Thanks for the insightful comments on this emerging technology in healthcare. I agree with everyone above the 3D printing has incredible potential as an alternative to living and deceased-donor organ transplants. Organ donation is a scarce resource that is allocated based on the recipient’s ability pay for, care for, and many times convince others to donate (at least with kidney donation). Overcoming these barriers obviously favors the educated and affluent and disqualifies an alarming number of disadvantaged suffering patients. 3D printing technology has the potential to even the playing field. I hope China’s MIIT can make it happen.
Dementia is an difficulty and expensive disease that is only growing as the population ages. This organizations supporters and 15-year runway seems ripe for open innovation. I wonder if DDF could enhance its probably of success if it not only accepts a wide range of ideas but also connects like-minded inventors in collaborative teams? A subset of divergent ideas with undoubtedly converge on overlapping innovations. Bringing together creative thinkers has the potential to unlock incredible discoveries across the ecosystem.
Wonderful analysis of this technology. The public’s concern with the discrepancy of perfect use vs. typical use failure rate (6.8%) is a major issue in all medical treatments. Healthcare researchers attempt to discern the efficacy of by using “intention to treat” analysis when interpreting outcomes data in clinical trials. Put simply, the analysis considers the outcomes of everyone randomized to the treatment arm not just those who strictly adhered to the protocol. There is also a trend toward conducting pragmatic clinical trials where treatments are given in the natural environment (not perfectly controlled likely traditional studies) to better determine the real-world impact of an intervention.
Predicting patient outcomes has been a never ending struggle in health-services research. Predictive models (such as the APACHE score, readmission calculators, cardiovascular risk scores) have become increasingly complex as access to data increases. However, you are right that data silos prevent the translation of these models into actionable intelligence. Patients and clinicians rarely see health predictions in real time to take action that can improve health outcomes. I agree that Cigna and other aggregators of health data across the ecosystem have a responsibility to society to use that data responsibly to help people.
It will be interesting to see if Alibaba can integrate their physical stores with online marketplace using AI. “Brick-to-click” customer purchasing is particularly important when consumers are shopping for expensive items where touching/feeling the product is essential. Embedding AI in physical stores where customers trial items can provide essential data to that target customers online and close the sale.
Great article! I too share your concern about the misconception that machine learning is capable of discerning causal relationships. For instance helio’s may determine collections between company performance (e.g. customer satisfaction, management evaluations) and their relationships to brand awareness. Many investors may see the performance decline as the root cause for poor brand awareness. In reality, the causal relationship may be the actual reverse, absent, or confounded by associated factors that share correlations with both the dependent and independent variables.