Tuesday, December 26, 2017

Emerging Healthcare Trends in 2018

Digital transformation is set to overhaul the global healthcare industry. As we move into 2018, here are some emerging healthcare trends that will talk about how personalized medicine and value based care will be adopted in the healthcare ecosystem.

Precision Medicine
In cancer, there are misspellings or mutations in important genes that drive the cell to grow out of control and eventually move around the body. Different patients have different misspellings and hence do not benefit from same treatment. Because of the differences across patients the conventional one-size-fits-all treatment paradigms has low response rate. Further, precious time is lost while physicians progress through successive standard therapies with no guidance on which will prove efficacious. This is where Precision medicine will offer the promise of averting unnecessary treatment, minimizing drug adverse events, and maximizing overall safety to ultimately maximize the efficacy and efficiency of the healthcare system. The rapid identification of the most beneficial personalized therapy would transform the patient experience.

Real Time Monitoring
Non-invasive monitoring approaches will enable in collecting patient data longitudinally across multiple time points. This is enabled by various sensors to track patient vital signs 24x7 through wearable devices, complemented by blood and saliva monitoring techniques. This data availability opens up opportunities to improving healthcare - predict onset, identify right treatments and track treatment impact.

Real Time Personalization
A Cancer characteristic in a patient is not static. It changes with time due to treatment pressures and other reasons. Hence the treatment strategy for a patient will need to evolve with time. With the ability to monitor impact of treatment on a disease -analogous to software world this will create the opportunity to debug why a treatment is not working and to learn and course-correct. This real time personalization will create an updated paradigm based on real time personalization. 

Use of Big Data
With the rise of the Internet of (Medical) Things (IoMT), mobile and wearable devices being increasingly connected, working together to create a cohesive medical report accessible anywhere by your health care provider will surface. This data can be used to identify the risk factors and provide preventative treatment to the patients. It can be pooled and studied collectively to predict health care trends for entire cultures and countries. Together, volume, variety, validity, velocity, volatility, and variability of data will produce the ultimate challenges of Big Data to apply in practices such as precision medicine, among others. However, the visualization of clear and concise clinical action that provides value to the patient, physician, and healthcare system will emerge as an effective solution.

Artificial Intelligence
Big data aggregated provides opportunity to learn from past and predict the future. Some clinical questions are better suited to use of artificial intelligence techniques because of available datasets. Early disease diagnosis and automated interpretation of images and other reports are few applications where AI will add value. Further, AI will help healthcare practitioners in mining of the data to identify risk factors for providing efficacious clinical treatment.

Mobility and cloud
Mobility and cloud access is and will help patients and doctors interact better and real-time. Globally, majority of doctors already use smartphones and medical app and access drug info on smart phones on a regular basis. Hospitals, insurance companies, and doctor's offices are now storing patient medical records in the cloud, with patients able to access test results online 24/7.Now, mobile devices perform ECGs, DIY blood tests, or serve as a thermometer, for 'anytime, anywhere' users. Going forward, with increasing automation, patients can enter their health results/ check-up into mobile patient portals as well as provide[the said] information to doctors - right and fast.

Overall, with rise of digital technology adoption by the healthcare ecosystem, the overall clinical care delivery for patient empowerment will be more streamlined and thereby improve the way healthcare facilities function as well.

Big Data in Healthcare - Hype or Reality

The Big Data Questions

Big data is generating a lot of hype in every industry including healthcare. People are looking for answers to questions like:

    When will I need big data?
    What should I do to prepare for big data?
    What’s the best way to use big data?
    What is Health Catalyst doing with big data?

It’s important to separate the reality from the hype and clearly describe the place of big data in healthcare today, along with the role it will play in the future.

Big Data in Healthcare Today

A number of use cases in healthcare are well suited for a big data solution.
Some academic- or research-focused healthcare institutions are either experimenting with big data or using it in advanced research projects.
This presentation will examine what’s being done to simplify big data and make it more accessible.

A Brief History of Big Data in Healthcare

In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data:
  • Volume
  • Velocity
  • Variety
Other analysts argued that this is too simplistic but for this purpose let’s start here.

EMRs alone collect huge amounts of data, but according to Brent James of Intermountain Healthcare most of the data is for recreational purposes.
Our work with health systems shows that only a small fraction of the tables in an EMR database (perhaps 400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases.

There is certainly variety in the data, but most systems collect very similar data objects with an occasional tweak to the model.
That said, new use cases that support genomics will certainly require a big data approach.

Health Systems Without Big Data

Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs.
We haven’t come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and using these databases effectively is a more valuable focus than worrying about big data.