How to Leverage AI for Predictive Analytics and Smarter Healthcare
- AI uses predictive analytics to take your healthcare business to the next level.
- Historical data predicts future outcomes.
- Using AI can reduce readmission rates.
How to Leverage AI for Predictive Analytics and Smarter Healthcare
When you’re in a leadership position in the healthcare industry, you always want to make the right moves. From scheduling enough workers to ensuring that you have all the needed supplies on hand, you feel like you need to be able to see the future to prepare for it. In some ways, this is the case. When you leverage the skills of artificial intelligence (AI) for predictive analytics, you can make better choices in a range of areas for your healthcare practice, making you more effective for patients and staff while maximizing profits.
Beyond Reporting—How AI Turns Historical Data into Future Insight.
AI is better able to see patterns in the data than a person or a more simplistic algorithm. It can look at historical data, identity patterns, and determine how these patterns will continue into the future. It might be the number of patients or types of cases you might see.
Clinical Predictive Analytics:
Patient Risk Stratification AI focuses on a patient in a new way. It looks at the patient’s past and current medical conditions to determine the best course of action with the given information and possible outcomes. AI is able to process more information and possibilities than a doctor can on their own. Check out these case studies:
Case 1: Early Sepsis Detection
Sepsis is life-threatening, and early detection can make a real difference in the overall outcome. AI can monitor real-time EHR data. This includes vitals, lab results, and nursing notes. AI can use this information to intervene hours before the doctor starts to see the clinic deterioration.
Case 2: Reducing Readmission Rates
You want to keep your readmission rates low, and AI can help. It looks at models of social determinants of health (SDoH) and post-discharge support and analyzes them to see a pattern of readmissions. The model can help patients who might struggle receive more support after leaving your facility and avoid readmissions. This can minimize CMS penalties.
Operational Predictive Analytics: Optimizing the Business of Care
AI can also be used for operational predictive analytics to see how the past has affected the current care and predict where your healthcare organization might be in a year or two. Review these cases to learn more:
Case 3: Forecasting Patient Flow.
AI can take advantage of your hospital’s operational analytics to predict ED surges. This can be based on seasonality, local health trends, and historical volume. You can use these forecasts for dynamic staffing models that reduce burnout among employees and minimize wait times for patients.
Case 4: Revenue Cycle Management (RCM)
You want the insurance companies to pay the claim the first time your facility submits the paperwork. AI can predict claim denials by identifying coding anomalies. When you use this to your advantage, you get a “clean claim” rate and liquid cash flow.
The Foundation: Why Clean Data and Strong Governance Are Essential
If there are problems with the data you use, there will be issues with the information you get from it. This is the theory of “Garbage In, Garbage Out.” Your AI model is only as accurate as the information you feed it. You should start by breaking down data silos. Data integrity ensures the accuracy and a lack of bias in all of your information. You can’t have a black box approach in a regulated environment. A black box approach ensures that you know the AI input and output, but you don’t have access to the internal logic. The approach lacks transparency for governance.
Conclusion: Solving Tomorrow’s Problems Today
If you want to be a leader instead of a follower, you should be an early adopter of predictive analytics in the healthcare industry. HPG wants to partner with you to help you take AI from theory to practice. We can help you effectively manage your predictive models that require extensive security measures to protect them. Contact us now to get started. Learn how to manage the risks and maintain HIPAA compliance in our comprehensive guide.
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