Healthcare Predictive Analytics

Healthcare predictive analysis is a technique used to obtain and combine current and historical healthcare data to make predictions. This involves predictive modeling, data mining, and machine learning to effectively and accurately predict health events for a patient or population before symptoms manifest. The analysis of current and historical clinical information for a particular patient helps healthcare providers better manage and direct resources and treatments that prevent illness.

Various predictive analytics applications exist that offer easy to use interfaces and improved outcomes. These predictive analytics software applications are designed for healthcare entities to help optimize healthcare delivery in turn saving valuable resources and lives.

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.