How can UM use predictive analytics to identify and manage high-risk patients and reduce readmissions?

1 What is UM and why is it important?

UM is the process of evaluating and monitoring the appropriateness, efficiency, and quality of health care services, procedures, and facilities. It aims to ensure that patients receive the right care at the right time and place, while avoiding unnecessary or excessive interventions. UM is important for health care organizations because it can help them improve patient satisfaction, comply with regulatory standards, and control costs.

2 What are predictive analytics and how do they work?

Predictive analytics are a type of data analytics that use historical and current data, along with statistical and machine learning techniques, to forecast future events or behaviors. They can help health care organizations identify patterns, trends, and risks, and provide actionable insights for decision making. Predictive analytics work by collecting and processing large amounts of data from various sources, such as electronic health records, claims, lab results, surveys, and social media. Then, they apply algorithms and models to analyze the data and generate predictions, such as the likelihood of a patient developing a certain condition, or the impact of a treatment on their health.

4 How can UM use predictive analytics to manage high-risk patients?

Another application of predictive analytics in UM is to manage high-risk patients and optimize their care pathways. By using predictive analytics, UM can monitor and evaluate the effectiveness and efficiency of the services and procedures that high-risk patients receive, and compare them with evidence-based guidelines and best practices. This can help UM identify gaps, variations, or deviations in care delivery, and suggest appropriate actions or recommendations to improve quality and outcomes. For example, predictive analytics can help UM determine the optimal length of stay, level of care, discharge plan, or follow-up care for high-risk patients.

5 How can UM use predictive analytics to reduce readmissions?

Reducing readmissions is one of the major goals and challenges for health care organizations, as they can indicate poor quality of care, patient dissatisfaction, and increased costs. Predictive analytics can help UM reduce readmissions by identifying the factors that contribute to them, such as clinical conditions, medication errors, patient behavior, or social support. By using predictive analytics, UM can estimate the probability of a patient being readmitted within a certain period, such as 30 days or 90 days, and provide alerts or notifications to the care team. This can help UM implement preventive or proactive measures to reduce readmissions, such as transitional care, medication reconciliation, home health care, or telehealth.