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Healthcare systems are increasingly relying on technology like electronic health records (EHR), big data, and machine learning to improve patient care quality.
When it comes to patient care, clinicians and healthcare organizations are particularly starting to apply the use of predictive analytics to develop actionable insights and better outcomes for clinical decisions.
What is healthcare predictive analytics, and how can analytics technology help you improve your patient outcomes and workflow as a provider? Read on to learn more about the predictive analytics tool below with Carda.
Predictive analytics is a form of artificial intelligence (AI) that can anticipate and help foresee future needs for an organization or business. These future needs could be operational, such as staffing demands or resource and supply needs. The future needs may also be financial, such as project or staffing costs.
Aircraft or automobile companies can use predictive analytics to predict when certain mechanical parts might require maintenance or break down. The forecast can help them fix machines well before they break in the first place.
Similarly, in a healthcare setting, clinical operations can use predictive analytics to foresee when medical equipment, such as a CT scanning machine, might break or need maintenance.
In a healthcare setting, providers and clinical practices also often use predictive analytics to guess the probability of risk factors occurring in a medical procedure or event.
A hospital might use this form of AI to predict intake and prioritization needs in the event of an unforeseen circumstance, such as a new disease outbreak, for example. Predictive analytics would help the hospital better allocate and distribute its resources should an unlikely medical event arise. The ultimate goal is to reduce the likelihood of negative outcomes for the situation.
For example, a surgeon might employ predictive analytics to determine the unique risk factor for an individual patient undergoing the procedure. This would help the surgeon forecast how likely it is that this patient will experience complications.
As another example, the same patient's healthcare team could utilize predictive analytics to forecast how likely it is that this patient will be readmitted to the hospital if they are discharged 48 hours after the procedure. They could then compare this likelihood to different time frames for discharge in order to find the best option.
Predictive analytics relies on data, algorithms, and machine learning to forecast future outcomes. The data can be from the past, current, or a combination of present-day and historical data. AI uses this data to create statistical models and, from these models, analyzes and predicts outcomes.
While AI analyzes the data to come to predictions, predictive analytics still needs the help of people, namely data scientists. Data scientists can help define a problem or pose a question, such as "what will happen next in X scenario?" The scenario is often called a “use case,” and the goal is to apply the use case results as foreshadowing data to predict future events.
Data scientists help gather, organize, and input the use case data into a computer system before AI takes over. Once AI has the data, the first step is usually data mining, which means that machine learning algorithms sort broader information into more digestible datasets. Then, AI incorporates data science to create a statistical model and, through predictive modeling, analyzes the healthcare data in real-time to generate predictions.
The healthcare industry consistently invests more in AI each year, particularly as medical facilities become more reliant on technology for their operation. Predictive analytics is the most prevalent AI application in healthcare.
Predictive analytics is increasingly popular for healthcare providers and clinical companies because it can yield data-driven predictions that offer clarity and visibility around operational needs.
Predictive analytics can bring many different benefits, specifically to a healthcare setting, that center around clinical capacity, decision-making, operations, and patient and public needs, outcomes, and costs.
Predictive analytics can analyze past patient data with the same or similar medical conditions in order to predict outcomes for current or future patients. The data can include prior patient treatment outcomes and the most up-to-date medical research and study results.
From this data, AI can predict how a patient might respond to treatment, including factors such as their recovery time, their likelihood to experience complications, and their expected response to prescribed medications during their recovery process.
Predictive analytics can also help project how likely a patient is to be readmitted to the hospital after treatment for a certain condition or procedure.
AI can gather data from different methods of suturing or different ways to perform a surgical procedure to project the probability that a patient will develop an infection from a procedure.
Predictive analytics can help providers make an accurate diagnosis and can help optimize their decision-making skills when it comes to determining a proper treatment plan. For example, if a patient presents with certain symptoms, providers can input these symptoms into an online system.
The provider could then run predictive analytics to determine whether they should send the patient for emergency care or effectively manage the patient within their own scope of care.
Because predictive analytics can help improve individual patient outcomes, they have the longer-term potential to improve population health outcomes and public health as a whole. The larger the number of individual patient outcomes that improve, the higher the likelihood of an overarching improvement in a population’s outcome.
As such, by improving individual patient outcomes, this analytics tool can inherently improve disease patterns and healthcare outcomes for a larger demographic, potentially improving public health over time.
Using risk factors and other patient and family medical and personal history, predictive analytics can help providers identify early warning signs for chronic conditions. If providers can identify risk factors and early onset symptoms soon enough in a patient's disease progression, they can intervene more quickly to help better manage their condition.
Healthcare providers can use known data for a patient, including biological or genetic factors and socioeconomic risk factors, to predict whether their unique history makes them more prone to a certain condition or disease.
Predictive analytics can compare the data for an individual patient against data from many different patients with many different risk factors to determine individual risk factors for a patient and a specific medical condition.
With information such as staff pacing and abilities, as well as projected growth for a certain medical field and necessary medical equipment, this AI form can use data analytics to help a clinical company figure out what their potential medical costs may be in the future.
The predictions can be even more accurate when data scientists pull information from other clinical practices, as well as from insurance companies’ databases. Sometimes, hospitals or clinical practices collectively share their data, working with insurance providers to create a larger database whose predictive analytics can yield a broader, more encompassing prediction when it comes to healthcare costs.
Predictive analytics is a type of artificial intelligence that analyzes data to predict future outcomes. Healthcare settings are increasingly utilizing this type of artificial intelligence to improve patient outcomes. With this type of AI, predictions have the opportunity for a wide scope, such as how a patient might respond to treatment, what their recovery period might look like, and how likely they are to experience complications in their procedure, medications, or discharge and recovery time.
In addition to improving patient health outcomes, using predictive analytics in healthcare has many benefits, such as improving provider decision-making, aiding with chronic disease management, and effectively identifying at-risk patients. If you think your patients are at high risk for heart or lung disease, Carda can help.
We offer at-home Gold Standard cardiac care so that your patients can experience the treatment they need right from home. With no transportation costs and on-demand appointments that fit a patient's schedule, Carda is cheaper and more effective than in-person cardiac rehab. We also offer the only home program with live monitoring of vital signs by exercise physiologists overseen by a cardiologist.
Plus, we partner with health systems so your patients can experience empathetic and interactive rehab at home — without dealing with long waitlists or a scalable staffing model. Get started with Carda today, to support patient engagement and offer them a personalized, comfortable, and private rehabilitation journey.
Sources:
Leveraging Big Data Analytics to Reduce Healthcare Costs | IEEE Journals & Magazine
Exploring the path to big data analytics success in healthcare | ScienceDirect
