Blog Post for “NeuronNet AI Solutions”

The adoption of AI has exploded in just about every industry in the last few years. It seems like you can’t go anywhere without hearing about some new tool or revolutionary use for it. But the healthcare industry has been pretty slow to adopt, mainly due to patient and provider distrust. Which is a shame, because some of the biggest breakthroughs are likely to come from AI in the medical field.

ChatGPT has already proved it can pass the U.S. medical licensing exam, has been named as co-author on several research papers, and is being used in appeals for medical insurance claims. And this barely scratches the surface of what it is capable of. 

If we harness AI’s capabilities for predictive analytics, we will start seeing some truly incredible breakthroughs in our ability to care for patients and improve overall population health, as well as save hundreds of billions of dollars a year in healthcare costs. 

What is Predictive Analytics?

The healthcare industry generates an incredible 30% of the world’s data, yet 97% of that data goes largely unused. 

We are inundated with data, but too often it is isolated within several different systems, in dozens of different incompatible formats. The electronic health records your doctors have don’t connect to the insights your smart watch compiles. Or maybe you went to an urgent care last month and your primary care doctor never received those records to update your chart. Maybe the latest clinical trial of an effective medication was published last week, but the news hasn’t reached your doctor’s office yet. The medical field changes and adapts so quickly, the challenge is to be able to use the most current and accurate data from all sources all at once to come up with the best treatment options. We just can’t put all this information to use in a meaningful way. 

But with predictive analytics we are able to sift through the sheer volume of data pouring in from all sides to uncover useful insights for patients, as well as overall populations. Predictive analytics is going to become the cornerstone of medicine and public health in the next few years. It uses AI technology to take in this vast ocean of collected data and find patterns, trends, and insights that us mere mortals wouldn’t otherwise be able to see. And then it uses that data to forecast future events or point out likely outcomes. 

Other industries are already putting this technology to good use. In the finance sector, predictive AI is being used to predict future cash flows in businesses and markets. Marketers can analyze and segment their audiences more effectively, and deliver more compelling campaigns. Manufacturing plants are able to predict when their assembly line machines need maintenance to avoid a breakdown. But some of the most promising applications of this technology are starting to happen in the healthcare sector. 

And the best part is that tech companies are making these tools extremely user-friendly. You won’t have to go out and get another degree to make use of this incredible technology.

What Are the Uses in Healthcare? 

As more and more of our health data gets digitized, these machine-learning engines have greater access to electronic health records, lab scans and tests, insurance claim data, and more. Predictive analytics can be used to help providers diagnose illnesses with more accuracy, predict future health risks based on genetic data and current health records, and optimize tailored treatment plans to a patient’s unique chemical make-up. 

Using predictive analytics, doctors can better predict patient outcomes, and personalize treatment plans based on a patient’s unique genetics and medical history. The opportunities are endless and will result in healthier populations and better healthcare resource management. 

Sure, that sounds incredible. But what will that do to the cost of care? It is actually going to save patients and healthcare providers huge amounts of money! A rare win-win scenario. Let’s look at how predictive analytics can accomplish this.

Improving Patient Care

With access to the vast amount of healthcare and personal data out there, AI will be able to suggest specific patient procedures and anticipated outcomes for a wide range of different health options. AI is already being used in some hospitals to tell surgeons which different methods of suturing during procedures will be most effective! These evidence- and data-based predictive solutions will reduce the number of post-operative infections, and minimize hospital readmissions. 

Identify Patient Risks

Usually when you step into the doctor’s office, they have your chart which has the information your doctor has gathered from however long you’ve been coming to see them. Maybe they’ve received records from other physicians you’ve gone to over the years if you completed the correct transfer forms. 

But predictive analytics is going to be able to combine all the genetic, lifestyle, and socioeconomic data that you give it access to and tell you exactly how likely you are to develop cancer or a chronic illness.  

Detect Disease Earlier

We can use large amounts of patient data, such as medical history, current health markers, genetic, and lifestyle data to identify patterns and anomalies that could indicate the early stages of cancers,  diabetes, or heart disease and introduce preventative measures early. 

AI can also anticipate potential infectious disease outbreaks by analyzing trends in community patient symptoms, environmental factors, and population health data to predict spread and try to introduce countermeasures earlier to head it off at the pass. 

Personalize Treatments

As we continue to standardize our methods for documenting patient outcomes and our records get better, we will be able to use this outcome data to predict better patient treatment plans. AI will be able to tell doctors how patients with similar physiologies and underlying conditions have responded to different treatment options and select the ones that are most likely to yield the best success. 

With patient permissions, it will also be able to use smart device information such as daily heart rates, weight tracked over time, sleeping patterns, and anything else your smart watch or scale tells you to predict which treatments are best suited to your unique lifestyle. How likely are you to follow through on a daily treatment plan? Are you more likely to stop taking your antibiotic 4 days early once you start to feel better? Maybe you are going to follow through with 45 minutes of physical therapy a day instead of 30, so you will recover 2 weeks earlier from that knee replacement than the average Joe. 

Reducing Healthcare Costs

Not only is predictive analytics a huge win for patient care, healthcare executives also believe it can reduce costs for providers by up to 25%. The paradigm shift from symptom treatment to illness prevention will not only improve quality of life for every single person on the planet, it will also save healthcare companies and hospitals hundreds of billions of dollars per year. 

By 2030, global healthcare spending is expected to reach $18.3 trillion USD. According to the Organisation for Economic Co-operation and Development (OECD), at least 20% of annual healthcare spending in the U.S. is wasted due to unused medicines expiring on the shelves, medications or procedures being prescribed unnecessarily, and other completely avoidable causes. That means $900 billion is being mismanaged every year by our healthcare system. But there’s good news. As we’ve been discussing, there’s an AI solution to reduce these systemic inefficiencies. 

Another area of waste is inefficiency in creating treatment plans. Either the treatment doesn’t resolve the primary complaint, or the treatment causes an unintended secondary complication. This is a huge contributor to high healthcare costs worldwide and affects as many as 20% of patients. Personalized treatment plans that take into account the whole person and their specific genetic, lifestyle, and socioeconomic conditions will greatly reduce the costs resulting from needing recurring treatments or from post-treatment complications.  

A predictive analytics algorithm could be used to examine staff abilities and work pacing to schedule shifts more efficiently. Or like in the manufacturing industry, AI can estimate more accurate maintenance schedules for important hospital equipment, such as CT or MRI machines. 

How Can You Use Predictive Analytics? 

All of this sounds great, in theory, but how can you as a healthcare provider actually start using predictive analytics today? 

NeuronNet AI Solutions is the top solution for healthcare providers to quickly and safely integrate predictive analytics into their existing data systems. You could be up and running with this powerful tool in less than a day with our highly-trained team of installation experts. 

We’ll also teach you and your team to create powerful dashboards. You’ll be able to predict staffing needs based on patient volume and expected wait times. And your physicians will be able to access visual dashboards of patient metrics over time and past responses to medications, to predict future health needs and outcomes. The opportunities are endless!

With data encryption, strict access controls, and regular data audits and monitoring for breaches, NeuronNet is the top choice for healthcare organizations looking to implement predictive analytics while protecting sensitive patient and hospital records. 

Patients can choose whether to connect their health apps securely in their patient portal to provide their doctors and our AI system with even more data to improve their care. 

Learn more about how AI is changing the way we do healthcare. Check out How AI is Revolutionizing Healthcare Efficiency and Slashing Costs.