A Look At How Clinical AI Is Changing Patient Experience

As digital transformation makes its presence felt in the healthcare sector, patients are positioned at the receiving end of all the benefits. With the integration of artificial intelligence with clinical data and processes, the entire medical system is going to be lifted to provide the next level of healthcare services.

As AI models focus firmly on delivering the most positive patient outcomes, the whole healthcare system also pivots to become more patient-centric. 

Here is a look at how patient experience will be enhanced with Healthcare analytics –

1. Improved bedside assistance
   Bedside patient rescue (BPR) is an area that has experienced positive outcomes with the introduction of AI in healthcare – hospitals have reported a drop of 8% in mortality due to timely detection and faster patient intervention. The AI-based models measure the deterioration risk for all patients in the hospital, and alert caregivers in case intervention are needed.
   
   
2. Overall better patient care and experience
   More active patient engagement is linked directly to positive health outcomes! To achieve higher patient impact, clinical AI identifies gaps in patient care, which might lead to a negative hospital experience for the patients and their family. By aligning patients’ needs and values with the care provided, smart healthcare systems drive the entire healthcare system towards a patient-centered care model. 
   
   
3. Continued care outside the hospital
   Usually, the work of hospitals ended once you walked outside its doors - the family or the primary physician took over and worked in isolation. Now with clinical AI, the care continues in an integrated and seamless manner which works towards keeping the patient healthy in the long term.

The healthcare sector, on the whole, is moving towards a patient-centered model. One key factor for achieving quality based care is the standardizing and measuring of patient experience and engagement, and it has been widely adopted by Centers for Medicare and Medicaid Services’ (CMS’s). 

In this changing landscape, clinical AI models are of immense help to all stakeholders - healthcare administrators, caregivers, and patients.

3 preventive harm vectors that can be avoided by Healthcare AI

Most of us enter hospitals to get better; to heal. But the sad reality of the current healthcare system is that some patients actually end up getting worse in healthcare facilities – close to 400,000 deaths every year can be attributed to preventable harm.

By smart integration of healthcare predictiveanalytics, hospitals can cut these numbers down drastically.  Intelligent healthcare solutions use the power of artificial intelligence to track patients on a high-risk trajectory, alerts caregivers in time, and even recommend patient-specific interventions to change the predicted negative outcome.

Here are 3 scenarios of preventable harm that is been positively impacted by predictive healthcare solutions –

1.  Hospital-acquired conditions
   Thousands die each year, and many more suffer continued ill-health due to medical errors. Some of these preventable conditions are falls, pressure injuries such as ulcers, or infections which can occur even in a professional environment.
   
   
   Healthcare machinelearning has enabled caregivers to be alerted regarding patients who might be heading towards such an event.
   
   
2. Healthcare-associated infections (HAIs)

   An understaffed and overworked hospital scenario often leads to patient deterioration due to HAIs. Infection prevention policies suffer when time and resources are stretched, and this leads to a situation where vulnerable patients are exposed to new infections.

   Healthcare Predictive Analytics sees into the future state of each patient and identifies those who are more at risk of contracting these infections. The clinical AI system also passes on recommendations to caregivers to avoid the HAIs altogether.

   
   
3. Readmissions
   Many patients –especially the ones with chronic illnesses – are at higher risk of readmission after being discharged. Most of these do so because of preventable causes such as a fall at home, missed medications, limited access to a pharmacy, or undesirable lifestyle or food habits. A

Healthcare AI models assess a host of environmental, socio-economic and other personal attributes in conjunction with patient health records to pinpoint people on a high-risk trajectory for readmission and helps these patients stay on a healthy course.

How Healthcare Cognitive Science Is Changing Patient Experience

Data analytics has been playing a pivotal role in revolutionizing the user experience across industries, and the healthcare industry is also a part of this new digital revolution. The scenario in the healthcare industry is changing as cutting edge healthcare data analytics is playing a significant role to bring about a paradigm shift.

How can healthcare cognitive science become a game changer?

Leveraging the power of healthcare data analytics and machine intelligence, doctors and medical practitioners are not just able to provide better care, but they are now also able to identify patients who are on a heightened risk trajectory and thus can provide timely interventions. The power of machine intelligence is enormous. It lets doctors predict future health risks and enable them to alter it for a better patient outcome. 

Recently several healthcare technology innovators have come up with innovative solutions where they are using machine learning and massive data sets to identify such patients and suggest patient-specific intervention plans using prescriptive analytics.

Apart from producing better health outcomes, it is also able to save a lot of money that is spent globally every year on the healthcare system.

How will this prescriptive analytics machine be a life savior?

Medical practitioners are of the opinion that by using this smart machine backed by prescriptive analytics, caregivers are now able to provide better care to patients. Some of the scenarios in which cognitive clinical AI is helping healthcare professionals are - avoiding readmissions, identifying and attending more quickly to patients at risk of mortality, providing personalized intervention to patients, reducing instances of hospital-acquired diseases and so on.

The power of healthcare cognitive science and how far it can be leveraged is unimaginable. However, if done right, we would surely be taking a giant leap in medical sciences.

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.