DISEÑO METODOLÓGICO
MUY FRECUENTE 1 50% 2 100% 1 100% 4 100%
Measure Details
Diabetes Measures
HbA1C Control - Percentage of adult patients with diabetes who had HbA1c control (<8.0
percent).
LDL Control - Percentage of adult patients with diabetes with most recent LDL-C <130 mg/
dL; LDL-C <100 mg/dL.
BP Control - Percentage of patient visits with blood pressure measurement recorded, for
patients with diagnosed hypertension.
Eye Exam - Percentage of adult patients with diabetes who received a dilated eye exam. Kidney Disease Screen - Percentage of adult patients with diabetes who had at least one
test for microalbumin or who had evidence of medical attention for existing nephropathy.
Aspirin Prophylaxis - Percentage of diabetes patients who are taking aspirin on a daily
basis.
CAD Measures
Drug therapy for lowering LDL - Percentage of patients with CAD who were prescribed a
lipid – lowering therapy.
Aspirin Prophylaxis - Percentage of vascular disease patients who are taking aspirin on a
daily basis.
CHF Measures
Persistence of Beta-Blocker Treatment after a Heart Attack - The percentage of
patients 18 years of age and older during the measurement year who were hospitalized and discharged alive, from July 1 of the year prior to the measurement year to June 30 of the measurement year, with a diagnosis of acute myocardial infarction (AMI) and received persistent beta-blocker treatment for six months after discharge.
Beta-Blocker Treatment after a Heart Attack - The percentage of patients 35 years of age
and older during the measurement year, who were hospitalized and discharged alive from January 1 – December 24 of the measurement year, with a diagnosis of AMI and received an ambulatory prescription for beta-blockers upon discharge.
IVD: Blood Pressure Management - The percentage of patients 18 years of age and older
who had blood pressure <140/90 mmHg.
IVD: LDL-C <100 - Percentage of patients 18 years and older with IVD whose most recent
LDL-C screening <100.
Hypertension Measure
BP Control - The percentage of patients 18-85 years of age who had a diagnosis of
hypertension (HTN) and whose blood pressure (BP) was adequately controlled (<140/90) during the measurement year.
Population Health Measures
Advising Smokers To Quit - The number of patients in the denominator who responded to
the survey and indicated they had received advice to quit smoking from a doctor or other health provider, during the measurement year.
Discussing Smoking Cessation Medication - The number of patients in the denominator
who responded to the survey and indicated that medication to assist with quitting smoking was recommended or discussed.
Discussing Smoking Cessation Strategies - The number of patients in the denominator
who responded to the survey and indicated that their doctor or health care provider recommended or discussed methods and strategies other than medication, to assist with quitting smoking.
Childhood immunizations - Percentage of children two years of age who had four
diphtheria, tetanus and acellular pertussis (DTaP), three polio (IPV), one measles, mumps and rubella (MMR), three H influenza type B (HiB), three hepatitis B, one chicken pox vaccine (VZV),four pneumococcal conjugate vaccines (PCV), two hepatitis A (Hep A), two or three rotavirus vaccine (RV) and two influenza (flu) vaccines by their second birthday. The last three were added in 2010.
Adult Body Mass Index (BMI) Assessment - Percentage of patients 18-74 years old who
had an outpatient visit and who had their BMI documented during the measurement year.
BMI records / Children (WCC) - Percentage of patients 2-17 years old who had
an outpatient visit with a PCP or PB/GYN and who had evidence of BMI percentile documentation, counseling for nutrition and counseling for physical activity during the measurement year. Because BMI norms for youth vary with age and gender, this measure evaluates whether BMI percentile is assessed rather than the absolute BMI value.
Flu Shots for Adults Ages 50-64 - The percentage of patients 50-64 years of age as of
September 1 of the measurement year who received an influenza vaccination.
Influenza vaccine - Percentage of patients 65 years of age and older as of January 1 of the
measurement year who received an influenza vaccination.
Pneumovax vaccine - Percentage of patients with pneumonia, age 65 and older, who have
ever received the pneumococcal vaccine.
Medication reconciliation - Percentage of discharges from January 1 – December 1 of
the measurement year for patients 65 years of age and older, for whom medications were reconciled on or within 30 days of discharge.
Establishing Performance Benchmarks and Targets
There are several ways to take performance metrics into account under an ACO accountability framework. Multiple models are currently in use around the country tying performance attainment to financial incentives.
We have laid out a basic framework for linking the performance targets to shared savings based on the starter set of measures. These principles could be applied against the identified starter set of measures as well as additional, desirable performance
measures that payers and ACOs find useful. The basic framework is described below:
•
Each performance measure will have an associated threshold. A minimum level of performance attainment (e.g., achieving the 50th percentile of a national or regional distribution of provider performance) could be required to “earn” performance points, with more points earned based on how far the minimum threshold has been exceeded.•
A minimum number of points are needed across the performance measure set in order for the ACO to become eligible for shared savings. An ACO could achieve a sufficient number of points by significantly exceeding performance targets for most but not all measures.•
In addition to – or instead of – earning points by achieving certain performance levels, ACOs could also earn points by demonstrating significant improvement in their performance as compared to the last time their performance was measured. Because there are variations in current performance across ACOs, the use of improvement thresholds – such as reducing the gap between current and national benchmark performance by 10 percent – may be seen as more equitable by some.Details on how performance measurement will be tied to bonus payments are being developed for the Brookings-Dartmouth pilot sites and will be discussed more broadly when additional information is available.
Performance Calculation
There are several issues that need to be considered when determining how to calculate quality measures in the ACO framework. Foremost, one needs to determine the eligible patient population for whom the ACO providers assume accountability for the costs and quality of care. In order to have reliable and valid results, a sufficient population size is required.
Below discusses several considerations for performance measurement calculation, including patient attribution, performance period, sample size, composite measures, and risk adjustment.
Physician/Patient Attribution
Previously, we describe the patient attribution process which aligns patients with ACOs, and these patients become the basis for determining whether the cost benchmarks are met. The same population should be used to determine eligibility for quality measurement purposes.
It is possible that not all patients in the ACO would be included in each of the performance measurement calculations, as not all patients will have relevant conditions. Also, each measure may require a certain period of enrollment (e.g., a “look- back” period) in order to calculate the measure. Most of the performance measures in the starter set require access to at least a full year of data to determine if the quality criterion was met. However, some measures may require less time, such as the use of imaging studies for low back pain.
Performance Period
The same performance period should be used for evaluating the quality measures and the financial performance. For example, if financial performance is evaluated on an annual basis, quality measures should be calculated on the same annual basis as well. This will permit quality and cost of care to be evaluated together, helping to guard against reductions in costs that reflect stinting on care. Performance calculations should also be done periodically to verify data integrity and to ensure timely actions are taken when performance goals are not met.
Reliability (Sample Size)
While other factors, such as measurement error, can contribute to the issue of reliability, sufficient sample size is a major determinant. There are few hard and fast rules on an appropriate sample size, as different
stakeholders have different comfort levels on margins of errors. For example, HEDIS measures are only publicly reported when a health plan has at least 30 observations for the denominator.
Several options are available for ensuring the sample size is sufficient to produce statistically valid measures, including not using measures with small populations, expanding the timeframe of the measures, or aggregating data at the ACO level for all participating payers. Not using certain measures has the drawback of throwing out data that could provide useful insights on the quality of care provided by ACOs. Expanding the timeframe can add to the sample size, but it would take longer for ACOs to observe changes in the quality measures. Aggregating measures at the ACO level across payers involves summing up the numerators and denominators for each of the payers within an ACO. The ACO-level measure would be the basis for the comparisons to targets. In order to have a valid measure at the ACO level, it is essential that measures are calculated consistently across all payers and all provider groups participating in ACOs.
In considering whether to aggregate measures, particularly for bonus-payment determination purposes, providers and payers may choose to make the decisions on a measure-by-measure basis. That is, not all measures require aggregation across payers to reach the minimum sample size to be statistically reliable. Furthermore, ACOs may choose different aggregation rules depending on the market characteristics. For example, assume that an ACO contracts with three payers in the market with one payer having a dominant market share. In this scenario, the large payer may not need to aggregate its results, while the two smaller payers may need to aggregate in order to achieve statistically reliable results.
Composite measures, which are discussed below, can also be used to deal with small sample size issues. In this case, a larger sample size is obtained by combining data from various measures.
Composite Measures
There are several ways to assess the value of the measure being used for performance determination. The simplest approach is to evaluate each measure separately in determining whether the benchmark is met. This means that each measure is given the same weight of importance.
One alternative is to rely on composite measures. Composite measures provide a comprehensive view of the overall quality of care delivered by combining individual measures into a single measure. Composite measures offer several advantages. It offers a simple way to identify and reward providers who are delivering high-quality care comprehensively. It also provides an easy way to rank provider performance.6 Furthermore, composite measures can improve the statistical reliability of quality measures, which is a particular problem when assessing care for small patient panels or relatively rare outcomes measures. In order for composite measures to not impede actionability, it is recommended that underlying details for all measures making up the composite be provided to ACO providers.
Risk-Adjustment
Risk-adjustment of measures takes into
consideration the underlying risk and severity of the patients, and supports more equitable and consistent comparisons. Risk-adjusted measures can provide meaningful comparisons across
different ACOs or over different periods for an ACO. Since factors other than the quality of care
rendered – such as patients’ age, gender, severity of illness, and comorbid conditions – can affect patient outcomes, risk-adjusted measures allow the analysis to focus on the quality of care rendered
and not the risk characteristic of the patient or mix of patients. However, since only measurable and reported risk factors can be accounted for, the extent to which data can be risk adjusted is limited. It is critical for measures to be risk adjusted
appropriately, especially when using national or regional “norms” to determine targets. ACOs should conform to the standard risk adjustment methodologies applicable to endorsed measures. Additional information on best practices for risk adjustment is provided in the Part 3 Appendix. Validation of Measure Results
Validation should be incorporated into the quality measurement process. It is critical to ensure that both the payers and providers are confident in the measure results. Validation should ensure all calculations are done in accordance with technical specifications. Consistency in the implementation of data collection/aggregation methods will assist in evaluations of the effectiveness across various ACOs. Consistency also allows aggregating the measures to increase the sample size.
The validation process should ensure:
•
Complete data are used in measure calculation;•
Programming algorithms are used accurately;•
Data checks (e.g., logic checks to identify if calculated results are plausible) are available; and,•
There is statistical precision and reliability. A major component of data verification could be a full data audit. Audit ensures the validity of reported data and addresses data accuracy concerns. Audit programs typically assure the measure results for all parties are computed in accordance with pre- defined rules using comprehensive data.Operationally, each ACO will need to determine how and who will be conducting the validation process, as well as verifying the measure calculation. One option is to rely on the payers to perform these
functions for some of the measures. In Part 4, we discuss the pros and cons an ACO should consider in determining whether to perform these tasks in-house or to contract with an external vendor. Engaging a third party that is agreeable to both the payer and the ACO can alleviate concerns of gaming by either party. For ease of administrative burden, ACOs may want to standardize the processes of performance reporting and validation across different payers.
Public Reporting
A core ACO principle is to be accountable for the quality of care provided. As such, publically reporting the measures is a key aspect of
implementing an ACO performance measurement program. Publically reporting the measures is intended to equip consumers with quality of care information that would help them make more informed decisions about their health care, while encouraging hospitals and clinicians to improve the quality of care provided to all patients.
Consistency is an important attribute of the measures to ensure that they are comparable across providers and understandable to consumers. Quality Measurement in Other Reform Models
Quality measurement in accountability payment systems should send consistent signals to providers regarding priority areas for improvement. There is a wide range of payment reform initiatives, including expanded use of pay-for-performance programs and medical homes, as well as ACOs. Each of these requires the use of performance measures. Having consistent and standardized measures across these models will greatly assist in the evaluation and implementation of these programs. Consistency also needs to extend to incentive payments made by CMS to providers, in promoting the “meaningful use” of health IT, in particular EHRs.