4 Ways Predictive Analytics in Healthcare is Improving Patient Outcomes
With increasing expectations in healthcare delivery, there is a demand for enhanced diagnoses and accelerated care. This need is leading many to explore the benefits of predictive analytics.
Predictive analytics in healthcare uses data modeling, process automation, and artificial intelligence to quickly assess and analyze large amounts of data. When compared to traditional concurrent care management, effective application of predictive analytics in healthcare results in improved patient outcomes at lower costs predicted upon making informed, accurate, and timely clinical decisions. Here, we dive into four specific improvements to patient care as a direct reflection of predictive analytics.
1. Puts Patients on the Path Toward Homeostasis
Uncovering time and cost savings in healthcare can be achieved by applying data modeling to patient risk scores combined with medication adherence/compliance. Although there are many proven outcomes derived by improving medication adherence, predicting the future cost of medications results in an increased ability to deploy proactive solutions affecting patient compliance and overall health.
Drug pricing is often perceived as mysterious as it can oscillate somewhat erratically. However, by applying appropriate data models benefits managers can predict the increases in cost and the resulting reduction in utilization or deterioration of health due to reduced compliance when costs go up. By being able to more easily identify the effects of cost, benefits managers and their providers can steer patients in alternative directions before non-adherence occurs.
Additionally, an increase in accessible data helps providers compare results to a wider pool and identify patterns, trends, and other correlations that could provide insight on how to properly manage patient medication therapy.
2. Gives an Indication of Future Costs
One of the best predictors of medical costs is pharmacy claims as medications leave obvious clues about the treatment and overall health of patients, along with future care and cost. For example; someone picking up medication intended for the treatment of diabetes for the first time is likely an indication that they are a new diabetic patient. This not only points to future pharmacy costs but also predicts long-term medical costs that will eventually be associated with that patient. This means that although it will take 30+ days for the medical claim to process, benefits managers can prepare to change how the patient is managed.
Because pharmacy happens immediately, data is accruing immediately, in turn, giving benefits managers the ability to implement changes in care without waiting for medical claims to process weeks/months in arrears.
These types of scenarios point to solutions like Xevant, where making the transition in care faster and more accurately will result in lower overall medical and pharmacy costs and improved patient outcomes. With Xevant’s near real-time data analytics, future medical costs for a patient can be easily identified allowing for earlier action and adjustment resulting in more immediate savings opportunities and health improvement.
3. Increased Efficiency for Operational Management
According to a recent article published by Deloitte, data analytics can provide “real-time patient admittance rates to determine ebb and flow, while also providing a capability to evaluate and analyze staff performance in real-time.”
This can draw attention to anomalies, and evaluate care quality while simultaneously working to improve it. Some issues, such as drug supply chain issues, surges in hospitals resulting in overcrowding, and a lack of resources, can be identified and analyzed in order to prevent the issue that led to the surge in the first place.
Data can also help identify where staff members are most needed, and whether seasonal adjustments should be put in place. These changes aided by data can improve the overall patient experience, whether they come to a hospital for a diagnosis or a longer treatment.
4. Allows for Cohort Treatment
Health records and performance requirement testing provides large sets of data to hospitals that gives them insights into the health of not only patients but the community as a whole.
The results obtained from predictive analytics can be used in health initiatives aimed at those already at risk, such as anti-smoking campaigns or messages on how to prevent and deal with obesity.
This data can also be utilized in the pharmaceutical sector. By identifying clusters of disorders, diseases, or specific conditions, data can help ensure that demand for medication will be met and eliminate shortages.
Step Into the Future With Xevant
With the ability to identify patterns, predictive analytics in healthcare is changing the future of the health sector. Individuals, larger populations, and the organization of the healthcare industry can benefit from data and improve patient care. Providers can also benefit from real-time analytics, as they can receive feedback and information right away giving them the knowledge to do their job with higher efficiency.
Xevant is the key to enhancing patient care through data. With nearly real-time data analytics and daily snapshots, changes and decisions can be made faster in order to increase both the quality of care for patients and the quality of service from providers.