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It's quite apparent that healthcare enterprises abound with loads of notes and imaging that are difficult to handle. Some researchers argue that biomedical datasets aren't large and raw enough to require big data approach, while others expect the market of big data in healthcare to break $34.27 billion by 2022.
Looking further ahead, how can medical companies benefit from big data solutions and make the most of them for the clients? We believe that big data-powered business intelligence can foster great improvements in patient care. Now, let's get into what we mean by it below.
With overall digitization and cloud computing, medical science obtains access to rich sources of healthcare-related data. Take EHRs and patient portals, for a start. On top of streamlining record turnaround and saving time for critical tasks, they serve as warehouses for loads of demographic data, basic vital signs, allergies, and medications.
Add to this:
1. Lab information systems that manage clinical test orders and harbor results
2. mHhealth or telehealth apps for remote doctor-patient sessions
3. Wearable devices and implantable sensors gleaning data on physical activity level, energy expenditure, and fitness
4. Social networks with numerous hubs for discussions around health.
5. How to use this variety of immediately accessible records to contribute to medical R&D and improve patient care?
Imagine, say, an interactive dashboard that communicates with the data endpoints mentioned above. Via that large searchable database, medical scientists can track changes in health condition over time, even at community level. Deriving valuable insights on public health, specialists will be able to further implement them in demographics management solutions.
The wealth of open data on vital signs and lab tests can help spot symptoms early on. Practicing predictive diagnostics and designing more efficient treatment patterns, doctors will be more likely to succeed in disease prevention.
With medical errors being the third leading cause of death in the US, 40% of Americans pick healthcare as their major concern. Even though most of the modern EHRs are programmed to detect risky drug interactions or overdoses, mistakes occur. Today, scientists can address the issue via big data-enabled software that integrates with EHRs to pinpoint inaccurate prescriptions.
To preclude the errors, the big data-powered systems check if a drug matches patient's condition as it's described in an EHR. If it doesn't, the prescription gets blocked as inappropriate, pending until it is either approved or cancelled.
With its ability to determine the entire genome order and identify disease risk or cause, DNA or genomic sequencing has become a major biotech in medicine. To ensure accuracy while investigating changes in genes, scientists have to analyze billions of DNA strands at a time. This is where big data approach becomes instrumental in pinpointing anomalies that are likely to affect health condition.
Analyzing big data on patient's genomic background helps handle complex chronic cases when neither lab test results nor symptoms are decisive enough to detect the underlying cause of disease. To diagnose rare anomalies, specialists can now rely on advanced sequencing solutions connected to patient portals. Capable of mapping individual genome 30 times in 40 hours, the systems enable doctors to precisely locate mutations.
Opioid crisis is a major healthcare issue that takes thousands of lives every year in the U.S. alone. What makes opioids so dangerous is a high risk of becoming an addict, as misusing them leads to drug tolerance, meaning the patients are to take greater amounts or switch to more powerful substances for a relief.
Researchers say that most addicts start overdosing once they borrow or steal opioids from their families or friends. Quite naturally, governments come up with initiatives around imposing tough standards on prescription drug accounting. How can big data help handle it?
First way is to build solutions that scan EHR records to detect unnecessary prescriptions. Second, with programs like The National Prescription Drug Take Back, startups can offer data visualization tools that help find drug take back locations and better stock treatment resources.
Medical imaging is a critical diagnostic tool that helps physicians quickly spot treatment targets. With an increasing image overload, the trend is to migrate petabytes of scans to cloud-based storage that grow into a world-scale anatomical and physiological database.
Currently, radiologists obtained a cost- and time-efficient assistive diagnostic solution that pairs medical imaging big data to AI algorithms. To tell normal and pathological patterns apart, specialists harness neural networks that have been trained on vast datasets. By applying AI algorithms on CT images, physicians can also calculate bone density and assess the risk of fracture.
A holistic approach to aggregation, governance, and analytics of biomedical big data ensures a great shift towards a wider adoption of precision or personalized medicine. This patient-centric and value-based medication model is designed to help detect diseases at an early stage, prevent outbreaks or complications, and decisively cure the cause.
The personalized medicine project obtained the US government's support back in 2015, with the growth of the NIH's initiative to develop cancer genomics and improve treatment methods. By combining big data with medical R&D, researchers are well on their way to yield innovative precision healthcare approaches.
Perhaps the best known techniques of personalized diagnosis come down to processing patient DNA with AI algorithms. However, the current tooling builds upon the knowledge that extends far beyond genomics. To tailor treatment patterns to each disease case, specialists bring together robust computing, real-time imaging, as well as advances in biochemistry, molecular biology, and infrared spectroscopy.
What's still in common between most of precision medicine techniques is that they can't do without big data. In this regard, the better the access to patient health records, the more informed are physician's decisions.
To ensure accurate individual treatment plans, stakeholders are looking to build healthcare business intelligence solutions that analyze big data across as many sources as possible. These may include lab results, progress notes, diagnosis and procedure codes, allergies and side effects, medication, admission, and discharge data, all the way to patient's access to therapeutic recreation, food, and housing security.
A surefire way to put the obtained insights to good use is to harness them while developing population health management systems. With that rich value-based info at hand, it is possible to efficiently identify risk groups, control epidemies, spot service gaps, and design community-level healthcare strategies for better outcomes.
Avoidable readmissions cost American healthcare providers tens of billions a year. To handle the issue, medical organizations undertake initiatives like Hospital Readmissions Reduction Program. Relying on big data-powered business intelligence, stakeholders analyze loads of EHRs, admissions, transfer stats, and create value-based roadmaps.
Big data approach unlocks actionable insights that enable medical companies reduce hospital stays, cut readmission rates, improve patient care, and achieve better health outcomes. Researchers work hand in hand with software engineers, bringing to the table full-fledged solutions for this purpose.
Take, for instance, a compound R&D team that helped develop a big data-powered risk scores system. The solution utilizes a machine learning algorithm to predict 30-day readmissions for patients suffering from heart failure. To estimate the possibility of another stay, the system analyzes vital signs and other clinical and demographic metrics.
Using AI-enabled monitoring systems that analyze patient data to flag changes in, say, blood glucose level or weight, medical institutions can reduce spendings on human staff. Big data-backed online diagnostic tools and genetic sequencing ordering services ensure better patient engagement and optimize treatment and decision-making efforts.
Additionally, healthcare providers leverage mHealth and telehealth apps to collect biomedical data that helps design individual post-discharge treatment roadmaps. In 2017, researchers found that real-time mHealth messaging apps ensure that 86% of patients follow their medication guidelines, as they got the instructions immediately available.
Virtual sessions via telehealth apps also help expand the access to healthcare services, streamline clinical workflows, and capitalize on avoiding in-house treatment and transportation expenses. In this regard, smartphones and wearables can do much good by tracking vitals, handling emergency alerts and e-prescriptions — providing urgent care on the go.
Ultimately, biomedical big data analytics yields preventive solutions to fraud and inaccurate claims. By exchanging real-time updates on admission, discharge, and transfer, healthcare providers can reduce unnecessary stays and save millions of budget across the states.
Today, healthcare market contributors make great strides towards a wiser usage of biomedical big data. Sure enough, privacy controversy poses certain barriers — that's the way things are when it comes to data digitization. Nevertheless, the gains from big data-powered intelligence can translate into a true makeover in the healthcare industry — from streamlined workflows and advanced diagnostics to personalized patient care and, ultimately, better outcomes.
