IBM Big Data & Analytics Hub

The United States spends around 17 to 18 percent of its GDP on healthcare every year. Translated into dollars, this equals a mind-boggling $2.9 trillion.

Unfortunately, that spending will grow at a faster rate now due to baby boomers becoming an aging population, and they are the largest demographic in the US (Baby boomers are about 76 million, which accounts for 25 percent of the population of the US).  The healthcare related spending is expected to grow at a faster pace than the under 5 percent annual rate it grew over the last decade.

Unless the US gets these spiraling healthcare costs under control, in a few short years we will spend almost 25 percent of our entire GDP on healthcare instead of redirecting those funds to other necessary priorities. For example, chronic diseases account for about 86 percent of healthcare expenditures in the US. Severe chronic conditions such as heart disease, arthritis, asthma and diabetes alone cost 33 percent of total spending. Obviously, we can’t stop the population from aging, but we can make the healthcare system more efficient.

In the healthcare system, “mass medicine” without individual diagnoses is no solution unless there is an epidemic that must be localized and cured. Although physicians have the authority to institute personalized medicine, wait times to see doctors are getting longer while doctor/patient visits are getting shorter—typically lasting 30 minutes or less. Even during that short visit, the diagnosis is based on how well the patient tracks the symptoms and, more importantly, how effectively the symptoms are communicated to the physician. Most times, the physicians try to associate the symptoms based on the local happenings to figure out the root cause. While this works most of the time, with the increasing load, there are times the opportunity for right diagnosis might be missed in the first visit. This could lead to costly hospitalization later. Especially when you take into account the monitoring of the symptoms and the effective communication of that to the physician is based on the patient’s knowledge and self-monitoring the results can lead to subjective decision making. Most times, symptoms won’t surface when at doctor’s office, much the same way car problems never surface when at a repair shop.

Solving this problem has not been easy because we have mostly closed-down patient record systems, no remote monitoring devices that can accurately monitor a patient’s vitals and a system that makes it challenging to get that information back into patient records. Plus, onerous government and insurance regulations exist on what can be monitored, collected, communicated, stored and so on.

In the past, digitization of patient records was just for recordkeeping purposes. But with advances in the Internet of Things, predictive analytics, cognitive computing and application programming interfaces (APIs) to connect them all, things have changed dramatically.

While the goal is not to replace doctors in the process, the “quantified self” concept will help ensure the right information for future diagnoses, and help predict and prescribe treatments in enough time to save lives in a more cost-effective manner.

Predicting readmission through remote monitoring

Readmission for patients with heart failures is very high in the US. In a simple study conducted on 1,095 patients, only two automated phone calls were made within 30 days to check on the status of their health. Of those reported having a negative response, 37 percent were readmitted—compared with 16 percent positive and 14 percent neutral respondents’ readmissions. The only problem with this test was that the self-monitoring and assessment were left to the patient, which makes the results subjective.

A predictive analysis can identify patients at a higher risk for readmission even before discharge. This allows for the doctors, nurses and other caregivers to train the patients about managing their health and reporting the status back on a regular basis. An educated patient is the best patient to control his/her own destiny.

With advances in wearable and mobile health technologies, vital signs such as heart rate, blood pressure, glucose levels, respiration rate, blood oxygen saturation levels and even ECG patterns can all be monitored almost to the clinical grade and the information can be fed back into a patient’s records in real time. This helps make physician-directed patient self-monitoring a reality.

This process removes the subjective nature from the equation, and fairly accurately predicts the path toward readmission very early. With this level of detection, patients can be put on customized, proactive programs where they will be constantly monitored. They will be guided to participate in healthy lifestyles through behavioral modifications, and constant in-person coaching will be provided as necessary.

Telehealth for the veterans: We all know the fiasco the VA (Veterans Affairs) went through recently. But they seem to be doing the telehealth portion of it more effectively now. Over half a million veterans get healthcare through telehealth home monitors every year. Many of these veterans live in remote areas and may not have access to necessary healthcare. According to Dr. Adam Darkins, who leads the National Telehealth Programs for the US Department of Veterans Affairs, the agency can take digital pictures of the retina or skin lesions and send them securely for analysis and recommended next steps. This service keeps about 40,000 people in their homes instead of nursing homes or other assisted care facilities. A quarter of a million patients are screened for eye diseases without overcrowding eye clinics. Darkins says the home telehealth program has reduced hospital admissions by over 30 percent.

Remote monitoring of patient’s health and safety: M2M technologies, a customer of IBM API Economy solutions, decided to take their security and comfort monitoring for the elderly, called Kizuna One, to the next level. Kizuna One, which collects data from wearable health devices to a repository, has decided to expose the information as APIs to their business partners to create new business opportunities. Essentially, keep the inflexible back end systems, but convert them into flexible and customized APIs to their business partners as their Chief Architect suggests. This allows them to scale up their business and IT systems without a very costly engagement. Those resold APIs allowed their business partners to create newer, modern and engaging digital apps and business models using the same baseline service that is offered by M2M.

To make all this happen, IBM is working with Mayo Clinic, MD Anderson Cancer Center, Memorial Sloan-Kettering Cancer Center, Apple, CVS, Epic and numerous other companies to collect data from the Internet of Things, get that data securely into existing patient medical record systems, analyze it using predictive analytics, and then recommend smarter decisions using IBM Watson cognitive systems to move healthcare from siloed legacy systems to a mainstream data economy (or API economy as it is sometimes called).

Going forward, we can diagnose correctly the first time, make the right predictions all the time, treat more efficiently, follow up effectively and reduce costs along the way. This is the care we all deserve.

Reach out to me at @AndyThurai to find out more about the above use cases or how APIs are the foundational piece for accelerating digital healthcare.

This article was originally published in IBMbigdatahub on Aug 28, 2015 – https://www.ibmbigdatahub.com/blog/digital-future-healthcare-here

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