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. 

New Healthcare Payment Models

With ICD 10 transition around the corner payment reforms in the healthcare industry can be confusing. However, the new healthcare payment models make the whole exercise much simpler. Healthcare providers are required to change their economic incentives to encourage value rather than volume. A fee for service or FFS model is the traditional way that many healthcare providers are paid. Over the course of a long treatment individual expenses such as blood tests, CT scans, and doctor’s visits can add up to a significant sum. Healthcare technology companies like Jvion offer predictive modeling healthcare with healthcare benchmarks analysis to help providers define their clinical quality improvement goals.

Patient centered payment models

 

Patient-centered medical homes can also benefit from these new healthcare payment models. They provide set monthly payments in addition to existing funding models to fund a team of primary care professionals. This could include physicians, medical assistants, psychologists, specialists, and nurse practitioners, to name a few. The team works closely in building a strong network with patients and caregivers. The funds can be used to hire nurse or provide special care and attention to high risk patients with the objective to reduce visits to the emergency room and other related problems that may arise in the future.

Forecast Readmissions and Financial Losses with a Patient-Phenotype Platform

Jvion strives on to build a patient-phenotype big information system that utilized innovative research and strong device intellect to estimate activities within a medical center. They modified these activities into the medical and financial use cases that consist of their RevEgis solution. However, the RevEgis patient-phenotype centered big information device learning system can help medical centers estimate which sufferers are more likely to drive their Efficiency Ranking up or down, stratify those sufferers depending on risk of having more intense results and apply focused treatments that would best benefit the affected person and have effect on helping the Total Efficiency Ranking. The system informs physicians and supervisors about maximum care process for an individual patient right from the time of first touch to discharge and beyond.

Moreover, RevEgis is considered among one of the best cost reduction strategies that prevents the economical effects associated with medical difficulties such as hospital readmissions relevant threats along with other revenue-related activities such as payor acquiring and ICD-10 using predictive intellect. Through a high level patient phenotype design, RevEgis generates more workable ideas that help medical centers focus on preventions to quit compensation failures by avoiding the medical conditions resulting in financial charges. Through this same strategy and technology, RevEgis forecasts other areas of leak within the income pattern so that providers can proactively quit the loss of sources before they spend their hard earned money. 

Find the Best Financial Analysis Tool in the Market

Jvion’s RevCore solution is the highly effective and smooth financial analysis tool available easily in the market today. This is because Jvion is being able to pin-point ICD-10’s exact economical impact by department/specialty, DRG, MDC, process rule, analysis rule, doctor, and programmer. Through this remedy, providers get an ICD 10 historical claims analysis that shows their unique case mix and business structure. It is exclusively designed, pushes risk minimization through workable suggestions, and better adjusts to ideal and functional main concerns. Moreover, RevCore is the prime component of their ICD complete solution. It uses predictive technological innovation to comprehend your ICD 10 financial/revenue threat and provide an extensive effect research.

Jvion's Application Alternatives Drive Financially Neutral 

Jvion helps doctor's methods and out-patient features evaluate ICD 10's economical threat, develop strategies to address that threat, and improve payments within the ICD 9 scheme

Outpatient Remedy that Provides ICD 10 Financial Neutrality

Jvion's out-patient software solution allows an ICD10 financial/revenue fairly neutral transformation through a traditional statements research.

Through RevCore, you will easily come to know:

• Which doctors are associated with high risky codes?
• Where to concentrate certification and training efforts
• The techniques and connections associated with high business risk 
• The ICD-9 requirements that have the biggest possibility of medical requirement triggers
• How to best arrange your examining way to economical threat areas and much more.

Analyze Disparate Data to Identify Fraud Waste and Abuse

The healthcare industry is going through a seismic move designed to reduce healthcare fraud, waste and abuse and improve the quality of patient care and reduce financial looses. However, the prime aim is to decrease management pressure and reduce waste within the system, the near-term effects actually increase the cost of running a medical center and reduced overall provider earnings. But a million dollar question comes how providers are impacted from Fraud Waste and Abuse:

• Increased technology execution expenses as providers try to adhere to required transformation dates
• Increased functional expenses due to improved returns, are attractive, and claim adjustments
• Longer Consideration Receivable periods
• Increased income threats such as reduced reimbursements
• Increased patient bad debt

So, if you are looking for the Healthcare fraud waste and abuse solution; Jvion offers big data solutions to address regulating conformity and get ready providers for new payment models. Also, the company uses big sets of disparate data from several resources to appear sensible of current threats and future opportunities as a result from the ICD-10 program code transformation.