Digital technology has become a major factor in the healthcare industry with the health-tech market being recorded at as much as USD 106 Billion in 2019. Robust growth is projected for the sector too. Among the technologies driving health care forward, Analytics is one of the most attractive options because it seems to hold the potential to enable significant healthcare outcomes.
Analytics refers to the computational analysis of massive volumes of data to discover, interpret, and communicate identified patterns. The aim is to derive hidden insights that allow more timely, better directed, and well-considered actions. Such actions help improve care. This is why Big Data analytics and Business Intelligence technologies are getting widely adopted in the healthcare sector in the quest for effective outcomes.
Big Data analytics in healthcare
The application of Big Data analytics in healthcare has life-saving outcomes for patients and also simplifies the tasks for the medical professionals and caregivers. Many of the analytics applications in healthcare are already quite common and have a history of providing exceptional outcomes.
Real-time Alerting
Clinical Decision Support, CDS systems are implemented in hospitals and medical centers to continually analyze medical data and generate alerts for better prescriptive decisions. Asthmapolis, for example, combines analytics with a GPS-tracking system in the inhalers to identify asthma trends and issue alerts that apply across groups and vulnerable sections.
Remote health monitoring is also enabled with the application of analytics to generate real-time alerts. Wearables, such as smartwatches and fitness bands record the health signs of people and issue alerts in the case of discrepancies. For example, high blood pressure levels lead to alerts and notifications to the user and can also be shared with medical professionals for quick assistance.
Predictive Analytics using EHRs
A majority of healthcare decisions are based on anticipation and reduction of the risk to the patient using current and historical medical information. Electronic Health Records, EHRs are now used by hospitals and medical centers to collate comprehensive data pictures of the patient. These comprise digital medical records of the patients. Predictive analysis can be conducted on EHRs to determine the likelihood of the different events in light of specific circumstances, leading to better diagnosis and treatment.
• Predictive analytics is applied to the medical records and health signs of the ICU patients to detect early signs of patient deterioration. The vital signs of the patients are continually recorded and predictive algorithms are applied to calculate the probability of the patients requiring medical intervention in the next one hour and so on. Such data can also be tapped to identify possibilities of patient readmission and the likely reasons to mitigate against them.
• The data from multiple sources, such as EHRs, wearable biosensors, fall detection pendants, etc. is used to predict the patients that may require emergency services in the upcoming days. The data-driven analytical approach can also deliver targeted predictive care to at-risk patients.
• Predictive analysis is also applicable in determining the maintenance requirements of healthcare tools and equipment. For example, MRI scanners can degrade with continuous usage over a period of time. Scheduled maintenance can be implemented with predictive analysis for effective outcomes and better use of the healthcare equipment.
Suicide and Self-harm Prevention
The global suicide rate as published by the World Health Organization (WHO) is 10.5 per 100,000 people. Also, 17% of the global population will self-harm during their lifetime. These statistics and numbers are disturbing and alarming at the same time.
Mental health further took a hit during the Covid-19 pandemic due to reduced social interaction, fear of job security, business loss, and numerous other reasons. Big Data analytics and business intelligence can identify the patterns involved in suicide and self-harm to prevent such occurrences. Analytics can help identify vulnerable populations or triggers and possible timely support can be provided or remedial action delivered to help make an impact.
Future Health Conditions and Pandemics
Covid-19 pandemic shook the entire world in 2020. Analytics in healthcare can help predict the outbreak and spread of such pandemics in the future based on the collective analysis of the health information, environment data, exposure, etc.
The future health conditions can also be predicted at an individual or population level to drive the precise implementation of preventive controls.
Analytics Combined with Medical Imaging
Approximately, 600 million imaging procedures are performed in the United States annually. Medical imaging is one of those vital procedures involved in healthcare services.
Big Data analytics has the potential to improve the way medical images are read and diagnosed. The analytics algorithms can analyze millions of images to develop specific patterns in pixels and convert them into numbers for better diagnosis and treatment. Various studies have shown that such diagnosis is faster and more accurate than that done by humans. Using such insights, radiologists can easily determine what needs to be done to achieve the desired outcomes based on the patterns recognized by the algorithms.
Current Obstacles in Widespread Adoption
Medical information is currently distributed across multiple sources, such as hospital databases, medical associations, government-owned sources, patient devices, etc. One of the major issues to conduct large-scale analytics in healthcare is the compilation of the information from all these sources.
Healthcare information is sensitive and private data subject to several government regulations and demanding adherence to onerous standards for protection and restricting access. The involvement of Big Data technologies combined with cloud computing applications exposes the information to security risks and issues. There are also implementation concerns identified with these applications and tools, especially with how to drive up acceptance and usage among doctors and caregivers.
To Sum it Up
Analytics in healthcare has abundant potential with many of the applications already showing their value in use. The integration of analytics applications with advanced security controls, such as data encryption and access control can address security concerns. It’s clear that Big Data analytics combined with intelligence can drive better-informed decisions and outcomes in healthcare.
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