Memorial day weekend (#MDW) is an important time to reflect on all of those who have served and have given the ultimate sacrifice for our way of life. Unfortunately, this weekend is also associated with many clinicians as a time to “catch up” on all those looming deadlines with some state licenses due for renewal.
Dr. Ian Madom, CEO and Co-Founder of Mocingbird had the chance to sit down with our partner StartUp Health for a Moonshot Moment interview while at VIVE 2022.
Published by Katie Finneran in News
eam had the pleasure to sit down for a chat with Dr. Dawn Forbes, neonatologist, founder, and CEO of NASCEND, one of our CME content providers at Mocingbird.
Published by Katie Finneran in Clinician Corner
What is AI?
Artificial intelligence (AI) refers to computer systems that simulate or exhibit a specific aspect of human intelligence or intelligent behavior, such as learning, reasoning, and problem-solving.
It is not a single technology, but rather a range of computational models and algorithms, the most important of which being Machine Learning (ML), which refers to a system that identifies patterns of data from input and makes predictions from new, never-before-seen data.
ML algorithms can automatically learn and improve from experience without being explicitly programmed, and such “learnability” represents a key feature of AI.
The benefits of AI in healthcare
Healthcare is a field in which AI is rapidly evolving, leading to a digital transformation that is:
- improving the access, quality, safety, and efficiency of various health services
- supporting evidence-based decision making
- fostering communication and coordination
- allowing for the optimization of health systems' performance
- unlocking big data to gain insights into patients
- delivering value at reduced costs, while still improving outcomes and patient experience
Current AI uses in healthcare
Recently, many ML algorithms have been approved for safe use in healthcare by the US FDA. Stanford developed an AI algorithm that can diagnose up to 14 types of medical conditions simultaneously from imaging, Mayo Clinic started using AI to find molecular biomarkers in MRI imaging instead of testing samples collected during surgery, while MIT developed an AI prediction model that can anticipate the development of breast cancer up to 5 years in advance.
Many big tech companies such as Google, Microsoft, Apple, Amazon, and IBM are providing organizations with AI cloud platforms and services as well as ML algorithms. There have also been various cross-sector collaborations, with Apple partnering with over 100 hospitals and clinics for its health records project, allowing consumers to exchange their health data with healthcare providers, or IBM partnering with various hospitals, enabling them to use Watson to make cancer diagnoses and treatment recommendations, a system that can digest information and make recommendations much more quickly and more intelligently than perhaps any machine before it, processing up to 60 million pages of text per second.
AI-assisted surgical robots are an example of this, as they can be operated both locally and remotely and are able to analyze data from pre-operative medical records to physically guide a surgeon’s instrument in real-time.
Robots can also be used in the rehabilitation of patients with stroke, to deliver equipment and medical supplies as well as to assist in the care of elderly patients.
Many hospitals in the United States have also started using ML algorithms for predictive analytics (eg, predicting adverse events, mortality rates, the number of patients in the emergency department), obtaining data that allows them to take proactive measures for the foreseen events days in advance.
EHRs (Electronic Health Records) are the backbone of this process and currently, many EHR vendors, including Epic, Cerner, Allscripts, and Athena have started to add AI into their systems with the purpose of supporting workflows, and clinical decision-making as well as patient engagement.
Another potential use that can enable clinicians to make quicker and smarter decisions about their patients uses the smartphone as a catalyst. Various companies are developing sensors that attach to phones to collect all sorts of biological data.
Ultimately, these applications can fall into 4 different categories:
- Patient-facing
- Doctor-facing
- Research
- Telehealth
The future of AI in healthcare
As more and more data are captured, and as computers become better and faster at processing them autonomously, the possibilities keep expanding. The application of AI in healthcare is disruptive, so a good question to ask is whether it will change not only how medicine is practiced, but who is practicing it. As some Silicon Valley investors are speculating, one day AI could take the place of doctors, serving as a diagnostician and even a surgeon, maybe doing the same work with better results for less money.
But physicians, after all, do more than data processing. They educate and walk the patients through the difficult times of disease and perhaps death, attend at their bedsides, and counsel them. They grasp nuance and learn to master uncertainty. No AI could replace that humanity in the eyes of a sick patient.
Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020 Jan;33(1):10-18. doi: 10.1177/0840470419873123. Epub 2019 Sep 24. PMID: 31550922.
Published by Katie Finneran in Honestly, From M
The global healthcare system we experience today is the consequence of an extremely efficient, yet fragile, supply chain. This efficiency, therefore, comes at the expense of resilience, meaning it is vulnerable to “black swan” events like Covid-19.
Healthcare facilities work with one another in a group purchasing organization. They contract with two or three large distributors, which either purchase from wholesalers or contract directly with manufacturers.
As the Covid-19 pandemic ravaged the world, we can all remember the shortages the healthcare system suffered. As the caseload kept increasing, the need for PPEs soared, filling the news with images of healthcare workers improvising hospital gowns and face shields. The same thing happened with some critical medical supplies. By April 2020, prices for N95 masks were up 6,136%, while those of isolation gowns had spiked by 2,000%. U.S. health care leaders had to resort to protocols for rationing testing and ventilators as the shortages continued.
This supply chain failure led to a paradigm shift from a centralized supply chain in the US into thousands of domestic manufacturers stepping up and producing PPEs as well as other critical medical supplies. An example of this included Austin-based Tito’s, which transitioned from producing vodka to hand sanitizer. Cumbersome vendor-approval processes and inflexible funding rules did not help.
In a world where large-scale disruptions such as climate change and natural disasters, shifting global economic or geopolitical conditions as well as cyberattacks are likely to become more common, the Covid-19 pandemic has opened our eyes to the dangers of a supply chain that focuses exclusively on efficiency.
https://hbr.org/2021/02/one-way-to-build-more-resilient-medical-supply-chains-in-the-u-s
Published by Katie Finneran in Honestly, From M
Tags: Featured
The US demand for physicians has been higher than the number of US medical school graduates. One way to fulfill this is by seeking IMGs.
Published by Katie Finneran in Honestly, From M
Tags: Featured
Mocingbird is excited to announce the company’s co-founder, Ian Madom, MD, MBA, will be taking on responsibilities as chief executive officer.
Are you able to answer your patient’s medical marijuana questions? Well, if your answer is ‘no,’ you are definitely not alone.
Published by Katie Finneran in Clinician Corner
There's no doubt that the Great Resignation has exponentially impacted the healthcare industry. Here's how you can protect your organization.
Published by Katie Finneran in Honestly, From M