How do you use data analytics to identify high-risk patients for readmission?

2 How to collect data for readmission risk assessment?

To identify high-risk patients for readmission, data analytics should be used to collect relevant data from multiple sources, such as electronic health records, claims data, patient-generated information, and social determinants of health. Various methods can be employed to collect the data, such as surveys, interviews, focus groups, sensors, wearables, or mobile apps. It is also important to ensure that the data are accurate, complete, timely, and secure.

3 How to analyze data for readmission risk prediction?

To use data analytics to predict readmission risk, you need to apply statistical or machine learning techniques to the data. These techniques can help you identify patterns, trends, correlations, and causal relationships among the data. Examples include regression analysis, which measures the relationship between a dependent variable and one or more independent variables; classification analysis, which assigns a label to a data point based on a set of criteria or rules; clustering analysis, which groups data points based on their similarity or proximity; and association analysis, which identifies frequent or co-occurring items or events in the data. You can use various tools or software to perform data analysis, such as Excel, SPSS, SAS, R, Python, or Tableau. It's important to validate and test your analysis results and compare them with existing evidence or benchmarks.

5 How to improve data analytics for readmission risk management?

To use data analytics to improve your readmission risk management, you need to continuously monitor and update your data and analysis methods. It is also beneficial to learn from best practices and evidence-based guidelines on readmission risk assessment and intervention, as well as participating in training and education programs on data analytics tools and techniques. Collaborating with data experts and analysts can provide guidance and support, while sharing your data insights and experiences with other case managers and stakeholders can offer feedback and suggestions. By using data analytics, you can identify high-risk patients for readmission and intervene on their risk factors, thereby enhancing your clinical judgment, decision-making, and problem-solving skills. Doing so will help you optimize your case management practice, improve patient care, and deliver more effective and efficient care.