Patient disengagement leads to serious negative consequences including hospitalization and preventable deaths. It is imperative that healthcare providers take innovative steps to improve patient engagement and consequent health outcomes. As in many other fields, big data can lend valuable insights into viable engagement strategies.
The Problem of Patient Disengagement
Patient disengagement is associated with a lack of trust in the healthcare industry and the rising costs of health services. It manifests as patients refusing to accept an active role in their health care, which includes a lack of compliance, personal research, and allocating funds to their care.
Patient engagement statistics show that people are increasingly open to using apps or data aggregations to supplement their health care, but the actual use of existing health apps, ratings, and diagnostics is relatively low.
When patient participation is improved, we can hope for increased patient loyalty and outcomes.
How Data Can Help
It’s time for the healthcare industry to follow the lead of most modern businesses by using data to craft customer-centric approaches to business. Health data can help build patient engagement tools that target specific segments of patients.
Aggregated data that considers demographics and medical conditions along with compliance and outcomes can provide insight into what programs may help specific patients and which patients are at risk for non-compliance.
Patient data can also help healthcare providers find personalized solutions by increasing diagnostic abilities. Learning more about patients should ultimately help physicians improve patient relationships and get patients more involved in their care.
Sources of Healthcare Data
Many avenues can be pursued to collect healthcare-relevant data. Common sources include administrative data, medical records, surveys, and standardized clinical data.
Most healthcare organizations employ electronic health record (EHR) systems to collect and organize patient health information. EHR can be used in compliance with HIPAA to identify patterns among patients and expand the database of diagnosable diseases, which can improve patient outcomes.
Healthcare Customer Relationship Management (HCRM) is another tool that integrates information from EHR with demographics for a more holistic picture of the patients. Physician Relationship Management (PRM) works similarly but instead gathers data about physicians to identify and retain excellent providers.
Personal interactions with patients can also be sources of data, from call centers that field questions and complaints to interactions between physicians and patients.
How to Use Big Data in Healthcare Patient Engagement
Machine learning and big data models are increasingly being used to inform nuanced intervention that might improve patient engagement. Apt text message reminders and targeted content have been made possible by AI and used effectively.
Patient health engagement models can help predict the risk factor of a patient becoming disengaged or non-compliant. This helps move physicians from a reactive mindset to offering proactive care.
Clinical propensity models with demographic, personal, and health-specific variables can help physicians develop treatment options and tools for patient engagement that target groups using correlations in the data.
Patient engagement specialists can benefit from statistics as they look for methods of increasing patient involvement and removing barriers to care.
Other Exciting Uses for Health Data
Models can be built from health and demographic data to help tailor chronic disease management; correlations in aggregated data can be used to design individualized treatment strategies.
Screening tools can also make it easier for physicians to relay the full range of treatment options to patients with rare diseases. Physician inexperience in treating or diagnosing a particular condition can be offset by a database that collects and organizes national or even global health information.
Big data may even have an important role to play in uncovering and preventing healthcare fraud. Artificial Intelligence (AI) systems may be able to find patterns in data that point to medical billing errors and waste, which are prevalent because of the industry’s complexity.
A review of the role of EHR in improving patient care found that a leading reason for medical errors is lacking medical data processing systems. Using standardized systems is effective at minimizing errors.
How to Increase Patient Engagement
Patient outcomes rely heavily on patient action, or inaction, regarding their health. An important part of any patient engagement system is supplementing patient knowledge of their conditions so they know the responsibilities and risks associated with their conditions.
Health literacy is an important aspect of this issue; natural language process (NLP) tools can be employed to help patients understand the wealth of information available to them through patient portals and databases.
Apps and wearable devices have also been found to improve clinical trial participation and patient engagement. Making participation more convenient and easier to follow can improve patient responses.
Finally, personal outreach must be a major part of patient engagement systems. Patient/physician relationships can increase trust, loyalty, and participation in primary care to disease specialist programs.
The speed of AI in healthcare is one advantage that demonstrates the impact of AI in all aspects of healthcare, including care delivery, research, early detection and pharmacovigilance, diagnosis, and treatment. Here is a list of examples of the impact of AI in healthcare. Read More
Dangers of Using Healthcare Data
As with any novel tool, big data in the patient care realm is not without dangers. Misinterpretation of the data can easily lead to mistaken treatments. Healthcare facilities should use reliable data analytics to avoid these problems.
With the right support, AI transforms healthcare by improving the efficiency, distribution, and diagnostic ability of the industry.
Healthcare providers should also be wary of changing regulations surrounding the collection and use of health data. Data privacy is an important aspect of any data management plan.
Big Data Analytics
AI in healthcare can pay off in improved outcomes for many patients. Machine learning, an application of AI, uses data to constantly improve algorithms.
Trained data scientists can extract valuable insights from raw data and present the important aspects of visual diagrams or statistics. This can help providers move past the noise of data and target important information.
The proper collection and reporting of healthcare data are revolutionizing the industry and helping physicians save lives. Syntelli Solutions is a leader in data managing and engineering and can employ predictive analytics to help you build a targeted system to improve patient engagement.
Contact us to learn how our modern business solutions can help you improve your healthcare facility.
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