How Predictive Analytics Improves Population Health and Reduces Costs for Providers

As the healthcare industry emerges from the pandemic, providers continue to focus on ways to provide care more efficiently. Staffing shortages and ballooning costs have placed hospital operations under a microscope. Organizations face increased pressure to better identify at-risk populations and reduce admissions and procedures that could be preventable with effective analysis.

Tthe medical profession is built on data. While the industry has advanced its approach to analyzing patient data, the next steps offer possibilities to impact population health. Through predictive analytics, your hospital or clinic can gain a clearer picture of the trends impacting your patient population.

Whether your organization services Medicare or Medicaid patients or operates commercially, you can apply your data to understand more than a patient’s condition. You can identify associated risk factors faster to improve health outcomes while also lowering expenses.

Population Health Analytics Starts with Data Quality

The advent of electronic medical records (EMR) ushered in a new era of analytics in patient care. Text mining and natural language processing (NLP) technologies have opened additional possibilities for incorporating physician’s notes and capturing new insights into medical data.

But even with both of these technologies, your organization needs to resolve what’s in your EMR system with your claims data. Often, fields won’t match between systems. Or, a member ID dedicated to a patient by one provider won’t match the name in the EMR record.

To take full advantage of population health analytics, you need to ensure all your data matches with the same person before creating the right data model. Ultimately, even the most advanced analytics strategy is only as good as its data. If your database is rife with errors, duplications, and inconsistencies, you won’t produce useful results.

Predictive Analytics in Healthcare Demands Collaboration

With a data model in place, your organization can leverage the right analytics tools to create the insights and action plans to improve population health. But before implementation, you need to bring your teams together to collaborate on developing the correct next steps.

Whether your organization has a data team or works with an outside partner, your data scientists should meet with your clinicians. Together, they can interpret the results from your model and plan how the findings should be leveraged.

From that point, you can create action plans to follow up on the findings and implement changes to your processes. But crafting a united message from data scientists and clinicians is critical to gaining buy-in from your CFO and other stakeholders.

Fundamentally, your leadership team needs to have a thirst for analytics-driven changes to your operations. If the people at the top don’t recognize the value of data and its capacity to improve decision-making in healthcare, any analytics project will fall short of its goals.

3 Ways Predictive Analytics Impacts Population Health

Healthcare continues to evolve toward a quality-of-care reimbursement model over its traditional, fee-for-service structure. Consequently, population health management is an increased priority. Organizations like yours need to identify patient risk factors faster while delivering care that’s more proactive and cost-effective.

When you work with an IT partner like Zencos, you gain access to our proprietary healthcare data model. From there, we work with our software partner SAS to apply predictive analytics to your data. Once implemented, your organization can improve population health in the following three ways:

1. Preventing Hospital Readmissions

Predictive analytics provides greater visibility into the risk factors impacting your patient population. By identifying patients at high risk of readmission after discharge from the hospital, your organization can intervene earlier and provide more targeted care.

For example, the Veterans Health Administration found that access to care, social support, and substance abuse contributed to patient readmissions. Analyzing these social and environmental factors among patients enables your organization to set up care strategies to offset these risks.

2. Identifying High-risk Patients

Chronic diseases such as diabetes, heart disease, and cancer often result in long-term care and emergency room visits among patients. Predictive models enable your organization to identify patients who are at high risk of developing such diseases and provide targeted interventions.

Developing plans for treatment and prevention provide a way to improve outcomes among the most vulnerable populations in your community. Plus, they minimize ultimately unnecessary ER visits, which both improves your ability to deliver emergency care to those in need and reduces your operating costs.

3. Improving Care Coordination

Predictive models enable your organization to identify patients in need of additional services such as home health care, physical therapy, and occupational therapy. Recognizing at-risk patients sooner enables providers to provide more targeted care and further prevent readmissions or emergency room visits.

Predictive Analytics Enables Proactive Care and Reduced Costs

Implementing predictive analytics enables providers like you to better identify the trends and risk factors that impact your community. Along with clearing a path toward a more proactive approach to care, it improves your organization’s ability to better understand your operations and effectively allocate resources.

Establishing the foundation to support these gains is complex, but you don’t have to attempt to build it alone. At Zencos, we specialize in helping organizations like yours streamline operations through their data. If predictive analytics sounds like a way to gain greater visibility into the needs of your patients, we should talk.