly when mapping diagnosis codes to the comorbid CCs.
Step 3
Determine HCCs for each comorbid CC identified. Refer to Table HCC—Rank.
For each stay’s comorbid CC list, match the comorbid CC code to the comorbid CC
code in the table, and assign:
 The ranking group.
 The rank.
 The HCC.
©2014 National Committee for Quality Assurance
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6
For comorbid CCs that do not match to Table HCC—Rank, use the comorbid CC as
the HCC and assign a rank of 1.
Note: One comorbid CC can map to multiple HCCs; each HCC can have one or more
comorbid CCs.
Step 4
Assess each ranking group separately and select only the highest ranked HCC in each
ranking group using the Rank column (1 is the highest rank possible).
Drop all other HCCs in each ranking group, and de-duplicate the HCC list if necessary.
Example
Assume a stay with the following comorbid CCs: CC-15, CC-19 and CC-80 (assume no
other CCs).
 CC-80 does not have a map to the ranking table and becomes HCC-80.
 HCC-15 is part of Ranking Group 1 and HCC-19 is part of Ranking Groups
Diabetes 1–Diabetes 4. Because CC-15 is ranked higher than CC-19 in Ranking
Group Diabetes 1, the comorbidity is assigned as HCC-15 for Ranking Group 1.
Because CC-19 is ranked higher in Ranking Groups Diabetes 2–4, the
comorbidity is assigned as HCC-19 for these ranking groups.
 The final comorbidities for this discharge are HCC-15, HCC-19 and HCC-80.
Example: Table HCC—Rank
Ranking Group
CC
Description
Rank
HCC
NyA
HyCC-80
Diabetes With Renal or Peripheral Circulatory
Manifestation
1
HCC-15
CC-16
Diabetes With Neurologic or Other Specified
Manifestation
2
HCC-16
CC-17
Diabetes With Acute Complications
3
HCC-17
CC-18
Diabetes With Ophthalmologic or Unspecified
Manifestation
4
HCC-18
CC-19
Diabetes Without Complications
5
HCC-19
CC-16
Diabetes With Neurologic or Other Specified
Manifestation
1
HCC-16
CC-17
Diabetes With Acute Complications
2
HCC-17
CC-18
Diabetes With Ophthalmologic or Unspecified
Manifestation
3
HCC-18
CC-19
Diabetes Without Complication
4
HCC-19
CC-17
Diabetes With Acute Complications
1
HCC-17
CC-18
Diabetes With Ophthalmologic or Unspecified
Manifestation
2
HCC-18
CC-19
Diabetes Without Complication
3
HCC-19
CC-18
Diabetes With Ophthalmologic or Unspecified
Manifestation
Diabetes Without Complication
1
HCC-18
2
HCC-19
NA
CC-80
Congestive Heart Failure
Diabetes 1
CC-15
Diabetes 2
Diabetes 3
Diabetes 4
CC-19
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
Step 5
7
Identify combination HCCs listed in Table HCC—Comb.
Some combinations suggest a greater amount of risk when observed together. For example,
when diabetes and CHF are present, an increased amount of risk is evident. Additional HCCs
are selected to account for these relationships.
Compare each stay’s list of unique HCCs to those in the HCC column in Table HCC—Comb
and assign any additional HCC conditions.
For fully nested combinations (e.g., the diabetes/CHF combination is nested in the diabetes/
CHF/renal combination), use only the more comprehensive pattern. In this example, only the
diabetes/CHF/renal combination is counted.
For overlapping combinations (e.g., the CHF, COPD combination overlaps the CHR/renal/
diabetes combination), use both sets of combinations. In this example, both CHF/COPD and
CHF/renal/diabetes combinations are counted.
Based on the combinations, a member can have none, one or more of these added HCCs.
Example
For a stay with comorbidities HCC-15, HCC-19 and HCC-80 (assume no other HCCs), assign
HCC-901 in addition to HCC-15, HCC-19 and HCC-80. This does not replace HCC-15, HCC19 or HCC-80.
Example: Table HCC—Comb
Comorbid HCC
HCC-15
HCC-16
HCC-17
HCC-18
HCC-19
Combination: Diabetes and CHF
Comorbid HCC
Comorbid HCC
HCC-80
NA
HCC-80
NA
HCC-80
NA
HCC-80
NA
HCC-80
NA
Combination HCC
HCC-901
HCC-901
HCC-901
HCC-901
HCC-901
Risk Adjustment Weighting
For each IHS, use the following steps to identify risk adjustment weights based on presence of surgeries,
discharge condition, comorbidity, age and gender.
Note: The final weights table will be released on November 1, 2013.
Step 1
For each IHS with a surgery, link the surgery weight.
 For Medicare product lines ages 18–64: Use Table PCR-MA-OtherWeights-Under65.
 For Medicare product lines ages 65 and older: Use Table PCR-MA-OtherWeights65plus.
 For commercial product lines: Use Table PCR-Comm-OtherWeights.
Step 2
For each IHS with a discharge CC Category, link the primary discharge weights.
 For Medicare product lines ages 18-64: Use Table PCR-MA-DischCC-Weight-Under65.
 For Medicare product lines ages 65 and older: Use Table PCR-MA-DischCC-Weight65plus.
 For commercial product lines: Use Table PCR-Comm-DischCC-Weight.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
Step 3
8
For each IHS with a comorbidity HCC Category, link the weights.
 For Medicare product lines ages 18–64: Use Table PCR-MA-ComorbHCC-WeightUnder65.
 For Medicare product lines ages 65 and older: Use Table PCR-MA-ComorbHCC-Weight65plus.
 For commercial product lines: Use Table PCR-Comm-ComorbHCC-Weight.
Step 4
Link the age and gender weights for each IHS.
 For Medicare product lines ages 18–64: Use Table PCR-MA-OtherWeights-Under65.
 For Medicare product lines ages 65 and older: Use Table PCR-MA-OtherWeights-65plus.
 For commercial product lines: Use Table PCR-Comm-OtherWeights.
Step 5
Identify the base risk weight.
 For Medicare product lines ages 18–64: Use Table PCR-MA-OtherWeights-Under65.
 For Medicare product lines ages 65 and older: Use Table PCR-MA-OtherWeights-65plus.
 For commercial product lines: Use Table PCR-Comm-OtherWeights to determine the
base risk weight.
Step 6
Sum all weights associated with the IHS (i.e., presence of surgery, primary discharge diagnosis,
comorbidities, age, gender and base risk weight).
Step 7
Use the formula below to calculate the adjusted probability of a readmission based on the sum
of the weights for each IHS.
∑ WeightsForIHS
Adjusted probability of readmission =
∑ WeightsForIHS
OR
Adjusted probability of readmission = [exp (sum of weights for IHS )] / [ 1 + exp (sum of weights
for IHS) ]
Note: “Exp” refers to the exponential or antilog function.
Step 8
Use the formula below and the adjusted probability of readmission calculated in step 7 to
calculate the variance for each IHS.
Variance = Adjusted probability of readmission x (1 – Adjusted probability of readmission)
Example: If the adjusted probability of readmission is 0.1518450741 for an IHS, then the
variance for this IHS is 0.1518450741 x 0.8481549259 = 0.1287881476.
Note: The variance is calculated at the IHS level. Organizations must sum the variances for
each age/gender and total category when populating the Total Variance cells in the reporting
tables.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
9
Sample Table: PCR—Risk Adjustment Weighting
Gender
Age
and
Gender
Weight
Discharge CC
Member
ID*
Admiss.
Counter
Base
Risk
Weight
1250
1
-1.08883
67
Female
0.1000
-0.2800
250.4
15
0.0700
4010
1
-1.08883
50.00
Male
0.1200
NA
007.4
5
0.0300
4010
2
-1.08883
50.00
Male
0.1200
NA
298.00
77
0.0600
Age
Surgical
Weight
ICD-9
Diagnosis
Code
Category
Weight
HCC-PCR
Category
20
25
NA 5
Weight
0.1400
0.2000
NA 0.0100
47
0.3300
Sum of
Weights
Adjusted
Probability
Variance
-0.8600
0.2976
0.2090
-0.9400
0.2811
0.2021
-0.5700
0.3615
0.2308
*Each Member ID field with a value represents a unique IHS.
Numerator
At least one acute readmission for any diagnosis within 30 days of the Index Discharge Date.
Step 1
Identify all acute inpatient stays with an admission date on or between January 2 and December 31 of the measurement year.
Step 2
Acute-to-acute transfers: Keep the original admission date as the Index Admission Date, but use the transfer’s discharge date as the
Index Discharge Date.
Step 3
Exclude acute inpatient hospital discharges with a principal diagnosis of pregnancy (Pregnancy Value Set) or a principal diagnosis for a
condition originating in the perinatal period (Perinatal Conditions Value Set).
Step 4
For each IHS, determine if any of the acute inpatient stays have an admission date within 30 days after the Index Discharge Date.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
10
Reporting: Denominator
Count the number of IHS for each age, gender and total combination and enter these values into the reporting
table.
Reporting: Risk Adjustment
Step 1
Calculate the average adjusted probability for each IHS for each age, gender and total
combinations and the overall total.
Organizations must calculate the probability of readmission for each hospital stay within the
applicable age and gender group to calculate the average (which is reported to NCQA). For
the total age/gender category, the probability of readmission for all hospital stays in the
age/gender categories must be averaged together; organizations cannot take the average
of the average adjusted probabilities reported for each age/gender.
Step 2
Round to four decimal places using the .5 rule and enter these values into the reporting
table.
Note: Do not take the average of the cells in the reporting table.
Example
For the “18–44” age category:
 Identify all IHS by 18–44 year-old males and calculate the average adjusted
probability.
 Identify all IHS by 18–44 year-old females and calculate the average adjusted
probability.
 Identify all IHS by all 18–44 year-olds and calculate the average adjusted probability.
Repeat for each subsequent group.
Step 3
Calculate the total (sum) variance for each age, gender and total combinations and the
overall total.
Step 4
Round to four decimal places using the .5 rule and enter these values into the reporting
table.
Reporting: Numerator
Count the number of IHS with a readmission within 30 days for each age, gender and total combination and
enter these values into the reporting table.
Note
 Organizations may not use Risk Assessment Protocols to supplement diagnoses for calculation of the risk
adjustment scores for this measure. The PCR measurement model was developed and tested using only
claims-based diagnoses and diagnoses from additional data sources would affect the validity of the models
as they are current implemented in the specification.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
11
Table PCR-A-2/3: Plan All-Cause Readmissions Rates by Age, Gender and Risk Adjustment
Age
1844
4554
5564
Total
Sex
Male
Female
Total:
Male
Female
Total:
Male
Female
Total:
Male
Female
Total:
Count of Index
Stays
(Denominator)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Count of 30Day Readmit
(Numerator)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Observed
Readmit
(Num/Den)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Average
Adjusted
Probability
__________
__________
__________
__________
__________
__________
__________
__________
__________
__________
__________
__________
Total Variance
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
O/E Ratio
(Obs. Readmit/
Avg. Adjusted
Probability)
Lower
Confidence
Interval (O/E
Ratio)
Upper
Confidence
Interval (O/E
Ratio)
___________
___________
___________
O/E Ratio
(Obs. Readmit/
Avg. Adjusted
Probability)
Lower
Confidence
Interval (O/E
Ratio)
Upper
Confidence
Interval (O/E
Ratio)
___________
___________
___________
Table PCR-B-3: Plan All-Cause Readmissions Rates by Age, Gender and Risk Adjustment
Age
85+
Sex
Male
Female
Total:
Male
Female
Total:
Male
Female
Total
Total:
Male
Female
65-74
75-84
Total:
Count of Index
Stays
(Denominator)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Count of 30Day Readmit
(Numerator)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Observed
Readmit
(Num/Den)
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Average
Adjusted
Probability
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
Total Variance
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
___________
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
12
Plan All Cause Readmission
Measure Work-Up
Measure Description
For members 18 years of age and older, the number of acute inpatient stays during the measurement year
that were followed by an unplanned acute readmission for any diagnosis within 30 days and the predicted
probability of an acute readmission. Data are reported in the following categories:
1. Count of Index Hospital Stays (IHS) (denominator).
2. Count of 30-Day Readmissions (numerator).
3. Average Adjusted Probability of Readmission.
A final rate is reported as ratio of the observed rate of readmission over the expected rate of readmission
based on the age, gender, discharge diagnosis and comorbid conditions of the discharged population.
Topic Overview
Importance and Prevalence
Health
importance
Discharge from the hospital is a critical transition point in a patient’s care. Incomplete
handoffs at discharge and poor care coordination can lead to adverse events for patients
and avoidable rehospitalization. Hospital readmissions may indicate poor care or missed
opportunities to better coordinate care (MedPAC, 2007). Hospital readmission is
associated with longer lengths of stay and higher mortality for patients. A DartmouthHitchcock Medical Center found that hospital mortality was significantly higher for
readmitted patients in the intensive care unit (ICU). This retrospective cohort study also
showed that a hospital’s length of stay was higher for patients that were readmitted
(Cook, 2006).
Similarly, a United Kingdom study showed that mortality was significantly higher for
patients readmitted to the ICU (death rates were 1.5 to almost 10 times higher among
readmission patients). Even after risk adjusting for disease and severity category,
studies found that the odds of death remained six and seven times higher among
readmitted patients (Rosenberg, 2000). Recent studies also suggest that older patients
tend to experience substantial cognitive decline following hospitalization, even after
controlling for severity of illness and cognitive decline that took place before hospital
admission (Rockwood, 2012; Wilson et al., 2012).
Prevalence
A recent MEDPAC report to Congress stated that despite a recent slight decline in
readmission rates, 12.3 percent of all 2011 Medicare admissions were followed by a
potentially preventable readmission. Readmission rates ranged from 9.9 percent for the
hospital at the 10th percentile of the distribution to 15.3 percent at the 90th percentile
(MEDPAC, 2013). Nationally representative data collected from 2011 and 2012 and
reported by the Department of Health and Human Services on Hospital Compare
(http://www.hospitalcompare.hhs.gov) shows an average hospital readmission rate of
16.0 percent. Historically, rates of hospital readmission for Medicare patients were as
high as 19 percent within 30 days of discharge (Jencks et al., 2009).
Hospital readmissions are commonly related to conditions such as congestive heart
failure, acute myocardial infarction, chronic obstructive pulmonary disease and
pneumonia (MEDPAC, 2013). Nationally representative data collected from 2009–
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
13
2012 and reported by the Department of Health and Human Services on Hospital
Compare showed that heart failure patients had the highest rate of hospital
readmission (23 percent), followed by heart attack patients (18.3 percent), and
pneumonia patients (17.6 percent) (http://www.hospitalcompare.hhs.gov).
Many experts, including the Institute for Healthcare Improvement, concur that
congestive heart failure as a reason for hospital readmission can be considered
potentially preventable (MedPAC, 2007; IHI, 2004). For Medicare patients with
congestive heart failure, 15-day readmission rates average 12.5 percent. Roughly 20
percent of hospitals that treat patients who have congestive heart failure have
inpatient readmission rates more than 4 percent higher than expected (MedPAC,
2007).
Financial
importance and
cost effectiveness
Unplanned hospitalizations are not only a burden for patients, they also have huge
financial costs. Studies have found that patients who receive post-discharge
interventions have reduced hospital readmission and less total health care costs than
those who do not receive such interventions (Constantino et al., 2013; Harrison et al.,
2011).
According to the Medicare Provider Analysis and Review file data, in 2005, 5.2
percent of Medicare patient readmissions within 7 days of discharge were considered
potentially preventable; 8.8 percent of readmissions within 15 days were potentially
preventable; and 13.3 percent of readmissions within 30 days were potentially
preventable. This equates to $5 billion, $8 billion and $12 billion dollars, respectively,
for potentially preventable readmissions.
The average Medicare payment for a potentially preventable readmission totaled
approximately $7,200 (MedPac, 2007). In 2005, the 30-day hospital readmission
rates for Medicare patients ranged from 14 percent–22 percent. If readmission rates
were lowered to the levels achieved by the top-performing regions, Medicare would
save $1.9 billion annually (Commonwealth Fund, 2006).
Supporting Evidence for Reducing Unplanned Readmissions
This measure is not based on clinical guidelines, but is supported by research and policy interest in reducing
costs and improving patient care. Research shows that specific hospital-based initiatives to improve
communication with beneficiaries and their caregivers, coordination of care after discharge and improving the
quality of care during the initial admission can avert many readmissions (MedPac, 2007). Measuring
readmissions must be clearly defined to distinguish between measures of all readmissions that may not
correlate to the quality of care provided or may not be preventable, and measures that focus on potentially
preventable admissions.
A large number of interventional and observational studies, including a number of randomized controlled
trials, have explored methods for preventing readmissions. In a meta-review of published systematic reviews
of the effect of clinical interventions on hospital readmission rates, Benbassat and Targin (2013) found that
disease management programs in the community significantly reduced readmission rates in patients with
heart failure, coronary heart disease and bronchial asthma. They found less consistent support for in-hospital
interventions: some studies and meta-analyses showed in-hospital interventions lead to reduced
readmissions; others found limited evidence of reduced readmissions post-intervention. Despite inconsistent
findings in the literature, there is a strong and growing consensus, as evidenced by examples of health plan
successes detailed below, that a substantial subset of readmissions are avoidable and that more effective
care can reduce their number.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
Health plan
role
14
Health plans can play a critical role in improving the quality of care transitions and
reducing the rate of hospital readmissions. Numerous health plans have established
quality improvement efforts that have resulted in a reduction in readmission rates. Below
are a few examples of successful health plan interventions to reduce readmissions.
 Kaiser Permanente Northwest Region reduced readmission rates from December
2010–November 2012, from 12.8 percent–11 percent. The change in O/E ratio was
1.0–0.8. The plan implemented a multidimensional intervention that included risk
stratification, standardized discharge summary, medication reconciliation, postdischarge phone calls, timely follow-up with a primary care physician, a special
transition phone number, palliative care consult and complex-case conferences
(Tuso et al., 2013).
 In California and Massachusetts Medicare Advantage plans, a model of pharmacist
in-home visits with patients after discharge and comprehensive medication
management resulted in up to a 30 percent reduction in hospital readmission rates
(Novac et al., 2012).
 In a retrospective study of more than 100,000 Medicare Advantage beneficiaries,
Costantino et al. (2013) found that implementation of a post-discharge telephone
intervention reduced readmissions, compared with a control group (9.3 percent and
11.5 percent, respectively; p<0.0001). As a group, overall cost savings were
$499,458 for members who received the intervention, with $13,964,773 in savings
to the health plan.
A similar randomized controlled trial of a post-discharge telephone intervention in a
commercial health insurance population found a 22 percent reduction in readmissions for
the treatment group, compared with the control group (Melton et al., 2012).
Gaps in care
Recent data from the HEDIS Health Plan measure showed average rates of hospital
readmission in 2012 to be 15.3 (SD=3.5) for 18–64-year-olds in Medicare Advantage
HMO plans and 15.4 percent (SD=3.5) for 18–64-year-olds in Medicare Advantage PPO
plans. The average hospital readmission rate for 65 and older in Medicare Advantage
HMO was 14.0 percent (SD=3.0) and was 13.0 percent (SD=2.8) for those in Medicare
Advantage PPO.
Medicare Advantage plans serving 18–64 had the most variation in performance, with an
8.5 percent difference in rates between HMO plans at the 10th and 90th percentiles, and
a 7.7 percent difference for PPO plans at the 10th and 90th percentiles.
Commercial plans had lower rates of hospital readmissions and less variation between
the high and low performers.
Commercial HMO plans averaged 9.0 percent (SD=4.8) and PPO plans averaged 8.2
percent (SD=1.1).
These data suggest there is significant room for improvement, particularly for Medicare
plans.
International comparisons of hospital readmission rates also suggest a need for
improvement in the United States. In a study comparing readmission rates in 17 different
countries, Kociol et al. (2012) found that 30-day post-discharge readmission rates for
patients with myocardial infarction were 68 percent higher in the United States than the
average for European countries from 2006 through 2008. Jencks et al. (2009) found great
variation in hospital readmission rates across different states and hospitals, suggesting
there is room for improvement. Currently, use of recommended practices to reduce
readmissions varies widely across hospitals in the United States (Bradley et al., 2012).
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
Health care
disparities
15
Research suggests that racial and ethnic minorities are more likely to experience hospital
readmissions than Whites. In a study of hospital readmissions within 6 months,
researchers at the Agency for Healthcare Research and Quality found that older African
Americans, Native Americans and Hispanics had significantly higher rates of readmission
than older White adults (Friedman and Basu, 2004).
In evaluations of 180-day readmission rates for patients with diabetes, Jiang et al. (2005)
found readmission rates varied by race/ethnicity and type of insurance, as follows:
Commercial
Medicaid
Medicare
White
21.4%
32.5%
27.9%
African American
20.2%
33.0%
30.7%
Hispanic
23.1%
34.2%
34.0%
Rathore et al. (2003) found that Black Medicare patients had higher rates of readmission
following heart failure treatment than White Medicare patients. In a more recent study of
2008 Medicare hospital readmission data, McHugh et al. (2010) found that Black
Medicare beneficiaries with heart failure, acute myocardial infarction and pneumonia were
more likely than Whites to be readmitted following an initial hospitalization, while Hispanic
beneficiaries had significantly higher odds of readmission for acute myocardial infarction.
Lower socioeconomic status has been found to be a risk factor for hospital readmission.
In a retrospective analysis of medical record data from a national sample of Medicare
beneficiaries hospitalized with heart failure (n=25,086), researchers found that lower
socioeconomic status of patients was correlated with higher hospital readmission rates
(Rathore et al., 2006).
On a facility level, hospitals that serve a higher share of low-income patients are
30 percent more likely to have 30-day hospital readmission rates above the national
average than hospitals that serve a lower share (Berenson and Shih, 2012). Recent
analysis by the Medicare Payment Advisory Commission also suggests that hospitals’
readmission rates are positively correlated with their low-income patient share.
In 2013, the Commission’s analysis found that a hospital’s share of low-income patients
(defined as Medicare patients receiving Social Security income) was a stronger and more
consistent predictor of readmissions than was patient race (MEDPAC, 2013).
References
Benbassat, J., and M.I. Taragin. 2013. The effect of clinical interventions on hospital readmissions: a metareview of published meta-analyses. Israel Journal of Health Policy Research, 2(1):1–15.
Bradley, E.H., L. Curry, L.I. Horwitz, H. Sipsma, J.W. Thompson, M. Elma, ... and H.M. Krumholz. 2012.
Contemporary Evidence About Hospital Strategies for Reducing 30-Day Readmissions: A National Study.
Journal of the American College of Cardiology, 60(7):607-614.
Berenson, J. and A. Shih. 2012. December 2012. Higher Readmissions at Safety-Net Hospitals and Potential
Policy Solutions, The Commonwealth Fund.
Costantino, M.E., B. Frey, B. Hall, and P. Painter. 2013. The Influence of a Postdischarge Intervention on
Reducing Hospital Readmissions in a Medicare Population. Population Health Management, 16(5).
Friedman, B., J. Basu. 2004. The rate and cost of hospital readmissions for preventable conditions. Medical
Care Research and Review. 61:225–39.
Harrison, P.L., P.A. Hara, J.E. Pope, M.C. Young, and E.Y. Rula. 2011. The impact of postdischarge
telephonic follow-up on hospital readmissions. Population Health Management. 14(1):27–32.
Institute for Healthcare Improvement. 2004. Reducing readmissions for heart failure patients: Hackensack
University Medical Center. http://www.ihi.org.
Jencks, S.F., M.V. Williams, and E.A. Coleman. 2009. Rehospitalizations among patients in the icare fee-forservice program. New England Journal of Medicine. 360(14):1418–28.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
16
Jiang, H.J., R. Andrews, D. Stryer, and B. Friedman. 2005. Racial/Ethnic Disparities in Potentially Preventable
Readmissions: The Case of Diabetes. American Journal of Public Health. 95(9):1561–67.
Kociol, R.D., R.D. Lopes, R. Clare, L. Thomas, R.H. Mehta, P. Kaul, ... and M.R. Patel. 2012. International
variation in and factors associated with hospital readmission after myocardial infarction. JAMA: the journal
of the American Medical Association. 307(1):66–74.
McHugh, M.D., J.M.B. Carthon, and X.L. Kang. 2010. Medicare readmissions policies and racial and ethnic
health disparities: a cautionary tale. Policy, Politics, & Nursing Practice. 11(4):309-316.
Medicare Payment Advisory Commission. June 2007. Report to the Congress: Promoting Greater Efficiency
in Medicare. http://www.medpac.gov/documents/Jun07_EntireReport.pdf (October 13, 2008)
Medicare Payment Advisory Commission. June 2013. “Refining the Hospital Readmissions Reduction
Program,” Chapter 4 in Report to Congress: Medicare and the Health Care Delivery System (Washington,
D.C.: MedPAC).
Melton, et al. 2012. Prioritized Post-Discharge Telephonic Outreach Reduces Hospital Readmissions for
Select High-Risk Patients. American Journal of Managed Care. 18(12).
Novak, C.J., S. Hastanan, M. Moradi, and D.F. Terry. 2012. Reducing unnecessary hospital readmissions: the
pharmacist's role in care transitions. The Consultant Pharmacist, 27(3):174–9
The Commonwealth Fund. September 2006. The Commonwealth Fund Commission on a High Performance
Health System, Why Not the Best? Results from a National Scorecard on U.S. Health System
Performance.
The Commonwealth Fund. 2008. The Commonwealth Fund Commission on a Case Study: Reducing Hospital
Readmissions Among Heart Failure Patients at Catholic Healthcare Partners.
Rathore, S.S., J.A.M. Foody, Y. Wang, G.L. Smith, J. Herrin, F.A. Masoudi, … H.M. Krumholz. 2003. Race,
quality of care, and outcomes of elderly patients hospitalized with heart failure. Journal of the American
Medical Association. 289:2517.
Rathore, S.S., F.A. Masoudi, Y. Wang, J.P. Curtis, J.M. Foody, E.P. Havranek, and H.M. Krumholz. 2006.
Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure:
findings from the national heart failure project. American Heart Journal. 152(2):371–8.
Rockwood, K. 2012. Hospitalization and effects on cognition. Neurology. 78(13):e86–7.
Rosenberg, A.L., and C. Watts. 2000. Patients Readmitted to ICUs. A systematic review of risk factors and
outcomes. Critical Care Reviews. Chest. 118:492–502.
Tuso, P., D.N. Huynh, D.P.P.D. Lynn Garofalo, G. Lindsay, M.H.A. Brandy Florence, J. Jones, ... and M.H.
Kanter. 2013. The Readmission Reduction Program of Kaiser Permanente Southern California—
Knowledge Transfer and Performance Improvement. The Permanente Journal. 17(3).
Wilson, R.S., L.E. Hebert, P.A. Scherr, et al. 2012. Cognitive decline after hospitalization in a community
population of older persons. Neurology. 78(13):950–6.
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
17
HEDIS® Health Plan Performance Rates: Plan All-Cause Readmission (PCR)
Note: Lower rates signify better performance
PRODUCT LINE: COMMERCIAL
Table 1. HEDIS PCR Measure Performance—Observed Rate of Readmission—Commercial HMO Plans
Year
2012
2013
Age
Group
18–64
Total Number
of Plans
221
219
Plans Able to
Report (%)
206 (93.2)
210 (95.9)
Average
8.4
9.0
Standard
Deviation
1.5
4.8
10th
Percentile
6.8
7.1
25th
Percentile
7.6
7.8
50th
Percentile
8.3
8.5
75th
Percentile
9.3
9.2
90th
Percentile
10.1
10.4
Table 2. HEDIS PCR Measure Performance—Observed Rate of Readmission—Commercial PPO Plans
Year
2012
2013
Age
Group
18–64
Total Number
of Plans
189
200
Plans Able to
Report (%)
187 (99.0)
197 (98.5)
Average
8.2
8.2
Standard
Deviation
1.1
1.1
10th
Percentile
6.9
6.9
25th
Percentile
7.5
7.8
50th
Percentile
8.3
8.4
75th
Percentile
8.9
8.8
90th
Percentile
9.3
9.3
25th
Percentile
0.74
0.73
50th
Percentile
0.80
0.79
75th
Percentile
0.87
0.87
90th
Percentile
0.97
0.96
25th
Percentile
0.76
0.73
50th
Percentile
0.81
0.78
75th
Percentile
0.85
0.83
90th
Percentile
0.90
0.87
Table 3. HEDIS PCR Measure Performance—O/E Ratio—Commercial HMO Plans
Year
2012
2013
Age
Group
18–64
Total Number
of Plans
221
219
Plans Able to
Report (%)
206 (93.2)
210 (95.9)
Average
0.81
0.88
Standard
Deviation
0.14
0.83
10th
Percentile
0.67
0.68
Table 4. HEDIS PCR Measure Performance—O/E Ratio—Commercial PPO Plans
Year
2012
2013
Age
Group
18–64
Total Number
of Plans
189
200
Plans Able to
Report (%)
187 (99.0)
197 (98.5)
Average
0.80
0.78
Standard
Deviation
0.10
0.09
10th
Percentile
0.69
0.68
©2014 National Committee for Quality Assurance
Draft Document for HEDIS 2015 Public Comment—Obsolete After March 19, 2014
18
PRODUCT LINE: MEDICARE
Table 1. HEDIS PCR Measure Performance—Observed Rate of Readmission—Medicare HMO Plans
Year
2012
2013
Age
Group
18–64
≥65
18–64
≥65
Total Number
of Plans
316
316
361
361
Plans Able to
Report (%)
278 (88.0)
304 (96.2)
290 (80.3)
322 (89.2)
Average
16.0
14.3
15.3
14.0
Standard
Deviation
4.2
3.2
3.5
3.0
10th
Percentile
11.1
10.9
11.1
10.7
25th
Percentile
13.6
12.5
13.4
12.4
50th
Percentile
15.9
14.2
15.4
13.7
75th
Percentile
18.0
15.9
17.2
15.6
90th
Percentile
20.1
17.9
19.6
17.3
Table 2. HEDIS PCR Measure Performance—Observed Rate of Readmission—Medicare PPO Plans
Year
2012
2013
Age
Group
18–64
≥65
18–64
≥65
Total Number
of Plans
139
139
155
155
Plans Able to
Report (%)
119 (85.6)
137 (98.6)
126 (81.3
148 (95.5)
Average
15.3
13.0
15.4
13.0
Standard
Deviation
4.0
2.8
3.5
2.8
10th
Percentile
10.6
9.6
11.7
9.6
25th
Percentile
12.9
11.7
13.2
11.3
50th
Percentile
14.9
13.0
15.5
13.0
75th
Percentile
17.5
14.1
17.2
14.0
90th
Percentile
20.0
16.0
19.4
15.5
25th
Percentile
0.77
0.80
0.74
0.78
50th
Percentile
0.88
0.90
0.84
0.85
75th
Percentile
1.01
1.00
0.95
0.95
90th
Percentile
1.17
1.11
1.06
1.06
25th
Percentile
0.77
0.79
0.76
0.75
50th
Percentile
0.90
0.88
0.87
0.85
75th
Percentile
1.04
0.96
0.97
0.92
90th
Percentile
1.15
1.05
1.07
1.01
Table 3. HEDIS PCR Measure Performance—O/E Ratio—Medicare HMO Plans
Year
2012
2013
Age
Group
18–64
≥65
18–64
≥65
Total Number
of Plans
316
316
361
361
Plans Able to
Report (%)
278 (88.0)
304 (96.2)
290 (80.3)
322 (89.2)
Average
0.91
0.91
0.84
0.86
Standard
Deviation
0.25
0.20
0.20
0.16
10th
Percentile
0.65
0.71
0.62
0.67
Table 4. HEDIS PCR Measure Performance—O/E Ratio—Medicare PPO Plans
Year
2012
2013
Age
Group
18–64
≥65
18–64
≥65
Total Number
of Plans
139
139
155
155
Plans Able to
Report (%)
119 (85.6)
137 (98.6)
126 (81.3)
148 (95.5)
Average
0.89
0.88
0.88
0.86
Standard
Deviation
0.22
0.18
0.24
0.20
10th
Percentile
0.60
0.63
0.63
0.66
©2014 National Committee for Quality Assurance

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