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Role of Big Medical Data in Patient Engagement

Data analytics has become a game-changer with changing tides in the healthcare sector. How can stakeholders use it to improve patient engagement?

When it comes to quality healthcare, patient engagement has been the critical element. In order to maintain well-being, it is very important to understand what to do and what not to do. And with the advent of technological advancements, wearable devices, patient portals, and social media have become commonplace for both–patients and providers as they have access to more big data than ever before. 

Healthcare providers can leverage technology and big data analytical tools in order to draw useful conclusions while also ensuring that patients actively participate in their own care. In this article, we will jot down a few ways in which healthcare leaders can boost patient engagement in the clinical, research, and home environment. 

Utilizing Patient Data to Tailor Chronic Disease Management

For patients with chronic diseases, it’s a prerequisite to engage in management strategies and adhere to the line of treatment in order to maintain their well-being and cut care costs. By having a self-management approach, patients can independently track their health and actively participate in their own care. 

Even with such accessibility of treatment plans, patients are seen losing motivation to self-manage programs if the content, goals, and benefits are not tailored to their needs. In order to make patients gain trust and reap its benefits, it is important for providers to ensure their active participation in the chronic disease management plan. While with access to patient data, organizations can develop predictive risk scores and design personalized treatment strategies.

A behavioral health management system–Beacon Health Option’s clinicians are using ML tools to excerpt actionable insights from structured and unstructured patient data. With this information, the organization aims to enhance care coordination for patients while ensuring patients stay engaged and adhere to their care plans. 

The goal of the organization is to move from being a reactive model that solely looks at what is happening historically to being more proactive and predictive. With such an approach healthcare leaders are looking at opportunities to bridge the silos that exist in healthcare delivery systems, thus utilizing ML to its full potential in order to bulldoze through traditional barriers. 

Using Natural Language Processing Tools to Enhance Patient Health Literacy 

For patients to take an active role in their individualized care, it is very important for them to have a crystal-clear understanding of their medical data while also having the knowledge to use this data and make informed decisions. While the adoption of patient portals has made it easier for patients to access their clinical data, many still cannot make a sense of their medical information. Back by low health literacy rate and complicated medical jargon leading to poor medication adherence and deficient disease management. 

With such issues, Natural Language Processing (NLP) has emerged as a viable solution, allowing individuals to translate complex medical information and improvise their health literacy.
Researchers from Yale, Umass, and the VA, in 2017, applied NLP algorithms to EHR data and matched clinical terms with layman’s language. They found that the NP tools outperformed baseline systems in precision when presented with evaluation data. Another study published in 2018 by Jamia also demonstrated the potential of NLP to improve patient EHR comprehension. 

Leveraging AI to Increase Clinical Trial Engagement

Its time and again proven that clinical trials can facilitate the development of novel treatments and innovative cures for debilitating diseases. However, research indicates that it may not accurately represent real-world demographics with multiple trials failing to address women and minorities; leading to insufficient drugs for patient safety and effectiveness. 

Additionally, one of the drawbacks is the travel distance to reach the clinical research sites which present a barrier to participation. A 2016 survey reported that 65 percent of patients are unlikely to enroll in clinical trials concerning the reason for traveling to the research locations for treatment.  

Whereas a recent case study from Delloite found that virtual trials that utilize AI-driven tools support both passive and active data collection which can help to enhance patient enrollment and engagement in clinical research. By utilizing such tools, the travel burden on the patient is cut down and they can take trials from the comfort of their homes. Additionally, with the inculcation of AI, the time-consuming process of matching patients to appropriate clinical trials can be accelerated. 

Mayo Clinic and IBM Watson Health recently collaborated to develop an AI-driven tool. After using the tools for 11 months the clinic saw an 80 percent rise in the enrollment rate. The organizations plan to continue their collaboration in developing the tool for other elements of cancer treatment such as radiation, supportive care, and surgery. 

Conclusion:

So we can conclude by saying that big data analytics can help stakeholders across the healthcare continuum to enhance patient engagement in care. By leveraging the power of AI, ML, and NLP, organizations can help patients actively participate in their own care advancing care delivery and health outcomes.

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