2024 MIPS Peer-Reviewed Journal Article Requirement
~ Standard Example ~
Section 101(c)(1) of the Medicare Access and CHIP Reauthorization Act of
2015 (MACRA) requires submission of new measures for publication in
applicable specialty-appropriate, peer-reviewed journals prior to
implementing in the Merit-based Incentive Payment System (MIPS). Such
measures will be submitted by the Centers for Medicare & Medicaid
Services (CMS), to a journal(s), before including any new measure on the
MIPS Quality Measures List. The measure submitter shall provide the
required information for article submission under the MACRA per the MIPS
Annual Call for Quality Measures submission process.
Interested parties submitting measures for consideration through the MIPS
Annual Call for Quality Measures must complete the required information by
the CMS Annual Call for Measures deadline (8 p.m. ET on May 10, 2024).
Some of the information requested below may be listed in specific fields in
the CMS Measures Under Consideration (MUC) Entry/Review Information
Tool (MERIT); however, to ensure that CMS has all of the necessary
information and avoid delays in the evaluation of your submission, please
fully complete this form as an attached Word document. The information in
MERIT must be consistent with the example measure information below,
including the following, but not limited to:
Please refer to resources
posted on the CMS Pre-
Rulemaking website for
more information
regarding the fields to be
completed.
• Measure Title: HIV Screening
• Meaningful Measures 2.0 Framework Domain: Wellness and
Prevention
Measure Steward: Centers for Disease Control and Prevention
Measure Developer: Mathematica Policy Research
Description:
Percentage of patients 15-65 years of age who have
been tested for human immunodeficiency virus (HIV).
Please choose the
appropriate Meaningful
Measures 2.0
Framework Domain
from the following list:
• Person-Centered
Care
• Equity
• Safety
• Affordability and
Efficiency
• Chronic Conditions
• Wellness and
Prevention
• Seamless Care
Coordination
• Behavioral Health
I. Statement
• Background (Why is this measure important?). This measure
is designed to promote higher implementation levels of
existing HIV screening guidelines and recommendations,
including the U.S. Preventive Services Task Force (USPSTF)
recommendation that clinicians screen for HIV infection in all
adolescents and adults ages 15 to 65. In the United States,
an estimated 1.2 million people are living with human
immunodeficiency virus (HIV), a serious, communicable
infection that, if untreated, leads to illness and premature
death (CDC 2016). In 2014, approximately 37,600 persons in
the United States were newly infected with HIV (CDC 2017).
If identified, persons living with HIV can use antiretroviral
therapy (ART) to achieve a suppressed viral load (a very low
level of the virus), allowing them a near-normal life
expectancy. Unfortunately, too many people living with HIV
are undiagnosed and unaware of their status. At the end of
2013, 13 percent, or about 161,200, of those infected with
HIV were undiagnosed, and almost 23 percent of the people
who were diagnosed had a Stage 3 (AIDS) classification at
the time of diagnosis (CDC 2016).
Targeted testing on the basis of risk behaviors fails to identify
many people who are HIV infected (Klein 2003; Alpert 1996;
Chen 1998). A substantial number of persons, including many
of those who are infected, do not perceive themselves to be
at risk for HIV, or do not disclose their risk factors (Nunn et al.
2011; Pringle et al. 2013). Routine HIV testing lessens the
stigma associated with an assessment of risk behaviors (Irwin
1996; Copenhaver 2006). More patients agree to be tested
for HIV when testing is offered routinely to everyone without
requiring a risk assessment (Fincher-Mergi 2002; CDC
2005a). Diagnostic testing in health care settings continues to
be the mechanism by which nearly half of new HIV infections
are identified (CDC 2006).
National goals emphasize the importance of increasing the
percentage of HIV-infected persons who are diagnosed, stay
in medical care, and achieve viral suppression. More
specifically, with respect to HIV testing, progress has been
defined by, and is tracked against, the following indicator:
o Increase the percentage of people living with HIV who
know their serostatus to at least 90 percent.
Achieving this national benchmark will require substantially
improving the levels at which guideline-concordant HIV
testing is provided and practiced in health care settings and
by clinicians, clinics, and health systems. This quality
measure will support and possibly incentivize efforts to
implement these necessary improvements to practice quality
(CDC 2015).
References
Alpert, P.L., J. Shuter, M.G. DeShaw, M.P. Webber, and R.S.
Klein. “Factors Associated with Unrecognized HIV-1
Infection in an Inner-City Emergency Department.”
Annals of Emergency Medicine, vol. 28, 1996,
pp.159–164.
Centers for Disease Control (CDC). “HIV Prevalence,
Unrecognized Infection, and HIV Testing Among Men
Who Have Sex with Men—Five U.S. Cities, June
2004–April 2005.” Morbidity and Mortality Weekly
Report, vol. 54, 2005, pp. 597–601.
CDC. “HIV Incidence: Estimated Annual Infections in the
U.S., 2008-2014 Overall and by Transmission Route.”
Washington, DC: U.S. Department of Health and
Human Services, 2017. Available at
https://www.cdc.gov/nchhstp/newsroom/docs/factshee
ts/HIV-Incidence-Fact- Sheet_508.pdf. Accessed
6/7/2017.
CDC. “NCHHSTP Strategic Plan Through 2020.” Washington,
DC: U.S. Department of Health and Human Services,
2015. Available at
https://www.cdc.gov/nchhstp/strategicpriorities/docs/n
chhstp-strategic-plan-through-2020-508.pdf. Accessed
June 30, 2017.
CDC. “Revised Recommendations for HIV Testing of Adults,
Adolescents, and Pregnant Women in Health-Care
Settings.” Morbidity and Mortality Weekly Report, vol.
55, no. RR-14, 2006.
CDC. “Monitoring Selected National HIV Prevention and Care
Objectives by Using HIV Surveillance Data—United
States and 6 Dependent Areas, 2014.” HIV
Surveill
ance Supplemental Report, vol. 21, no. 4,
2016. Available at
http://www.cdc.gov/hiv/library/reports/surveillance-
archive.html/. Published July 2016. Accessed May
31, 2017.
Chen, Z., B. Branson, A. Ballenger, and T.A. Peterman. “Risk
Assessment to Improve Targeting of HIV Counseling
and Testing Services for STD Clinic Patients.”
Sexually Transmitted Diseases, vol. 25, 1998, pp.
539–543.
Copenhaver, M.M., and J.D. Fisher. “Experts Outline Ways to
Decrease the Decade-Long Yearly Rate of 40,000
New HIV Infections in the U.S.” AIDS and Behavior,
vol. 10, 2006, pp. 105–114.
Fincher-Mergi, M., K.J. Cartone, J. Mischler, P. Pasieka, E.B.
Lerner, and A.J. Billittier IV. “Assessment of
Emergency Department Healthcare Professionals'
Behaviors Regarding HIV Testing and Referral for
Patients with STDs.” AIDS Patient Care and STDs,
vol. 16, 2002, pp. 549–553.
Irwin, K.L., R.O. Valdiserri, and S.D. Holmberg. “The
Acceptability of Voluntary HIV Antibody Testing in the
United States: A Decade of Lessons Learned.” AIDS,
vol.10, 1996, pp. 1707–1717.
Klein, D., L.B. Hurley, D. Merrill, and C.P. Quesenberry, Jr.
“Review of Medical Encounters in the 5 Years Before
a Diagnosis of HIV-1 Infection: Implications for Early
Detection.” Journal of Acquired Immune Deficiency
Syndrome, vol. 32, 2003; pp. 143–152.
Nunn, A., Zaller, N., Cornwall, A., Mayer, K., Moore, E.,
Dickman, A., Beckwith, C., Kwakwa, H. "Low
Perceived Risk and High HIV Prevalence Among a
Predominantly African American Population
Participating in Philadelphia's Rapid HIV Testing
Program." AIDS Patient Care STDS, vol. 25, no. 4,
2011; pp. 229–235. Available at
https://pubmed.ncbi.nlm.nih.gov/21406004/ Accessed
June 30, 2017.
Pringle, K., Merchant, R., Clar
k, M. "Is Self-Perceived HIV
Risk Congruent with Reported HIV Risk Among
Traditionally Lower HIV Risk and Prevalence Adult
Emergency Department Patients? Implications for HIV
Testing." AIDS Patient Care STDS, vol. 27, no. 10,
2013. Available at
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC383756
2/. Accessed June 30, 2017.
• Environmental scan (Are there existing measures in this
area?). We aren’t aware of any related or competing
measures. There are numerous measures pertaining to care
of patients with HIV. There are a few measures pertaining to
HIV testing for specific populations (for example, pregnant
women), but they aren’t in use in federal programs or
currently endorsed by a Consensus-Based Entity (CBE) (i.e.,
Partnership for Quality Measures (PQM)).
Please review for
duplicative quality
measures concepts
within MIPS. If a similar
quality measure is
identified, provide a
rationale to justify the
use of the submitted
measure over the
current quality measure.
II. Gap Analysis
• Provide evidence for the measure (What are the gaps and
opportunities to improve care?). HIV testing is essential for
improving the health of people living with HIV and helping to
prevent new infections. CDC and the USPSTF recommend
that all adolescents and adults get tested at least once for
HIV as part of their routine medical care, and that gay and
bisexual men and members of other populations at high risk
get tested more frequently. While testing rates have steadily
increased, CDC estimates that one in eight Americans living
with HIV remain unaware of their infection (CDC 2014).
Overall, National Health Interview Survey (NHIS) data from
2015 suggest that only 38.6 percent of adults ages 18 and
older have ever been tested for HIV (excluding testing
performed during blood donations) (CDC 2016). Meanwhile, a
recently published analysis of data from the National Youth
Risk Behavior Survey (YRBS) and Behavioral Risk Factor
Surveillance System (BRFSS) showed that only 22 percent of
high school students and 33 percent of young adults ages 18
to 24 who had ever had sexual intercourse reported that they
had been tested for HIV at any time in the past (Van Handel
et al. 2016). Finally, data from the National Survey of Family
Growth, 2011–2013, indicate that only 19 percent of persons
between the ages of 15 and 44 had been tested for HIV in the
In the Gap Analysis
section, explain the gap
between actual
healthcare and ideal
healthcare and how this
quality measure will
assist in closing the
gap. Provide statistical
data supporting the
existence of a gap in
healthcare which may
include average
performance rates,
ratios, and performance
range.
past year, including 22 percent of females and 16 percent of
males (Copen et al. 2015). Given that all of these survey
estimates are based on self-reported data, and that some
people may erroneously assume they have been tested for
HIV as part of routine preventive care, the true percentage of
persons in the United States who have ever been tested for
HIV is likely to be lower than these survey results suggest.
Analyses of administrative claims data offer more evidence to
support that HIV testing in general, and routine HIV screening
in accordance with CDC and USPSTF recommendations in
particular, is likely rare. In an analysis of 2012 outpatient
medical visits captured by the Truven Marketscan database,
89,242 of 2,069,536 patients (4.3 percent) with Medicaid
coverage had at least one HIV test, and 850 (1.0 percent) of
those tested received a new HIV diagnosis. Among
27,206,804 patients with commercial insurance, 757,646 (2.8
percent) had at least one HIV test, and 5,884 (0.8 percent) of
those tested received a new HIV diagnosis (Dietz et al. 2015).
This analysis of claims offers little evidence that routine HIV
screening was being widely implemented during outpatient
medical visits in 2012.
Similarly, CDC recently estimated the mean annual number of
visits by males ages 18–39, and of HIV testing at those visits,
using 2009–2012 National Ambulatory Medical Care Survey
(NAMCS) and U.S. Census data (Hoover et al. 2016). The
study showed that, overall, only 1.3 percent of males ages 18
to 39 were tested for HIV, based on an estimated 58.4 million
annual visits to physician offices. The study also showed that
with current HIV testing rates, most males would not be
tested by the age of 39 and that a fourfold increase in HIV
testing at visits to U.S. physicians’ offices could achieve high
HIV testing coverage of persons up to age 39.
References
CDC. Early Release of Selected Estimates Based on Data
From the National Health Interview Survey, 2015.
(2016). Retrieved May 12, 2017, from
http://www.cdc.gov/nchs/data/nhis/earlyrelease/
earlyrelease201605.pdf.
CDC. “Monitoring Selected National HIV Prevention and Care
Objectives by Using HIV Surveillance Data—United
States and 6 Dependent Areas—2012 [PDF -
1.79MB].” HIV Surveillance Supplemental Report,
vol.19, no. 3, 2014.
Copen, C.E., A. Chandra, and I. Febo-Vazquez. “HIV Testing
in the Past Year Among the U.S. Household
Population Aged 15–44: 2011–2013.” NCHS data brief
no 202. Hyattsville, MD: National Center for Health
Statistics. 2015.
Dietz, P.M., M. Van Handel, H. Wang, P.J. Peters, J. Zhang,
A. Viall et al. HIV Testing among Outpatients with
Medicaid and Commercial Insurance. PLOS ONE, vol.
10, no. 12, 2015. e0144965.
doi:10.1371/journal.pone.0144965.
Hoover, K.W., C.E. Rose, and P.J. Peters. “Setting a
Benchmark for HIV Testing at Visits to U.S. Physician
Offices by Males.” Available at
http://www.croiconference.org/sites/default/files/uploa
ds/croi2016-abstract-book.pdf. Presented at
Conference for Retroviruses and Opportunistic
Infections on February 24, 2016, Boston, MA.
Van Handel, M, L. Kann, E.O. Olsen, and P. Dietz. “HIV
Testing Among US High School Students and Young
Adults”. Pediatrics, vol. 137, no. 2, 2016. doi:
10.1542/peds.2015–2700. Epub 2016 Jan 19.
• Expected outcome (patient care/patient health improvements,
cost savings). As illustrated below, HIV screening ensures
that more persons living with HIV are made aware of their
infections and linked to clinical and prevention services. In
2014, approximately 37,600 persons in the United States
were newly infected with HIV (CDC 2017). The CDC
estimates that almost 13 percent of the people living with HIV
infection in the United States are unaware of their infection
(Centers for Disease Control and Prevention 2016).
Antiretroviral therapy (ART) delays this progression and
increases the length of survival, but it is most effective when
initiated during the asymptomatic phase. It is estimated that
on average, an HIV-infected person who is 25 years old and
receives high quality health care will live another 38 years
(Farnham 2013).
Viral
Suppression
Prescribed
ART
Engaged in
Care
Linked to
Care
Diagnosed
HIV
Screening
References
Centers for Disease Control and Prevention. “Monitoring
Selected National HIV Prevention and Care
Objectives by Using HIV Surveillanc
e Data—United
States and 6 Dependent Areas, 2014.” HIV
Surveillance Supplemental Report, vol. 21, no. 4,
2016. Available at https://www.cdc.gov/hiv/library/
reports/hiv-surveillance-archive.html.
Published July 2016. Accessed May 10, 2017.
Centers for Disease Control and Prevention. “HIV Incidence:
Estimated Annual Infections in the U.S., 2008-2014
Overall and by Transmission Route.” Washington, DC:
U.S. Department of Health and Human Services,
2017. Available at
https://www.cdc.gov/nchhstp/newsroom/docs/factshee
ts/HIV-Incidence
-Fact-Sheet_508.pdf. Accessed
6/7/2017.
Farnham, P
.G., Gopalappa, C., Sansom, S.L., Hutchinson,
A.B., Brooks, J.T., Weidle, P.J., Marconi, V.C.,
Rimland, D. “Updates of Lifetime Costs of Care and
Quality-of-Life Estimates for HIV-Infected Persons in
the United States: Late Versus Early Diagnosis and
Entry Into Care.” Journal of Acquired Immune
Deficiency Syndromes, vol. 64, no. 2, 2013, pp. 183-
189.
• Recommendation for the Measure (Is it based on a study,
consensus opinion, USPSTF recommendation etc.?). The
USPSTF recommends that clinicians screen for HIV infection
in adolescents and adults ages 15 to 65. Younger
adolescents and older adults who are at increased risk should
also be screened (A Recommendation) (Moyer 2013).
Since 2006, CDC has recommended routine opt-out HIV
screening (that is, the patient is notified that testing will be
performed unless the patient declines) of adolescents and
adults ages 13 to 64, and required health care facilities to
perform HIV diagnostic testing of adolescents and adults with
clinical signs or symptoms consistent with HIV infection
(Centers for Disease Control and Prevention 2006).
References
Centers for Disease Control and Prevention. “Revised
Recommendations for HIV Testing of Adults,
Adolescents, and Pregnant Women in Health-Care
Settings.” Morbidity and Mortality Weekly Report, vol.
55, no. RR-14, 2006.
Moyer, V.A., on behalf of the U.S. Preventive Services Task
Force. “Screening for HIV: U.S. Preventive Services
Task Force Recommendation Statement.” Annals of
Internal Medicine, 2013. Available at
http://annals.org/aim/article/1700660/screening-hiv-u-
s-preventive-services-task-force-recommendation-
statement. Accessed June 15, 2016.
The Recommendation
for the Measure section
should list the
recommendations or
guidelines that support
the quality measure.
Quality measures should
reflect current
guidelines.
III. Reliability/Validity
• What testing has been performed at the clinician level?
Please provide testing results including the N value,
correlation coefficient and any other pertinent information or
values to be considered. We are currently using electronic
health records (EHR) data from three clinician practices to
test the measure’s reliability and validity, as well as the
sensitivity of measure scores to an alternative numerator
specification. CDC tested the reliability and validity of a
previous version of the measure using EHR data from five
community health centers, which included data on 87,969
eligible patients and over 400,000 encounters. The previous
version failed to obtain a CBE endorsement because it
included patients identified as HIV positive prior to the
measurement period in the denominator. The current version
of the measure excludes patients identified as HIV positive
before the measure period from the denominator.
The quality measure is
required to be fully
developed and tested
at the level of
implementation to
progress through the
MUC process. MIPS
CQM and eCQM
collection types require
testing at the individual
clinician level (may also
be tested at the group
level). Administrative
Claims collection type
may require a reliability
threshold.
o Reliability Testing Results
We assessed the reliability of the measure score using
the signal-to-noise ratio (SNR) approach described by
Adams (2009). The goal of these tests was to determine
how well the measure scores distinguish between strong
and poor performers based on true differences in clinician
performance. Briefly, SNR methods assume that a
patient’s observed state, in Adam’s approach, whether or
not the patient was tested for HIV during the
measurement period, reflects the combined effects of
each patient’s true state (signal) plus some overlying
measurement error (noise). SNR methods use of a
binomial or beta binomial function to estimate patient-level
“true” scores. Analysts can then aggregate results at the
clinician level to produce individual clinician-level “true”
scores. SNR methods also use a binomial or beta
binomial function to estimate clinician-level scores—that
is, assuming that the clinician’s score is a binomial
random variable conditional on the true value that comes
from the beta distribution (Adams 2009). In SNR analysis,
reliability is measured as the ratio of the variance in
clinician-level “true” scores to the variance in clinician-
level actual observed scores (true score + error). An SNR
indicates lower reliability when it is closer to zero and
higher reliability when it is closer to one. Measures with
reliability coefficients of 0.70 are generally considered
adequately reliable (Nunnally and Bernstein 1994).
Using data extracted from three primary care clinics, we
found the median reliability across all clinicians with at
least 1 patient in the measure’s denominator was 0.93,
meaning that half of clinicians’ scores had a reliability
estimate of 0.93 or higher. We also calculated reliability
In the Reliability Testing
Results section, please
include the N,
correlation coefficient
(signal-to-noise ratio),
and any other pertinent
information or values in
a table format, if
possible, so that the
information can be
readily available and
easily inferred to
support the reliability of
the quality measure.
Patient/Encounter Data
Element reliability
testing can also be
included here.
when limiting the population to physicians with a minimum
number of eligible patients, and median reliability was
0.94 when we set the threshold to 10, 20, and 30 patients;
at 50 patients, median reliability increased to 0.96. These
results indicate high reliability and precision in clinician-
level scores.
Table 1. Provider Reliability Scores by Number of Patients in the
Provider’s Denominator
Provider
Type
Per Provider in
Provider N
Average
Reliability
Coefficient
PCPs
281
0.93
PCPs
505
0.94
PCPs
264
0.96
o Validity Testing Results, Clinician Sites
At two of the three clinician practices,
1
we first extracted
EHR data for all patients ages 15 to 65 who had an
encounter with a clinician during the measurement period.
We then completed chart abstraction on a random sample
of 400 patients at the two testing sites. We calculated the
minimum sample size needed to perform a validity
analysis, and found it was necessary to abstract
information from a minimum of 200 patient charts at each
site. After we collected the data, we assessed the validity
of the individual data elements by comparing the manually
abstracted data to the data collected from the EHR
extraction. We used both general agreement rates and
Cohen’s kappa coefficient to assess the agreement by
individual data element and by overall score. The kappa
statistic, which accounts for agreement occurring by
chance as well as by intention, is generally a better
indicator of data element validity than agreement rates
alone.
Provide statistical data
supporting the validity of
the quality measure in a
table format along with
the level testing
performed.
Patient/Encounter Data
Element validity testing
can also be included
here.
Data Element/Patient Encounter Level Testing
Our data showed the agreement rates for the
denominator and numerator were high, 98.3 and
92.5 percent respectively. The kappa statistic for
the denominator was 0.66, suggesting moderate
agreement between electronically extracted data
and manually abstracted data, but showed
excellent agreement (0.76) for the numerator. The
agreement rate for the HIV diagnosis date—a key
variable for accurately attributing patients to the
denominator or numerator—was 95.3 percent, but
the weighted kappa was only 0.57, suggesting
moderate agreement. In most cases where the
dates did not agree, the abstractors identified
1
We excluded the third site from the abstraction effort given the fact that a previous version of
the measure was tested in multiple clinics and in recognition of resource constraints.
older HIV diagnoses in the patients’ records than
were included in the electronic health record
extract. This finding suggests that the measures
might not accurately exclude all patients with HIV
diagnoses prior to the measurement period, which
may reduce a provider’s score. Additionally, of the
electronic records that showed no HIV test,
manual abstractors found an HIV test in about 6%
of the records, indicating that some over-testing
may occur as a result of the disagreement
between patient records.
o Empiric Validity Testing Results at the Accountable
Entity Level
For the earlier testing at community health centers, CDC’s
testing partner first extracted EHR data for all patients
ages 15 to 65 who had at least one encounter in 2013.
The organization then completed chart abstraction on a
random sample of 300 patients. Based on the results of
power calculation and greater concern about false
negatives (type II errors) than false positives (type I
errors), twice as many patients who “failed” the measure
were sampled as those who “passed” it. As a result,
through random selection, 100 patients who met the
measure and 200 patients who did not meet the measure
were pulled for chart review. Under this approach, the
CDC only fully assessed numerator data element validity.
CDC’s testing partners examined denominator validity for
charts that were selected, but the sampling approach
didn’t include charts of patients who weren’t included in
denominator. Consequently, the results address whether
denominator elements are there when the measure says
they are, but not whether they were there when the
measure indicated they weren’t (because the study didn’t
sample records that didn’t meet the denominator).
Overall, the automated calculation (EHR extract) for
patients who were not screened for HIV performed almost
equally to the manual review with the exception of
calculations for four patients (Table 1). The automated
calculation for patients who were screened for HIV was
100 percent accurate. The measure results are highly
accurate representations of the information contained in
patient’s EHRs. False positives are likely to be
exceedingly rare, and false negatives are unlikely to be so
common that they fundamentally distort the picture of
performance that emerges when measure results are
calculated from the EHR.
Table 2. Agreement Between Automatically Determined and Manually Extracted Records of HIV
Testing Among 300 Adults Ages 15 to 65: Community Health Centers
--
Manual Abstraction
Calculation
Total
Sensitivity
[SE]
(95% CI)
Specificity
[SE]
(95% CI)
% Agreement
[SE]
(95% CI)
Kappa
[SE]
(95% CI)
Met
Numerator
Did Not
Meet
Numerator
EHR
Automated
Calculation
Met the
Numerator
100 0 100
0.96
[0.019]
(0.92-0.99)
1
[0]
98.6
[0.68]
(97.3-99.9)
0.97
[0.015]
(0.94-0.99)
Did Not
Meet the
Numerator
4 196 200
Total
104
196
300
--
--
--
--
Note 1: Sensitivity and specificity were calculated considering the manually
extracted records of HIV testing as the gold standard.
Note 2: CI = confidence interval; SE = standard error.
o Alternative Numerator Specification
We are testing an alternative measure specification that
requires the presence of an HIV test result in the EHR to
qualify a patient for inclusion in the provider’s numerator.
The default specification only requires the documentation
that a test occurred, but it does not require the presence
of a result. We are examining performance under this
alternative specification, and also calculating reliability
estimates for this specification using the same approach
outlined above.
References
Adams, J. L. “The Reliability of Provider Profiling: A Tutorial.”
TR-653-NCQA. Santa Monica, CA: RAND
Corporation, 2009.
Nunnally J. C., and Bernstein, I. H. Psychometric Theory, 3rd
ed. New York: McGraw-Hill, 1994.
o What were the minimum sample sizes used for reliability
results? Analysis of measure reliability is ongoing.
• Other Information
o Is it risk adjusted? If so, how? This measure is not risk
adjusted.
o What benchmarking information is available? We have not
studied or established any benchmarks for this measure.
o Collection Type: Specify the data collection type. eCQM.
Collection Type:
- Electronic clinical
quality measures
(eCQMs)
- MIPS clinical quality
measures (MIPS CQMs)
- Administrative claims
measures (collected
solely by claims data)
IV. Endorsement
• Provide the CBE (i.e., PQM) endorsement status (and CBE
ID) and/or other endorsing body. If the measure is only
endorsed for paper records, please note endorsement for
only the data source being submitted. This measure is not
currently endorsed by a CBE.
Endorsement isn’t
required but is
encouraged. If currently
seeking endorsement,
document what stage of
the endorsement process
the measure is in.
V. Summary
• Alignment with CMS Meaningful Measures Initiative or
MACRA (if applicable). This measure falls into the Promote
Effective Prevention and Treatment of Disease goal of CMS’s
Meaningful Measures Initiative, and the domain of
Community/Population Health within MACRA.
• Importance to MIPS or other CMS programs. This measure
will incentivize clinicians to check whether their patients have
been screened for HIV and to offer screening to those who
have not. Higher levels of HIV screening will ensure that more
people know their HIV status and are better empowered to
protect their health. This will both improve clinical outcomes
for patients with the disease and help to prevent future
transmission.
• Rationale: Use of measure for inclusion in program (specialty
society, regional collaborative, other). This measure has not
yet been implemented in an existing program; however, we
believe this measure would receive support as a meaningful
and useful quality care concept. It is aligned with USPSTF
and CDC guidelines for HIV screening and is consistent with
CDC’s goals for increasing HIV screening rates. Our testing
results and the feedback we have received from experts
indicate that the measure can be successfully implemented to
assess clinicians’ performance.
• Public reporting (if applicable). Because this measure has not
been implemented yet, it is not publicly reported.
• Preferable relevant Peer-Reviewed Journal for publication.
We recommend submitting this measure to Clinical Infectious
Diseases (first choice) or AIDS Care (second choice).
Quality measures must
be linked to existing and
related cost measures
and improvement
activities, as applicable
and feasible. MIPS
quality measure
stewards will be
required to provide a
rationale as to how they
believe their measure
correlates to other
performance category
measures and activities.