Literature DB >> 21320938

Exceptions to outpatient quality measures for coronary artery disease in electronic health records.

Karen S Kmetik1, Michael F O'Toole, Heidi Bossley, Carmen A Brutico, Gary Fischer, Sherry L Grund, Bridget M Gulotta, Mark Hennessey, Stasia Kahn, Karen M Murphy, Ted Pacheco, L Greg Pawlson, John Schaeffer, Patricia A Schwamberger, Sarah H Scholle, Gregory Wozniak.   

Abstract

BACKGROUND: Physicians report outpatient quality measures from data in electronic health records to facilitate care improvement and qualify for incentive payments.
OBJECTIVE: To determine the frequency and validity of exceptions to quality measures and to test a system for classifying the reasons for these exceptions.
DESIGN: Cross-sectional observational study.
SETTING: 5 internal medicine or cardiology practices. PARTICIPANTS: 47,075 patients with coronary artery disease between 2006 and 2007. MEASUREMENTS: Counts of adherence with and exceptions to 4 quality measures, on the basis of automatic reports of recommended drug therapy by computer software and separate manual reviews of electronic health records.
RESULTS: 3.5% of patients who had a drug recommended had an exception to the drug and were not prescribed it (95% CI, 3.4% to 3.7%). Clinicians did prescribe the recommended drug for many other patients with exceptions. In 538 randomly selected records, 92.6% (CI, 90.3% to 94.9%) of the exceptions reported automatically by computer software were also exceptions during manual review. Most medical exceptions were clinical contraindications, drug allergies, or drug intolerances. In 592 randomly selected records, an unreported exception or a drug prescription was found during manual review for 74.6% (CI, 71.1% to 78.1%) of patients for whom automatic reporting recorded a quality failure. LIMITATION: The study used a convenience sample of practices, nonstandardized data extraction methods, only drug-related quality measures, and no financial incentives.
CONCLUSION: Exceptions to recommended therapy occur infrequently and are usually valid. Physicians frequently prescribed drugs even when exceptions were present. Automated reports of quality failure often miss critical information. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

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Year:  2011        PMID: 21320938     DOI: 10.7326/0003-4819-154-4-201102150-00003

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  7 in total

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Authors:  Michael A Steinman; Sei J Lee; Carolyn A Peterson; Kathy Z Fung; Mary K Goldstein
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2.  Agreement of Medicaid claims and electronic health records for assessing preventive care quality among adults.

Authors:  John Heintzman; Steffani R Bailey; Megan J Hoopes; Thuy Le; Rachel Gold; Jean P O'Malley; Stuart Cowburn; Miguel Marino; Alex Krist; Jennifer E DeVoe
Journal:  J Am Med Inform Assoc       Date:  2014-02-07       Impact factor: 4.497

3.  Improving Performance on Preventive Health Quality Measures Using Clinical Decision Support to Capture Care Done Elsewhere and Patient Exceptions.

Authors:  Michael E Bowen; Deepa Bhat; Jason Fish; Brett Moran; Temple Howell-Stampley; Lynne Kirk; Stephen D Persell; Ethan A Halm
Journal:  Am J Med Qual       Date:  2017-10-14       Impact factor: 1.852

4.  Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

Authors:  Steffani R Bailey; John D Heintzman; Miguel Marino; Megan J Hoopes; Brigit A Hatch; Rachel Gold; Stuart C Cowburn; Christine A Nelson; Heather E Angier; Jennifer E DeVoe
Journal:  Am J Prev Med       Date:  2016-08-10       Impact factor: 5.043

5.  Reasons for not prescribing guideline-recommended medications to adults with heart failure.

Authors:  Michael A Steinman; Liezel Dimaano; Carolyn A Peterson; Paul A Heidenreich; Sara J Knight; Kathy Z Fung; Peter J Kaboli
Journal:  Med Care       Date:  2013-10       Impact factor: 2.983

6.  Patient preference and contraindications in measuring quality of care: what do administrative data miss?

Authors:  Joan J Ryoo; Diana L Ordin; Anna Liza M Antonio; Sabine M Oishi; Michael K Gould; Steven M Asch; Jennifer L Malin
Journal:  J Clin Oncol       Date:  2013-06-10       Impact factor: 44.544

7.  Cardiac risk is not associated with hypertension treatment intensification.

Authors:  Jeremy B Sussman; Donna M Zulman; Rodney Hayward; Timothy P Hofer; Eve A Kerr
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  7 in total

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