Examining the Importance of Organizational Structure On Voluntary Medical Error Reporting Over Time

1. Examining the Importance of Organizational Structure On Voluntary Medical Error Reporting Over Time James G. Anderson, Ph.D. Rangaraj Ramanujam, PhD Devon J. Hensel, Ph.D.

2. Patient Safety Organizations Larger Project Objective • To examine whether participants in a data-sharing system experience increased reporting over time • This project: • Investigate the nature and volume of error reporting trends over time • Assess how organizational characteristics affect these trends

3. Measures • Outcome variables: Number of total errors • Predictor Variables: • Number of hospitals beds • Accreditation score • Teaching status (non-teaching/teaching)

4. Sample • N = 26 hospitals • Teaching hospitals: 38.4% (N=10) • Average number of beds: 303 (SD=203) • Mean accreditation score: 92.7 (SD=2.06)

5. Sample: Number of Errors

6. Procedure • Latent growth curve analysis: • Shape, magnitude and direction of change over time • Group and individual level • Components: • Observed measures • Latent (unobserved) factors

7. Graph: Number of Errors Over Time

8. Graph: Number of Errors Over Time Intercept

9. Graph: Number of Errors Over Time Slope Intercept

10. The Basic Model Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) Slope Intercept

11. The Basic Model Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) 1 1 1 Slope 1 Intercept

12. The Basic Model Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) 3 4 2 Slope Intercept 1

13. The Basic Model Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) Slope Intercept Number of Beds Accreditation Score Teaching Status

14. Unconditional Model Results Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) Slope Intercept Mean: 38.87* Var: 1369.386* Mean: 9.749* Var: 359.02 *p<.05

15. Conditional Model Results (standardized) Number of Reported Errors (Q1) Number of Reported Errors (Q2) Number of Reported Errors (Q3) Number of Reported Errors (Q4) Slope Intercept 0.297 0.644 -0.267 -0.391 Accreditation Score Number of Beds Teaching Status

16. Discussion • Linear increase in error reporting over time • Error reporting is proportional to system experience • Increase in comfort certain error types

17. Discussion • Hospital size affects error opportunity and type of error • Accreditation score may reflect degree of medical reliability and pressure to perform • Teaching hospitals may have greater fallibility but more stability over time.

18. Limitations • Assessing validity of error reports • Medication error patterns likely to differ

19. The Role of Information Technology in Patient Safety Initiatives: A Simulation

20. Objectives • Develop a simulation model of a voluntary medication error reporting system. • Explore organizational changes required to improve patient safety.

21. Pittsburgh Regional Healthcare Initiative • Organized in 1997 • Includes • 44 hospitals • 4 major insurers • 32 business health care purchasers • Goals • Improve quality on 2 patient safety issues • Design • Focus • 5 clinical areas for quality improvement • Patient safety • Nosocomial infections • Medication errors

22. Medmarx • Sponsored by the U. S. Pharmacopeia (USP) • Implemented in August 1998 • Anonymous • Voluntary participating hospital sand health care systems • Internet accessible • Standardized format for reporting medication errors • USP analyzes the reports, identifies patterns, makes recommendation and shares information with participating organizations • Uses the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) definition of medication errors

23. Medmarx • Information collected includes; • Inpatient or outpatient setting • Type of error • Severity • Cause of error • Location • Staff and products involved • Contributing factors • Corrective actions taken

24. Research Methods Data • 17, 000 reports of medication errors • 25 Pennsylvania hospitals Analytic Strategy • Computer simulation.

25. Table 1. Number and Type of Errors Reported over Time by the Coalition of Hospitals

26. Percentage of Medication Errors Reported by Severity

27. Simulation ModelThree Stages: • Generation of medical orders and medical errors • Communication about the errors to patients and staff. • Organizational changes: Personnel Organizational policies and practices Technology

33. Clinical Information System Applications • [BL] Existing information system • [1] Computer-based physician order entry system • [2] Computer-based physician order entry system that provides dosing information about drugs at the time orders are written • [3] Pharmacists participation on physician rounds • [4] Pharmacists participation and organizational commitment to identify causes of errors and make system changes to improve patient safety

35. Estimated Average Number of Medication Errors that Could Have Resulted in ADEs by Quarter

36. Implications • Implementation of a basic CPOE system would have little effect on the rate of serious medication errors • Inclusion of pharmacists on physician rounds would reduce medication errors by 20 percent • Organizational commitment to system changes following medication errors would reduce the error rate by 70 percent

37. Conclusions • There is a mismatch between patient safety goals and hospital actions to reduce the risk of future medication errors. • Hospitals increasingly recognize the need to implement error reporting systems. • At the same time they fail to implement organizational and IT changes needed to improve patient safety. • Actual error reduction will require organizational changes and IT to be carefully institutionalized and integrated into long term plans.

38. Influences on Nurse Perception of Hospital Unit Safety Climate: an HLM Approach Rangaraj Ramanujam, PhD Kathleen Abrahamson, RN, MS James G. Anderson, PhD

39. Nursing and Patient Safety • Medical errors are a significant problem in healthcare. • Most serious errors are preventable • Research links nurse staffing levels to adverse patient outcomes

40. Purpose of Current Project • Determine the influence of unit level characteristics on nurse perceptions of hospital unit safety • Does the overall level of nurse education, licensure and experience affect individual nurse perception of patient safety? • Shift in focus from individual to group characteristics

41. Data and Subjects • 2004 survey of nurses at two Midwestern hospitals • 6 months minimum tenure • 95% female, 98.6 % Caucasian • n=430

42. Method • Multilevel approach addresses nesting, or clustering, of nurses within hospital units • IV’s do not vary within hospital units, violates the assumption of error independence • HLM allows for accurate analysis of multilevel data

43. HLM – Step 1 • ‘Within unit’ variation and ‘Between unit’ variation • 29 hospital units= 29 within unit models • Step 1 asks, “How much variation in Nurse Perception of Patient Safety can be accounted for by between unit variation?” • A significant amount of total variance that is explained by between unit variance supports a hypothesis that unit level characteristics are predictive of individual nurse perceptions

44. HLM – Step 1 • Results support the unit level hypothesis: Coefficient 3.932, Standard Error .046, t-ratio 85.655, p< .000. • Results remained significant after adding the controls of individual nurse education, nurse experience, and nurse employment status (full vs. part time)

45. HLM- Step 2 • How much between unit variance can be explained by the independent variables? • Hypothesized that nurse perception of patient safety (measured at the individual level) will increase with increased co-worker qualifications (measured at the unit level)

46. Results • Average tenure in years has a significant positive relationship to nurse perception of patient safety (p<.052) • A significant amount of variance in nurse perception of patient safety between units can be explained by variance in nurse tenure

47. Table 2. HLM Results: Influence of Unit Level Variables on Nurse Perception of Patient Safety

48. Discussion • Positive relationship between nurse perception of patient safety and average tenure of nurses on unit confirms the importance of providing nurses with co-workers who are able to provide collegial support • Experience may contribute to support more than education, licensure or full time status

49. Policy Implications • Retention of experienced nurses may have important safety implications • Technological upgrades provide nurses with information support, have been shown to reduce error • Error reduction may be best achieved through a combination of information technology and social support

50. Limitations • Nurses included in sample from similar hospital environments and close geographic proximity • Cross-sectional data • Need to examine interaction effects: Does influence of collegial support differ for inexperienced and very experienced nurses? • Further research needed to examine collegial support in relation to level of available information technology