By Zac Amos, Features Editor, ReHack
LinkedIn:Â Zachary Amos
LinkedIn:Â ReHack Magazine
Preventing unnecessary hospital readmissions is critical for improving patient outcomes and controlling healthcare costs. Each readmission adds to healthcare costs and disrupts a patient’s recovery, impacting quality of life and increasing risk.
Here are some ways predictive analytics can help reduce hospital readmissions, as well as practical ways healthcare professionals and providers can harness its potential.
Understanding Predictive Analytics in Healthcare
Predictive analytics uses historical and real-time data to anticipate future events or outcomes. In healthcare, it allows professionals to analyze patient data and determine the likelihood of certain conditions or events, such as readmissions.
By leveraging advanced machine learning models, predictive analytics can uncover patterns that indicate which patients may be at higher risk of readmission. These insights allow providers to act early and prevent readmissions wherever possible.
Why Reducing Readmissions Matters
Hospital readmissions are costly and can signify gaps in patient care, such as inadequate discharge planning, poor follow-up or lack of adherence to prescribed care.
Approximately 20% of Medicare patients are readmitted within 30 days of discharge, and unplanned readmissions cost the U.S. healthcare system $15 billion to $20 billion annually. Avoidable readmissions have, therefore, become a priority for healthcare providers aiming to improve care quality and manage operational costs effectively.
Key Applications of Predictive Analytics in Reducing Readmissions
Predictive analytics offers healthcare providers powerful tools to reduce readmission risks proactively. Here are the key ways it’s being applied to address this critical challenge:
Enhancing Discharge Planning
Effective discharge planning is essential to preventing readmissions. Predictive analytics can optimize discharge processes by assessing each patient’s unique needs. In fact, comprehensive discharge planning reduced readmission rates from 23% to 10%.
For example, if a predictive model indicates a high likelihood of readmission due to medication non-adherence, providers can arrange a follow-up with a pharmacist or offer medication management resources.
Identifying High-Risk Patients
Predictive models can analyze various data points, including demographic information, medical history, lab results and previous hospitalizations, to assess a patient’s readmission risk.
Improving Post-Discharge Follow-Up
Predictive analytics also informs providers on when and how to conduct post-discharge follow-up. Some models suggest optimal follow-up times based on each patient’s readmission risk, helping providers schedule timely appointments.
For instance, a patient discharged after heart surgery may benefit from closer follow-up within the first two weeks, while another patient might need a follow-up at a different interval. By customizing follow-up schedules, hospitals can ensure that high-risk patients receive the appropriate care when they need it most.
Steps for Implementing Predictive Analytics in Readmission Reduction
Effectively implementing predictive analytics requires a strategic approach. Here’s a step-by-step guide to getting started and making the most of these tools:
Data Collection and Integration
The first step in implementing predictive analytics is collecting data from diverse sources, including electronic health records (EHRs), medical imaging and even patient-reported information. Healthcare providers need a centralized data repository that integrates data from these sources, ensuring a comprehensive view of each patient’s health profile.
Additionally, many hospitals are now exploring ways to incorporate social determinants of health (SDOH) — such as education level and socioeconomic status — which have been shown to impact readmission rates significantly.
Model Development and Validation
Healthcare IT teams can work with data scientists to develop predictive models specifically tailored to their patient populations. These models must be validated rigorously, often using a test set of patient data, to ensure they accurately predict readmissions.
Models should be reviewed regularly and updated based on new data, especially as patient demographics and healthcare needs evolve.
Real-Time Risk Scoring and Alerts
Many hospitals are implementing real-time risk-scoring systems to make predictive analytics actionable. These systems provide alerts within EHRs, enabling clinicians to see each patient’s readmission risk at the point of care. For example, a high-risk score may prompt the healthcare provider to conduct additional assessments or coordinate with other specialists.
Tracking Success and Refining Predictive Models
Implementing predictive analytics is a continuous process. Healthcare IT professionals need to monitor outcomes and adjust models based on results. Metrics such as readmission rates, patient satisfaction and cost savings can help gauge the success of predictive models.
Additionally, feedback from clinicians who interact with these tools daily can provide insights on areas for improvement, ensuring that predictive analytics continues to meet the evolving needs of healthcare providers and patients.
Leveraging Predictive Analytics for Sustainable Change
Predictive analytics is transforming patient care by enabling proactive measures to reduce readmissions. Healthcare providers can make significant strides in lowering readmission rates by accurately identifying high-risk patients, enhancing discharge processes and facilitating timely follow-up care.
The effective integration of predictive analytics into care workflows promises cost savings and improvements in patient satisfaction and outcomes. As the healthcare sector continues to adopt and refine predictive analytics, its potential to deliver sustainable change will only grow.