Making the Healthcare system more accountable and efficient

The healthcare model of the 21^st century requires a radical change because of the gradual increase in the medical expenses. Every year people pay huge premiums toward medical insurance. Currently, the predominant payment mode for patients is fee for service. Doctors charge separately for each procedure. Patients have to pay every time they access these services. It is a huge drain on resources; therefore, new healthcare payment models have come into existence to address the imbalance and duplicity. One of the best models is the accountablecare organization (ACO) model. Under this scheme, different hospitals, clinics, and physicians combine to share quality, treatment and care for a large segment of the population to eliminate duplicate tests and overlapping care.

The role of modern healthcare is to improve the well being of people:

• Apart from the payment method, the new approach to healthcare will go a long way in carrying out sepsis prevention
• Big data in healthcare facilitates the analysis of the historical information in detail to understand which group of people is most vulnerable to disease
• After identification, doctors design the treatment plan as per the medical history of the patients
• Advance analytics pinpoint the individuals who have higher chances of falling prey to chronic diseases
• Prevention is better than the cure is an old saying that perfectly fits the modern healthcare scenario. Instead of waiting for the disease to become worse, doctors can start the treatment in early stages to save the patient’s life. For example, predictive analytics applications can also accurately predict whether an individual is vulnerable to pressure ulcers. Doctors can take immediate steps to prevent the occurrence and worsening of the problem

Data Mining to Deliver Efficient Medical Services to Patients

The advent of prediction applications in the health sector is a shot in the arm for hospitals and the medical team. Even highly specialized doctors face problems prescribing treatment to patients suffering from chronic diseases. As a result, individuals visit hospitals more frequently and medical insurance continues to rise. To correct the problem, doctors analyze the medical history of the patient and target only those individuals that are at serious risk of getting infected. For others, low cost interventions could provide the same results.

Boosting the efficiency of the medical care

Installing a system based on artificial intelligence in hospitals has proven to be a boon for users. Medical staff understands and streamlines the hospital workflow to ensure optimum efficiency. Instead of providing highest-level care to each patient, patients receive customized treatment according to their individual problems. People suffering from multiple comorbidities gain access to top of the line medical consultation. Doctors define their objectives regarding patients and explain the situation in simple language devoid of medical jargons. At each visit, medical staff focuses on problems that may lead to organ failure or even death in the future.

Pressure Ulcer example

• Similarly, in case of pressure ulcers, doctors can analyze historical data and decide whether the patient will visit the clinic more often
• They plan prevention strategies to eliminate the occurrence of emergency
• Therefore, the doctors advise the patients to change the sleeping position on the bed frequently to eliminate bed sores in the skin
• While administering treatment, the medical consultant use evidence based medication to benefit the health of the patients
• Studying the treatment history of the individual will also provide valuable information about the effectiveness of the prescribed medicines

Predictive analytics in preventing Hospital Fraud, Waste and Abuse

One of the major concerns of healthcare providers, physicians and other stakeholders is fraud, waste and abuse. Billions of dollars are lost each year due to fraud, abuse and waste. This fact makes it essential for every stakeholder to implement solutions to detect, correct and prevent healthcare fraud waste and abuse. The goal is to make healthcare affordable for everyone with the onus on healthcare providers and business partners. 

Service provider fraud is one of the most common types where fraudsters resort to over billing or billing for services not rendered with the intention of generating insurance payments. In some cases, patient IDs are stolen and used to make claims and also to file DME or Durable Medical Equipment claims for services and supplies not provided. 

Jvion’s RevEgis is a robust solution designed to help reduce healthcare fraud waste and abuse through predictive analytics. Estimates by the US Department of Health and Human Services suggest that over $270 is lost every year to healthcare fraud. As costs continue to escalate, this figure could rise significantly if immediate steps are not taken by stakeholders to prevent hospital fraud waste and abuse. 

There are several benefits of utilizing such solutions. To begin with, they are designed to detect patterns of fraud in billing by profiling and segmenting claimants. This helps identify potential fraudsters and any patterns in medical events. Unstructured healthcare data can be analyzed to identify fraud where data extracted

from call center logs can raise red flags wherever any suspicious activity is found. Most solutions utilize multiple methods of analytics for organizations to detect fraud and abuse sooner. This also helps stakeholders build an effective method to detect fraud before money is disbursed. 

Predictive analytics and Personalized Healthcare

The healthcare system is undergoing revolutionary changes with plenty of challenges to meet with the use of new and existing sources of data to deliver personalized care. Clinicians are required to not only make decisions about healthcare but incorporate volumes of health data generated and controlled by patients. Integrating the data into healthcare enables stakeholders to make better decisions, which is made possible with predictive analytics software.

The multiple benefits of predictive analytics

There are numerous benefits of implementing a predictive analytics solution, which include the ability to provide better patient care and significant reduction of costs. While the thought that healthcare could be reduced to algorithms may be intimidating, the reality is that predictive analytics is very promising with the ability to deliver accurate results. Predictive analytics is something doctors have been doing on a large scale for a long time. However, predictive analytics software it a step further and helps to better collate and measure previous data that was hard to obtain.

Combining data with existing sciences of clinical medicine enables a better understanding of the relationship between external factors and various aspects of human biology and medicine. This results in improved ability to deliver personalized care.

The role of historical data

A predictive analytics solution is the best way to allow patient care to be personalized for each individual by studying historical data. It helps physicians make better clinical decisions and avoid adverse events. These solutions like Jvion's RevEgis are designed to reduce readmission rates and help in chronic disease management and patient matching. The objective is to treat individual patient better by widening the data set.

The Role of Predictive Analytics in Hospital Acquired Infection Reduction

To avoid unnecessary costs and patient suffering, healthcare providers need to consider infection control solutions and have zero tolerance for hospital acquired conditions (HACs) that only result in patients suffering from infections that result in longer hospital stays. Predictive analytics is one of the ideal ways to help in hospital acquired infection reduction and reduce readmission rates. Infection control prevention solutions from healthcare technology provider Jvion utilize big data and deep machine algorithms to predict and prevent HACs and reduce patient suffering. 

Reduce Patient Suffering and Save Lives

An evidence based approach to infection control prevention is highly effective in reducing infection rates. Patient-centered care can be improved significantly with the use of predictive analytics so that preventative measures can be implemented. Jvion’s RevEgis is one of the solutions that can help predict disease and infection by analyzing phenotypes. The objective is to not only optimize the cost of care but reduce patient suffering and save lives. The well being of patients after they are discharged is now a key element of the Healthcare Reform Act. This is designed to prevent 30 day readmissions, which is something that hospitals are now well aware of since the Centers for Medicare and Medicaid Services (CMS) are set to reduce payments to hospitals with high readmission rates. 

Fraud, Waste & Abuse need not be a major concern with Jvion’s RevEgis

Healthcare fraud waste and abuse affects everyone either directly or indirectly where billions of dollars are lost annually. These losses in turn lead to skyrocketing healthcare costs and ever increasing insurance premiums. The sheer size of the healthcare sector and the money involved facilitates the need for a robust hospital fraud waste and abuse solution to reduce costs associated with the healthcare system.

Use an Intelligent Healthcare System

Unstructured Data use within healthcare holds the key to the development of intelligent healthcare systems. Clinical data is innately complex and 80 percent of all data remains unstructured. However, the important part is realizing value from big data. Healthcare fraud is a reality where individuals and organizations perpetrating fraud are a constantly evolving group looking to make the most of loopholes in the healthcare system. Equally important is healthcare abuse where a fraction of providers believe they can beat the system and earn profits through unwanted surgeries and exaggerated claims. Then again, waste is another major factor that indicates inefficiencies in administrative and operational levels.

  

Hospital Acquired Condition Prevention is Now Easier than Ever

Hospital acquired conditions have been a stone in the shoe of the healthcare industry. Inspite of extensive sterilization or quarantining, even a minor error can be disastrous for a patient. Considerable resources are being spent on pressure ulcer prevention, a technical name for bedsores. These resources could be optimized and can be allocated to other areas where the need is greater. Predicting hospital acquired infections is becoming more critical every day. Fortunately with RevEgis predicting hospital acquired conditions has become easier for healthcare providers.

What is RevEgis? It is a holistic software application that predicts patient conditions and possible risk factors. It is a data analytics system that incorporates deep machine learning to provide the most optimized solution for patients, as well as institutions. The software application is not a replacement formedical diagnoses but it helps healthcare providers make better clinical decisions. The resulting ability to predict hospital acquired infections helps save crucial hospital resources and lives.

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5 Ways That a Predictive Modeling Healthcare System Helps

The advancement in technology has led us to a point where the man-machine system has integrated into one seamless predictive algorithm. The ingenuity of the human mind in creating mind-boggling algorithms combined with the deep knowledge of medical science, and a yearning to create a better healthcare system gave birth to a new predictive software application. It is a software application that helpsoptimizestandards of medical management by combining healthcare benchmarks and hospital peer comparisons with the ability to develop a patient-centered approach. 

1.Preventing waste of resources by predicting underlying conditions leading to deteriorated health outcomes

2.Providing a clear picture of weak links and steps needed to improve performance

3.Improving inter-departmental collaboration and laying down organizational goals for healthcare providers

4.Providing a comprehensive and customized analysis of various demographics with goal oriented comparisons

5.Recognizing the innate factors that result in a deviation from the set standards and comprehend the effect of the new healthcare payment models

 The above factors are responsible for creating a holistic health care model focused on patientswhile providing an optimized system of management that saves valuable hospital resources and saves lives in turn.

Predictive Analytics Software Help Treat Healthcare Acquired Conditions Effectively

An algorithm that can be an all inclusive solution to the challenges in healthcare systems is surely an asset. Such a technology has been developed and is helpful in preventing complications including hospital-acquired conditions and infections contracted in a hospital setting. With new regulations in place predicting and preventing hospital-acquired conditions has become necessary for hospitals to avoid denial of complete reimbursements. Moreover, it is essential for the reputation of the hospital to achieve high levels of patient satisfaction.

Certain algorithms have been developed, that help predict hospital acquired infections before they occur by analyzing the patient phenotype and background. Pressure ulcer reduction is an important part of hospital acquired infection prediction as it is a commonly occurring condition among long-term patients. Using such predictive analytics software, pressure ulcers and other HAIs that plague the patients after discharge are reduced. Overall, the application's domain in prediction and analysis help doctors treats patients more effectively, and hospitals maintain their reputation while patients achieve better health outcomes. 

The Changing Future of the Healthcare System

The world is changing with time, and as Big Data takes its baby steps there are a few technologies that are way ahead of it. One of them is based on an evidence-based metrics healthcare system that derives its power from the genius of a medical mind and a deft programmer. It is a giant leap in the healthcare system. A healthcarepredictive analytics algorithm is the next step in achieving a risk-stratified model of care that aims at reducing the risk of infections while treating the original illness. An algorithm with the power to predict and self-correct is a break through achievement in coding as well as for the healthcare system.

It is essential to have a closed loop system in an algorithm to make it smart and unique. A healthcare predictive modeling system that works on a feedback loop can produce even better results with time as it gathers more and more data about the background of the patient. Hospitals can also benefit from new technologies including a healthcare provider fraud waste and abuse solution. 

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.