The biggest takeaway from COVID-19 is that we will be more often than not be surprised by new virus strands that can bring the entire world to a screeching halt. If there is one industry that has been stress-tested over the last two years, it is healthcare. In such situations, all eyes turn to the healthcare world, from caregivers to pharmaceutical companies to hospitals.
AI (Artificial Intelligence) can play a pivotal role in such unprecedented situations. It can work effectively to identify the disease hotspots, monitor active cases, predict future outbreaks, diagnose the virus, disease management, patient care and much more.
Let us time travel to 2025 and imagine a day in the life of Mike. This time travel will depict the role of AI in creating a better patient journey and its potential in reducing the high burnout physicians face in addressing patient needs.
On a typical day in Mike’s life, his activity tracker recognizes an unusual heart rate and recognizes a possible myocardial infarction. It automatically looks into the schedule of his physician or cardiologist brings an appointment on the calendar for him to consider. Meanwhile, it also skims thru all the previous medical records and comes up with a short, summarized recommendation that pops up on his mobile screen, highlighting the need to take the scheduled appointment. Before Mike visits his doctor’s office, his digital assistant has updated the patient portal with the latest heart rate readings and the necessary insurance details. As Mike’s doctor prepares for the diagnosis, AI-assisted ECG helps the doctor with critical insights by comparing with several other anonymized ECGS and identifying a possible left ventricular dysfunction. As the doctor uses the NLP-based EMR system to record the notes, his robotic nurse EVA brings in the required medical supplies to examine and provide the necessary care. Mike leaves the hospital facility, with his required medications being promptly delivered via the robotic pharmacy assistant.
While this scenario looks like a pipedream, we are not far from it being an absolute reality in the next couple of years. Today, most of these AI-assisted technologies are in the pilot phase or have a moderate user base, and some may be in the experimental stage before the actual rollout. With AI and ML growing leaps and bounds just in the last two years through several open-source AI initiatives, the potential to transform the dream into reality is enormous.
In healthcare 4.0, patient experience from pre-care to post-treatment monitoring will be leveraging AI-enabled technology. This transformation will change the care providers, pharmacies, physicians, and payers by bringing in higher efficiency, lowering emergency scenarios, reducing costs, preventing frauds, improving patient well-being, and helping patients with less turnaround time with more accuracy.
As AI gains higher traction to become an integral part of the healthcare industry, all stakeholders must respond to the embrace the changing business landscape. This makes it imperative for physicians, nurse practitioners, caregivers, healthcare executives, Insurance executives to understand the factors contributing to this change and how AI will reshape their work. With this understanding, they can start to build the skills and talent, embrace emerging technologies, and create the ecosystem that elevates the patient experience.
Here’s a look at some of how AI is helping healthcare organizations make significant improvements in patient experience and outcomes.
- Healthcare Bots – can empower healthcare organizations to provide superior experience at scale. With built-in medical intelligence combined with natural language capabilities, extensibility tools, and compliance constructs, healthcare organizations such as Providers, Payers, Pharma, HMOs, and Telehealth can give people access to trusted and relevant healthcare services and information. They can perform a variety of tasks such as the mundane appointment booking by identifying the right service provider, helping mental health patients beat stress and anxiety by striking a conversation, assisting with refilling prescriptions, offering healthcare providers data with the correct information on drug dosage, adverse drug effects, and the right therapeutic option for various diseases.
- In Medical diagnosis – Deep neural networks have demonstrated that they can outperform radiologists in detecting pneumonia from chest X-rays. AI can assist radiologists in dealing with multiple and rising imaging volumes, thereby increasing the capacity and diagnosis reliability. AI further offers the capability to combine whole-genome and a range of other patient data for use by machine learning algorithms that will ultimately allow early detection, diagnosis, differential diagnosis, subclassification, and outcome prediction in an integrated fashion.
- AI-assisted EMR – Healthcare delivery thrives on efficiency, speed, and productivity. AI-enabled EHR systems help clinicians rapidly access, extract, and electronically export patient data with minimal error. AI-based speech-to-text technologies can reduce the administrative burden of clinical documentation and self-learning capability of AI solutions of emerging data points and enable more personalized care. Further AI-based EMR creates interoperability, preventing information from getting lost when shared digitally with multiple health providers. The enormous amounts of data accumulated in this process can be further processed thru various algorithms and identify patient patterns and effective diagnosis.
- AI-enabled wearables – Wearables have been part of our lives for quite some time now, starting from hearing aid to fitness trackers. Machine Learning, AI technologies do a fantastic job of converting the plethora of data they collect and providing users with actionable intelligence. The application here is multifold:
a) the visually impaired can have wearables that guide them to move around comfortably with the optimal use of the natural visual cues from the data collected by the movements of sighted people spotting paths, buildings, sidewalks, curbs, etc.
b) Epileptic patients can get alerts when they likely have seizures from the historical patterns.
c) With the help of machine learning, meaningful data can be created by monitoring the physiological markers of stressors and emotional arousal in children with an autism spectrum disorder. - Robotic Nurse assistants – In recent years, several robotic platforms have been developed and deployed in healthcare to assist with hospital logistics, disinfection of spaces, and patient screening. Slightly advanced robots can help people in rehabilitation by being their walking assistants or monitoring the vital signs or fetching medical supplies for consumption.
Fast-paced development in the next couple of years will lead to serious disruption of the healthcare industry. Patient-centered healthcare providers will leverage AI & ML to innovate the service experience, harness the insights to improve diagnosis, and create personalized care. The underlying point here is that healthcare providers will have to adopt a growth mindset and courage to experiment with these disruptive technologies in different aspects of their business.
As next steps what should a healthcare organization do?
If you are looking for an actionable framework to adopt AI, then:
- Go for the low-hanging fruits – Adopt conversation agents for patient interaction with the front desk as it comes at a low cost and provides tangible results.
- Work with a technology partner to develop POC (Proof of Concept) in areas that have a high impact.
- Develop an AI-first healthcare strategy by organizing talent and infrastructure needed for experiments
- Collaborate with other players in the ecosystem to collectively use AI to improve the patient experience.