Technology Alone Won’t Save Healthcare, But It Will Redefine It

When companies first try to explore the potential of new technology, they tend to think within current frameworks, but often miss its deep implications. Echoing the quote attributed to Henry Ford—“If I had asked people what they wanted, they would have said faster horses”—we start using new technology to improve what we already do before realizing that we should question what we do in the first place.

With hindsight, the most significant effect of data-driven technological progress has been the transformation of the system (or game) itself. Consumers have experienced this change in many industries such as media, transportation, and retail. So far, this has yet to happen in healthcare. Our use of technology in healthcare has been superficial at best. For example, we’ve used IT systems to computerize paper processes that take filing cabinets, load their contents into computers, and call those electronic health records (EHRs). 

Today, a number of powerful technologies allow us to define some of healthcare’s greatest challenges in terms of data and how we manage it. Through genomics, wearables, and digitally connected consumers, we can generate health “signal” at a scale we had never dreamt of. Through advances in machine learning and artificial intelligence (AI), we have the ability to reason about and utilize this data at an unprecedented scale in order to predict, prevent, and treat disease more effectively. 

The full Forbes article can be viewed at this link.  

Solving Healthcare’s Big Epidemic—Physician Burnout

As someone who has spent years focusing on improving patient outcomes, I’ve seen a distressing shift in the work of physicians, one that is no fault of their own.

The job has become less and less focused on patients, and more and more preoccupied with the back-office beast of modern healthcare—the electronic health record (EHR)—and the attention and time it requires. Ultimately this takes away from caring for patients. Even in the outpatient setting, the need to use EHRs is slowing down treatment. 

As healthcare policy and practices have evolved in recent years, it has become increasingly difficult for doctors to focus on what matters most—the patient. Administrative and data-entry tasks have gotten more time-consuming and at times seem all-consuming. Regulatory compliance is unnecessarily laborious. Keeping current with the latest medical research is a constant struggle.

The full Forbes article can be viewed at this link.  

Confronting One Of Healthcare’s Biggest Challenges: Cyber Risk

Online attackers are keen to steal healthcare data or hold it for ransom for a simple reason—the return on investment. That’s one reason why healthcare firms are one of cyber criminals’ favorite target. In 2017, a typical healthcare organization suffered an average of 32,000 intrusion attacks per day, compared to 14,300 per day at organizations in other industries. Personal health information is 50 times more valuable on the black market than financial information, according to Cybersecurity Ventures, and stolen patient health records fetch upwards of $50 per record (10 to 20 times more than credit card information).

One big cause: Healthcare security teams unintentionally leave gaps in online security by not implementing security tools which, while important, might slow or block the flow of medical data that clinicians need at a moment’s notice. That’s a scary scenario for patients and clinicians, who are caught between the need to maintain access to critical-care machines and data, while also pushing back against hackers intent on making a quick buck.

The full Forbes article can be viewed at this link.  

Accenture 2019 Digital Health Consumer Survey

Healthcare consumers today are changing, and their expectations for convenience, affordability and quality are redefining how they engage at each stage of care.  Younger consumers are not satisfied with healthcare's status quo and consumers of all generations are more willing to try non-traditional services.  The providers and payers who heed the shifts and deliver what patients are looking for will be the ones to earn loyalty, navigate disruption and be strongly positioned as the future of healthcare consumerism unfolds.  

The full Accenture survey results can be downloaded below.  

Patient, Heal Thyself (With AI)

Solutions are emerging that put powerful capabilities back into the hands of patients themselves. We are living in a time of an extraordinary confluence of rapidly evolving human biologic knowledge and advanced technology, including miniaturization of components, advanced sensing capabilities, artificial intelligence (AI) and machine learning. Combined, they are empowering patients to shoulder more responsibility for their own healthcare through personalized data and tech tools that can monitor and manage their own well-being in new ways. Machine learning and AI applications are opening many of those doors.

The full Forbes article can be viewed at this link.  

AI And Healthcare: A Giant Opportunity

Artificial intelligence’s (AI) transformative power is reverberating across many industries, but in one—healthcare—its impact promises to be truly life-changing. From hospital care to clinical research, drug development and insurance, AI applications are revolutionizing how the health sector works to reduce spending and improve patient outcomes.

The total public and private sector investment in healthcare AI is stunning: All told, it is expected to reach $6.6 billion by 2021, according to some estimates. Even more staggering, Accenture predicts that the top AI applications may result in annual savings of $150 billion by 2026.

These benefits will accrue incrementally, from automated operations, precision surgery, and preventive medical intervention (thanks to predictive diagnostics), but within a decade they will fundamentally reshape the healthcare landscape as we know it.

“It’s going to take years to get the full promise, but it does bring a particular tool into the dialogue that was never before available,” says Kaveh Safavi, head of Accenture’s global health practice.

The full Forbes article can be found at this link.  

How Machine Learning Is Crafting Precision Medicine

Medicine has become more and more individualized since the days of leeches and humors, but in the last 15 years, an explosion of patient data in the form of genetic information and electronic health records (EHRs) has sharpened the doctor’s picture of the individual patient—and of treatments tailored to their precise needs.

Such targeted care is referred to as precision medicine—drugs or treatments designed for small groups, rather than large populations, based on characteristics such as medical history, genetic makeup, and data recorded by wearable devices. In 2003, the completion of the Human Genome Project was attended by fanatic promises about the imminence of these treatments, but results have so far underwhelmed. Today, new technologies are revitalizing the promise.

The full Forbes article can be found at this link.  

Artificial intelligence systems for complex decision-making in acute care medicine: a review

The integration of artificial intelligence (AI) into acute care brings a new source of intellectual thought to the bedside. This offers great potential for synergy between AI systems and the human intellect already delivering care. This much needed help should be embraced, if proven effective. However, there is a risk that the present role of physicians and nurses as the primary arbiters of acute care in hospitals may be overtaken by computers. While many argue that this transition is inevitable, the process of developing a formal plan to prevent the need to pass control of patient care to computers should not be further delayed.

The first step in the interdiction process is to recognize; the limitations of existing hospital protocols, why we need AI in acute care, and finally how the focus of medical decision making will change with the integration of AI based analysis. The second step is to develop a strategy for changing the focus of medical education to empower physicians to maintain oversight of AI. Physicians, nurses, and experts in the field of safe hospital communication must control the transition to AI integrated care because there is significant risk during the transition period and much of this risk is subtle, unique to the hospital environment, and outside the expertise of AI designers.

AI is needed in acute care because AI detects complex relational time-series patterns within datasets and this level of analysis transcends conventional threshold based analysis applied in hospital protocols in use today. For this reason medical education will have to change to provide healthcare workers with the ability to understand and over-read relational time pattern centered communications from AI. Medical education will need to place less emphasis on threshold decision making and a greater focus on detection, analysis, and the pathophysiologic basis of relational time patterns. This should be an early part of a medical student’s education because this is what their hospital companion (the AI) will be doing.

Effective communication between human and artificial intelligence requires a common pattern centered knowledge base. Experts in safety focused human to human communication in hospitals should lead during this transition process.

The full article can be downloaded below.  

Can social robots help children in healthcare contexts? A scoping review

This review identified 73 studies that explored the use of social robots for children in healthcare applications. Robots were used to serve a range of purposes, including a companion role, teacher/coach, to connect unwell children to school and to assist in therapeutic and educational endeavours. The wide range of target populations highlights many potential applications, in particular for children with disabilities, impairments, and diabetes, who require intensive ongoing care. Although hospitalisation is not necessarily long term, anxiety, pain and distress are often heightened during hospitalisation. There are potential benefits of using social robots if they can help reduce burden in all three of these contexts. Some of the key findings suggest that social robots can help children with diabetes to improve knowledge; reduce anxiety, anger and depression in children with cancer, and engage children with cerebral palsy in exercises to help improve physical functioning.

The humanoid NAO robot was the most widely used, likely due to its commercial availability, ability to be personalised and relatively autonomous capabilities. Its size and appearance makes it appropriate and appealing. The level of control of robots ranged from almost fully autonomous, to entirely controlled by a human operator. There is a clear need for technological developments to increase the autonomy of all of the robots, particularly in speech recognition and speech production.

The results highlight the significant promise and potential held by social robots to help children in healthcare, but demonstrate the need for more and higher quality research. In particular, more randomised control trials (RCTs), experimental designs and longer-terms studies are required, with larger sample sizes. There is considerable excitement surrounding the use of robotics in healthcare, but there remains a long way to go in terms of technological developments, integration into the healthcare system and establishment of effectiveness.

The full article can be downloaded below.  

Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities

Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. To advance the translation of machine learning into clinical care, health system leaders must address barriers to progress and make strategic investments necessary to bring health care into a new digital age. Machine learning can improve clinical workflows in subtle ways that are distinct from how statistics has shaped medicine. However, most machine learning research occurs in siloes, and there are important, unresolved questions about how to retrain and validate models post-deployment. Academic medical centers that cultivate and value transdisciplinary collaboration are ideally suited to integrate machine learning in clinical care. Along with fostering collaborative environments, health system leaders must invest in developing new capabilities within the workforce and technology infrastructure beyond standard electronic health records. Now is the opportunity to break down barriers and achieve scalable growth in the number of high-impact collaborations between clinical researchers and machine learning experts to transform clinical care.

The full article can be downloaded below.