Monitoring prescribing patterns using regression and electronic health records
Abstract Background It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and condit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-017-0575-5 |
| _version_ | 1818911023894102016 |
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| author | Daniel Backenroth Herbert S. Chase Ying Wei Carol Friedman |
| author_facet | Daniel Backenroth Herbert S. Chase Ying Wei Carol Friedman |
| author_sort | Daniel Backenroth |
| collection | DOAJ |
| description | Abstract Background It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. Methods As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. Results Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. Conclusions Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment. |
| first_indexed | 2024-12-19T22:52:07Z |
| format | Article |
| id | doaj.art-65403bf2949d488886f5ff2b0db083d8 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-19T22:52:07Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-65403bf2949d488886f5ff2b0db083d82022-12-21T20:02:46ZengBMCBMC Medical Informatics and Decision Making1472-69472017-12-011711810.1186/s12911-017-0575-5Monitoring prescribing patterns using regression and electronic health recordsDaniel Backenroth0Herbert S. Chase1Ying Wei2Carol Friedman3Columbia University Mailman School of Public HealthDepartment of Biomedical Informatics, Columbia UniversityColumbia University Mailman School of Public HealthDepartment of Biomedical Informatics, Columbia UniversityAbstract Background It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. Methods As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. Results Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. Conclusions Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment.http://link.springer.com/article/10.1186/s12911-017-0575-5Health care quality controlElectronic health recordsPrescribing patterns |
| spellingShingle | Daniel Backenroth Herbert S. Chase Ying Wei Carol Friedman Monitoring prescribing patterns using regression and electronic health records BMC Medical Informatics and Decision Making Health care quality control Electronic health records Prescribing patterns |
| title | Monitoring prescribing patterns using regression and electronic health records |
| title_full | Monitoring prescribing patterns using regression and electronic health records |
| title_fullStr | Monitoring prescribing patterns using regression and electronic health records |
| title_full_unstemmed | Monitoring prescribing patterns using regression and electronic health records |
| title_short | Monitoring prescribing patterns using regression and electronic health records |
| title_sort | monitoring prescribing patterns using regression and electronic health records |
| topic | Health care quality control Electronic health records Prescribing patterns |
| url | http://link.springer.com/article/10.1186/s12911-017-0575-5 |
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