‘Fixing health care’ is a disservice to society


We all know — and the presidential candidates keep reminding us at every debate and in the run-up to Super Tuesday — that our health care system is struggling to provide Americans with affordable care. While we broadly agree that health care needs to be fixed, the conversation on “how” is headed down the wrong path. Instead of looking for solutions to patch up the current system, we should think anew for higher efficiencies, lower costs and, most importantly, better outcomes.  

We should start by asking how we use existing and emerging technologies to invent a preventive, proactive, predictive, and personalized self-care system that delivers tenfold cost-effectiveness enhancements. How do we seize the new economics of a tech-enabled national health care system? Many of the tools needed to affect this transformation are now available; others are rapidly evolving. Health care policymakers need to focus on cultivating and rapidly incorporating a new tech-enabled paradigm of health management while phasing out the old.  

The “old” features a long and costly drug discovery-to-delivery process with a 90 percent failure rate, and a drug target discovery rate of five per year, despite a $30 billion annual investment. Its surgeons guess the extent of patients’ clogged arteries using grayscale images that tell a vague story. The “old” consists of overburdened hospitals and clinics, antiquated administrative systems, well-intended care professionals suffering the highest-ever burnout rate, and three-in-ten Americans foregoing or deferring treatment due to the cost of care.

But, the new paradigm changes everything.

The “new” is artificial-intelligence-enabled and highly targeted, featuring personalized drug discovery and delivery capabilities. Already being used to hunt down new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, diabetes, and many other disease areas, AI predicts clinical trial outcomes ahead of the trial. AI is used to identify drug targets by leveraging neural networks’ capacities to analyze enormous datasets and determine targeted proteins. In these capacities and others, AI will drive enormous cost and time savings compared to traditional testing processes, with far greater care benefit to patients.

The “new” is augmented reality (AR) and 3D printing technologies that drive precision intervention at a scale with efficacy never seen before. Surgeons can now use AR to visualize the depth of vessels and identify optimal incision locations, while 3D printing is already used to create personalized tools used in surgery to minimize associated risks. Soon, real-time patient data and dynamic 3D imagery will become available to further optimize the efficiency and success rate of everything from reconstructive surgeries to tumor removals.  

The “new” is telehealth-delivered clinical advice and intervention, such as operating remote surgery bots – precision robots that perform vascular and coronary procedures (i.e. remote heart surgery).

And the “new” is also disease prevention and longevity extension. Emerging, quick genetic sequencing at $100 per person will bring incredible transparency and precision to boost both effective disease prevention and management interventions. The unit for which health care is designed will shift from an N of millions to an N of one. Personalized health solutions will drive greater prevention, early detection, precision medication and intervention, and self-care-health monitoring, with a completely different economic value, likely to finally save us from our current reality where a CT scan costs $1,100 in the United States and $140 in Holland. 

To achieve this higher-level, efficacious, cost-effective health care system, these technologies and their integration have to be both strategically cultivated and mandated. A new national health care strategy needs to attend to several key issues:

1. Investment in new technologies is required

Technologies that transform patient-doctor interactions, diagnostic tools, and preventive and personalized treatments require greater investment in the automation of health systems, offerings, and new delivery methods. Investment in AI, machine learning, biodata, 3D printing, and IoT devices will initially drive health care costs up before cost savings are realized, but this investment is strategic and necessary to escape the antiquated, ineffective, and awfully costly system currently has. 

2. A plan for phasing in the new” is needed

We should recognize that phasing-in medical, digital, and intelligent technologies while phasing-out antiquated health care systems and delivery methods is a complex change-management challenge. Coordination, up-skilling, communication, and promotion of new technologies and offerings, all while maintaining consistency in health care service and economic feasibility, are the biggest issues.

3. A cyber-secure system is a must. A future of personalized, preventive and predictive health care hinges on sharing personal health data. The technology that gleans insights from massive datasets is here, but without consumer confidence that their data is safe, we can’t leverage it fully. Payers, providers and regulators must come together to form a universal implementation of data security.

4. Advancing the ethical framework is essential. AI-enabled health care presents enormous ethical challenges, mostly because of the transparent and predictive quality of health care-related data. Presently we have no framework to regulate and monitor against biased and discriminatory AI and address the new level of privacy concerns and the use of personal health data. We need a code of ethics to regulate our use of AI in health care.

While candidates tout various approaches to fastening a band-aid to health care’s broken leg, the tools are now within our reach to create an entirely new system focused on keeping people healthy, rather than engaging them only when they’re ill. Fostering healthier populations could one day save us as much as one $trillion annually — the one-third of total U.S. health care spend logged as clinical waste.

But, in order for this future to arrive, we must act with vision and drive to replace our outdated system with one that truly places patient care and improved health at the center.

Anat Lechner is a clinical associate professor of Management and Organizations at NYU’s Stern School of Business. She specializes in disruptive innovation and consults to the Fortune 500. She served as a McKinsey research fellow and is also the recipient of the GE Teaching Excellence award.

 Ian Marks is currently consulting with Northern Heritage Capital.


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