1. Missed Diagnoses of Acute Myocardial Infarction in the Emergency Department: An Exploration Using HCUP Data AHRQ Annual Meeting September 28, 2010

2. Team Agency for Healthcare Research and Quality Agency for Healthcare Research and Quality – Ernest Moy, MD, MPH Thomson Reuters Thomson Reuters – Cheryl Kassed, PhD, MSPH – Marguerite Barrett, MS – Rosanna Coffey, PhD – Anika Hines, PhD, MPH 2

3. Outline Background Background Specific Aims Specific Aims Methods Methods Results Results Conclusion Conclusion Implications Implications 3

4. Background Some patients with acute myocardial infarction (AMI) are mistakenly released from the emergency department (2-5%) Some patients with acute myocardial infarction (AMI) are mistakenly released from the emergency department (2-5%) Such patients may have increased mortality Such patients may have increased mortality Failure to hospitalize may be related to race, gender, and the absence of typical cardiac symptoms Failure to hospitalize may be related to race, gender, and the absence of typical cardiac symptoms Little work comparing rates across institutions Little work comparing rates across institutions

5. Specific Aims To explore the use of administrative data to identify missed diagnoses of AMI To explore the use of administrative data to identify missed diagnoses of AMI – How do HCUP estimates compare to the literature? – How do rates of missed diagnosis of AMI vary across subgroups? – How do rates of missed diagnosis of AMI vary across hospitals?

6. Data: HCUP Healthcare Cost and Utilization Project (HCUP) is a family of health care databases developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP) is a family of health care databases developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ) – SID: State Inpatient Databases = universe of inpatient discharge records from 42 states – SEDD: State Emergency Department Databases = hospital-affiliated emergency departments visits that do not result in hospitalizations

7. Methods Sample: HCUP data for 9 states with reliable person linkages and race/ethnicity data—AZ, FL, MA, MO, NH, NY, SC, TN, UT Sample: HCUP data for 9 states with reliable person linkages and race/ethnicity data—AZ, FL, MA, MO, NH, NY, SC, TN, UT Design: Cross-sectional analysis of adults Design: Cross-sectional analysis of adults 18 years or older 18 years or older First AMI admission between Feb and Dec 2007 First AMI admission between Feb and Dec 2007 Analysis: Subgroup estimates compared using t- tests (p-value<0.05) Analysis: Subgroup estimates compared using t- tests (p-value<0.05)

8. Methods Key Measure: Key Measure: – Percentage of patients with an AMI admission who were seen in the ED within the prior 2 to 7 days for a cardiac-related issue cardiac diagnosis/symptom cardiac diagnosis/symptom abdominal pain abdominal pain

9. Percent of patients with an ED visit with likely missed AMI—patient attributes *p<0.05

10. Percent of patients with an ED visit with likely missed AMI—hospital attributes *p<0.05

11. Percent of patients with an ED visit with likely missed AMI—other attributes * p<0.05

12. Conclusions Study rate of AMI missed diagnosis=1.85% Study rate of AMI missed diagnosis=1.85% – Pope et al, study found 2.1% Administrative data are a reasonable source for estimating missed AMI diagnoses Administrative data are a reasonable source for estimating missed AMI diagnoses

13. Conclusions Unsurprising results Unsurprising results – Vulnerable populations have higher rates of missed diagnoses for AMI—minorities, the uninsured, those with low-income, and those visiting hospitals in rural areas Surprising results Surprising results – Busy hospitals have lower rates of AMI missed diagnoses (i.e. hospitals with higher occupancy rates, higher bed volume, and residency programs) – Weekend visits and slow ED days have higher rates of AMI missed diagnoses

14. Limitations Administrative data  lower estimates of missed diagnoses Administrative data  lower estimates of missed diagnoses Data not representative—9 states Data not representative—9 states

15. Implications Administrative data may be useful for studying other types of missed diagnoses. Administrative data may be useful for studying other types of missed diagnoses. Reporting on variation in missed diagnoses could lead to better quality of care. Reporting on variation in missed diagnoses could lead to better quality of care.