Over the years, medical advances have been largely focused on changing the ways we diagnose, treat, and prevent illness. As medical professionals have learned more about their patients, standards of care have improved. Today, big data analytics stands poised to unlock the next wave of medical advancement as modern technology digitizes healthcare to allow for the collection of vast amounts of data.
Big data analytics combines information collected through systems like imaging, pharmaceutical records, and lab results into an electronic health record. These types of records offer a big-picture overview of patient health, unlocking valuable information about a patient’s well-being and offering clinicians insights that improve patient outcomes.
Take the power of big data analytics data one step further, and healthcare providers begin to assemble what’s known as patient 360. Health organizations leveraging the modern data stack through a cloud provider like Snowflake are able to access a more robust view of their patient, combining medical data, test results, and patient history alongside self reported patient feedback, demographic data, and lifestyle activities to create a true and complete view of patient health. This comprehensive view of both medical and patient reported data is what’s known as patient 360, and for many healthcare professionals, it represents the future of patient care.
Here are the 5 ways data analytics in healthcare is transforming how clinicians diagnose and provide treatments to their patients.
Improved Decision-Making for Faster Diagnosis
Access to Big Data from any location is becoming mainstream. This accessibility benefits doctors, who can use the information to improve patient outcomes. Big data analytics uses information from patient portals, smartphones, wearable devices, electronic health records, and research studies to help doctors make informed decisions and healthcare organizations improve efficiencies. So, how does data analytics in healthcare work?
Artificial intelligence (AI) and machine learning (ML) rely on precise and thorough exploratory data analysis (EDA) to analyze massive volumes of patient data, revealing insightful and actionable information about a patient’s care. This information then gets fed into data analytics systems. From there, the data analytics platform can perform a variety of functions, such as reviewing historical data in a patient’s medical records to uncover patterns.
Predictive analytics uses current and historical patient information to predict health problems. Physicians can then use this information to assess a patient’s risk for specific medical conditions. For example, a team at Massachusetts General Hospital conducted a study that showed how AI, paired with thorough exploratory data analysis, can predict patient risk better than traditional models.
Shortened Hospital Stays and Fewer Readmissions
Business data analytics in healthcare gives clinicians the tools needed to monitor patient treatment to determine the best time to release the patient. For instance, healthcare professionals can require patients to use wearables that continuously collect patient health information such as glucose levels and heart rate. Real-time data streams instantly deliver the information to the doctor so they can make decisions quicker. That way, the doctor can recommend a custom treatment plan that can help get the patient out of the hospital sooner.
Moreover, the data can be used to monitor chronic conditions so doctors can respond before the patient needs to visit the hospital. A University of Washington Tacoma study, for example, developed an AI model to predict and flag patients with a high readmission score. This score represents how likely a patient is to return to the hospital within 30 days. Essentially, predictive data analytics help physicians deliver individualized care and outreach to ensure the patient sticks to their treatment plan. Those with a low readmission score can receive an email as follow-up; those with median risk can receive a text message; patients with a high readmission score can receive a telephone call.
Patient data can also be used to lower healthcare costs for both healthcare providers and patients. Hakkoda’s team of healthcare expert data scientists created this functionality for health organizations in the form of the ALOHA care accelerator. ALOHA stands for appropriate length of hospital admissions. With ALOHA, hospitals can analyze patient data in real-time, freeing up beds for those in need of urgent care, preventing rates of infection and readmission, all while driving down costs for the individual.
Improved Evaluation and Monitoring for Physicians
Today’s healthcare systems are focusing more on patient experiences and value-based care with their medical teams. To do this, healthcare systems gather information from patients regarding their experiences with their providers. The insight gained informs additional training physicians may need to improve patient outcomes, satisfaction, and trust.
Consider this example: Dr. Helen Riess, director of the Empathy and Relational Science Program at Massachusetts General Hospital, conducted a study that asked patients to rate their doctors on a scale of how they perceived their doctor’s empathy during a visit. As a part of the study, a group of doctors underwent empathy training. When asked to evaluate the doctor after training, the doctor’s empathy rating improved.
Analytics are also useful in measuring whether a doctor’s treatment plan was effective. For example, the doctor would receive a score based on whether the patient’s condition improved or worsened after the treatment. This information gives physicians objective feedback to help them improve how they treat patients.
Improved Lab Analysis Efficiency
Medical professionals rely heavily on lab data to inform patient care, which means that the consequences of faulty lab data, or even inaccurately interpreted lab data, are potentially catastrophic for both patient and provider. For example, incorrect results could lead doctors to prescribe the wrong medication or treatment plan. In fact, the National Library of Medicine reports that the costs associated with caring for patients with medication-associated errors exceed $40 billion each year.
Data analytics tools provide greater accuracy in analyzing lab results. Based on this information, the tools can spot prescription errors before the medication gets delivered to the patient. Medical labs are subject to regulatory data governance and compliance quality standards. Big Data analytics can monitor several data points to identify quality assurance issues.
Improved Medication Therapy Management
Data analytics have the power to inform treatment recommendations so providers can customize a patient’s treatment plan. Key insights like an understanding of a patient’s risk for addiction, genetic history that may indicate a likelihood for allergic reaction, and more are revealed through a patient 360 view of health via big data analytics. This additional information ultimately increases the likelihood of improving patient outcomes and reducing adverse reactions.
Healthcare data systems reveal insights that improve patient care. AI and ML analyze the data in these systems to provide details of a patient’s medical history, and ultimately, a medical provider is able to establish a 360 degree view of patient health. This information guides physicians in creating individualized treatment plans that minimize the risk, anticipate health problems, and improve patient experience. In the years to come, big data analytics will be critical to improving patient outcomes and revolutionizing healthcare.
Leverage Big Data Analytics With Hakkoda
Big data analytics is transforming healthcare by giving physicians the information they need for improved decision-making. Trends like patient 360 are only just beginning to become a standard pursuit for healthcare providers, and with greater access to real-time data, it’s likely that the healthcare industry as we know it is at the cusp of core transformation. Over the next few decades, big data will become invaluable to patient care.
Hakkoda is a specialized modern data services provider with deep domain expertise in the healthcare space. With a team of SnowPro certified data scientists that hail from the world of healthcare, Hakkoda helps medical practices implement big data architectures, building systems that enable secure access to information from multiple sources. With Hakkoda, providers can harness the power of their data to improve outcomes with a 360 patient view. Reach out to one of our experts to learn more.