Pressure Ulcer Prevention in Hospitals - An Overview

Pressure Ulcer Definition

A pressure ulcer is a kind of skin wound created by friction, shear or pressure. They could occur when pressure reduces or cuts the blood supply to a specific part of the body for a long period of time. Prevention is definitely an ideal form of protection from pressure ulcers.

Pressure Ulcer Prevention In Hospitals

The pressure ulcer prevention can be nursing intensive. After the development of pressure ulcer,the goal for the healthcare staff is to help the healthcare unit in closing the ulcer as early as possible. It includes preventing further ulcer deterioration, keep the area clean and minimize possible infections from developing while keeping the patient pain free. Several aspects of managing pressure ulcers are parallel to prevention (nutrition, support surfaces and mechanical loading). Some major guidelines include recommendations on policies such as utilization of pressure redistributing support surfaces, repositioning, nutritional support, wound care and biophysical agents.

Predictive Analytics To The Rescue

Another way to prevent pressure ulcers is by ensuring they don’t occur in the first place. Jvion’s RevEgis, built on advanced artificial intelligence using the Clinical Patient Pod technology delivers predictions using the data for patients that a provider already has on hand. The solution looks at a patient data and predicts any possibility of pressure ulcers before any clinical signs are present. This helps a provider target preventions that are low cost, non-invasive, and easy to apply where they are needed the most. As a result, providers are able to reduce readmissions, length-of-stay, save resources and most of all, reduce patient suffering caused by pressure ulcers.

Predictive Analytics – A Proven Approach for Readmission Reduction

Readmissions to hospitals have become a major concern for hospitals and post acute care providers as it may lead to increased penalties. Most of the hospitals are striving hard to find suitable measures for reducing the rates of readmissions.

One of the most preferred and effective ways to readmission reduction is predictive analytics. It helps the hospitals quickly pinpoint patients who are at greater risk of readmission.

How predictive modelling works?

Readmission prediction is possible with the help of predictive analytics. Being a distinctive statistical technique, predictive analytics uses data mining and modeling to identify patterns and trends. With the help of complex calculations, patients who are at an increased risk of readmission can be shortlisted. Both, inpatient and outpatient environments could be considered to ensure more accuracy in predictions and in turn yield better outcomes.

On the basis of calculated risk scores, post acute care providers are able to proactively take measures towards readmission reduction. With daily reports obtained from predictive analytics, allotting resources when and where they are needed the most to focus on high-risk patients becomes very easy.Such an approach also helps reduce readmissions, save resources, and improve patient satisfaction while reducing suffering.

Hospital Acquired Condition Reduction Strategies with Big Data Analytics

Hospital acquired conditions or HACs emerge as a great challenge for healthcare systems and clinicians as it is one of the major causes for mortality and penalties since such situations occur during a patient’s stay in the hospital. The main causes behind the occurrence of such infections may include – poor maintenance of healthcare facilities or inappropriate treatment procedures adopted by the staff.

Being a prominent safety concern for patients and healthcare providers, such conditions create a distinctive urge for the deployment of hospital acquired infection prevention measures. 

Who is at High Risk?

Prior to making any significant changes toward hospital acquired infection prevention measures, information about high-risk individuals needs to be reviewed. The major important factors behind the development of such infections include –

• Patients within the age-group of 65 and above
• Patients who consume antibiotics 
• Shock treatment or any major trauma
• Acute renal failures may also result in such conditions

Hospital Acquired Infection Reduction Program

The increasing rate of mortality, morbidity and lengthy stay of patients has resulted in the introduction of hospital acquired infection reduction program by CMS (Center for Medicare and Medicaid Services). According to this program, CMS is liable to penalize the healthcare organizations and hospitals that have higher patient complication rates in comparison to their peers. The qualifying hospitals will require paying one percent of every Medicare payment they receive.

Hospital Big Data – A Preventive Measure

As per the study of CDC (Centers for Disease Control and Prevention), the hospital acquired conditions are directly becoming a major cause for more than 15000 deaths every year. However, providers are striving hard to seek better methods for managing such conditions by gathering reliable data concerning these problems.Of course, hospital big data can greatly help in the prevention of such infections. In fact, some of the suggested measures for its control according to CDC include –

• Ensuring appropriate hand hygiene
• Making use of equipment for personal protection (PPE)
• Establish proper cough etiquette and respiratory hygiene
• Making sure complete injection site safety 
• Effective handling and storing of medication
• Disinfecting and cleaning the surgery instruments as well as devices
• Improvising the techniques of sterilization.

Reducing the Penalties

Having the right clinical analytical software such as Jvion’s RevEgis can help improve the HAC scores of a hospital. Here are some of the important tips for healthcare organizations to reduce penalties under the HAI reduction program of CMC –

• Remain proactive in evaluating, measuring and optimizing the processes as well as outcomes of critical care. 
• Putting in place the right processes of coding in order to capture accurate present on admission (POA) data of patient. 
• Implement active systems of surveillance for identification of HACs and any potential harm to patients. 

Summary: You can ensure absolute prevention from hospital acquired condition with the help of big data analytics. It combines and analyzes the patient history to predict the probability for the occurrence of such infections.

Patient Predictive Data Warehouse – A Must-Have for Effective Functioning of Healthcare Systems

The healthcare industry is showing a tremendous shift towards the adoption of predictive analytics to serve an array of purposes. Experts view it as a major pre-requisite for the management of community health. Most of the hospitals employ such statistical tools to pinpoint the patients at a high-risk of readmissions. Whereas, some use it as a patient predictive data warehouse to identify as well as intervene the patients with severe and chronic illnesses.

Although, the process of using predictive analytics software may not be new for the healthcare industry, it may come with several challenges specially with gathering data. This calls fora requirement for well-structured patient predictive data warehouse. Hospitals and healthcare organizations can reap the following key benefits –

Ensure Patient Stratification

Patient stratification is a distinctive measure that integrates the chronic conditions, cost trends as well as social determinants of risk, together to identify the individuals who can benefit from the proactive programs of care management. It can further expand the capability to identify the candidates who are critical for the programs of care management.

Helps In Predicting Hospital Acquired Infection

Almost every healthcare system is facing the similar dual challenges of wringing out their expenses and matching up with the regulatory challenges imposed by the government over HAI (Hospital Acquired Infection). Previously, the implementation of traditional EDW (Enterprise Data Warehouse) by medical centers resulted in poor outcomes. However, with patient predictive data warehouse, EHR software gets more agile resulting in better outcomes.

This data can help you predict hospital acquired infections with accuracy thereby avoiding any kind of penalties. It is undoubtedly a cost-effective measure that is beneficial for both the patients as well as healthcare organizations.

Promotes Community Health Solution

When it comes to addressing the major challenges of community health solution, big data warehouse plays a vital role in predicting patients who are at a highest risk. It is a faster, smarter as well as more actionable approach to ensure community health welfare.This can further provide granular insights driving improved outcomes for both the hospital as well as patients.

Such predictions can also turn out to be valuable in the process of policy making, spreading education, interventions as well as care coordination for community health.

Summary: Having a patient predictive data warehouse can help the healthcare organizations store the past and current clinical information for every patient. It ensures better interpretations and predictions of hospital-acquired infections, community health and patient stratification.

Predictive Analytics– A Revolutionary Approach to Systematic Healthcare

One of the much talked-about topics in the realm of healthcare analytics is predictive analytics. It is an advanced approach to predicting clinical occurrences before they manifest. By using techniques such as statistics, data mining, machine learning and artificial intelligence,predictive analytics solutions analyze current and historic data to make predictions. Such predictions help the healthcare industry to improve patient care, community health as well as manage chronic diseases.

Working With Predictive Analytics Software

To ensure better healthcare, numerous predictive analytics software are available in the market. They are well equipped and user-friendly which help accelerate the performance of healthcare industry thereby benefiting both the patients as well as physicians. Such analytics also change the patient’s role and help them become more informed consumers who work with their physicians collaboratively to achieve better outcomes. 

Predictive Modeling Healthcare Benefits

Predictive analytics finds prominence in almost every branch of healthcare. Adopting a reliable predictive analytics solution helps in the following ways –

• Increases accuracy of the diagnoses resulting in decreased suffering, saved resources and lives
• Helps providers make better decisions about treatments as in when and where resources need to be directed
• Provides physicians with better direction to treat patients on their individual needs
• Helps monitor and maintain community health with preventive medicine
• Supports hospitals as well as employers with better prediction of insurance costs 

Reducing Hospital Readmissions with Infection Control Measures

The hospital readmission refers to an occurrence when a patient is discharged from a hospital and his readmission takes place within a specific time – 30-day readmissions. The rate of readmissions affects the quality benchmark for the healthcare system and depends on various factors such as diagnoses, severity of illness, and the availability and quality of post-discharge care.

Increased readmissions could result inthe following situations:

• Unnecessary treatment expenditures
• Improper reimbursement for services
• Compromised professionally recognized standards of care.

A hospital's readmission rate is calculated to adjust the associated risks.A measure of a hospital’s readmission performance compared to the national average for the hospital’s set of patients with a similar medical conditions is the hospital’s excess readmission ratio.

Hospitals have been engaging a number of strategies to reduce preventable readmissions. These include providing improved care during the inpatient stay which leads to reduced risk of hospital acquired infections,more careful administration of patient medications and discharge planning with improved communication about follow-up care.One of the most effective ways to reduce readmissions is to deploy infection control measures in healthcare facilities.

By industrializing infection control workflows and implementing real-time patient monitoring, hospitals would be able to better identify high-risk patients and enable clinicians to proactively take appropriate action in real-time to reduce hospital-acquired infection so rout breaks on a population level.

Hospitals should implement strategies that can go across the continuum of care for effective reduction of readmission rates. Data connectivity and information sharing crucial for inter operability of patient data, will improve care coordination between healthcare personnel and disparate health information systems. 

Using Jvion’s RevEgis, providers can pin point high-risk patients and proactively intervene to provide appropriate care when needed. This helps healthcare facilities reduce readmissions, reduce length of stay related complications, and stop the loss of vital hospital resources while improving quality of care and in turn improved patient satisfaction.