AI & ML in Healthcare: What’s Next in HealthTech Innovation?

• Posted on June 24, 2022
• |
• 6 minutes read

As a trusted partner for Healthcare innovators, Digicorp has always had the front row seat to product development and design innovation in the HealthTech space. By co-creating a number of products in close collaboration with eminent Healthcare changemakers, we have developed a keen insight into the pulse of the industry – particularly the dramatic rise of AI and ML applications in this domain. 

It is no secret that healthcare leaders are increasingly embracing Artificial Intelligence (AI) and Machine Learning (ML) to augment the patient care value chain. For example, the Philips’ Future Health Index 2021 recently reported that 74% of healthcare leaders see AI as a crucial area for future investment. AI in Healthcare is expected to grow from $2.1 billion to $36.1 billion by 2025. At present, 46% of healthcare-focused AI is used in service operations, 28% in product and service development, 19% in risk management, 21% in supply chain management, and 17% in marketing and sales.

But what’s the next revolution to watch out for in HealthTech? How will AI and ML transform Healthcare as we know it in the near future? In this blog, we share our two cents on these mission-critical questions.

1. Democratizing Healthcare Access Through Smart Health Applications

By anticipating potentially critical scenarios well in advance and proactively seeking help, such applications help to reduce uncertainty and missed opportunities. Furthermore, by monitoring and analyzing diet, sleep, and physical activity levels on the go, the health-conscious segment of the population can finally take matters into their own hands.

With smart wearables and implantable technologies, patients now have better control over their health and greater access to a wide range of healthcare services. Powerful AI-ML algorithms embedded in smart health applications uncover the underlying patterns by correlating the various health indicators with the onset of medical episodes (like a heart attack or a sudden rise/fall in blood pressure, for example). Real-time alerts can be sent, both to patients and their families as well as Healthcare service providers, saving countless lives in the process.

All these data when combined with the patient’s healthcare records like EMR and EHR serve to fast-track the process of diagnosis in the hands of Healthcare professionals as well. In fact, today there are real-time, location-aware, and need-based AI-healthcare applications that help to ensure that patients are able to implement medical necessities like:

Taking medicines on time

Auto-ordering medicines when the time for refill is near

Recommending a date and time for the next visit with the doctor (based on personal calendar and doctor’s availability)

Auto-booking a visit with the doctor/telemedicine consultation

Provide alerts related to device performance and safety

Suggesting doctors and hospitals nearby, based on the nature and criticality of the condition, and so on.

The vast majority of HealthTech applications today are geared to answer one simple question: how do we bring the best of Healthcare services to one and all in the shortest possible time? Thanks to advances in AI and ML, “Quality Healthcare, Anywhere” is now a real possibility. 

Click here to learn how Digicorp helped a leading nonprofit in the U.S. to build an app to empower expectant mothers by fostering awareness about healthy pregnancies along with the option for end-to-end tracking and monitoring their journey into motherhood.

2. Process Automation & Clinical Decision Support

Let’s face it – hospitals, clinics, diagnostic centers, and pharmacies are all overwhelmed with the ever-increasing patient burden. 30% of healthcare costs can be attributed to administrative chores, according to Business Insider Intelligence. Process automation using AI-ML can successfully deploy the superpowers of HealthTech to lighten the burden from a wide range of activities like: 

An Accenture report suggests that at present, AI and ML can handle up to 20% of all clinical requests. As this ratio goes up in the near future, one can expect HealthTech to play a larger role in reducing the number of unnecessary clinic visits. 

Click here to learn how Digicorp helped a leading HealthTech company based in the USA to develop a full-spectrum Electronic Health Records (EHR) on a subscription basis, along with Practice Management and Billing Services.

Purposeful, targeted algorithms are now also enabling clinical decisions in more ways than ever, particularly in the field of diagnostics. For example, a combination of patient symptoms, medical and family history, and underlying genetic factors are now being digested by AI-ML algorithms in order to narrow down the differentials based on the patterns reflected in a large volume of similar medical data.

Another example is the Radiology arm of Healthcare where specific algorithms can be put in place to prioritize critical cases (like intracranial hemorrhage or pulmonary embolism)  over other less critical ones. This, in turn, would help radiologists to focus on items based on the degree of concern and the complexity involved. 

Lastly, by integrating the workflows of diagnosticians, doctors, and support staff, HealthTech can take process efficiency and decision-making effectiveness to the next level using AI and ML.

3. Leveraging Bio-medical Literature Using Natural Language Processing (NLP)

Manually combing through thousands of pages of Bio-medical literature is not only tiring and tedious but also near impossible. Even if dedicated teams are put in place to accomplish this mission, cognitive biases and human errors might creep in, compromising the whole process. Besides, scooping up the right nugget of information at the right time for the right problem is a herculean task.

Earlier, data science could primarily deal with structured data only. Now, unstructured data like Bio-medical literature are also a cakewalk. This is where a specific AI algorithm, that is, the Natural Language Processing(NLP) comes into play.

NLP is a highly scalable technology, which means that it can be used to tap into as many reports, studies, articles, opinion papers, journals, and other unstructured documents as required. The relevant answer to a medical question can be culled out at blazing fast speed without doctors and staff having to pore over pages after pages of documents. Further, no-code/low-code ML applications can be custom-built to find solutions to specific problems as and when they arise. This is a revolution in itself and has major implications for clinical practice and decision-making. 

Conclusion

Countries like the U.S., Germany, the United Kingdom, Finland, Israel, and China are already betting their money on AI-related research in Healthcare. While AI-ML can’t replace doctors, medical staff, and scientists, it can surely take away some of the burdens from their shoulders and place more autonomy and control in the hands of patients as well as healthcare providers. The COVID-19 pandemic has further accelerated the need for solutions that allow all healthcare stakeholders to be proactive, empowered, and informed.

Digicorp has been working tirelessly to help changemakers in the Healthcare industry usher in the next revolution through products that save time, money, and above all – lives. Our designers, engineers, and developers understand the space well and bring the right amount of sensitivity, expertise, and experience to solve the most challenges. If you are looking to bring your healthcare innovation to life, please reach out to us at sales@digi-corp.com, or visit https://digi-corp.com/about-us/ to know more about our work and capabilities.

Reference Credits:

https://ai-med.io/more-news/future-health-index-2021-reveals-shift-to-ai-investment/
https://www.timesnownews.com/health/article/revolution-by-artificial-intelligence-machine-learning-and-deep-learning-in-the-healthcare-industry/728736
https://ai4.io/blog/2021/08/26/how-ai-and-iot-play-an-important-role-in-healthcare-today/