Healthcare analytics: How data informs critical healthcare decisions
Although the healthcare sector has traditionally lagged behind other industries when it comes to digital transformation, that’s no longer the case when it comes to data analytics. According to RBC Capital Markets, the healthcare industry is generating approximately 30% of the world’s data volume. By 2025, the compound annual growth rate (CAGR) for healthcare data will be 36%. Healthcare will reach this milestone 11% faster than media and entertainment, 10% faster than financial services, and six percent faster than manufacturing.
To help you understand why healthcare analytics is experiencing such impressive growth, we’ll explain how healthcare analytics works and why it’s important. We’ll share examples of how healthcare organizations are already using powerful data insights to diagnose patients more quickly and effectively, issue timely alerts to keep patients safe, cut costs, and even tap the data collected by wearable devices to elevate the standard of patient care. Here’s everything you need to know about healthcare analytics and how it’s already shaping the future of healthcare.
Use these links below to jump to what you need:
- What is healthcare analytics?
- Why are healthcare analytics important?
- 3 types of healthcare data
- Advantages of healthcare analytics
- Real-world use cases of healthcare analytics
What is healthcare analytics?
Healthcare organizations use data analytics to spot trends, enable more accurate decision-making, and identify opportunities for improvements. Healthcare analytics is valuable for a wide range of use cases, such as personalizing patient care, evaluating physicians’ performance, improving health decisions at a population level, or optimizing internal business operations. The data used in healthcare analytics can include patient information included in electronic health records (EHRs), data from clinical trials, imaging data from x-rays and magnetic resonance imaging (MRI), healthcare insurance data, sensor data from medical equipment connected to the internet of things (IoT), sensor data from wearable devices, prescription data, and other sources.
Why are healthcare analytics important?
Timely access to accurate information is critical in healthcare settings, where life and death decisions are made under challenging conditions every day. Healthcare analytics help doctors quickly diagnose and treat patients before their emerging conditions worsen, preventing serious negative outcomes. They can even provide pharmacists with important alerts about medication errors before they fill prescriptions, catching drug conflicts that would otherwise unintentionally degrade patient health or potentially even result in death. Analytics also play an important role when it comes time to file a claim, a notoriously slow process that many insurers are now automating and accelerating with AI-enabled tools.
Healthcare analytics have been essential during the COVID-19 pandemic, allowing public health officials to discover emerging outbreaks and proactively direct critical resources there so that people in those communities can access the care they need. Going forward, these insights will help epidemiologists better understand how and why the pandemic has evolved in the way that it has, helping governments around the world improve their preparations and strengthen their healthcare systems in advance of the next pandemic.
Recommended reading: Applications of IoT for Healthcare
3 types of healthcare data
Healthcare analytics typically relies on three types of healthcare data: predictive analytics, descriptive analytics, and prescriptive analytics. These data types enable healthcare organizations and providers to predict future scenarios, diagnose patients more quickly, understand patterns in historical data, and make better decisions about patient care.
1) Predictive analytics
Predictive analytics uses current and past patient data to help predict what could happen to the patients health in the future. Healthcare organizations can use predictive analytics to proactively treat patients and improve the quality of patient care. For example, researchers at the University of Michigan Rogel Cancer Center are creating a blood test that can tell doctors whether a particular treatment method for HPV-positive throat cancer is working far sooner than traditional imaging scans would be able to indicate. Researchers can also use predictive analytics to determine the likelihood of a patient getting serious illnesses like cardiovascular disease and guide patient care accordingly to ensure the best possible result.
2) Descriptive analytics
Just as predictive analytics illuminates the future, descriptive analytics can shed light on the past. Healthcare organizations can use descriptive analytics to evaluate historical data and understand previous events, identifying patterns or comparing outcomes if desired.
3) Prescriptive analytics
Prescriptive analytics is especially well-suited to decision-making. With the insights derived from machine learning (ML), healthcare organizations and providers can predict what is most likely to happen in a given scenario and then make decisions based on that understanding. Healthcare organizations and providers can use predictive analytics when planning treatments, evaluating high-risk patients, and managing care for a large volume of patients.
Advantages of healthcare analytics
Patients, providers, organizations, and insurers all benefit from healthcare data analytics. The insights from this data can reduce medical errors, prevent complications, and improve patient treatment. Doctors can even analyze past data to predict future health problems, delivering preventive care that improves patient well-being and cuts costs. Healthcare insurers can leverage analytics to optimize performance, save money, and boost customer satisfaction. And as wearable technology becomes ubiquitous, doctors can explore the rich data connected devices collect and provide their patients with tailored care.
Improves patient's comfort and healthcare
Medical errors can setback a patient’s progress or even harm the patient, but healthcare data analytics can help prevent these errors from taking place. With the benefit of AI analysis, healthcare organizations can reduce or even eliminate adverse outcomes like surgical complications, mistaken diagnoses, or hospital-acquired infections. In addition, healthcare providers can tap healthcare analytics to diagnose critical diseases like lung cancer and more accurately interpret digital images. These insights can help providers improve a patient’s comfort and enhance the quality of the healthcare they provide.
Precision predictive maintenance for treatment
Health conditions can impact patients' quality of life and result in costly medical bills, especially when they are not diagnosed early on. Precision predictive maintenance allows healthcare professionals to analyze patients’ medical records and spot emerging issues before they worsen into chronic illnesses. With the necessary insights at the right moment, healthcare professionals can offer the exact care that is needed — whether that’s a preventive treatment plan or a cure. Not only do patients benefit from better health and well-being, but the overall costs associated with their care are often lower over the long term, too.
Quicker patient diagnostics
Healthcare analytics can help healthcare professionals diagnose patients faster, so treatment can begin as soon as possible. For example, radiologists can use AI-enabled analytics to accurately interpret images, spot patterns that indicate specific illnesses and diagnose patients faster. Medical researchers also use healthcare analytics to conduct their research, which in turn helps doctors personalize the care they provide and diagnose patients more quickly.
Reduces overall healthcare costs
Healthcare providers and insurers use analytics to cut costs, make their internal operations more efficient, and improve patient satisfaction. For example, Allina Health System (https://www.healthcarefinancenews.com/news/allina-applies-analytics-patient-data-save-45-million-over-5-years) achieved more than $45 million in performance improvement savings across five years by using data to improve its approach to cardiovascular care at 13 hospitals and 82 clinics. In doing so, Allina was often able to address problems earlier so patients could be discharged sooner.
Provides real-time alerting
Healthcare analytics can also help enable issue timely alerts when a mistake has been made, enhancing patient safety and preventing adverse outcomes. For example, pharmacies can leverage this information to avoid medication errors associated with allergies, drug interactions, and other conflicts. According to Statista, between 44,000 and 98,000 people die each year from medication errors in the U.S., making this problem the eighth leading cause of death in the country. Real-time alerting can help pharmacists spot potential medication errors before filling prescriptions, keeping patients safe, and raising their standard of care.
Improved healthcare with wearable devices
More people than ever are using wearable devices such as smartwatches to track their health and boost their well-being. Early adopters of wearables sought to optimize their personal performance as part of the quantified self movement, which promoted self-improvement through self-knowledge. Because these connected devices are part of the IoT and include sensors that can take readings on vital health indicators, healthcare organizations and providers can now also use this information to innovate the healthcare experience.
Doctors whose patients take advantage of wearable devices to track their health can collect more data over a longer period of time than they could during one-off visits to the doctor’s office, even triggering alerts when potentially serious health issues are arising. For example, glucose monitors placed on the back of patients’ arms can alert them if their readings are too high or too low, helping them avoid complications. With access to a larger set of patient health data, doctors can better understand a patient’s internal state and give them more tailored, personalized care.
Recommended reading: Wearable healthcare technology: 12 incredible IoT applications
Real-world use cases of healthcare analytics
Healthcare analytics are already providing valuable insight at the macro and micro levels, whether that’s by helping public health experts understand how the COVID-19 pandemic is evolving and direct resources accordingly or allowing researchers to identify potential cures for deadly diseases. Insurance companies also make use of healthcare data to accelerate the claims process, optimize their internal operations, and cut costs.
Early disease detection
Healthcare analytics have proved crucial during the COVID-19 pandemic, for example by allowing public health organizations to identify emerging outbreaks and rapidly respond to them. Surveillance analytics, such as the wastewater data collected in specific communities, are one such example. Local health authorities have also used healthcare analytics to monitor increasing strain on the healthcare system and shift resources to the areas of greatest need so people in locations with high transmission can still get the treatment they require. At the provider level, healthcare professionals can tap healthcare analytics to identify patients that are at highest risk for severe complications associated with COVID-19 and give them prompt, personalized care in the event that they need it.
Researching cures for deadly diseases such as cancer
When collected and analyzed in the aggregate, large quantities of anonymized healthcare data can even help researchers identify cures for deadly diseases such as cancer. Although traditional clinical studies relied on comparatively modest data sets representing tens of thousands of people, it is now possible to collect and analyze health data on a vast scale. For example, the Mayo Clinic’s unified data platform has over ten petabytes of data, and the popular 23AndMe consumer genealogy platform has genomic and self-reported health data from over 12 million people. With access to the insights that these voluminous data sets offer, researchers now have far greater potential to uncover cures for a broad range of serious illnesses.
Speeding up insurance claims
Insurers are tapping healthcare data to accelerate the claims process, returning decisions in less time and improving customer satisfaction. With AI assistance, healthcare insurance companies can fast track straightforward and inexpensive claims, freeing up internal resources to focus on more complicated or costly ones. Insurers can also identify a potentially costly claim in advance so they can assign experienced adjusters to those cases early on. With greater efficiency and fewer manual processes, insurers can avoid errors and provide more responsive customer service.
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The healthcare sector is rapidly transforming, tapping data insights to improve outcomes from individual patient diagnoses to monitoring and safely navigating the global COVID-19 pandemic. Although other industries are also harnessing data analytics to boost innovation and maximize performance, healthcare is expected to leverage data at an even more rapid clip than many of them. IoT will play a key role in enabling this acceleration, facilitating the collection and transfer of valuable information that improves healthcare results for individuals, providers, insurers, organizations, and even entire societies.
With Hologram’s easy, reliable, and versatile cellular IoT connectivity, healthcare providers, businesses, and innovators can take advantage of all that healthcare analytics has to offer. Get connected today!