Beating Back Healthcare Associated Infections with Artificial Intelligence

We associate hospitals and clinics with good health – after all, this is where we go when things are less than perfect with our bodies. However, the reality is a bit different –a WHO study reveals that out of every 100 hospitalized patients, 7 in developed and 10 in developing countries will acquire at least one healthcare-associated infection.In high-income countries, almost 30% of patients in ICU set-ups get at least one healthcare-associated infection.

This means patients can actually end up with more add-on illnesses and problems during their stay at a hospital. Some of these Hospital Acquired Conditions such as Urinary Tract infections are easier to treat, but some patients, especially the elderly and newborns, can be seriously impacted. 

Along with negatively impacting patient experience, Hospital Acquired Conditions also cost the institution and the healthcare system millions of dollars –It is estimated that it directly accounts for an annual financial loss of around US$ 6.5 billion just in the USA.

Using Healthcare AI to Handle the Problem

Prevention and better care conditions are naturally the first steps towards reducing Healthcare Associated Infections, and while creating and maintaining standards and investing in training is vital, it is obvious that vigilance in care routines is not enough to stop the rise of Hospital-acquired conditions.  

This is where technology steps in - innovations in the field of Artificial Intelligence have led to the creation of Healthcare AI solutions. These software models can be integrated within the hospital systems and work with complex layers of data to ascertain, which patients are at a higher risk of acquiring hospital related conditions! 

While it might sound very much like looking into the future, it is, in fact, cutting-edge technology using predictive analysis to forecast with accuracy. Along with predicting problem areas or identifying high-risk patients, these Healthcare solutions also produce road-maps and suggestions to correct the problem in time. 

This is not future tech! Several hospitals across the US are already using Healthcare AI systems with great success. Next step is to ensure that technology can benefit more people across the globe.

Preventing Patient Deterioration from Hospital Acquired Conditions with Cognitive Machines

As the name suggests, hospital-acquired conditions (HAC) is a medical condition or a complication that the patient acquires from his/her stay in the hospital. The condition or complication was not present prior to hospital admission. Some of the common HACs are ventilator-associated pneumonia, surgical site infection, urinary tract infection, falls and trauma, stage III and stage IV pressure ulcers, air embolism, etc. 

With the rising risk and chronic healthcare management, CMS and hospitals are on the constant lookout for a permanent solution to prevent patient deterioration from hospital-acquired conditions. 

Predictive analytics and healthcare

With the growing popularity of predictive analytics, some industries have already implemented it successfully and are reaping its benefits. The scope of predictive analytics in healthcare is immense. However, we need to keep in mind that we can leverage predictive analytics only when we can have actionableinsight into it.

For example, using a cognitive machine that can predict a ‘hospital acquired condition’ in a patient and offeran actionablesolution to reverse the impact in a patient’s health, his/her duration of stay in the hospital and can also impact the overall healthcare economy. 

In short, millions of dollars can be saved on healthcare that is otherwise wasted through HAC by leveraging healthcare analytics. 

How Cognitive Machines can reverse the condition?

Some hospitals have already implemented a Cognitive Machine that is capable of predicting and delivering insights about a patient’s future state of health. This tool not only predicts the likelihood of a patient developing HAC but also providesa roadmap to avoid it. This cognitive machine is 20X more capable than a human mind in assimilating and assessing information about a patient’s future health condition. By leveraging this cutting-edge tool, hospitals will surely be able to limit and reduce the number of HAC cases every year.  

What is The Importance of Cognitive Machines for Offering Patient-Centered Care?

Healthcare sector has seen a lot of advancements in the past few years, and the cognitive machine is one of the best solutions the healthcare providers have come across. Identifying a medical disease in advance can eliminate the need for hospitalization by preventing the disease from worsening. Whether a person suffers from a treatable disease or a critical one, patient deterioration can be prevented with help of predictive analytics technology as it can recognize a patient’s condition in advance. Once a medical condition is identified, it is easier to deliver patient-centered care without delay.

Need for Predictive Analytic Solutions

Ever since cognitive machines came into existence, predictive analytic companies have been over stressing the real capabilities of these machines. Generally, right intervention in a medical case can impact the outcomes for some patients, but it may not be true for everyone. There are some cases where consequences may not be positive even after doing everything it takes. Both outcomes are different not because of the type of provided care but the time the disease is discovered. Disease and deterioration are evaluated within the context of the patient and his or her environment to determine the cognitive propensity. Cognitive machines, the predictive analytic solution allows healthcare professionals to provide patient-centered care to deliver the best outcomes.

Cognitive machines are highly dependable not just for recognizing the category of diseases but also for identifying them with high accuracy. Generally, it can be extremely complicated to distinguish the symptoms of a disease from the symptoms of another disease or recognize healthcare associated infections. However, cognitive machines can understand the exact disease or infection a patient is on the verge of developing. Once the cognitive propensity is determined, it becomes easier for healthcare providers to address an illness effectively and provide patients with the right treatment.

How has Cognitive Science helped in Curing Patients from Deadly Diseases?

Technology has always been advantageous for all kinds of industries, and the healthcare industry is no different. Whether it is the diagnosis or monitoring of a disease, artificial intelligence (AI) is reimagining the healthcare sector around the world. As AI technology helps in improving accuracy and efficiency in identifying and treating patients faster than ever, it serves the purpose most efficiently. Considering the rising risk of diseases that patients die of, AI is widely being implemented for the following.

Diagnosis- It is certainly one of the best benefits of the AI technology. There have been many surveys across the healthcare industry that indicate that AI could help discover the disease a patient is likely to suffer from in the near future. This not only helps in preventing the disease but also makes sure the disease doesn’t get worsened.

Monitoring- Diabetes, cholesterol, cardiac health, fertility issues are managed by regular monitoring and some lifestyle changes. AI can help in managing various health conditions and adjust the dosage of medicines along with lifestyle information, such as exercise, food habits, etc.

Image Analysis- A lot of pathological evolutions depend on image analysis. AI can help in screening the image in to give faster and more accurate results.

Apart from the above-cited ones, there are many other benefits of AI technology that can help in diminishing the rising risk of various chronic diseases.

Detecting Sepsis

Among deadly diseases that are difficult to cure, sepsis is one of the most expensive and lethal syndromes. It’s quite challenging to detect sepsis in the large part, as its symptoms can be mistaken for many other conditions. Inability to detect sepsis can increase the chances of an escalating problem and possible death too among the sepsis community.

Use of cognitive clinical success machine helps in identifying and preventing sepsis from increasing the deterioration of health. As per many reports across the healthcare industry, over 40% cases developed within the sepsis community were with specific kind of diseases, led to sepsis usually including the infections of the gut, lungs, urinary tract, etc. Use of cognitive machines has significantly helped in bringing down the number of sepsis patients.

Predictive Analytics Software for CJR Readmissions

Today physicians are well skilled and make efforts to stay current with the latest studies and practices. However, it is not expected for them to memorize each individual patient’s records. But in today’s technology era, this can be made possible with clinical predictive analytics. Healthcare providers have the predictions at their fingertips, which help them make appropriate decisions and deliver better care.

The comprehensive care for joint replacement model helps support effective and efficient care for patients who undergo the most frequent inpatient surgeries such as hip and knee replacements. This model examines bundled payments and aspect measurements for an episode of care related with hip and knee replacements to motivate hospitals, physicians, and post-acute care providers to work in sync to improve the quality of treatment.

A hospital readmission is an episode when a patient who been discharged from the hospital gets admitted again in a specified time interval (e.g. 30-day readmissions). CJR readmissions refer to the patients who have undergone the CJR surgeries and get readmitted to the hospital within a specific time interval.

Jvion’s artificial intelligence predictive analytic solution uses deep machine learning and clinical data to deliver the most advanced and accurate CJR readmissions predictions. RevEgis helps providers improve care quality, drive down cost, and meet the demands of value-based models of care.

Predictive Analytics and Its Major Role in Healthcare

Clinical Predictions cannot be beneficial until they are transformed into actions. This applies to predictive analytics solutions that provide early notifications to doctors and medical staff about a specific patient. Healthcare facilities do not have to wait for symptoms to manifest before the treatment begins. With the analysis of current and historical patient data from hospitals, high-risk patients can be effectively pinpointed and resources can be diverted where they are needed the most.

Clinical Predictive Analytics

Clinical predictive analytics is an effective way to reduce readmission rates in hospital settings. Healthcare analytics software not only reduces readmissions, but also provides patient-level predictions to determine interventions that prevent specific diseases and infections. 

Few ways in which clinical analytics help: 

• Considers different components such as patient phenotype, patient specific sensitivities and case history
• Predicts the possible state of a patient with reference to current and historical information before symptoms are evident
• Optimizes the allocation and utilization of resources to boost healthcare cost reduction techniques
• Predicts hospital acquired conditions, one of the primary reasons of loss of resources and increased LoSto adequately contain and handle any such cases before they happen 
• Helps examine live information and numbers assisting hospital staff proficiently function towards reducing readmissions, reducing suffering while being patient-centric.

Importance of big data healthcare

The healthcare sector has realized the importance of big data. In this era of open information in hospitals, stakeholders and the federal government are quickly moving toward transparency through creating the decades of disparate data more searchable, operational and actionable for the healthcare industry. This data helps pharmaceutical companies, payors and providers to develop proactive plans to thrive in the new healthcare environment. The remarkable increase in electronic health records enable doctors to take better clinical decisions and provide improved care yielding better outcomes.  

The leading clinical predictive solution like Jvion’s RevEgis for providers, utilizes big data and helps predict patient level diseases, hospital acquired conditions, improve community health, drive predictive infection control, reduce readmissions and more. 

An Insight into Infection Control and Prevention

Infection control is a measure to reduce and prevent nosocomial or healthcare facility related infections. Infection control solutions address aspects related to the spread of infection within the healthcare setting and investigation with monitoring of suspected or confirmed spread of infection within a specific health-care facility. Healthcare providers engage a substantial amount of resources handling nosocomial infections resulting in wasted resources and increased suffering.

In addition to measures such as hand hygiene, sterilization and disinfection, an evidence-based approach to infection control and prevention is also effective to reduce infection rates. With the help of predictive analytics, it is easier to improve the quality of patient centered medicine.  

Use of clinical analytics for patient stratification is the primary step in the risk management measures adopted by stakeholders like ACOs. Its major objective is to pinpoint high-risk patients so that medical staff can plan proactively to ensure that the chances of patients being exposed to infections are minimized.

All these measures help reduce and control hospital acquired infections which in turn improve patient satisfaction, reduce suffering, save resources and improve community health.