Identifying Organizational Effects on Medical Productivity

Transcription

1 Identifying Organizational Effects on Medical Productivity Robert S. Huckman AcademyHealth June 29, 2009

2 Preliminaries Medical productivity = quality-adjusted output per unit of input Not just how much but also how well Assumption: Hospitals are points of interaction between professionals Wide variation in the determinants of medical productivity (Wennberg and Gittelsohn, 1973) Broad research question: To what extent do organizational (i.e., hospital) factors impact medical productivity? Technological choice (what gets done)* Operational performance (how well things get done) for any given technology** * Robert S. Huckman, The Utilization of Competing Technologies within the Firm: Evidence from Cardiac Procedures, Management Science, 49(5): ** Robert S. Huckman and Gary P. Pisano, The Firm Specificity of Individual Performance: Evidence from Cardiac Surgery, Management Science, 52(4):

3 (Some) Organizational Determinants of Medical Productivity ENTRY INTO SYSTEM Decision to Seek Care TECHNOLOGIAL CHOICE Decision Regarding Treatment OPERATIONAL PERFORMANCE Quality and Cost of Treatment Diffuse turf battles Separate skill and status Leverage residual variation from multiple sources of data Build team familiarity Separate individual and organizational effects Leverage individual mobility Managerial Objective Empirical Challenge Approach 3

4 Research Setting: Major Cardiac Procedures Source: Thomas Burton, Bypass Surpasses Angioplasty in Study, Wall Street Journal, May 4,

5 Comparison of Bypass Surgery and Angioplasty Angioplasty Bypass Surgery Date of Introduction 1970s 1960s Type of Physician Cardiologist Cardiac surgeon Location (in Hospital) Catheterization Lab Cardiac OR Severity of Patient Condition Low High Patient Discomfort Low High Effectiveness Medium (with rapid improvement) High (with slight improvement) Financial Return for Hospitals High Very high 5

6 Bypass Surgery and Angioplasty in New York State 6.00 Procedure Rate/1,000 Population Age Year Bypass Surgery Angioplasty Source: David M. Cutler and Robert S. Huckman (2003), Technological Development and Medical Productivity: The Diffusion of Angioplasty in New York State," Journal of Health Economics. 6

7 Across-Hospital Variation in Bypass Rates Hospitals Mean Adjusted Bypass Rate = Bypass/(Bypass+Angioplasty) Standard Deviation 25th Percentile 75th Percentile Patient Severity Low Medium High Note: The means and standard deviations are weighted by the total number of revascularization cases at the given severity level for the three-year period from 1993 to Source: Robert S. Huckman (2003), The Utilization of Competing Technologies within the Firm: Evidence from Cardiac Procedures," Management Science. 7

8 Possible Explanations for Variation Across Hospitals Differences in Patient characteristics Objective quality of bypass surgery relative to angioplasty Relative profitability of bypass surgery and angioplasty Overall financial performance Intensity of turf battles between surgeons and cardiologists 8

9 Cardiac Turf Battles Surgeons are not the best friends of angioplasts * where the open-heart alternative is available, the number of angioplasty procedures performed is usually small, and the hospitals make no effort to recruit (physicians) who are skilled in angioplasty. * It s like the Wild West, the turf wars with the vascular surgeons You ve got interventional cardiologists, interventional neuroradiologists, interventional neurologists all wanting in. (Alejandro Bernstein, M.D., neuroradiologist at Beth Israel Medical Center, New York)** Sources: *Robert Hamilton, Hospital Proposes Bypass Alternative, New York Times, March 16, 1986; **Joanne Kaufman, The Uncut Version, New York Magazine, October 13,

10 Data Patient-level records for every individual receiving either bypass surgery or angioplasty in New York, ~30 hospitals and ~100,000 procedures in sample Substantial detail Characteristics of patient (e.g., age, gender, clinical characteristics) Surgeon and hospital identifiers In-hospital mortality outcomes Data are used as the basis of public report cards about bypass and angioplasty quality 10

11 Variables Dependent variable: Risk-adjusted rate of bypass relative to angioplasty for a given hospital-year Key independent variables Cardiac surgeons status = hospital s count of bypass citations in the medical literature ( ) Cardiologists status = hospital s count of angioplasty citations in the medical literature ( ) Controls Objective quality for each procedure Bypass surgery: Risk-adjusted mortality Angioplasty: Risk-adjusted rate of emergency bypass following angioplasty Hospital characteristics (e.g., size, lagged profitability, payer mix, teaching status, county population density) 11

12 Measuring the Impact of Turf Battles on Technological Choice Dependent Variable: Risk-Adjusted Bypass Rate Low Severity Medium Severity High Severity Emergency AMI Average Value of Dependent Variable 4.8% 36.0% 84.7% 93.4% Impact of 1 SD Increase in Bypass Citations 1.2% * 5.2% *** 2.4% *** 2.1% *** Impact of 1 SD Increase in Angioplasty Citations 0.5% -0.8% -0.9% ** 0.2% *,**,*** denote statistical significance at the 10%, 5%, and 1% levels, respectively Source: Huckman (2003) 12

13 ENTRY INTO SYSTEM Decision to Seek Care TECHNOLOGIAL CHOICE Decision Regarding Treatment OPERATIONAL PERFORMANCE Quality and Cost of Treatment Diffuse turf battles Separate skill and status Leverage residual variation from multiple sources of data Build team familiarity Separate individual and organizational effects Leverage individual mobility Managerial Objective Empirical Challenge Approach 13

14 Team Familiarity and Surgical Outcomes Many physicians in the United States are freelancers may work at multiple hospitals Hospitals employ other team members (e.g., nurses, techs, anesthesiologists) who may be important in determining performance Test for importance of familiarity: Can the experience of a surgeon be fully transported across hospitals? 14

15 Are Volume-Outcome Effects For Individual Surgeons Hospital Specific? Risk-Adjusted Mortality Rate for Surgeon S at Hospital A Hospital B? Hospital B? Hospital A Hospital B? Surgeon S Volume 15

16 Why Might Hospital-Specific Volume Matter? Familiarity: high volume is associated with the productive benefits of organizational familiarity (e.g., team stability, experience with firm-specific processes) Status/Influence: high volume is associated with the political benefits of access to more or superior resources from the organization (e.g., staff, financial support) Adding proxies for status/influence does not affect base findings Surgeon s share of hospital s bypass volume in prior quarter (financial importance) Surgeon s share of hospital s total citations for bypass related publications (status) 16

17 Research Setting Data: Patient-level records for every bypass surgery in Pennsylvania, ~34,000 patients, 40 hospitals, and 190 surgeons Detail is similar to that in New York database and is also used for public report cards Why Pennsylvania? High degree of splitting activity (~25% of surgeons) Splitting occurs roughly simultaneously reduces concerns about changes in surgeon performance over time 17

18 Empirical Specification Logistic regression using patient-level observations (surgeon s and hospital h) Dependent variable: Indicator equal to one if patient died in the hospital; zero otherwise Key independent variables Surgeon s volume of cases at hospital h in prior quarter Surgeon s volume of cases at hospitals other than h in prior quarter Controls Surgeon quality: Surgeon s risk-adjusted mortality rate (across all hospitals) in prior quarter Hospital quality: Hospital h risk-adjusted mortality rate (across all surgeons) in prior quarter Patient characteristics (demographic and clinical) Influence proxies (robustness) 18

19 Results Did Bypass Patient Die in Hospital? Impact of One Standard Deviation Increase in: HOSPCASE : Surgeon s Cases at Hospital h (Prior Quarter) -0.32% *** OTHCASE : Surgeon s Cases at Hospitals Other Than h (Prior Quarter) -0.01% Average Predicted Probability of Mortality (Evaluated at Means of Independent Variables) 1.76% Level at which HOSPCASE is significantly different from OTHCASE 1% Note: *** denotes statistical significance at the 1% level. Source: Huckman and Pisano (2006) 19

20 Summing Up Broad Conclusion Managerial Objective Methodological Challenge Methodological Solution Organizations may not make decisions concerning technological choice, but they do affect the way in which such decisions are made Mitigate negative effects of turf battles between groups Identifying the intensity of turf battles identifying status independent of skill Leverage residual variation by using multiple sources of data The performance of highly-skilled workers is not perfectly transferrable across firms organizations matter Build team familiarity Separating individual and organizational effects Leverage individual mobility 20