Each healthcare facility has billions of individual data points. However, all of those data point are useless until they are properly interpreted. This is where data analytics comes in. According to a Health Catalyst survey, 90 percent of healthcare workers believe healthcare analytics will be important to their organizations.
Healthcare analytics specifically refers to the process of sifting through massive amounts of structured and unstructured data to draw actionable insights for more effective patient care. The data analytics industry in healthcare alone is expected to reach $34.27 billion globally by 2022 because it’s tremendous benefits. This post outlines the advantages data analytics provides to the healthcare field.
More accurate diagnoses
According to a 2018 BBC story, diagnostic errors result in 40,000 to 80,000 deaths every year in the U.S. alone. Data analytics can be used to mitigate that statistic and provide more accurate diagnoses. This process uses AI and machine learning algorithms to process current and previous patient data, as well as clinical and laboratory reports. AI technology then employs predictive analytics to provide the most accurate diagnosis possible based on analysis of the collected data.
Drug development is a long, complicated process that requires a significant amount of testing, time and money. AI algorithms can analyze data from prior tests, treatment results, and case studies to simulate a drug’s potential interaction with the body. In doing so, analysts can quickly and easily determine the probability of a drug’s success or failure early on. This process can greatly reduce the time and money invested in drug development, while also substantially lowering the risk of failure.
Electronic Health Records (EHRs)
An EHR is a digital version of a patient’s chart. EHRs contain entire patient histories, including demographics, medications, images, allergies, immunizations, previous visits, and lab and test results. These digital charts can be accessed from any healthcare facility and updated in real-time for the most current information on each patient. EHRs can analyze the data in a patient’s chart and draw insights and connections to potential health risks and diagnoses.
With the onset of smartwatches and health trackers, wearable tech can continuously monitor the body to get a more holistic view of a patient’s vitals throughout each day. More sophisticated monitors are also available from healthcare providers that can give more detailed analyses of blood pressure, oxygen, glucose levels, and more. These devices can help doctors predict possible health issues, disorders, or concerning symptoms.
According to Accenture top AI capabilities in the healthcare sector could result in $150 billion in annual savings by 2026. Data analytics can examine historical data to detect patterns in patients, assist in proper staff allocation and supply chains, and predict the best possible discharge protocols to reduce readmissions. Additionally, insurance companies can save money by providing wearable tech and health trackers so patients can self-monitor and minimize healthcare facility visits.
When it comes to healthcare, there is no such thing as too much data. However, analyzing that data can be extremely difficult without the help of AI, machine learning and predictive analytics. Advanced data analytics will be able to provide patients and healthcare professionals with more accurate diagnoses, more efficient drug discovery, preventative medicine, and lower costs.
This article written by Amanda Peterson