A person with diabetes, hypertension or other chronic health condition has to make more decisions on a daily basis than the average person. And with 6 in 10 adults living with a chronic health condition, that’s millions of added choices.
For instance, a landmark Stanford Medicine study found that people with Type 1 diabetes make about 180 decisions per day, most tied to blood sugar monitoring, food intake, insulin dosing and other health behaviors.
Not all chronic health conditions require the same vigilance as Type 1 diabetes, but successful management requires individuals to consider the relationship between their daily decisions and health, then act accordingly. After all, doctors, and healthcare in general, regularly advise people to manage chronic health conditions with diet and exercise but cannot provide individuals with day-to-day support for making those changes.
Ultimately, uncertainty is central to the experience of living with a chronic health condition. Fluctuating physical symptoms, increased mental burden, barriers to care access, and gaps in knowledge about treatment options or outcomes cause people with chronic health conditions to regularly question what is happening in their bodies and the best course of action. How will what I ate for breakfast impact my blood sugar four hours from now? At what intensity and interval should I exercise to meet my goal weight?
Fortunately, predictive analytics can offer some relief for decision-making fatigue by limiting guesswork. Combining sophisticated algorithms capable of sorting vast amounts of data with artificial intelligence and machine learning techniques (AI/ML) makes it possible. The resulting software can begin making personalized predictions about how diet, exercise, and other behaviors will impact a person's body in the hours, days, or weeks ahead with a high degree of accuracy. Then, based on an individual's health forecasts and trends, offer actionable advice, create alerts, and curate educational materials or digital tools to drive positive behavior change or product engagement.
The future of self-care will be predictive
For decades, predictive modeling has been used to forecast the likelihood of something happening in the future. It is regularly implemented in marketing and underwriting. In healthcare, predictive analytics is used to guide medical support based on a person's risk of developing a health condition. But rarely has it been used to empower individuals in day-to-day condition management. Why? Some explanations point to a lack of technology capable of accessing large volumes of data and sophisticated machine learning algorithms to sort it. Still, the healthcare system struggles with long-term investments in the prevention of chronic health conditions. Traditional healthcare operates on return on investment timelines of one year or less, making it challenging to quantify financial and patient outcomes.
Today, people have access to a number of tools that capture data about their bodies and activities, whether it's the pedometer built into every iPhone, activity monitors from FitBit or new wearables like the Oura Ring sleep tracker. These real-time data sources, together with increased mobile computing power, API technologies, and advancements in machine learning and artificial intelligence, make it possible to offer predictive analytics to empower the everyday consumer on their health journey.
Some digital health companies are already doing this, and as predictive analytics matures, so does the potential for helping people with chronic conditions make informed decisions for better health outcomes, peak performance, and more fulfilling lives.
Steering health in the right direction
Broadly speaking, predictive analytics uses data, statistical algorithms, and AI/ML to predict what will happen in the future. In support of preventive self-care, it works by synthesizing complex factors affecting an individual’s health into meaningful insights and predictions. Forecasting can help individuals identify actions that negatively impact their health or delay progress, like eating high-sugar cereal for breakfast while trying to lose weight, and reinforce positive choices, like taking a 15-minute walk after lunch to minimize a spike in blood sugar.
Artificial intelligence and machine learning enable predictive analytics software to learn automatically from patterns or features in data and tailor the product experience accordingly. For example, a digital platform for chronic condition management can use predictive analytics to serve up personalized notifications with exercise suggestions or recipes at just the right moment. Because health is both holistic and highly nuanced, smart companies will use AI/ML to complement and augment—not replace—human-based digital services (e.g., clinical health coaching) to optimize an individual's use of predictive insights and recommendations.
By leveraging aggregate data, predictive analytics tools are able to generate accurate predictions for one person based on millions of similar health profiles. This way, data privacy is maintained, and an individual can receive a prediction within minutes of entering their first data point, which will ultimately be added to the de-identified data pool and repurposed to help others.
Empower people to take charge of their health, not just monitor it
When you’re diagnosed with a chronic condition, you go from feeling in control of your destiny to feeling like you have no control at all. Having predictive analytics at our fingertips is empowering—future possibilities are revealed, and it becomes easier to identify the best course of action to prevent problems before they happen: high blood sugar, hypertension, heart disease.
As a person acts on these insights, their health improves; self-efficacy develops as they grow increasingly confident in their ability to make intelligent decisions and succeed in certain circumstances. Self-efficacy, thought to be intricately related to motivation and behavior change, is the key that unlocks our willingness to change our habits in favor of a healthier lifestyle. Equipped with predictive analytics tools designed to promote proactive decision-making and filled with confidence and motivation, people can reduce the burden of chronic care management and regain the capacity to focus on living their best life.
Keep in mind technology is not a silver bullet. We each need to remain accountable for our actions and take ownership of our health. But we don’t have to do it alone. Predictive analytics lightens the cognitive burden of managing a chronic condition, giving millions of people back their time and energy to spend on things that matter and with the people they love. We have the technology to change the future of health, so let’s use it.
Dan Goldner, Ph.D., is the executive vice president of advanced technologies research and discovery at digital health company One Drop.