The healthcare industry continues to be competitive, with organizations striving to lower costs, save time, and primarily, provide better patient outcomes. By adopting new technologies like Artificial Intelligence (AI), health care organizations are enhancing quality of care and gaining a competitive edge in the market.
There has been a lot of hype surrounding AI which previously seemed hypothetical. But AI is now moving from futuristic to business critical. It is rewiring the way we think about business, including the healthcare industry. It is changing how we predict outcomes, diagnose and treat patients for a better overall experience.
AI in the form of predictive analytics is a great way to improve patient outcomes. It has been used in multiple ways, from predicting the rate in which a patient may develop chronic illness, to assessing health insurance plan risks on exchanges.
What is Predictive Analytics?
Predictive analytics is a form of advanced analytics which uses data and machine learning to make predictions about future events. By using predictive analytics, and historical patient data from electronic health records (EHRs), a healthcare organization can create dashboards and build reports. Companies can then use their dashboard to determine patterns and trends, and address concerns. (Link to https://www.logic2020.com/insight/bi-customer-journey)
Predictive analytics does not allow companies to predict the future, but it will allow organizations to become proactive and forward thinking. It forecasts patient outcomes and behaviors, such as whether they are more likely to develop an addiction to medication or miss a scheduled appointment, with a certain level of assessment and reliability. This allows for a forward-looking perspective of your audience.
The growing volume of data, and easier to use technology, has led to more organizations turning to predictive analytics for a competitive advantage. It can help solve long-standing operational and clinical problems and streamline communication as well as processes across multiple channels in order to improve outcomes.
Predictive Analytics in Healthcare
The healthcare domain is being disrupted by artificial intelligence, especially predictive analytics. Some of the top benefits include:
• Improved Risk Management – Predictive analytics can create risk scores and identify patients with higher risk of developing addiction, or chronic conditions. Earlier identification leads to risk reduction and means a better chance for physicians to help patients avoid health problems.
• Using Hospital Data to Reduce Readmissions – Predictive analytics can cut costs by reducing the rate of hospital readmissions. It can also improve patient outcomes by using data to predict the likelihood a patient will develop a condition, relapse to addiction, or need to come back and be readmitted.
• Improved Operations and Hospital Management – Technology can help to improve patient experience by forecasting operating room demands, optimizing staffing, and streamlining care. This will allow for an overall better patient experience at your facility. Many companies also use predictive analytics to forecast inventory and manage resources.
• Streamlining Communication to Boost Patient Satisfaction – Predicting patient behaviors is a key component of developing effective communications between doctor, office and patient. Predictive analytics can streamline communication. It can also give a heads up when the clinic is about to get busy to better manage wait times.
Having a Healthcare dashboard will serve as a tool to monitor not only your business KPIs, but your patient data as well. It can give healthcare professionals current as well as past patient information through an electronic health record (EHR), or a digital version of the patient’s paper chart. This makes patient data available in real time, in an all-encompassing view for better point of care assessment and diagnosis.
This single source of truth allows for an organized view of historical data in order to see areas where you could be improved and compare effectiveness of treatments. By streamlining the data in an easy to use way, healthcare professionals will be able to better diagnose, communicate to patients effectively, and save time, contributing to an improved overall patient experience. (Link to Healthcare Dashboard Blog)
One instance where predictive analytics is particularly helpful is with patients addicted to opioids. More than 130 people died every day from opioid-related drug overdoses in 2016 and 2017, according to the US Department of Health & Human Services (HHS). In order to change this, clinicians will have to change the way they prescribe opioids, with the goal of preventing abuse.
Combatting this epidemic is especially difficult because it has multiple causes and factors. According to IQVIA, although the numbers have been dropping in recent years, the number of opioid prescriptions given out by doctors peaked in 2012 at 282 million. Patient history, including past prescriptions or family history, needs to be considered. Predictive analytics can enable a comprehensive view of the situation. The tools have the potential to change the trajectory of patients that are high risk. By considering numerous patient factors such as medical conditions, mental health and healthcare use patterns, doctors can change how clinicians prescribe opioids, with the goal of preventing overuse, misuse, and abuse.