How Data Can Drive Digital Medicine Towards a Healthier Future

Technology has undoubtedly transformed our lives. As it grows faster than ever before thanks to advances in computing hardware, we’re lucky to live in this era of unprecedented innovation in which we get cool new upgrades and developments every few years.

Emerging technologies like artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) are all invaluable on their own, but when we combine and apply them to healthcare, we can unlock new potential across numerous niches in medicine and therapeutics. Luckily, the key to doing so lies in our data.

Using Data to Advance Medicine

We can enhance the performance of these technologies with a pool of historical information, which healthcare has no shortage of. Sharing the data globally can further accelerate how well medical applications perform, allowing us to deploy these implementations faster and with more accurate results.

The technologies not only improve current applications, but they can also usher in new innovations that haven’t been thought of before. And for those people around the world who don’t have healthcare options, these technologies expand access greatly.

Current emerging technologies are being used in the fitness and wellness space right now, and they’re working to help patients treat conditions like stress, anxiety, sleeplessness, and obesity. However, these conditions and illnesses can’t be fully resolved with an app or digital product; patients generally need human or medical intervention to help guide them to the end goal.

A major problem surfaces when you realize that most digital treatments on the market aren’t supported by scientific evidence like clinical trials. There is also no way to measure how effective these digital solutions are for the people who use them.

To fix this, even if a digital tool is backed-up by clinical trial data, digital medicine should be assessed and reviewed frequently about their effect on real-world populations. The assessment should be data- and number-focused to be replicable by any outside third-party.

Data Determines Outcomes

As an industry, healthcare would benefit from taking large amounts of patient data and cross-referencing it with other health datasets. Doing so would elucidate ideas about what does and doesn’t work inpatient treatment and therapy. Using this combined data, we could enhance current medical techniques and treatments while simultaneously building new digital tools to improve the patient experience.

In the field of mental health, app developers are using data from outcomes and interactions between practitioners and patients. Using advanced data science and data mining techniques to study patient interventions, characteristics, and outcomes, clinicians can get a better idea of the best treatments.

Ieso Digital Health is a UK-based company located an hour and a half outside of London. The company recently released information about its “first of its kind” research surrounding a deep learning model. The algorithm quantifies how impactful the therapists’ language can be on a patient during psychotherapy sessions. The data involved over 90,000 hours of internet-enabled Cognitive Behavioral Therapy transcripts, and it was combined with data about clinical patient outcomes.

The study leads the effort in combining datasets to unveil the answers to a variety of questions about patients, their outcomes, their response to treatment, and more. The type of data and research method is actively quality-controlled, ensuring that the deep learning algorithm gets better and better at its task. This work can show what’s possible and drive digital therapy to be based on more real-world evidence and data.

Data Drives Healthcare Tech on a Global Scale

When analyzing data about tens of thousands of patients, we discover patterns and common experiences. These insights can guide us in changing, updating, and removing certain aspects of the healthcare experience for patients. With the mental health data analysis, natural language processing was able to understand high-level clinical concepts and apply them to the words both the patient and the therapist said.

In “decoding” therapy using data, we’re able to apply newfound best practices to areas of the world where people suffer from the same mental health conditions but lack access to a therapist nearby. Ultimately, one day we might be able to use these datasets to train computers and AI apps to perform some aspects of therapy without any need for human intervention. Of course, therapists will be needed to check in on sessions and ensure the computer is working correctly, but these therapists need not be locally-based to do their job.

Healthcare systems across the world are facing issues with a scarcity of investment, high demand, lack of practitioners, and limited resources. Relying on effective, scientifically-proven digital treatments and tools will become commonplace for those who need it globally. This could mean access to tools that are available anytime-anywhere, whether it’s a mobile app or a wearable device.

The Future of Data in Medicine

The opportunities and potential are endless for algorithms and connected devices to treat patients, and the whole world is ready to adopt new tools that fit the bill. If we continue applying AI and deep learning to medicine, we can revolutionize how patients receive and access healthcare.

By combining various datasets, sharing data globally, using scientifically-proven research, and expanding access to patients that can’t get therapy locally, we can change (for the better) how healthcare is delivered. It means that in-demand therapy areas can use their professionals for patients that need them the most.

What do you think of data’s place in healthcare? As always, let us know your thoughts in the comments below!