Reflection Paper
Baba0914
AC21620-Chapter9.pptxA Practical Approach to Analyzing Healthcare Data, Fourth Edition Chapter 9, Benchmarking and Analyzing Externally Reported Data
Susan White, PhD, RHIA, CHDA
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Learning Objectives
Explain the types of benchmarking
Link benchmarking to value-based purchasing programs
Discuss healthcare report cards
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Types of Benchmarking
Benchmarking – comparing performance to a standard
Internal benchmarking – comparison to internal goals or year-over-year
External benchmarking – comparison to external norms or competitors
Benefits
Identify strong or weak areas
Part of quality improvement culture
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Benchmarking Steps
1. Identify the issue to benchmark
2. Locate internal data related to the issue
3. Analyze internal data
4. Identify external data available for benchmarking
5. Collect public domain data or purchase data, if appropriate
6. Compare internal and external data
7. Determine whether a performance gap exists
8. Communicate benchmarking findings
9. Establish performance-level targets and action plans for achievement
10. Implement plans; monitor and communicate progress
11. Recalibrate benchmarks as necessary
12. Repeat the process
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Hospital Value Based Purchasing Programs (HVBP)
CMS HVBP is example of a formal benchmarking program
HVBP includes four domains
Process of care
Outcomes
Patient experience
Efficiency of care
Generates Total Performance Score (TPS) that is used to determine an incentive payment added to Medicare inpatient payments for participating hospitals
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Dashboards and Scorecards
Method to represent performance in terms of key performance indicators (KPI)
Guide management decisions
Include a combination of indicators measured on a ‘per unit’ basis for comparability across time
Categories may include:
Clinical
Operational
Financial
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Example Dashboard – Medicare Spending
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Example Dashboard – Medicare Chronic Conditions
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National Quality Forum (NQF)
Provides a framework for endorsing healthcare quality measures by:
Convenes working groups to foster quality improvement in both public- and private-sectors;
Endorses consensus standards for performance measurement;
Ensures that consistent, high-quality performance information is publicly available; and
Seeks real time feedback to ensure measures are meaningful and accurate.
Endorsement of a quality measure requires the following steps:
Measure is proposed and supported with scientific evidence
Validity and reliability of the measure is established
Feasibility is tested typically via pilot testing; includes cost and potential administrative burden for data collection
Usability is assessed; does the measure provide enough feedback so that users can improve performance
Assessment of related or competing measures
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Medicare Quality Measures
Data.medicare.gov
Hospital Compare
Nursing Home Compare
Physician Compare
Home Health Compare
Dialysis Facility Compare
Data provided in online query and comparison format as well as a bulk download of national statistics
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Hospital Compare Example
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Risk adjustment
Quality measurement should include an adjustment for the risk of an adverse outcome
Patient level adjustment
Age/gender
Comorbidities
Provider level adjustment
Teaching status
Location (urban/rural)
Socio-economic attributes of patient mix
Payer mix
Used to compare actual performance to expected performance based on the risk factors
SIR – standardized infection rate (observed infection rate divided by the expected infection rate)
SRR – standardized readmission rate
SMR – standardized mortality rate
For all standardized rates, a value of greater than one is interpreted that a facility’s rate is higher than expected given the risk attributed to their patient mix
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CMS Risk Adjustment – CLABSI in ICU
CLABSI = central line-associated bloodstream infections
Observed and expected infection rates are calculated for each hospital
Expected rates are risk adjusted
The graph depicts the SIR or observed to expected rate (O/E) for each hospital
O/E = 1.0 means that the hospital’s infection rate is equal to that expected after risk adjustment
The dark shaded areas represent the 95% confidence interval for the O/E
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