5 Examples of How Machine Learning Can Improve your Healthcare SaaS Product

By Marcelo Lopez, UruIT CEO

The global healthcare industry is overwhelmed. While the COVID-19 pandemic is dominating headlines, it has also shed light on just how embattled the system has become, dealing with issues like aging populations, increasing burdens of illness, and rising demand, combined with aging infrastructure and even older, outdated policy.

Now more than ever, these past few months have shown us just how vital it is for health systems to be able to meet the demands of the market. When the healthcare system is not able to serve properly, precious lives are lost.

Luckily, there is also no shortage of SaaS innovators in the digital health space, dedicated to “curing” the healthcare industry. According to Techcrunch, digital health startups have raised more than $10 billion in funding over nearly 1,000 deals over the past two years.

One area to watch here is no doubt artificial intelligence, with numerous companies having taken it upon themselves to apply machine learning and deep learning to give themselves an edge in the industry. How can you argue against data, after all? In fact, it was reported that $4 billion was invested in the AI healthcare sector in 2019, up from $2.7 billion in 2018.

Discover how you can harness the power of ML to streamline and accelerate your health product, in order to save time, and save lives.

Challenge #1: How can I acquire data in order to train my health product’s machine learning model?

Solution: There are many AI/ML applications powered by sensors, IoT or wearable devices (which typically use short-range communication protocols like BLE: Bluetooth Low Energy) that can draw important data automatically. You can also rethink the data you already have with your product.

An app is only as powerful as its data, especially where healthcare and machine learning are concerned. However, acquiring data manually can be difficult and time-consuming. In hospital settings, for example, where individuals and teams have extremely demanding responsibilities along with even more hectic schedules, it is not easy to carve out time to put in said data accurately and consistently, even if the data is supposed to help ease processes in the long run. Ironically, this makes it difficult for the app to best fulfill its purpose—which is to improve the processes to be more efficient in the first place.

Having quality data is key—without it, it’s harder to create or train high quality predictive models. Once obtained, however, machine learning can take use of this data, combine it with data obtained from tests or screenings from healthcare facilities, and then feed it into algorithms that model health conditions, in order to parse lab results and panel results as well as claims data.

The truth is that we have more data than we think. There is an abundance of apps that track a myriad of things to do with health—heart rate, diet, exercise, sleep quality. Wearables are just one example of data obtained through smart devices, collecting data such as blood pressure, temperature, heart rate, and more. As for sensors and Bluetooth applications, a more pertinent example right now is seeing how countries are using geolocation from cell phone users to track and prevent the spread of COVID-19.

Challenge #2: I have data, but I’m not sure what I can do with it to benefit my health product.

Solution: One of the best ways to capitalize on your data is using its applications to streamline and automate processes, in order to make your product more efficient.

You can train your ML models with the information you have (that you may have collected as we mentioned previously, or by using other sources, such as existing historical data from your software) and optimize based on the needs of your business. For example, a healthcare management tool where employees manually audit insurance claims could use machine learning to make the processes more automated, increasing efficiency. They could also use ML to detect anomalies or frauds early.

The healthcare industry is heavily dependent on a steady rhythm of administration, tracking, and record-keeping—but when systematic issues arise, people and patients are inevitably impacted. Many SaaS companies have emerged to streamline or overhaul the process, creating applications that automate healthcare administrative workflows such as managing accounts receivable and checking claim statuses, and so on, making sure that no one falls through the cracks. Organizing health records and patient data are just some of the hugely important tasks that SaaS companies have set their problem-solving sights on.

Machine learning can become indispensable to SaaS operations, especially those working on administration and records-related issues because it can identify user patterns and system anomalies to make suggestions and solve issues more quickly, minimizing costs, time, and effort. In other apps, ML can assist the user by offering tailored prompts and providing relevant prompts for features that are specific for their situation.

Aside from benefits to overall healthcare operations, due to its ability to quickly highlight and find patterns in big data, ML can also more effectively organize health records to make healthcare itself more efficient. In some cases, clinic and healthcare organizations can even use one centralized program for a variety of needs and uses, from tracking symptoms, to keeping records, to filing insurance claims. Machine learning can make easy work out of prioritizing and sorting claims and identifying necessary costs, while also detecting anomalies to investigate situations on a case-by-case basis, streamlining the entire process.

Challenge #3: Sometimes it can be difficult to direct the patient to the most suitable doctor or give them the best possible recommendations.

Solution: Machine Learning can facilitate initial communication via chatbots and intelligent filters so the patient can get in touch with the right doctor and receive the right treatment as soon as possible.

Good communication is crucial for good healthcare. For many clinics and hospitals, one of the biggest challenges to providing high-quality patient care is the sheer volume of patients, and how to address each of their needs in a way that is timely and efficient. The problem is that good communication requires time—a resource that stressed, overworked health systems do not usually have in abundance. That’s where ML comes in.

Machine learning-aided chatbots can support patient care while streamlining the facility’s communication ecosystem. Most screening forms or chat platforms offer a simple setup that patients are already accustomed to—filling in their details, symptoms, and concerns on an online form, and so on. However, with the addition of machine learning, the application can streamline the attention flow for healthcare professionals. Using digital channels can also better support the patient’s time and wellbeing before, during, and after their treatment.

Response and patient wait times can be made more quickly with matching algorithms, which can ‘match’ patient and account managers more quickly based on a set of criteria. These matching algorithms will increase efficiency by decreasing wait times for patients. A good example of this kind of matching algorithms is the automatic Spoken Language Identification (LID), a common AI feature through which clinics can automatically redirect calls to someone who speaks the same language, thereby increasing the quality of care which is extremely important in emergency situations.

Challenge #4: How can we ensure all patients have the best possible treatment?

Solution: Machine Learning offers the possibility to personalize the treatment and cater to each patient’s individual needs.

Behavioral learning technology such as virtual assistants and chatbot apps are some of the most heavily invested-in startups nowadays—they deepen the level of customization with the user not just when they first enter the app experience but throughout. This enables the SaaS technology to better serve the user’s needs (people and their needs change over time, after all!) and furthermore, use this data to better serve other user experiences as a whole.

By analyzing ideal client outcomes, linguistic features, and context-specific patterns, machine learning can make even better recommendations for users to better support and accelerate the app’s efficacy. After the initial assessment occurs, then the people who need treatment can receive it in a more timely manner. That criteria can grow and expand as time goes on and more data is compiled. Personalized apps build an experience that is tailored towards the user and their desired outcomes—offering support but also providing the opportunity to connect with a real-life healthcare practitioner, whether in-person or virtually.

Challenge #5: Diagnosing a condition can take up too much time, delaying the patient’s ability to get access to treatment.

Solution: ML can speed up diagnosis in order to help doctors to prioritize and make better decisions for ideal patient outcomes.

Diagnostic errors account for about 10 percent of yearly patient deaths, mostly due to issues like poor tracking, misinformation, and miscommunication. This is a stat that can be directly addressed through improved screening and diagnosis methods through machine learning.

Automating technology can help save valuable time through data. AI technologies such as computer vision, natural language processing, and speech recognition, for instance, have a smaller rate error than humans. When used for prediction of diseases, important findings can be sent to human doctors for a second confirmation.

In healthcare situations, stakes are high—mistakes can not only cost money but lives. In such settings, it can be difficult to estimate correctly the time and cost of medical cases, since people can only look to the precedent or best-case practices. However, in the scenario of a hospital, for example, not only is it difficult to estimate, but one may not have enough data available to make an educated decision. For apps who are trying to predict early-stage cancers or pinpoint eligible clinical trial candidates or manage multiple drug pipelines, much of this data is reliant on the app’s users submitting or providing the data. Especially in the beginning stages, during the early adoption phase, this can be difficult for users to get on board.

ML is not only able to assimilate and draw patterns quickly from big data sets—the technology has access to that data, to begin with. It can offer a high-level perspective easily for users and furthermore use an algorithm to determine proper estimates and recommendations best suited to whatever current situation based on numerous previous examples. See here for a similar example in a software context.

Healthcare SaaS companies should turn to Machine Learning

Machine learning is the way for elevated SaaS. It sounds simple enough—through analyzing and parsing data (patient records, doctor’s written or audio notes, user preferences, the list goes on), but the possibilities become endless. Automating processes that you are currently handling manually not only frees up time but also increases accuracy and efficiency. By using the right data along with the right algorithm model, you are effectively making the humans behind the business smarter and freeing them up to do the work that really matters.

Learn more about AI applications for your health product with UruIT’s dedicated Machine Learning Consulting team

Published on May 19, 2020

Pin It on Pinterest

Share This