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Patient-centered Care Models Taking Center Stage in Life Sciences
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Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- Patient behavior-driven care, or treatment based on insights from patient behavior data, has the potential to revolutionize healthcare delivery.
- Behavior analysis of patients and insights from them can inform early behavioral interventions to improve patient outcomes.
- Together, technologies such as AI and machine learning (ML), IoT, cloud, and blockchain, and solutions such as wearable medical devices can enable behavior-driven care.
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Transforming care
Patient behavior-driven care can revolutionize healthcare.
When it comes to getting a treatment right, reading the signs from a patient, or in other words, the person’s behavioral changes, is just as important as understanding the physical symptoms he or she may show. Behavioral changes play a significant role in the development, prevention, and management of diseases. For instance, disturbed sleep patterns combined with an unhealthy lifestyle are known to increase the risks of heart attacks, strokes, or depression. By tracking and analyzing behavioral changes—mood swings, changes in sleep patterns, daily physical activities, cognitive functions, adherence to medication, appetite, or social interactions—healthcare professionals can make informed decisions and alter treatment plans as needed.
Behavioral insights
The potential for improved and targeted care by integrating behavioral insights into the treatment provided is huge.
Behavior-driven care can be effectively applied in post-surgery recovery, cancer treatment, psychiatric care, and treatments for chronic diseases. Think about post-surgery recovery. Many types of surgeries, such as joint replacement surgery, have moved from an in-patient setup to an out-patient one, providing greater convenience to patients. However, this also makes it critical to include behavioral aspects of patients, especially when they are away from the hospital, in the recovery protocol.
While the opportunities for enhancing patient outcomes by integrating behavioral insights into the treatment plans are huge, there are many challenges. Collecting and analyzing patient behavior data is complicated.
Manually collecting and analyzing data on parameters as varied as mood swings, daily physical activities or social interactions is impractical and almost impossible. Data privacy, compliance, and security concerns add another layer to the complexity.
Data is key
Technology can help in analyzing the inherent behavior of a patient throughout the treatment period.
Artificial intelligence (AI), machine learning (ML), IoT, cloud computing, and blockchain, along with wearable medical devices, can make patient data collection, processing, and communication easier. Insights from solutions powered by these next-gen technologies can help set up behavior-driven care systems for effective personalized treatment.
A behavior-driven care system will only be as good as the data it gets. It will require a constant stream of behavioral data, continuously collected and analyzed. Four key data sources should be considered for obtaining patients' inherent and evolving behavior data:
- Self-reported data: Patients can be encouraged to give out information about their anxiety level, perceived pain, or food intake through interactive applications.
- Data from wearable medical devices: Such devices can provide near real-time information on vital health data, activity details, medication adherence, and sleep patterns.
- Electronic medical records or electronic health record systems: These can provide a patient's historical healthcare data.
- Social health data: Patients’ social media interactions can serve as key indicators of their mental health and overall well-being.
An architecture for behavior-driven care
For a behavior-driven care system to work, an IT architecture that supports it is vital.
We can visualize such a system as comprising two main layers: a data collection layer and a data insight layer.
The data collection layer can handle the collection of various data from patients, including patient-provided pain scores or step counts from wearable medical devices or mobile applications. The collected data can be securely communicated from the data source and stored in a central storage location. Technologies such as IoT (for collection of data from connected devices), 5G (for powering health applications), and cloud as a unifying fabric enabling other technologies can play a key role.
Advanced analytics on the collected data using AI/ML technology can provide a deeper understanding of a patient’s condition or recovery. This is where the insight layer comes in. It should support powerful software that leverages AI-ML techniques to analyze the stored data and provide meaningful insights into patient behavior and health. Such software typically falls under the regulated medical software applications or software as a medical device category. They can also help with data privacy compliance. Health professionals can use the insights from such applications for timely interventions in care delivery.
By embracing patient behavior-driven care, medical device manufacturers, pharmaceutical companies, healthcare providers, and payers can transform care delivery. They need to invest in new technologies and robust technology architectures for such a care system now to make a difference in the lives of millions.
