Using Predictive Analytics for Hospital Acquired Condition Prevention

Hospital-acquired condition prevention is essential to avoiding infections that cause serious problems for patients. Healthcare providers must adopt a zero tolerance for hospital-acquired conditions, which can result in patients spending a longer time in the hospital and increase the risk of more serious complications or even death. One of the best ways to reduce readmissions and ensure hospital-acquired condition prevention is through patient-level predictive analytics. Predictive analytics can help healthcare providers identify patient disease cohorts and pin point individual patients at risk of target illnesses. Solutions from healthcare technology providers like Jvion, are designed to predict and prevent hospital acquired conditions to reduce patient suffering and achieve better health outcomes.

The Significance of Predictive Analytics - Use Cases

Predictive analytics can play an integral role in septicicemia prevention and pressure ulcer prevention. Volumes of patient data can be searched to identify high-risk patients so that timely and effective interventions can be applied to prevent disease. Solutions offered by organizations like Jvion deliver predictive analytics that include risk stratification, the simulation of what-if-scenarios, and risk mapping. With the Centers for Medicare & Medicaid penalizing hospitals that have high hospital-acquired condition rates, it is essential for providers to implement solutions that promote disease intervention and ensure sepsis prevention along with other hospital acquired conditions. 

Healthcare Predictive Analytics and Risk Stratification

The healthcare industry continues to adopt a more proactive approach toward patient engagement through a continuum care model that focuses on the delivery of patient-centered medicine. Healthcare predictive analytics play an important role in preparing providers for this new model. Many organizations that lack the resources to implement clinical analytics can utilize solutions that lead to better risk-stratified care management. Risk stratification is a relatively new term for what physicians have been doing for years: identifying high-risk patients and making sure they get what they need when they need it.

The Importance of Risk-Stratification

Healthcare predictive analytics involves identifying patients at risk of developing a target illness or condition to enable the most effective interventions. This includes factors such as level of risk, criteria, and limitations. A risk score can be assigned to each patient, which can be recorded in EHR or electronic health record system or database. For the most part, evidence-based metrics healthcare and risk stratification are planned and proactive processes that can be developed and deployed in a practice to plan for patient’s needs and care. The objective is to develop and define roles and responsibilities with a proactive approach to care and management of varied patient populations.

Risk Stratification and the Healthcare Industry

Risk stratification assessment is the process of grouping patient populations into high-risk, low-risk, and rising-risk groups. Possessing the right risk stratification tool to classify patients according to risk is critical to the success of any proactive health management initiative. Moreover, the management of population health and risk stratification are essential as Accountable Care Organizations (ACOs) and other value-based care delivery models become mainstays within the industry. Proactive health management is critical for organizations seeking to improve outcomes and lower the overall cost of care, especially for high-risk, high-cost patients. A risk stratification tool can help identify these high-risk patients so that their health can be carefully managed and interventions can be applied early.

Methods and Goals of Risk Stratification

HCCs or Hierarchical Condition Categories play a vital role in risk stratification where the goals are to predict a patient’s health risks, prioritize interventions, and alleviate adverse outcomes. The ACG or Adjusted Clinical Groups model is other approach that classifies patients into one of 93 categories based on both inpatient and outpatient diagnoses. In assessing risk under both schemes, it is essential to use multiple comorbidities to predict risk more accurately.