Costs are certainly a key issue when it comes to 3D printing in healthcare – especially considering these medical practices often need to hire additional employees with more technical backgrounds. Additionally, many surgeons need to spend additional time training to learn how to use this new software and hardware (which is time away from working at the top of their license). Eventually, the hope is that the costs can be significantly reduced given the trends in printer and material pricing, along with the potential that this process can become automated. Do you think the costs associated with 3D printing will ever reduce costs on the system? This is especially relevant in some new payer models where reimbursements can be tied to outcomes metrics and time driven costing.
This is a great read Alec. I imagine that in the future there will be an additional list of metrics that each physician and nurse can evaluate that is recorded on these AI cameras. We already see cameras used in patient care in Neurology when trying a record a patient’s seizures. Just as we look at BMPs or an ECGs, so too I can see us analyzing AI camera metrics such as the amount of sleep that a patient got, if a patient rotated enough times to prevent a pressure ulcer or even a recording of an episode of delirium that a healthcare provider did not witness. These findings can then help influence clinical decision making and one can even imagine the system then making recommendations based upon these findings.
Thanks for the great article. In terms of your first question – “what are the long-term effect of the 3D printed products on patient health? While we can observe their effectiveness in the short-term, we have yet to see how our bodies respond in the longer-term.” Considering much of the material used is similar to implants and prosthetics that have been used for years or are already a part of our anatomy, it is very likely that our bodies will respond well to these products in the longer term. In terms of your second question, “what are the effects on our global healthcare systems and economy?” – this is significantly more complex. We are still in the early stages of adopting these advances. 3D printing of organs is certainly not a reality as of yet but it certainly can be in the future. One could imagine that these technologies will be able to save people requiring organs that would otherwise have died on organ waitlists. While this is an incredible medical advancement, it also means significantly more costly procedures for patients. On the other hand, as these technologies advance, they will become cheaper and more automated. It is certainly possible that these technologies can make some operations more efficient and cost effective. Furthermore, considering the price of these technologies and the concern with potential reimbursement, it is likely that these advancements will initially only be accessed by wealthier patients in developed countries – which can exacerbate the current state of health inequity.
Thank you for a great read. Nowadays it seems as if every healthcare company with access to data is trying to create data guided recommendations. A challenge that Cigna may have is ensuring its tools that it may create or recommendations it may find are largely based upon retrospective data. I wonder if they can find a way to run rigorous trials within their patient populations testing the ability of certain recommendations to effect morbidity or mortality. Another concern would be that Cigna must enroll representative patient populations – or else it may end up creating a new set of recommendations for people that have relatively better access to care and can therefore further health inequity.
Thanks for this great read. Machine learning has many potential applications within healthcare, and as you alluded to, this is especially true when considering clinical decision making. A key way to maintain human safeguards is to use the algorithms as support tools – many of which are being rolled out in practices around the country. An important concern whenever creating a support tool to help make clinical decisions, is that each patient’s perspective on health value can be different. Therefore it is extremely important to ensure that there is no inherent bias encoded into these algorithms. Interestingly, just as a tool can be biased, so too can a physician. How can we ensure that this becomes an unbiased tool to improve informed consent, rather than an additional layer of bias through which a patient receives health information?
Thanks Joe – this is an especially interesting topic to discuss related to dementia considering the size of the burden along with the amount of new solutions that we have seen fail in the past decade. I wonder if this open innovation strategy is beneficial considering it can widen the net of potential solutions and hopefully find stronger solutions in the process. Do you think that this strategy is able to combine the best attributes of both the non-profit charities and the more profit driven funds, or can this prove to be inefficient as an imperfect version of both without a clear mission?