Hospital and emergency department factors associated with variations in missed diagnosis and costs for patients age 65 years and older with acute myocardial infarction who present to emergency departments

Academic Emergency Medicine

Volumn 21, Issue 10, 2014, Pages 1101-1108

  Wilson, Michael ^a   Welch, Jonathan ^a   Schuur, Jeremiah ^a   O'Laughlin, Kelli ^a   Cutler, David ^b  

^a HARVARD MEDICAL SCHOOL   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE HEART INFARCTION; ADVERSE OUTCOME; AGED; ARTICLE; EMERGENCY PHYSICIAN; EMERGENCY WARD; FEMALE; HEALTH CARE COST; HEART MUSCLE ISCHEMIA; HOSPITAL ADMISSION; HOSPITAL COST; HOSPITAL DISCHARGE; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICARE; OUTCOME ASSESSMENT; THORAX PAIN; COMORBIDITY; DIAGNOSTIC ERROR; ECONOMICS; EMERGENCY HEALTH SERVICE; HOSPITALIZATION; MYOCARDIAL INFARCTION; RISK FACTOR; STATISTICS AND NUMERICAL DATA; UNITED STATES; UTILIZATION; VERY ELDERLY;

AGED; AGED, 80 AND OVER; COMORBIDITY; DIAGNOSTIC ERRORS; EMERGENCY SERVICE, HOSPITAL; FEMALE; HOSPITALIZATION; HUMANS; MALE; MEDICARE; MYOCARDIAL INFARCTION; PATIENT DISCHARGE; RISK FACTORS; UNITED STATES;

EID: 84908149192     PISSN: 10696563     EISSN: 15532712     Source Type: Journal    
DOI: 10.1111/acem.12486     Document Type: Article

References (22)

1. Mensah GA, Brown DW. An overview of cardiovascular disease burden in the United States. Health Aff (Millwood) 2007;26:38-48.
2. McClellan M. Reforming payments to healthcare providers: the key to slowing healthcare cost growth while improving quality? J Econ Perspect 2011;25:69-92.
3. Schuur JD, Venkatesh AK. The growing role of emergency departments in hospital admissions. N Engl J Med 2012;367:391-3.
4. Lee TH, Rouan GW, Weisberg MC, et al. Clinical characteristics and natural history of patients with acute myocardial infarction sent home from the emergency room. Am J Cardiol 1987;60:219-24.
5. Pope JH, Aufderheide TP, Ruthazer R, et al. Missed diagnoses of acute cardiac ischemia in the emergency department. N Engl J Med 2000;342:1163-70.
6. Schor S, Behar S, Modan B, Barell V, Drory J, Kariv I. Disposition of presumed coronary patients from an emergency room. A follow-up study. JAMA 1976;236:941-3.
7. McCarthy BD, Beshansky JR, D'Agostino RB, Selker HP. Missed diagnoses of acute myocardial infarction in the emergency department: results from a multicenter study. Ann Emerg Med 1993;22:579-82.
8. Masoudi FA, Magid DJ, Vinson DR, et al. Implications of the failure to identify high-risk electrocardiogram findings for the quality of care of patients with acute myocardial infarction: results of the Emergency Department Quality in Myocardial Infarction (EDQMI) study. Circulation 2006;114:1565-71.
9. Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS). Available at: http://hcupnet.ahrq.gov/HCUPnet.jsp?Id=A013AF252A0D96E5&Form=MAINSEL&JS=Y&Action=%3E%3ENext%3E%3E&-MAINSEL=NEDS%20Statistics. Accessed Jul 25, 2014.
10. MaCurdy T, Bhattacharya J, Perlroth D, et al. Geographic Variation in Spending, Utilization and Quality: Medicare and Medicaid Beneficiaries. Available at: http://iom.edu/Reports/2013/Variation-in-Health-Care-Spending-Target-Decision-Making-Not-Geography/~/media/Files/Report%20Files/2013/Geographic-Variation2/Subcontractor-Reports/Updated%20Acumen%20Report.pdf. Accessed Jul 24, 2014.
11. Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH. Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J 2004;148:99-104.
12. Petersen LA, Wright S, Normand SL, Daley J. Positive predictive value of the diagnosis of acute myocardial infarction in an administrative database. J Gen Intern Med 1999;14:555-8.
13. Weintraub WS. Cardiovascular Health Care Economics. Totowa, NJ: Humana Press, 2003.
14. Hall SA, Kaufman JS, Ricketts TC. Defining urban and rural areas in U.S. epidemiologic studies. J Urban Health 2006;83:162-75.
15. Spetz J, Maiuro LS. Measuring levels of technology in hospitals. Q Rev Econ Finance 2004;44:430-47.
16. Silvio D, Laurence CB. Did the Butler Really Do It? Examining the Impact of Technology on Hospital Cost Growth. Available at: http://dx.doi.org/10.2139/ssrn.1905429. Accessed Jul 18, 2014.
17. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998;36:8-27.
18. Chen J, Normand SL, Wang Y, Drye EE, Schreiner GC, Krumholz HM. Recent declines in hospitalizations for acute myocardial infarction for Medicare fee-for-service beneficiaries: progress and continuing challenges. Circulation 2010;121:1322-8.
19. Rabe-Hesketh S, Skrondal A. Linear Random-Intercept Models. In: Rabe-Hesketh S, Skrondal A (editors). Multilevel and Longitudinal Modeling Using Stata. College Station, TX: Stata Press Publication, 2008, pp 91-7.
20. Noymer A. Alpha, significance level of test. In: Lavrakas PJ (editors). Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications, 2008.
21. Schreyogg J, Stargardt T. The trade-off between costs and outcomes: the case of acute myocardial infarction. Health Serv Res 2010;45:1585-601.
22. Chen LM, Jha AK, Guterman S, Ridgway AB, Orav EJ, Epstein AM. Hospital cost of care, quality of care, and readmission rates: penny wise and pound foolish? Arch Intern Med 2010;170:340-6.