ProactivePower App and Framework

SUMMARY: The idea is to have information flow from patient, to doctor, then to researcher, all in one framework. Then the information can flow back. From researcher/policy-make finding insights, back to the doctor and educator, and then to the patient. But not only is it just disparate pieces of random information, the patient (doctor and researcher) can see trends, effects of actions, and interaction of multiple variables and habits.

This leads to a "gamification" of their health. What is my progress this week? What is it this month? Doctors and educators work with goals, but research shows that those goals are rarely met. The gamification (perfect for children as well), lets patients see real-time progress, and more importantly, the impact of their habits on their health. That's where patient self-learning comes from.

HOW IT WORKS

One third of people born after 2000 are projected to have diabetes (1).

If chronic patients have a tool to help themselves, insurance companies stand to save 52% in hospital costs, and nearly $1000 per patient annually (2).

According to the American Diabetes Association, that's $245 billion dollars in annual costs, and accelerating.

ProactivePower App and Framework integrates current research to achieve success in the best practices of proactive patient self-management based on 1294 citations, 41 studies in 48 articles involving more than 200 practices and 48,000 patients.

Additionally, the ProactivePower App and Framework includes the ability for researchers to pass down new policies and methodologies back to caregivers and patients.

Meet Mrs Smith, a 45 year old patient with diabetes.

She comes in for her 15 minute visit with Dr Madden. After evaluating her acutely painful knee and treating her gastroesophageal reflux disease, Dr Madden has 3 minutes left to assess diabetic control.

Having fruitlessly searched through Mrs Smith's medical records, and sees that Mrs Smith isn't keeping good records, and can't readily find what's gone on in the past, Dr Madden gives up in frustration and schedules another visit during his off day to manage Mrs Smith's diabetes.

Unfortunately, Mrs Smith's poor record keeping, and Dr Madden's frustration is the norm. Both are victims of “Tyranny of the Urgent”(3).

Often acute symptoms crowd out the less urgent need to bring chronic illness under optimal management.(4) Even with Diabetes Educators, if the patient is not complying or keeping records, little can be done.

*This isn’t just about record keeping, it’s the patient seeing the impact of their actions. This leads to better actions.

Now let’s see Mrs Smith with ProactivePower.

Mrs Smith goes out for lunch with friends. She sends a text message to ProactivePower. Everything is recorded.

She checks her blood glucose, and tells ProactivePower. Recorded.

That weekend she goes for a bike ride around the neighborhood with her husband. She tells ProactivePower.

These seem like randomly recorded events, without context don’t really help her, her doctor, or the diabetes educator.

ProactivePower puts it all into a big picture context.

Now on her next doctor visit, there is more to talk about.

*When she sees the effects of her actions, she is self-educating herself, and feels empowered to take control of her diabetes. Then she records more. Which helps everyone more.

This ProactivePower framework is designed to help the physicians and educators do their job better by improving patience compliance, record keeping and more importantly seeing trends and seeing the patient's chronic care from a more holistic, data-driven view.

A side benefit is that researchers and policy makers get to see the larger picture trends and efficacies of their efforts. Lastly policy makers can take a research, data-supported approach to trying out new policies and procedures and then directly measuring successes right from the ProactivePower framework.

THE GREAT FEEDBACK LOOP

There are three data screens: Patient, Doctor, and Researcher:

(1) Patient can see what actions they are taking that is helping their diabetes, and positively reinforce behaviors that make it better. Also they get a weekly PDF Infographic telling them what they did right that week.

(2) Doctor can see what is going on with the patient. Monitor patient compliance, and proactively work with patients before something becomes critical or life-threatening. i.e. If the AI tells the doctor that the patient is always hypoglycemic on Tuesday afternoons, the doctor can work with the patient to see what habits are in place, and what can be done to protect the patient from a life-threatening or hospitalization event.

(3) Researchers can see aggregate data to find patterns. The researchers feed this data back to doctors and policy makers, which then feeds back to patients.

This framework is a data collection hub designed to make it easy for people with diabetes, and any socio-economic background to collect data, and more importantly, teach themselves how their habits influence their health. Data can come from a simple mobile phone via SMS, and connect data from other sources like Healthvault and devices. Second to that is an artificial intelligence approach to encouraging and reinforcing those good habits.

Surveys and studies look at people as numbers. Not as individuals. This app will change that by making the individual the center of importance. Data and statistics to help the individual. Everyone is different, each individual has different habits and biochemistry. Finally, this tool can help the individual find and discover habit to improve their health.

Here is a simple, but powerful use case example. If researchers see that statistically patients who check their blood glucose right before eating out, are more aware of their health, and choose better foods, this will show up in the data. Researchers can do a basic search in ProactivePower to find patterns like this. Then the researchers can pass this finding onto policy-makers, and doctors/educators so the system can remind patients, "Don't forget to check your glucose levels before you eat?" when it detects the patient is eating out. The results of this new policy can be directly measured in the ProactivePower platform to see the immediate effect of a new policy.

  • A side benefit is that researchers and policy makers get to see the larger picture trends and efficacies of their efforts. Lastly policy makers can take a research, data-supported approach to trying out new policies and procedures and then directly measuring successes right from the ProactivePower framework.

AN SMS BASED SYSTEM FOR HIGHER COMPLIANCE AND SUSTAINABILITY

Lower income people with basic phones can interact through the SMS system. Most disposable and pay-as-you-go plans now include unlimited SMS making this an affordable option for lower income families.

In addition, elderly patients are accommodated by only having to enter numbers and simple messages that work even from a flip phone. No smartphone required.

Lastly, SMS and text messaging have become ubiquitous as the go to application. People will often not load an app on their phone, but will always check their text messages.

Keys to patient compliance is to make the interaction as easy and effortless as possible. Not to assume that patients are lazy, but by simplifying the process it's easier to integrate new habits for patients, and it also means that there is less human error introduced into the process.

*The system is designed to have cultural files. So language and cultural patterns are used in the text messaging system for different cultures and language speakers.

ADDITIONAL ANALYTICS AVAILABLE

When I eat fast-food, I am doing a good job, or am I seeing spikes in blood sugar? The months that I eat fast food more than 6 times means my A1C is higher? The months that I eat at restaurants my A1C is lower?

If the system notices that your blood sugar is out of statistical range, it will ask you: "Strange numbers. Did you wash your hands to make sure there wasn't any food or lotion on your hand?

Will Include Crouching Tiger Hidden Sugar feature. Enter the fast food restaurant you are eating at, and it will give you a link of hidden sugars at that place.

Over age 60 is the most at risk population for severe hypoglycemia. The system can identify if the patient is immediately, or trending risk of hypoglycemia and contact the doctor or diabetes educator.

SCALABILITY

The system is built on a robust .NET infrastructure with Microsoft SQL Server for data collection and analytics. It is scalably designed to use Amazon Web Services or Microsoft Azure load balancing on multiple server clusters.

REFERENCES *Note, a significant number of resources were used for this project. The following resources are only in reference to this document.

  1. Proc (Bayl Univ Med Cent) 2010;23(3):230-234
  2. JAMA, Oct 16, 2002 - Vol 288, No 15, 1911
  3. JAMA, Oct 9, 2002 – Vol 288 No. 14 1775
  4. Wagner EH, et al, Organizing care for patients with chronic illness. Milbank Q. 1996:74:511-544

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