Examining Potentially Avoidable Emergency Department Encounters and Hospital Admissions

1. Examining Potentially Avoidable Emergency Department Encounters and Hospital Admissions Claudia Steiner MD, MPH Agency for Healthcare Research and Quality AHRQ Annual Meeting • September 2010

2. Assess the impact of including both IP and ED data when evaluating potentially preventable admissions and visits Previous research primarily considers IP data Friedman B., Basu J. The Rate and Cost of Hospital Readmissions for Preventable Conditions. Med Care Res Rev 2004; 61; 225

3. Objectives • Understand the impact of including both IP and ED data when evaluating: • the utilization of potentially preventable healthcare encounters • the costs of potentially preventable healthcare encounters

4. Study Design • Design: retrospective, observational cohort study • Timeframe: 23-months (January 2005 – November 2006) • Four states: AZ, FL, NE, and TN • Geographic and demographic variability • Represent 8.6 million discharges

5. SEDD SID Three Primary AHRQ Resources Prevention Quality Indictors (Asthma {pediatric, adult, elderly}, Diabetes {pediatric, adult}, CHF, Bacterial Pneumonia, and Pediatric Gastroenteritis ) Quality Measures HCUP State Inpatient Databases (AZ, FL, NE, TN) Hospital discharge databases HCUP State ED Databases (AZ, FL, NE, TN)

6. Study Design • HCUP SID • Encounter / discharge level • All discharges from all community hospitals in participating states • HCUP SEDD • Encounter / visit level • All treat and release encounters from all community hospital emergency departments in participating states • AHRQ PQIs • Applied the standard definitions (numerators and denominators) provided by the software package available through AHRQ • Each condition defined using the principle diagnosis field • Readmissions limited to the same condition

7. Impact of using all-listed vs. first-listed diagnosis (HCUP SEDD) Source: Agency for Healthcare Quality and Research, Healthcare Cost and Utilization Project, State Emergency Department Databases, Arizona and Nebraska, 2006-2007.

8. Study Results: Across 4 States

9. Study Results: Across 4 States

10. Study Results: Across 4 States

11. Study Results: Across 4 States

12. Study Results: Across 4 States

13. Distribution of IP and ED Events by PQI Condition

14. Which PQIs were most impacted by adding ED data? Percent of visits that were treat- and-release ED visits versus IP admissions: • Pediatric gastroenteritis (83%) • Asthma, Pediatric (82%) • Asthma, Adult (81%) • Asthma, Elderly (41%) • Bacterial pneumonia (28%) • Diabetes, Adult (24%) • Diabetes, Pediatric (20%) • Congestive heart failure (11%) Greatest ED impact on utilization Pediatric gastroenteritis and Non-elderly asthma Lowest ED impact on utilization CHF

15. Conclusions • Substantial impact of ED visits on overall hospital utilization for eight potentially preventable admissions • Accounting for ED visits more than doubled the number of visits (by 500K) • Variable impact of ED visits on overall hospital costs for eight potentially preventable admissions • Increased overall costs by 7% (by $243M)

16. Project Team • Claudia Steiner, MD, MPH • Barry Friedman, PhD • Joanna Jiang, PhD • Dan Whalen • Marguerite Barrett, MS • Minya Sheng • Chaya Merrill, PhD

17. Considerations Editor, Annals of Emergency Medicine (based upon almost identical comments from Reviewer1 and Reviewer2): “The eight selected conditions are very relevant to emergency physicians…..However, the reviewers had several concerns that limit our ability to publish your manuscript. First, ED care and hospitalization are non-mutually exclusive steps in a pathway in the United States -- the decisions about whether to visit the ED are up to patients while the decisions about whether to get hospitalized are up to emergency physicians and physicians in the community. In addition, PQIs were developed exclusively for use in the inpatient setting, and are not necessarily valid for ED visits.”

18. Considerations Reviewer 1, Annals of Emergency Medicine Misclassification Bias: “One means of assessing the degree of misclassification would be to calculate the proportion of the inpatient (IP) diagnoses that match the ED "reason for visit" for the IP hospitalizations that were admitted through the ED”

19. Considerations Reviewer 1, Annals of Emergency Medicine “Should preventable hospital care be viewed the same as "preventable" ED care? Perhaps these entities are different and should be analyzed as distinct entities.”

20. Considerations Reviewer 2, Annals of Emergency Medicine “ED care is ambulatory care and not inpatient care. This paper assumes that ED patients that have diagnoses that when admitted fall within PQI measures are the same as admitted patients with these diagnoses. But EDs treat and release patients who were likely less sick, did have their hospitalization prevented by definition, and the importance of including such treat and release patients in the PQI measure is unclear.”

21. Considerations Reviewer 2, Annals of Emergency Medicine “The PQI was developed for inpatient hospitalization measurement, whereas alternative methods such as the Billings algorithm have been developed to assess preventable ED care.”

22. Considerations Reviewer 2, Annals of Emergency Medicine “The authors even note that many ED codes are symptom-based codes, so the application of PQI to ED codes raises many concerns: a. PQI is designed for inpatient discharge codes not ambulatory codes.b. PQI has never been validated for application to ED patients; there is no reason to believe that ED patients with treat and release conditions and/or these codes for CHF, asthma or diabetes are similar. This needs to be studied.”

23. Healthcare Cost and Utilization Project (HCUP) THE LARGEST COLLECTION OF MULTI-LEVEL, ALL-PAYER, ENCOUNTER-LEVEL, HEALTH CARE DATA