Professor, Graduate School of Business
Professor (By courtesy), Bioengineering
To build enabling innovation frameworks for health care entrepreneurs to better identify, evaluate, and pursue entrepreneurial opportunities.Powerful frameworks have been developed to enable entrepreneurs and investors identify which opportunity areas are worth pursuing and which start-up ideas have the potential to succeed. These frameworks, however, have not been clearly defined and interpreted for innovations in health care. Having a better understanding of the process of innovation in health care allows physician entrepreneurs to innovate more successfully.A review of academic literature was conducted. Concepts and frameworks related to technology innovation were analyzed. A new set of health care specific frameworks was developed. These frameworks were then applied to innovations in various health care subsectors.Health care entrepreneurs would greatly benefit from distinguishing between incremental and disruptive innovations. The US regulatory and reimbursement systems favor incrementalism with a greater chance of success for established players. Small companies and individual groups, however, are more likely to thrive if they adopt a disruptive strategy. Disruption in health care occurs through various mechanisms as detailed in this article. While the main mechanism of disruption might vary across different health care subsectors, it is shown that disruptive innovations consistently require a component of contrarian interpretation to guarantee considerable payoff.If health care entrepreneurs choose to adopt an incrementalist approach, they need to build the risk of disruption into their models and also ascertain that they have a very strong intellectual property (IP) position to weather competition from established players. On the contrary, if they choose to pursue disruption in the market, albeit the competition will be less severe, they need to recognize that the regulatory and reimbursement hurdles are going to be very high. Thus, they would benefit from seeking market opportunities that are large enough to warrant greater regulatory and reimbursement risks.
View details for DOI 10.1097/SLA.0b013e3182251538
View details for Web of Science ID 000292908700005
View details for PubMedID 21685793
Recently, universities in the United States and abroad have developed dedicated educational programs in life science technology innovation. Here, we discuss the two major streams of educational theory and practice that have informed these programs: design thinking and entrepreneurship education. We make the case that the process of innovation for new medical technologies (medtech) is different from that for biopharmaceuticals and outline the challenges and opportunities associated with developing a discipline of medtech innovation.
View details for DOI 10.1126/scitranslmed.3002222
View details for Web of Science ID 000292982600001
View details for PubMedID 21775665
To determine trends in the significance of HLA matching and other risk factors in kidney transplantation, we analyzed data on graft survival in a consecutive sample of 33 443 transplant recipients who received deceased donor kidneys from December 1994 to December 1998 with a mean follow-up time of 2.2 years. HLA matching and other risk factors (peak panel reactive antibody, donor age, sex and cause of death, cold ischemia time, donor and recipient body size) were examined. Mean likelihood ratios of models, fit with and without each variable of interest, were calculated by generating bootstrapped samples from each single year cohort. Pooled censored and uncensored graft survival rates were 90.6% and 89.9% at 1 year, 85.8% and 84.5% at 2 years, and 80.7% and 78.6% at 3 years. HLA matching declined in significance while other factors retained similar levels of statistical significance over the four yearly cohorts. With evolving clinical practice, including the provision of safer and more potent immunosuppressive therapy, the significance of HLA matching has diminished. Non-immunologic factors continue to impede more marked improvements in long-term graft survival. Recognizing these trends, organ allocation algorithms may need to be revised.
View details for DOI 10.1111/j.1600-6143.2004.00535.x
View details for Web of Science ID 000223283900014
View details for PubMedID 15307838
Despite the acute shortage of cadaveric organs for kidney transplantation, more than 10% of cadaveric kidneys are discarded each year because of marginal quality. Transplant recipients' access to these kidneys and to information about their quality is limited. A Monte Carlo model was developed to simulate the operations of an organ procurement organization over a 10-yr period. Donor and recipient characteristics were generated from the United States Renal Data System. Kidneys were assigned one of five possible grades, which were determined by calculating the relative risk of graft failure associated with donor characteristics and HLA matching for every donor-candidate pair. Modeled were recipient decisions to accept or reject a kidney on the basis of the relative change in quality-adjusted life years (QALY). Compared were the United Network of Organ Sharing (UNOS) policy, the UNOS expanded donor criteria policy, two benchmark policies (one equity driven and the other efficiency driven), and a hybrid policy that incorporated recipient choice into the UNOS algorithm. Sensitivity analyses for major input variables were performed. Compared with UNOS, an algorithm that incorporated recipient choice predicted a 6% increase in QALY, a 12% decrease in median waiting time, a 39% increase in the likelihood of transplantation, and a 56% reduction in the number of discarded kidneys. Benefits were observed across categories of age, gender, and race. Incorporating recipient choice in kidney transplantation would improve equity, efficiency, and QALY of the end-stage renal disease population.
View details for DOI 10.1097/01.ASN.0000127866.34592.60
View details for Web of Science ID 000221649400031
View details for PubMedID 15153578
One proposal to increase kidney transplantation is to exchange kidneys between pairs of ABO-incompatible (or cross-match-incompatible) living donors and their recipients. One variation that has greater potential exchanges living donor kidneys for cadaveric donor kidneys (indirect exchanges). A primary concern with indirect exchanges is the potential to disadvantage blood group O wait list candidates. Using wait list modeling, we examine whether this proposal would disadvantage cadaveric kidney blood group O wait list candidates, and present an approach for neutralizing these negative effects.A probability model estimated the total number and blood type frequencies of donor-recipient pairs that would participate in indirect exchanges. A supply-to-demand model for the cadaveric kidney wait list estimated the mean wait time under different allocation policies and donor selection mechanisms for candidates on the wait list classified according to the candidates' race and blood type.Indirect exchanges will reduce the mean wait time for cadaveric kidney wait list candidates. The mean wait time of blood group O cadaveric kidney wait list candidates increases when the participating living donors self-select and when kidney allocation is determined by efficiency. This is neutralized when the transplant team preferentially selects blood group O living donors and cadaveric kidney allocation is determined by need.Indirect exchange programs will significantly shorten the wait times for cadaveric kidney wait list candidates. The wait times of blood group O candidates will not be affected adversely if blood group O living donors are selected preferentially and if allocation is based on need.
View details for Web of Science ID 000170968400015
View details for PubMedID 11544425
There are not enough cadaveric kidneys to meet the demands of transplant candidates. The equity and efficiency of alternative organ allocation strategies have not been rigorously compared.We developed a five-compartment Monte Carlo simulation model to compare alternative organ allocation strategies, accommodating dynamic changes in recipient and donor characteristics, patient and graft survival rates, and quality of life. The model simulated the operations of a single organ procurement organization and attempted to predict the evolution of the transplant waiting list for 10 years. Four allocation strategies were compared: a first-come first-transplanted system; a point system currently utilized by the United Network of Organ Sharing; an efficiency-based algorithm that incorporated correlates of patient and graft survival; and a distributive efficiency algorithm, which had an additional goal of promoting equitable allocation among African-American and other candidates.A 10-year computer simulation was performed. The distributive efficiency policy was associated with a 3.5%+/-0.8% (mean +/- SD) increase in quality-adjusted life expectancy (33.9 months vs 32.7 months), a decrease in the median waiting time to transplantation among those who were transplanted (6.6 months vs 16.3 months), and an increase in the overall likelihood of transplantation (61% vs 45%), compared with the United Network of Organ Sharing algorithm. Improved equity and efficiency were also seen by race (African-American vs other), sex, and age (<50 or > or =50 years). Sensitivity analyses did not appreciably change the qualitative results.Evidence-based organ allocation strategies in cadaveric kidney transplantation would yield improved equity and efficiency measures compared with existing algorithms.
View details for Web of Science ID 000081396100009
View details for PubMedID 10403353
We study pooled (or group) testing as a method for estimating the prevalence of HIV; rather than testing each sample individually, this method combines various samples into a pool and then tests the pool. Existing pooled testing procedures estimate the prevalence using dichotomous test outcomes. However, HIV test outcomes are inherently continuous, and their dichotomization may eliminate useful information. To overcome this problem, we develop a parametric procedure that utilizes the continuous outcomes. This procedure employs a hierarchical pooling model and estimates the prevalence using the likelihood equation. The likelihood equation is solved using an iterative algorithm, and a simulation study shows that our procedure yields very accurate estimates at a fraction of the cost of existing procedures.
View details for Web of Science ID 000074678800003
View details for PubMedID 9695191