Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach
Background. There is a great deal of interest in evaluating hospital performance in order to monitor and improve health care quality. Increasingly, risk-adjusted performance measures are available to the public and statistical approaches for estimating these measures are considered. Some methods in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2018-04-01
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| Series: | MDM Policy & Practice |
| Online Access: | https://doi.org/10.1177/2381468318761027 |
| _version_ | 1818350621383720960 |
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| author | Patricia Cooper Barfoot R. Jock MacKay Stefan H. Steiner |
| author_facet | Patricia Cooper Barfoot R. Jock MacKay Stefan H. Steiner |
| author_sort | Patricia Cooper Barfoot |
| collection | DOAJ |
| description | Background. There is a great deal of interest in evaluating hospital performance in order to monitor and improve health care quality. Increasingly, risk-adjusted performance measures are available to the public and statistical approaches for estimating these measures are considered. Some methods in use currently are based on 3-year aggregates of data since a small number of cases may lead to imprecise estimates and make it hard for stakeholders to detect differences across hospitals over time. However, if quality changes over time, a measure based on these data is a biased estimate of present performance. Methods. We present an alternative approach (weighted estimating equations [WEE]) for combining historical data in estimation that regulates the tradeoff between bias and precision in the measure of present performance. The WEE approach uses all available historical data through estimating functions that down-weight past data. Results. We compare the WEE approach to two current practices using a realistic dataset of the mortality of patients following an elective percutaneous coronary intervention procedure in New York State who meet certain criteria. The width of the uncertainty interval in the realistic example is up to 65% smaller and the difference is more pronounced for hospitals with a small number of cases. Conclusions. The advantage of this approach extends from the example dataset to other datasets. The WEE approach uses all available data rather than data from an arbitrary 3-year window. The effect of borrowing strength from historical data is a more precise estimate of present performance than current practices. Its advantages are important for the comparison of other aspects of medical performance, including surgical or medical practitioner performance. |
| first_indexed | 2024-12-13T18:24:45Z |
| format | Article |
| id | doaj.art-0b26ca4476a9429fb201d1c16ecddc6d |
| institution | Directory Open Access Journal |
| issn | 2381-4683 |
| language | English |
| last_indexed | 2024-12-13T18:24:45Z |
| publishDate | 2018-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | MDM Policy & Practice |
| spelling | doaj.art-0b26ca4476a9429fb201d1c16ecddc6d2022-12-21T23:35:37ZengSAGE PublishingMDM Policy & Practice2381-46832018-04-01310.1177/2381468318761027Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations ApproachPatricia Cooper BarfootR. Jock MacKayStefan H. SteinerBackground. There is a great deal of interest in evaluating hospital performance in order to monitor and improve health care quality. Increasingly, risk-adjusted performance measures are available to the public and statistical approaches for estimating these measures are considered. Some methods in use currently are based on 3-year aggregates of data since a small number of cases may lead to imprecise estimates and make it hard for stakeholders to detect differences across hospitals over time. However, if quality changes over time, a measure based on these data is a biased estimate of present performance. Methods. We present an alternative approach (weighted estimating equations [WEE]) for combining historical data in estimation that regulates the tradeoff between bias and precision in the measure of present performance. The WEE approach uses all available historical data through estimating functions that down-weight past data. Results. We compare the WEE approach to two current practices using a realistic dataset of the mortality of patients following an elective percutaneous coronary intervention procedure in New York State who meet certain criteria. The width of the uncertainty interval in the realistic example is up to 65% smaller and the difference is more pronounced for hospitals with a small number of cases. Conclusions. The advantage of this approach extends from the example dataset to other datasets. The WEE approach uses all available data rather than data from an arbitrary 3-year window. The effect of borrowing strength from historical data is a more precise estimate of present performance than current practices. Its advantages are important for the comparison of other aspects of medical performance, including surgical or medical practitioner performance.https://doi.org/10.1177/2381468318761027 |
| spellingShingle | Patricia Cooper Barfoot R. Jock MacKay Stefan H. Steiner Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach MDM Policy & Practice |
| title | Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach |
| title_full | Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach |
| title_fullStr | Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach |
| title_full_unstemmed | Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach |
| title_short | Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach |
| title_sort | comparing and monitoring risk adjusted hospital performance measures a weighted estimating equations approach |
| url | https://doi.org/10.1177/2381468318761027 |
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