The Health Care Benefits of Combining Wearables and AI

In southeast England, patients discharged from a group of hospitals serving 500,000 people are being fitted with a Wi-Fi-enabled armband that remotely monitors vital signs such as respiratory rate, oxygen levels, pulse, blood pressure, and body temperature.

Under a National Health Service pilot program that now incorporates artificial intelligence to analyze all that patient data in real time, hospital readmission rates are down, and emergency room visits have been reduced. What’s more, the need for costly home visits has dropped by 22%. Longer term, adherence to treatment plans have increased to 96%, compared to the industry average of 50%.

The AI pilot is targeting what Harvard Business School Professor and Innosight co-founder Clay Christensen calls “non-consumption.”  These are opportunity areas where consumers have a job to be done that isn’t currently addressed by an affordable or convenient solution.

Before the U.K. pilot at the Dartford and Gravesham hospitals, for instance, home monitoring had involved dispatching hospital staffers to drive up to 90 minutes round-trip to check in with patients in their homes about once per week. But with algorithms now constantly searching for warning signs in the data and alerting both patients and professionals instantly, a new capability is born: providing healthcare before you knew you even need it.

The biggest promise of artificial intelligence — accurate predictions at near-zero marginal cost — has rightly generated substantial interest in applying AI to nearly every area of healthcare. But not every application of AI in healthcare is equally well-suited to benefit. Moreover, very few applications serve as an appropriate strategic response to the largest problems facing nearly every health system: decentralization and margin pressure.

Take for example, medical imaging AI tools — an area in which hospitals are projected to spend $2 billion annually within four years. Accurately diagnosing diseases from cancers to cataracts is a complex task, with difficult-to-quantify but typically major consequences. However, the task is currently typically part of larger workflows performed by extensively trained, highly specialized physicians who are among some of the world’s best minds. These doctors might need help at the margins, but this is a job already being done. Such factors make disease diagnosis an extraordinarily difficult area for AI to create transformative change. And so the application of AI in such settings  —  even if beneficial  to patient outcomes —  is unlikely to fundamentally improve the way healthcare is delivered or to substantially lower costs in the near-term.

However, leading organizations seeking to decentralize care can deploy AI to do things that have never been done before. For example: There’s a wide array of non-acute health decisions that consumers make daily. These decisions do not warrant the attention of a skilled clinician but ultimately play a large role in determining patient’s health — and ultimately the cost of healthcare.

According to the World Health Organization, 60% of related factors to individual health and quality of life are correlated to lifestyle choices, including taking prescriptions such as blood-pressure medications correctly, getting exercise, and reducing stress. Aided by AI-driven models, it is now possible to provide patients with interventions and reminders throughout this day-to-day process based on changes to the patient’s vital signs.

Home health monitoring itself isn’t new. Active programs and pilot studies are underway through leading institutions ranging from Partners Healthcare, United Healthcare, and the Johns Hopkins School of Medicine, with positive results. But those efforts have yet to harness AI to make better judgements and recommendations in real time. Because of the massive volumes of data involved, machine learning algorithms are particularly well suited to scaling that task for large populations. After all, large sets of data are what power AI by making those algorithms smarter.

 By deploying AI, for instance, the NHS program is not only able to scale up in the U.K. but also internationally. Current Health, the venture-capital backed maker of the patient monitoring devices used in the program, recently received FDA clearance to pilot the system in the U.S. and is now testing it with New York’s Mount Sinai Hospital. It’s part of an effort to reduce patient readmissions, which costs U.S. hospitals about $40 billion annually.

 The early success of such efforts drives home three lessons in using AI to address non-consumption in the new world of patient-centric healthcare:

1) Focus on impacting critical metrics – for example, reducing costly hospital readmission rates.

Start small to home in on the goal of making an impact on a key metric tied to both patient outcomes and financial sustainability. As in the U.K. pilot, this can be done through a program with select hospitals or provider locations. In another case Grady Hospital, the largest public hospital in Atlanta, points to $4M in saving from reduced readmission rates by 31% over two years thanks to the adoption of an AI tool which identifies ‘at-risk’ patients. The system alerts clinical teams to initiate special patient touch points and interventions.

2) Reduce risk by relying on new kinds of partners.

Don’t try to do everything alone. Instead, form alliances with partners that are aiming to tackle similar problems. Consider the Synaptic Healthcare Alliance, a collaborative pilot program between Aetna, Ascension, Humana, Optum, and others. The alliance is using Blockchain to create a giant dataset across various health care providers, with AI trials on the data getting underway. The aim is to share and protect patient health data with the goal of reducing the cost of processing claims while also improving access to care. Going it alone can be risky due to data incompatibility issues alone. For instance, the M.D. Anderson Cancer Center had to write off millions in costs for a failed AI project due in part to incompatibility with its electronic health records system. By joining forces, Synaptic’s dataset will be in a standard format that makes records and results transportable.

3) Use AI to collaborate, not compete, with highly-trained professionals.

Clinicians are often looking to augment their knowledge and reasoning, and AI can help. Many medical AI applications do actually compete with doctors. In radiology, for instance, some algorithms have performed image-bases diagnosis as well as or better than human experts. Yet it’s unclear if patients and medical institutions will trust AI to automate that job entirely. A University of California at San Diego pilot in which AI successfully diagnosed childhood diseases more accurately than junior-level pediatricians still required senior doctors to personally review and sign off on the diagnosis. The real aim is always going to be to use AI to collaborate with clinicians seeking higher precision — not try to replace them.

MIT and MGH have developed a deep learning model which identifies patients likely to develop breast cancer in the future. Learning from data on 60,000 prior patients, the AI system allows physicians to personalize their approach to breast cancer screening, essentially creating a detailed risk profile for each patient.

Taken together, these three lessons paired with solutions targeted at non-consumption have the potential to provide a clear path to effectively harnessing a technology that has been subject to rampant over-promising. Longer term, we believe the one of the transformative benefits of AI will be deepening relationships between health providers and patients. The U.K. pilot, for instance, is resulting in more frequent proactive check-ins that never would have happened before. That’s good for both improving health as well as customer loyalty in the emerging consumer-centric healthcare marketplace.