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• W186177235 abstract "The achievable benchmark of care (ABC) approach identifies the performance of the top 10% of healthcare providers as the benchmark. Comparing individual performance with peer performance should be a powerful motivator to change behavior and improve quality of healthcare. This paper presents a SAS Macro to calculate ABC, also presents the utilization of Annotate data set to label ABCs on individual clinician’s performance bar chart. INTRODUCTION Benchmarking is generally considered to be an important tool for quality improvement. Achievable Benchmarks of Care (ABCs), calculated from existing performance data, represent attainable levels of excellence for a performance measure. ABCs allow individual healthcare provider to compare the outcomes for their patients relative to overall performance of the “peer-group”. As the Quality Improvement Organization (QIO) for the state of Alabama, AQAF works in partnership with physicians, hospitals, nursing homes and home health agencies to improve the quality of health care in Alabama. Historically, a provider’s performance has been compared to the “average” performance of “peer-group” for assessment. To help healthcare providers set attainable goal and make them more involved and proactive in quality improvement, we include both peer-group averages and ABCs in individual performance charts, which is a powerful motivator for providers to change behavior and provide appropriate care to patients. We are working with different healthcare providers on almost 50 clinical measures, and for each measure we have similar data layout: provider #, measure, period, numerator, denominator, and rate. Using a separate module would make the programming and the debugging a lot easier. By utilizing SAS Macro, we created the ABC Macro to calculate ABCs. Once ABCs are calculated, we use Annotate data set to label ABCs on providers’ performance charts to enhance graphics output. ABC MACRO This Macro has 8 parameters: benchmark(lib=,dataset=,ProvVar=,NumVar=,DenVar=,measure=,period=, range=) This paper will use diabetes mellitus (DM) patients receiving annual eye examinations as an example for illustration. Performance is measured by the proportion of DM patients having claims data reflecting an eye exam. Assume you have a LIB named SESUG, in which there is a SAS data set called Diab_Eye_2006Q3 with the following layout. Provider MEASURE_PERIOD Eye_NUM Eye_DEN Eye_PCT #1 2006Q3 25 46 54.35% #2 2006Q3 33 47 70.21% ... ... ... ... ... State 2006Q3 41293 81867 50.44% The key steps for ABC calculation are: determine eligible patient’s breakpoint, calculate the Bayesian Adjusted Performance Fraction (APF), and identify 10% Bayesian adjusted performance and any tied performance. The ABC Macro utilizes Macro variables, retain variable, and PROC SQL procedure to calculate ABC and save it into a SAS data set. Below are the steps for ABC Macro. See appendix for complete program. ABC Macro Calculation Steps First, create a copy of data set, renaming fields for calculation. Step 1: Determine eligible patient’s breakpoint. Assign the value to a Macro variable. Step 2: Create provider data set, excluding the record with overall data, like state average. Step 3: Calculate the Bayesian Adjusted Performance Fraction (APF) for each provider. Step 4: Rank all provider data in descending order of their APF." @default.
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• W186177235 date "2007-01-01" @default.
• W186177235 modified "2023-09-27" @default.
• W186177235 title "ABC Macro and Performance Chart with Benchmarks Annotation" @default.
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