Predicting the future is impossible. Otherwise, we wouldn’t need meteorologists, and more people would win the lottery.
There are tools, however, that can be utilized to help forecast trends — as long as enough accurate and reliable data is available. One such resource is predictive analytics, which employs customized models to help businesses determine historical patterns and predict future outcomes or trends. That’s why it’s so valuable in healthcare, an industry in which better outcomes can literally be life-changing.
Through a variety of techniques, including data mining, artificial intelligence and machine learning, predictive analytics aggregates vast amounts of real-time operational, financial and clinical data and transforms it into actionable insights. Hospitals, health systems, physician groups then use those insights to reduce operational costs, increase compliance, make more objective decisions and gain improvements in patient care and outcomes, population health management and clinical quality.
Predictive analytics is especially helpful for healthcare providers striving to increase their value-based reimbursement. Why? Because it aids in predicting the likelihood of their patients developing certain medical conditions and identifying warning signs before conditions become severe.
Advantages of Predictive Analytics
Healthcare provider organizations of all sizes are increasingly inundated with large amounts of data. Using predictive analytics gives them a cost-effective method to harness that data to enhance care coordination, improve care planning and management, increase patient safety and reduce risks. Other benefits of predictive analytics for the healthcare industry consist of:
- Improving efficiencies for operational management of health
- Delivering patient-centric care
- Enhancing cohort treatment
- Tailoring treatment to individual patient needs
- Increasing patient engagement and outreach
- Decreasing no-shows
Predictive Analytics in Action
The use of predictive analytics might sound too futuristic or complex, but it can be applied to numerous areas within healthcare, from optimizing staffing all the way to conducting population health management. And, as more and more healthcare provider organizations apply it to their operational and clinical strategies, there will likely be even more use cases that achieve advantages for both providers and patients.
Patient Flow and Staffing
The widespread healthcare staffing shortage in the United States was only compounded by the COVID-19 pandemic. As we mentioned in a previous blog, nearly 30 percent of healthcare workers are considering leaving their profession altogether.
With this ongoing staffing shortage and labor costs typically accounting for more than half of hospital expenses, healthcare provider organizations must find ways to maintain a high level of patient care without diminishing financial sustainability. Employing predictive analytics for staffing benefits them by enabling them to anticipate staffing needs while reducing overtime costs.
More advanced predictive analytics solutions for staffing integrate data from multiple sources and use that data to gain actionable insight into patient wait times, demographics, nurse-to-patient ratios, trends and patterns and more. Such tools provide healthcare provider organizations with the capability to forecast patient flow over multiple time spans and produce staffing recommendations based on that data.
Other perks of utilizing predictive analytics for staffing include:
- Improved ability to make real-time adjustments to staffing
- Increased patient and staff satisfaction
- Quicker notification of changes in patient wait times and other staffing KPIs
- Enhanced scheduling processes
- Improved coverage and quality of patient care
- Enhanced capability to staff appropriate clinicians
- Improved capacity setting and staff recruitment and retention
Chronic Disease Management
Approximately 60 percent of adults in the U.S. have a chronic disease. More than 75 percent of all healthcare costs are due to chronic conditions.
By employing predictive analytics, healthcare provider organizations can better identify patients with a high risk of chronic disease, resulting in the availability of more proactive intervention and treatment. They also have the ability to help to identify patients who are at risk for complications or relapse and provide interventions before problems occur. Both are essential to chronic disease management.
Incorporating social determinants of health (SDOH) into predictive analytics offers even more benefits. Healthcare providers can recommend treatment options that consider a patient’s socioeconomic and environmental factors, which not only improves the chance of a better outcome but also reduces the overall cost of care for that individual.
Population Health Management
As we’ve discussed in previous blogs, population health programs are designed to improve clinical metrics for specific groups of patients. The goal of population health management (PHM) is to proactively tackle health disparities and keep patients healthy outside their care visits to minimize costly interventions, including emergency department visits, hospitalizations, imaging tests and surgery.
PHM enables healthcare providers to use data for predictive analytics to offer insight into the health of specific populations, such as those with those priority conditions, yield actionable information and develop a care plan accordingly. From the insights gained through predictive analytics, the provider can identify a certain population of people with a chronic health condition and determine a more streamlined care plan to reduce the likelihood of a relapse.
Hospital Readmissions
Hospital readmissions are costly, often doubling the cost of care. In fiscal year 2022, only 17.81 percent of hospitals did not have a readmissions penalty.
Predictive analytics can be utilized to assess the probability a readmission will occur along with how often and when it might occur. It also aids healthcare provider organizations in forecasting likely reasons for readmissions. It’s especially helpful for healthcare organizations striving to increase their value-based reimbursement.
Predictive analytics also enables healthcare organizations to reduce hospital admissions by:
- Determining which patients are most likely to experience issues post-discharge
- Accurately addressing length of stay (LOS) management and hospital-acquired infections (HAIs)
- Triaging workflow across multiple areas of operations
- Reducing complications from a variety of services
- Integrating multiple data sources for analyzation of complete medical records
- Assisting providers in meeting various CMS goals to eliminate penalties
- Enhancing clinical records
- Intervening on clinical issues in at-risk populations
The Providertech Platform for Predictive Analytics
Although the benefits to utilizing predictive analytics in healthcare are many, there are some challenges to it. The biggest obstacles to using and implementing them include incomplete data, lack of sufficient technology, processes/infrastructure and/or skilled employees, too much data, a limited budget and regulatory issues.
At Providertech, our predictive analytics platform enables you to perform a variety of functions, including identifying gaps in care, predicting patient no-shows and alerting your staff to patient experience issues. Plus, our self-service dashboard aids you in ensuring HIPAA compliance and analyzing and managing your patient outreach efforts.
Read more about predictive analytics for healthcare here.
