Human Capital in the Nursing Workforce and Its Impact on Patient Outcomes

1. Human Capital in the Nursing Workforce and Its Impact on Patient Outcomes Ciaran S. Phibbs, Ph.D. June 2, 2007

2. Research Team • Investigators • Ann Bartel • Patricia Stone • Nancy Beaulieu • Programmers and Research Assistants • Lakshmi Ananath • Cecilia Machado • Susan Schmitt • Andrea Shane Health Economics Resource Center

3. Background • Relationship between hospital nurse staffing and quality of care is a significant concern for health services researchers, health services providers and policymakers. • Prior research has found evidence showing negative correlation between adverse patient outcomes and nurse staffing levels, but this literature has numerous limitations. Health Economics Resource Center

4. What Our Paper Contributes • Unit-level data • Monthly observations • Longitudinal 4 years. • Data from a single organization (VA). • Adds role played by human capital and relational capital in the nursing workforce Health Economics Resource Center

5. Hypotheses We Test • RNs with more general human capital (education, experience) deliver higher quality nursing care • RNs with more firm-specific human capital (job tenure) deliver higher quality nursing care • Relational capital (stability of nursing team) contributes to higher quality care because stable teams are better at sharing tacit knowledge and facilitating coordination. Health Economics Resource Center

6. Nurse Staffing Data • DSS, VA’s implementation of TSI, a comprehensive hospital activity based accounting system. • Inpatient nursing labor tracked at the unit level. Health Economics Resource Center

7. Nurse Staffing Data • Monthly extract that reports the number of hours, for each type of nursing labor, including: • RNs • LVNs • Aides • Advanced practice nurses • Others Health Economics Resource Center

8. Nurse Staffing Data • Hours worked and costs reported separately for “regular” hours, overtime, and vacation/sick/holiday hours. Thus, we can track nursing labor by the actual hours worked, not the hours nurses were paid. Health Economics Resource Center

9. Human Capital Data • We extracted human capital and other nursing characteristics from the VA payroll data (PAID). These data contain a wealth of information, including VA tenure, education, and age. • PAID also reports hours of overtime and shift premiums (track off-shift labor). Health Economics Resource Center

10. Human Capital Data • DSS tracks where people work to allocate costs. • DSS made a special extract for us that summarized, by PAID codes, how all nursing labor was allocated across units, for each facility. Health Economics Resource Center

11. Patient Data • Unlike most discharge abstracts, VA has a “bedsection” file, with one record for each bedsection stay, which are defined by the treating specialty of the physician. ICUs are separate bedsections. • Thus, most, but not all, unit stays have a corresponding bedsection record. Health Economics Resource Center

12. Empirical Model • PSIit = α1(RN Hrs PPD)it + α2(RN Hrs PPD2 )it + • α3 HCit + α4 Non-RN Hours PPDit + α5DRGit + α6AGEit + α7 LOSit + α8Dischargesit + • Year + λi + εit • HC is measured by percentage of RN hours provided by RNs with at least a B.S. degree, RN age (“experience”), RN tenure, and percentage of RN hours provided by part-time RNs. Health Economics Resource Center

13. Summary Statistics Health Economics Resource Center

14. 2006 average rate of failure to rescue, by ICU bed section Health Economics Resource Center

15. 2006 average rate of infection, by ICU bed section Health Economics Resource Center

16. Preliminary Findings • General human capital (education) is negatively correlated with the rate of infections due to medical care • Specific human capital (job tenure) is negatively correlated with the rate of infections due to medical care and rate of failure to rescue. The former correlation holds even controlling for nursing-unit fixed effects. • Data are only for intensive-care units and results are likely to be a lower bound on effects for more diverse units. Health Economics Resource Center

17. Summary of OLS Results: Human Capital Variables • General human capital (education) is negatively correlated with the rate of infections • Specific human capital (tenure) is negatively correlated with the rate of infections. A one standard deviation increase in RN tenure is associated with a 19% decrease in rate of infections • Specific human capital (tenure) and failure to rescue are negatively correlated (though less robust). A one standard deviation increase in RN tenure is associated with a 4% decrease in failure to rescue rate. Health Economics Resource Center

18. Summary of OLS Results: Other Variables • RN staffing has a negative but diminishing effect on infection rate, and is negatively correlated with failure to rescue rate • Non-RN hours has no effect • Very low patient volume is associated with higher failure to rescue rate Health Economics Resource Center

19. Summary of Fixed Effects Results • RN tenure negatively correlated with rate of infections and the measured impact is more than twice as large as the OLS effect. • RN tenure is insignificant in failure to rescue equation. • RN education is insignificant in both equations. Health Economics Resource Center

20. Plans for Future Work • Expand sample to include all acute-care units • Control for prior experience of RNs • Control for union membership • Study other measures of health care quality such as 30-day mortality rate, re-admission rates and patient-assessed quality of care • Add more information about human capital, especially for off-shifts • Add information from RN satisfaction survey • Construct measures of team stability to study relational capital Health Economics Resource Center

21. Health Economics Resource Center

22. Patient Data • 88% of the acute bedsection records link to only 1 IPD stay. • 437,465 acute hospitalizations in FY 06 • 54,323 had more than 1 IPD stay mapped to the same bedsection stay • 94% of these had 2 IPDs • 5.7% had 3 IPDs • 0.3% had 4 or 5 IPDs Health Economics Resource Center

23. Correlations Health Economics Resource Center

24. Rate of Infections, OLS Regressions Notes: N=2866 in columns (1) through (4). N=2821 in column 5. All regressions include dummy variables for quartiles of the number of discharges and year dummy variables. Health Economics Resource Center

25. Failure to Rescue Rate, OLS Regressions Notes: N=2673 in columns (1) through (4); N=2631 in column (5). All regressions include dummy variables for quartiles of discharges and dummy variables for years. Health Economics Resource Center

26. Rate of Infections Fixed Effects Notes: N=2866 in columns (1) through (4). N=2821 in column (5). All regressions include dummy variables for quartiles of discharges and dummy variables for years.l Health Economics Resource Center

27. Failure to Rescue Rate, Fixed Effects Notes: N=2673 in columns (1) through (4). N=2631 in column (5). All regressions include dummy variables for quartiles of discharges and dummy variables for years. Health Economics Resource Center