The Society of Actuaries surveyed in 2019, in which 60% of the health executives mentioned their organization having adopted predictive analytics. Out of this percentage, 42% have witnessed improved patient satisfaction using predictive analytics, and 39% have saved costs. The point to be noted here is that much of the medicine relates to predicting and mitigating risk based on historical and current patient data. The most significant advantage of predictive analytics in healthcare that has come to the fore is that clinicians can make decisions with absolute certainty.
What is Predictive Analytics in Healthcare?
Enhancing patient care, improving health outcomes, and decreasing costs by predicting when, where, and how care must be provided. This is how predictive analytics aids health organizations. Simply put, predictive analytics in healthcare alerts caregivers and clinicians of the probability of events and results, thus preventing and curing health issues beforehand. Driven by the increasing use of the Internet of Things (IoT) and Artificial Intelligence (AI), algorithms are fed with historical and real-time data to make meaningful predictions. Clinicians utilize these predictive algorithms to support clinical decision-making for their patients and inform treatments on a population or cohort level. Predictive analytics in healthcare even helps to meet administrative and operational challenges.
Examples of Predictive Analytics in Healthcare
Detecting Early Signs of Patient Deterioration
A predictive analytics tool at the University of Pennsylvania leveraged EHR data and machine learning. Doing so helped to detect patients on track for acute sepsis or septic shock 12 hours before such a condition took place. Huntsville Hospital, Alabama, took another initiative. This project found that integrating clinical decision support (CDS) tools and predictive analytics could mitigate sepsis mortality by more than half. Therefore, predictive algorithms can assist medical practitioners in recognizing patients with the highest probability of an intervention need in the next one hour. Caregivers can proactively interfere early after noticing subtle signs of deterioration in their patient’s condition. Predictive analytics in healthcare helps to determine the possibility that patients risk death or readmission within two days when discharged from the ICU. Caregivers can, hence, decide on the patients who can be discharged. With the further adoption of wearable biosensors, care providers could easily detect early signs of patient deterioration. These biosensors adhere cautiously to the patient’s chest to gather, store, measure, and transmit heart and respiratory rates every minute and note contextual parameters, like activity level, posture, and ambulation.
Predicting Patient Utilization Patterns
Predictive analytics tools help to inform healthcare providers of the timings when the clinic may get busy. Clinics use data analytics solutions to forecast patient utilization patterns, guarantee optimal staffing levels, decrease wait times, and increase patient satisfaction. Predictive data analytics strategies and visualization tools can model patient flow patterns and emphasize opportunities for scheduling changes or to make workflow adjustments. At Wake Forest Baptist Health, North Carolina, predictive analytics in healthcare assisted the oncology infusion center in forecasting peak utilization times to adjust its scheduling practices. Analyzing typical patient utilization rates, the infusion center figured out that popular mid-day appointment times created unsustainable highs in capacity, while the late afternoon and morning spots went unfilled. As the medical center changed specific scheduling procedures, the burden on nurses lowered, and patient satisfaction greatly improved.
Developing Precision Medicine
In today’s times, precision medicine and genomics are emerging strong. Therefore, researchers and healthcare providers resort to predictive analytics techniques to complement drug discovery techniques and clinical trials. The Center for Drug Evaluation and Research (CDER) by the U.S. Food and Drug Administration (FDA) utilizes simulation and modeling to forecast clinical outcomes. It also aims to inform clinical trial designs, optimize dosing, support evidence related to dose effectiveness, analyze potential adverse event mechanisms, and anticipate product safety. As we march towards an era of drug individualization, modeling and simulation, while including facets of individual physiology and genetics in drug-metabolizing enzymes, helps detect patient subgroups in need of dose adjustments. Therefore, clinical decision support tools and predictive analytics play instrumental roles in translating new drugs into precision therapies. Besides, CDS systems can estimate a patient’s response to a specific course of treatment. They do so by matching genetic information with earlier patient cohorts’ results. Healthcare providers can now choose the specific therapy with the highest likelihood of success.
The Future of Predictive Analytics in Healthcare
Predictive analytics has a solid and healthy position in the future of healthcare delivery. In the future, healthcare providers who embrace data and contemplate their technological investments would offer the best care to their patients while optimizing their operational costs. Compunnel Digital’s expertise and experience powering healthcare technology across organizations around the globe has helped strengthen processes and functions while empowering them to develop new products that aim to deliver improved services and care. Connect with us for a technology partnership to further improve the health of your business and clients.