Imagine a world where healthcare organizations can accurately predict a patient’s health before symptoms even emerge. That world is no longer a fantasy, thanks to the power of predictive analytics. Predictive analytics in healthcare refers to the use of data and advanced statistical techniques to identify future outcomes based on past data. In healthcare, it’s changing the game by providing valuable insights into patient health and allowing providers to make data-driven decisions that can improve patient outcomes.
What is predictive analytics in healthcare, and how can it be used?
Predictive analytics is an emerging technology that could revolutionize healthcare. It involves collecting and analyzing patient data to observe trends, make predictions, optimize patient experience, and manage care. By combining machine learning techniques with patient records to spot patterns or anomalies in the data, predictive analytics could be used to anticipate medical issues of individual patients before they arise. This would enable healthcare organizations to better adjust treatment and improve patient outcomes by anticipating problems rather than simply reacting to them when a crisis occurs. Additionally, predictive analytics can track changes in cost between treatment options, helping hospitals and insurance companies manage their budgets more effectively while providing quality health care. Overall, predictive analytics can be a powerful tool if used properly within the healthcare setting.
Case Examples
One example of the use of predictive analytics in healthcare is in the identification of patients at risk of readmission. By analyzing data such as medical history, vital signs, demographics and Social Determinants, healthcare organizations can identify patients at high risk of being readmitted to the hospital. This information can then be used to prioritize high-risk patients for interventions such as follow-up care, medication adjustments, and lifestyle counseling. By proactively managing these high-risk patients, healthcare organizations can reduce the likelihood of readmission and improve patient outcomes.
Another example of the use of predictive analytics in healthcare is in the optimization of hospital staffing levels. By analyzing historical data on patient admissions, healthcare organizations can predict the number of patients likely to be admitted on a monthly/quarterly basis. This information can then be used to optimize staffing levels to ensure enough healthcare professionals are available to meet patient demand. By using predictive analytics in this way, hospitals can reduce wait times, improve patient satisfaction, and increase operational efficiency.
Finally, predictive analytics can optimize cost and treatment plans for individual patients. For example, a healthcare provider can predict which treatment plans are most likely to be effective for a patient by considering a patient’s medical history, symptoms, and genetic markers. This information can then be used to personalize treatment plans and improve patient outcomes.
Advantages of predictive analytics in healthcare
Predictive analytics in healthcare is rapidly transforming the way patient care is managed. By leveraging data insights and incorporating machine learning algorithms, the healthcare system can more accurately identify potential risks, discover new treatment methodologies, and provide individualized patient care. This allows healthcare organizations to focus on preventing medical issues or illnesses before they occur, significantly improving patient experience. Additionally, this type of predictive analysis can allow for additional targeted treatments or interventions, resulting in improved patient outcomes and quality of life. Data gleaned from predictive analytics can also help healthcare organizations make better long-term decisions about allocating resources to improve overall patient satisfaction and enhance efficiency among caregivers.
Further, predictive analytics has become integral in population health management by predicting the spread of disease; as a result, resources can be allocated quickly and efficiently to combat outbreaks.
Challenges of Predictive analytics in healthcare
Implementing predictive analytics in healthcare can be a powerful tool to improve patient care. However, it’s important to understand that several challenges must be addressed before successful implementation. These challenges range from patient data privacy, data storage capabilities, and staying abreast of constantly evolving regulations. Additionally, healthcare organizations must have the right technology and infrastructure to collect, analyze and deploy the information provided by predictive analytics appropriately. Lastly, predictive analytics must be leveraged correctly within the larger organizational framework emphasizing patient health and experience, care management and cost-effectiveness. In doing this, the promise generated by predictive analytics of enhanced patient care and well-being is more likely to be fulfilled.
The future of predictive analytics in healthcare
Predictive analytics is rapidly transforming healthcare and becoming a major game-changer. By leveraging data to create actionable insights, predictive analytics enables healthcare organizations to make proactive decisions to improve patient health, enhance the overall patient experience and support effective care management. Healthcare organizations can now use predictive analytics to forecast potential emergency admission events and recommend interventions before they occur. It has also allowed for improved diagnosis accuracy and streamlined patient management. It is evident from this that predictive analytics will be a cornerstone of the future of healthcare, enabling improvements in patient outcomes, cost efficiency, and overall quality of care.
How HealthEM.AI uses predictive analytics in healthcare to predict hospital admissions and readmissions
A Florida-based primary care group wanted to improve patient engagement by creating 360-degree patient profiles and optimizing care management workflows. As a result, they partnered with HealthEM.AI to standardize and improve care and reduce costs and disease progression through risk stratification.
HealthEM.AI helped the client create a single trusted patient data source that included claims, eligibility, clinical and social determinants of health (SDOH), and electronic medical records (EMR). The platform enabled the client to assimilate and normalize historical patient data. By combining this data with social determinants of health, we created an AI/ML model that predicts future healthcare costs, hospital admissions, and emergency room visits based on this data. In addition, a 360-degree member record enabled care managers to identify members at the highest risk, both clinically and non-clinically. With this data, the client could determine which patients were at risk, the reason, and the required mitigation steps for immediate interventions.
Outcomes/impact:
- 28% reduction in hospital admissions, an enhanced assessment of high-risk patients
- 1,500 insights generated from patient health data, cost data, and social attributes
- 90.2% accurate and 51.7% precise readmission prediction for the next 12 months
- 75% change in patients making up the highest risk band compared to the baseline
