Skip to main content
Erschienen in: International Journal of Health Economics and Management 1/2018

30.08.2017 | Research Article

Organizational learning-by-doing in liver transplantation

verfasst von: Sarah S. Stith

Erschienen in: International Journal of Health Economics and Management | Ausgabe 1/2018

Einloggen, um Zugang zu erhalten

Abstract

Organizational learning-by-doing implies that production outcomes improve with experience. Prior empirical research documents the existence of organizational learning-by-doing, but provides little insight into why some firms learn while others do not. Among the 124 U.S. liver transplant centers that opened between 1987 and 2009, this paper shows evidence of organizational learning-by-doing, but only shortly after entry. Significant heterogeneity exists with learning only evident among those firms entering early in the sample period when liver transplantation was an experimental medical procedure. Firms that learn begin with lower quality outcomes before improving to the level of firms that do not learn, suggesting that early patient outcomes depend on the ability of new entrants to import best practices from existing liver transplant programs. Knowledge of best practices became increasingly available over time through the dissemination of academic research and increasingly specialized training programs, so that between 1987 and 2009, 6 month post-transplant survival rates increased from 64 to 90% and evidence of organization-level learning-by-doing disappeared. The lack of any recent evidence of organizational learning-by-doing implies that common insurer experience requirements may be reducing access to health care in non-experimental complex medical procedures without an improvement in quality.
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
Earlier articles outside of economics already had considered how outcomes improved with practice (e.g., Wright 1936).
 
3
Although most patients are waitlisted at some point during the transplant process, living donors can direct their donation to an individual patient, allowing that patient to opt out of the cadaveric donor waitlist. About 4% of transplants in the data used live donors, with the first successful live donor liver transplant performed on November 27, 1989. Directed cadaveric donation is possible, but it is not tracked in the data and rarely occurs according to conversations with staff at a major U.S. transplant hospital.
 
4
National Organ Transplant Act of 1984, Pub. L. No. 98-507, Sect. 372, Stat. 2339 (1984).
 
5
The UNOS website states October 1, 1987, but the reported transplant dates in the data start on September 30, 1987.
 
6
To briefly describe the differences between “incumbent” centers and centers entering during the panel period: the incumbent centers perform more transplants per year on average (115 vs. 75), are in larger hospitals (average daily census of 536 vs. 494), and are more likely to be government-owned (34 vs. 28%) and less likely to be privately owned (0 vs. 3%). (Ownership and average daily census data were obtained from the American Hospital Association (2009)). The mean year of entry and average 6-month survival rates for incumbents versus entrants are 1983 and 1990 and 85 and 88%, respectively.
 
7
In general, the literature on learning-by-doing in industrial organization uses a natural log transformation of a standard Cobb–Douglas production function \((f(x,y)=Ax^{\alpha }y^{\beta }\) (becomes \(\ln (f(x,y))=\ln (A)+\alpha \ln (x)+\beta \ln (y)\), where A is a constant measuring total factor productivity and x and y are factors of production.
 
8
Results from using a logit model are very similar and are provided in Online Appendix Table OA1.
 
9
Note that cumulative volume is based on total patients treated, i.e., patients dropped due to missing data are included in the cumulative volume measure.
 
10
Casemix controls for serum creatinine, Status 1 allocation ranking, wait time in days, age, gender, race (African American or other), diagnosis (AHN, biliary atresia, cholestatic liver disease, cirrhosis, metabolic disorder, neoplasm or other), multi-organ transplants, blood type, ICU and hospitalization status at transplant, life support, live donor, donor age, donor gender, donor race (African American or other), donor-recipient blood type compatibility, cold ischemia time, distance from donor hospital to transplant center, and whether a whole or split liver was transplanted. Further details on these variables are available in Online Appendix Table OA2.
 
11
Online Appendix Table OA3 details patient, donor, and match characteristics included in the regressions and shows that about half of the means of these variables differ between the first 20 patients and the 21st to 40th patient treated.
 
12
As universal donors, donor organs with blood type O can be matched with any other blood type, but type O transplant patients can only receive a type O organ. The allocation policy for Status 1 patients prioritizes these patients in receiving type O organs regardless of patient blood type. For non-Status 1 patients, organs are first matched to patients with identical blood types. Some studies indicate that patients with blood type O may have slightly worse access to organs due to the demand for donor organs of blood type O by all blood types. These studies document longer waiting times for patients with blood type O (Freeman and Edwards 2000; Barone et al. 2008).
 
13
As liver disease progresses, it negatively affects the kidneys leading the elevated serum creatinine levels. The current liver allocation algorithm uses serum creatinine as a measure of disease progression in prioritizing patients for cadaveric donor organs.
 
14
For example, improvements in immunosuppression mean an incompatible blood type match between donor and recipient matters less for survival than it did before the introduction of the new drugs. (An incompatible blood type between patient and donor in the early period is associated with a statistically significant three percentage point reduction in 6-month survival, but in the later period the effect is positive and insignificant.) The prevalence of most patient, donor, and match characteristics also changes over time and all the control variables differ statistically from each other between the two periods based on a two-sided t-test except for the following three recipient characteristics; diagnosis of adenomatous hyperplastic nodule, being African-American, and having a blood type O (See Online Appendix Table OA4).
 
15
The probability of being alive 6 months post-transplant is predicted by locally weighted regressions as a function of cumulative volume up to 100 patients, using a bandwidth of 0.8, i.e., 80% of the data. The predicted values are graphed by cumulative volume up to 100 patients. The triangles represent the mean 6 month survival across centers for that level of cumulative volume.
 
16
Because fourteen centers included in these regressions do not reach twenty transplants, I cannot calculate a center-specific learning penalty for those centers.
 
17
Because the sample sizes are quite small for some centers, the fixed effects estimates may be imprecise. As a robustness check, due to the small sample sizes at some centers, I use Empirical Bayes’ estimation to estimate individual center-specific effects on student outcomes (e.g., Kane and Staiger 2008; Jacob and Lefgren 2005). Although these new estimates suggest less heterogeneity and more learning-by-doing, Guarino et al. (2015) documents via simulation analysis that Empirical Bayes’ estimators diminish the variance in the estimated effects but at the price of bias and inconsistency, especially under non-random assignment of patients to transplant centers, which obviously arises in cases at children’s hospitals or for patients receiving multiple transplants. A histogram of the shrunken effects is overlaid on Fig. 3 in Online Appendix Figure OA1.
 
18
If each patient within the first 10 patients has a decreased probability of survival of 0.042 percentage points, then the decreased survival probability summed over the group of ten patients would be 0.42 percentage points. Only the first two categories are statistically significant, so on average a center would expect to lose 0.8 more patients during the first 50 transplants versus 50 transplants thereafter.
 
19
Using dummy variables for each period and interactions between those variables and cumulative volume less than or equal to 20, gives similar results. The coefficient on the Period 1 dummy variable is large in size and statistically significant, indicating survival improvements by period and the coefficient on the interaction between being within the first 20 transplants and being in Period 1 is essentially the same as in the main period-level results for the single cumulative volume category (cumulative volume \(=\) 20) in Table 3. The main effect of being within the first 20 transplants is statistically insignificant with the inclusion of the period-level dummy. Results splitting the second period into 1994–2002 and 2003–2009 are available in Online Appendix Table OA6. Measuring periods based on date of entry for each center rather than data of transplant or limiting the analysis to only new entrants in each period only increases the magnitude of learning-by-doing between 1987 and 1994 (See Online Appendix Table OA7 for more details).
 
20
A common concern in the healthcare literature, termed “selective referral”, is that rather than higher volume leading to better outcomes, better outcomes attract more patients. Although this is a clear issue when comparing across centers, within center reverse causality seems much less likely, especially with a learning curve that plateaus after twenty transplants and the long wait times for transplants.
 
21
Online Appendix Tables OA8 and OA9 provide results from regressions measuring center age in quarters and years, respectively.
 
24
Alaska, Idaho, Maine, Montana, Nevada, New Hampshire, North Dakota, Rhode Island, South Dakota, Vermont, West Virginia and Wyoming.
 
Literatur
Zurück zum Zitat American Hospital Association. (2009). AHA annual survey database (1987–2009). American Hospital Association. AHA data on liver transplant centers was obtained through the National Bureau of Economic Research (NBER). American Hospital Association. (2009). AHA annual survey database (1987–2009). American Hospital Association. AHA data on liver transplant centers was obtained through the National Bureau of Economic Research (NBER).
Zurück zum Zitat Arrow, K. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(3), 155–173.CrossRef Arrow, K. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(3), 155–173.CrossRef
Zurück zum Zitat Axelrod, D., Guidinger, M., McCullough, K., Leichtman, A., Punch, J., & Merion, R. (2004). Association of center volume with outcome after liver and kidney transplantation. American Journal of Transplantation, 4, 920–927.CrossRefPubMed Axelrod, D., Guidinger, M., McCullough, K., Leichtman, A., Punch, J., & Merion, R. (2004). Association of center volume with outcome after liver and kidney transplantation. American Journal of Transplantation, 4, 920–927.CrossRefPubMed
Zurück zum Zitat Balasubramanian, N., & Lieberman, M. (2010). Industry learning environments and the heterogeneity of firm performance. Strategic Management Journal, 31, 390–412. Balasubramanian, N., & Lieberman, M. (2010). Industry learning environments and the heterogeneity of firm performance. Strategic Management Journal, 31, 390–412.
Zurück zum Zitat Barone, M., Avolio, A., Di Leo, A., Burra, P., & Francavilla, A. (2008). ABO blood group-related waiting list disparities in liver transplant candidates: Effect of MELD adoption. Transplantation, 85(6), 844–849.CrossRefPubMed Barone, M., Avolio, A., Di Leo, A., Burra, P., & Francavilla, A. (2008). ABO blood group-related waiting list disparities in liver transplant candidates: Effect of MELD adoption. Transplantation, 85(6), 844–849.CrossRefPubMed
Zurück zum Zitat Benkard, C. L. (2000). Learning and forgetting: The dynamics of aircraft production. American Economic Review, 90(4), 1034–1054.CrossRef Benkard, C. L. (2000). Learning and forgetting: The dynamics of aircraft production. American Economic Review, 90(4), 1034–1054.CrossRef
Zurück zum Zitat Centers for Medicare and Medicaid Services (2006). National coverage determination (NCD) for adult liver transplantation (260.1). Publication number 100-3, version 2, effective 06/19/2006–06/21/2012. Centers for Medicare and Medicaid Services (2006). National coverage determination (NCD) for adult liver transplantation (260.1). Publication number 100-3, version 2, effective 06/19/2006–06/21/2012.
Zurück zum Zitat Centers for Medicare and Medicaid Services (2007). Medicare program; hospital conditions for participation: Requirements for approval and re-approval of transplant centers to perform transplants; Final rule. Title 42 code of federal regulations, parts 405, 482, 488 and 498. Federal Register, 72(61), 15201. Centers for Medicare and Medicaid Services (2007). Medicare program; hospital conditions for participation: Requirements for approval and re-approval of transplant centers to perform transplants; Final rule. Title 42 code of federal regulations, parts 405, 482, 488 and 498. Federal Register, 72(61), 15201.
Zurück zum Zitat Contreras, J. M., Kim, B., & Tristao, I. M. (2011). Does doctor’s experience matter in LASIK surgeries? Health Economics, 20, 699–722.CrossRefPubMed Contreras, J. M., Kim, B., & Tristao, I. M. (2011). Does doctor’s experience matter in LASIK surgeries? Health Economics, 20, 699–722.CrossRefPubMed
Zurück zum Zitat David, G., & Brachet, T. (2009). Retention, learning by doing, and performance in emergency medical services. Health Services Research, 44(3), 902–925.CrossRefPubMedPubMedCentral David, G., & Brachet, T. (2009). Retention, learning by doing, and performance in emergency medical services. Health Services Research, 44(3), 902–925.CrossRefPubMedPubMedCentral
Zurück zum Zitat David, G., & Brachet, T. (2011). On the determinants of organizational forgetting. American Economic Journal: Microeconomics, 3(3), 100–123. David, G., & Brachet, T. (2011). On the determinants of organizational forgetting. American Economic Journal: Microeconomics, 3(3), 100–123.
Zurück zum Zitat Department of Health and Human Services, Health Care Financing Administration (1998). Medicare and medicaid programs; hospital conditions of participation; identification of potential organ, tissue, and eye donors and transplant hospitals’ provision of transplant-related data, final rule. June 22, 1998. 42 CFR Part 482. FR Doc No: 98-16490, 33856-33875. http://www.gpo.gov/fdsys/pkg/FR-1998-06-22/html/98-16490.htm. Department of Health and Human Services, Health Care Financing Administration (1998). Medicare and medicaid programs; hospital conditions of participation; identification of potential organ, tissue, and eye donors and transplant hospitals’ provision of transplant-related data, final rule. June 22, 1998. 42 CFR Part 482. FR Doc No: 98-16490, 33856-33875. http://​www.​gpo.​gov/​fdsys/​pkg/​FR-1998-06-22/​html/​98-16490.​htm.
Zurück zum Zitat Edwards, E., Roberts, J. P., McBride, M., Schulak, J., & Hunsicker, L. (1999). The effect of the volume of procedures at transplantation centers on mortality after liver transplantation. The New England Journal of Medicine, 341(27), 2049–2053.CrossRefPubMed Edwards, E., Roberts, J. P., McBride, M., Schulak, J., & Hunsicker, L. (1999). The effect of the volume of procedures at transplantation centers on mortality after liver transplantation. The New England Journal of Medicine, 341(27), 2049–2053.CrossRefPubMed
Zurück zum Zitat Evans, R. W. (1991). Executive summary: The national cooperative transplantation study. Seattle, WA: Health and Population Research Center at the Battelle-Seattle Research Center. Evans, R. W. (1991). Executive summary: The national cooperative transplantation study. Seattle, WA: Health and Population Research Center at the Battelle-Seattle Research Center.
Zurück zum Zitat Freeman, R. B, Jr., & Edwards, E. B. (2000). Liver transplant waiting time does not correlate with waiting list mortality: Implications for liver allocation policy. Liver Transplantation, 6(5), 543–552.CrossRefPubMed Freeman, R. B, Jr., & Edwards, E. B. (2000). Liver transplant waiting time does not correlate with waiting list mortality: Implications for liver allocation policy. Liver Transplantation, 6(5), 543–552.CrossRefPubMed
Zurück zum Zitat Freeman, R. B., Steffick, D. E., Guidinger, M. K., Farmer, D. G., Berg, C. L., & Merion, R. M. (2008). Liver and intestine transplantation in the United States, 1997–2006. American Journal of Transplantation, 8(4), 958–976.CrossRefPubMed Freeman, R. B., Steffick, D. E., Guidinger, M. K., Farmer, D. G., Berg, C. L., & Merion, R. M. (2008). Liver and intestine transplantation in the United States, 1997–2006. American Journal of Transplantation, 8(4), 958–976.CrossRefPubMed
Zurück zum Zitat Gaynor, M., Seider, H., & Vogt, W. B. (2005). The volume-outcome effect, scale economies, and learning-by-doing. American Economic Review Articles and Proceedings, 95(2), 243–247.CrossRef Gaynor, M., Seider, H., & Vogt, W. B. (2005). The volume-outcome effect, scale economies, and learning-by-doing. American Economic Review Articles and Proceedings, 95(2), 243–247.CrossRef
Zurück zum Zitat Guarino, C., Maxfield, M., Reckase, M. D., Thompson, P., & Wooldridge, J. M. (2015). The evaluation of empirical Bayes’ estimation of value-added teacher performance measures. Journal of Educational and Behavioral Statistics, 40(2), 190–222.CrossRef Guarino, C., Maxfield, M., Reckase, M. D., Thompson, P., & Wooldridge, J. M. (2015). The evaluation of empirical Bayes’ estimation of value-added teacher performance measures. Journal of Educational and Behavioral Statistics, 40(2), 190–222.CrossRef
Zurück zum Zitat Ho, V. (2002). Learning and the evolution of medical technologies: The diffusion of coronary angioplasty. Journal of Health Economics, 21, 873–885.CrossRefPubMed Ho, V. (2002). Learning and the evolution of medical technologies: The diffusion of coronary angioplasty. Journal of Health Economics, 21, 873–885.CrossRefPubMed
Zurück zum Zitat Huckman, R. S., & Pisano, G. P. (2006). The firm specificity of individual performance: Evidence from cardiac surgery. Management Science, 52(4), 473–488.CrossRef Huckman, R. S., & Pisano, G. P. (2006). The firm specificity of individual performance: Evidence from cardiac surgery. Management Science, 52(4), 473–488.CrossRef
Zurück zum Zitat Huesch, M. D. (2009). Learning by doing, scale effects, or neither? Cardiac surgeons after residency. Health Services Research, 44(6), 1960–1982.CrossRefPubMedPubMedCentral Huesch, M. D. (2009). Learning by doing, scale effects, or neither? Cardiac surgeons after residency. Health Services Research, 44(6), 1960–1982.CrossRefPubMedPubMedCentral
Zurück zum Zitat Irwin, D. A., & Klenow, P. J. (1994). Learning-by-doing spillovers in the semiconductor industry. The Journal of Political Economy, 102(6), 1200–1227.CrossRef Irwin, D. A., & Klenow, P. J. (1994). Learning-by-doing spillovers in the semiconductor industry. The Journal of Political Economy, 102(6), 1200–1227.CrossRef
Zurück zum Zitat Jacob, B. A., & Lefgren, L. (2005). Principals as agents: Subjective performance measurement in education. In NBER working article no. 11463. Jacob, B. A., & Lefgren, L. (2005). Principals as agents: Subjective performance measurement in education. In NBER working article no. 11463.
Zurück zum Zitat Jovanovic, B., & Nyarko, Y. (1995). A Bayesian learning model fitted to a variety of empirical learning curves. Brookings Articles on Economic Activity. Microeconomics, 1995, 247–305.CrossRef Jovanovic, B., & Nyarko, Y. (1995). A Bayesian learning model fitted to a variety of empirical learning curves. Brookings Articles on Economic Activity. Microeconomics, 1995, 247–305.CrossRef
Zurück zum Zitat Kane, T. J., & Staiger, D. O. (2008). Estimating teacher impacts on student achievement: An experimental evaluation. In NBER working article no. 14607. Kane, T. J., & Staiger, D. O. (2008). Estimating teacher impacts on student achievement: An experimental evaluation. In NBER working article no. 14607.
Zurück zum Zitat Levenson, J. L., & Olbrisch, M. E. (1993). Psychosocial evaluation of organ transplant candidates. A comparative survey of process, criteria, and outcomes in heart, liver, and kidney procedures. Psychosomatics, 34(4), 314–323. Levenson, J. L., & Olbrisch, M. E. (1993). Psychosocial evaluation of organ transplant candidates. A comparative survey of process, criteria, and outcomes in heart, liver, and kidney procedures. Psychosomatics, 34(4), 314–323.
Zurück zum Zitat Levitt, S., List, J., & Syverson, C. (2013). Toward an understanding of learning by doing: Evidence from an automobile plant. Journal of Political Economy, 121(4), 643–681.CrossRef Levitt, S., List, J., & Syverson, C. (2013). Toward an understanding of learning by doing: Evidence from an automobile plant. Journal of Political Economy, 121(4), 643–681.CrossRef
Zurück zum Zitat Manthous, C., Nembhard, I. M., & Hollingshead, A. B. (2011). Building effective critical care teams. Critical Care Medicine. doi:10.1186/cc10255. Manthous, C., Nembhard, I. M., & Hollingshead, A. B. (2011). Building effective critical care teams. Critical Care Medicine. doi:10.​1186/​cc10255.
Zurück zum Zitat Nembhard, I., Cherian, P., & Bradley, E. H. (2014). Deliberate learning in health care: The effect of importing best practices and creative problem solving on hospital performance improvement. Medical Care Research and Review, 71(5), 450–471.CrossRefPubMedPubMedCentral Nembhard, I., Cherian, P., & Bradley, E. H. (2014). Deliberate learning in health care: The effect of importing best practices and creative problem solving on hospital performance improvement. Medical Care Research and Review, 71(5), 450–471.CrossRefPubMedPubMedCentral
Zurück zum Zitat Pisano, G. P., Bohmer, R., & Edmondson, A. (2001). Organizational differences in rates of learning: Evidence from the adoption of minimally invasive cardiac surgery. Management Science, 47(6), 752–773.CrossRef Pisano, G. P., Bohmer, R., & Edmondson, A. (2001). Organizational differences in rates of learning: Evidence from the adoption of minimally invasive cardiac surgery. Management Science, 47(6), 752–773.CrossRef
Zurück zum Zitat Stith, S., & Hirth, R. (2016). The effect of performance standards on healthcare provider behavior: Evidence from kidney transplantation. Journal of Economics and Management Strategy, 25(4), 789–825.CrossRef Stith, S., & Hirth, R. (2016). The effect of performance standards on healthcare provider behavior: Evidence from kidney transplantation. Journal of Economics and Management Strategy, 25(4), 789–825.CrossRef
Zurück zum Zitat Terasaki, P. I. (1986). Clinical transplants 1986. Los Angeles, CA: UCLA Tissue Typing Laboratory. Terasaki, P. I. (1986). Clinical transplants 1986. Los Angeles, CA: UCLA Tissue Typing Laboratory.
Zurück zum Zitat The Organ Procurement and Transplantation Network and United Network for Organ Sharing (2008). OPTN/UNOS living donor committee: Report to the board of directors, November 17–18, 2008 (pp. 3–18). The Organ Procurement and Transplantation Network and United Network for Organ Sharing (2008). OPTN/UNOS living donor committee: Report to the board of directors, November 17–18, 2008 (pp. 3–18).
Zurück zum Zitat Thornton, R., & Thompson, P. (2001). Learning from experience and learning from others: An exploration of learning and spillovers in wartime shipbuilding. American Economic Review, 91(5), 1350–1368.CrossRef Thornton, R., & Thompson, P. (2001). Learning from experience and learning from others: An exploration of learning and spillovers in wartime shipbuilding. American Economic Review, 91(5), 1350–1368.CrossRef
Zurück zum Zitat United Network for Organ Sharing. (2010). Standard Transplant Analysis Research (STAR) data files (1987–2009). In United network for organ sharing. www.unos.org. United Network for Organ Sharing. (2010). Standard Transplant Analysis Research (STAR) data files (1987–2009). In United network for organ sharing. www.​unos.​org.
Zurück zum Zitat Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122–128.CrossRef Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122–128.CrossRef
Metadaten
Titel
Organizational learning-by-doing in liver transplantation
verfasst von
Sarah S. Stith
Publikationsdatum
30.08.2017
Verlag
Springer US
Erschienen in
International Journal of Health Economics and Management / Ausgabe 1/2018
Print ISSN: 2199-9023
Elektronische ISSN: 2199-9031
DOI
https://doi.org/10.1007/s10754-017-9222-z

Weitere Artikel der Ausgabe 1/2018

International Journal of Health Economics and Management 1/2018 Zur Ausgabe