A Cognitive Risk Stratification Assessment Saves Resources and Saves Lives

Modern technology has a lot to offer today.Combined with life sciences it has changed the way we live. The healthcare industry is a prime example where the collaborative efforts between medicine and technology are clearly visible. This association has made saving lives easier than ever.

Although, the new ICD-10 standards require revamping of codes, they provide a more robust structure to create the algorithms for patient predictive datawarehouse applications. These software programs successfully predict events before they occur with accuracy up to 90%. These statistics are extremely crucial for accurate risk assessments and predictions.

Lot of resources are wasted on the disparity in diagnoses and the treatment of patients. While different monitoring techniques spend valuable resources, patient suffering escalates. A comprehensive module devised to predict a condition using population health predictive solution is very effective for patient risk stratification. Sustainable use of such technologies in a hospital setting helps divert the right resources where they are needed the most. Thus, compliance can be more cost-effective and resource-friendly if integrated into such an algorithm and that in turn helps reduce patient suffering and saves lives.

Revolutionary operational changes needed for ICD-10 clinics

A lot of time has elapsed since the announcement and the subsequent delay of induction of ICD-10 codes. The stage is set for the transition of health care to a mammoth 70,000-code system. The challenge now lies with the providers who have to invest time to train their personnel in addition to manage incurred expenses. The EHR (Electronic Health Record) and billing systems need updating in accordance with the specified standards. The transition from ICD-9 to ICD-10 will bring about a radical change in healthcare that will accommodate newly developed diagnoses and procedures, innovations in technology and treatment, performance-based payment systems, and more accurate billing requirements. ICD-10 ambulatory clinics are believed to have fully established themselves by now as October is looming.

Based on the new ICD norms, a complete overhaul of the technological inventory might be required ahead of this transition. All the devices, platforms, software, and tech assists, which were previously based on an ICD-9 construct, need to be remodeled according to the latest update. Now is not the time to get an insight over how the transition will affect your practice. ICD-10 has already been delayed, and the first of October is just a few months away. The simple fact is that changes have to be made, and in compliance with the standards.

In ICD-10, physicians would be required to populate detailed reports about the medical conditions and the procedures performed at various stages of treatment. Although, there are certain similarities with the former version, ICD-10 codes are a lot more specific and exhaustive. 

Predicting Hospital Acquired Conditions Saves Your Bottom Line and Saves Lives

Differential diagnosis can get doctors in a spot. Similar symptoms and numerous conditions intersecting with each other sometimes cause a fault in the diagnosis. It results in the wrong line of treatment hence, further escalation of problems for the patient. Another added factor is healthcare acquired infections. Sometimes patients develop infections due to poor conditions at a hospital or a healthcare facility, or due to hospital staff not following proper procedures and hygiene. This results in increased patient suffering, long length of stay, loss of resources for the hospital and even loss of lives. 

Measures for prevention of healthcare acquired infections should take precedence in hospital settings. This would result in helping stop the unnecessary loss of resources and even save patient lives. Modern software applications that can predict an infection before it occurs contribute largely to the reduction of hospital acquired infections, which further reduces the stress on the hospital resources. These resources can being turn allocated to places where they are required the most. Although, many hospitals vehemently deny such occurrences, unfortunately, they do happen. While all contingencies cannot be covered, it is important to predict the occurrences of infections in hospital or healthcare settings to optimize and maximize resource allocation while saving lives.

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.