Jim MGowan, ElectrifAI

Tech Q&A: Artificial Intelligence Has Promise of Streamlining Hospital Processes, Diagnostic Tools

By Maria Fontanazza
Jim MGowan, ElectrifAI

Companies developing technologies that integrate AI need to consider regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

The global pandemic is pushing the healthcare system even harder to find ways to help hospitals efficiently address cost and streamline operations. From managing healthcare billing and the insurance process to providing a faster diagnosis of a serious disease, artificial intelligence (AI) has the potential to completely change how hospitals operate. MedTech Intelligence recently discussed some of the areas of impact with Jim McGowan, head of product at ElectrifAI.

MedTech Intelligence: How is AI helping hospitals manage healthcare bills and the insurance process?

Jim McGowan: The original areas within a hospital where AI created efficiency were in registration and insurance processing, most notably in revenue cycle management (RCM). RCM was envisioned as a seamless process across patient appointment and registration; claim coding and submission; payment reconciliation; and appeals. Over time these solutions grew so complex that parallel industries around “Pay and Chase” emerged, in which providers needed incremental support to capture all their revenue. With margins in the low single digits each dollar counts.

These RCM systems are rule based, which is antiquated AI technology. [Our] RevCaptureAi solution combats the limitations of these traditional revenue cycles with the dynamic intelligence of artificial intelligence (AI) and machine learning (ML) that track, analyze and generate insights about your missed charges. In a billion-dollar health system, just 1% of missed total charges adds up to $10 million in lost revenue. This is the opportunity.

Both providers and payers are implementing chatbots to more efficiently engage with patients/members by automating common support topics like confirming eligibility, getting claims/payment status, scheduling appointments and more. Machine learning is in the early stages of adoption. ElectrifAi has used machine learning to capture missed codes on hospital bills for [more than] five years, and building practical solutions to AI problems for [more than] 15 [years].

ElectrifAI’s CEO Edward Scott discusses artificial intelligence and machine learning during the coronavirus crisis in “Beating COVID-19 Is a Team Sport”MTI: How is the technology streamlining medication management? What is its role in managing procedures?

McGowan: Medication errors are still a significant issue in hospitals. EMR solutions were implemented to improve workflow and data capture for a complete patient view. These solutions have reduced adverse drug events (ADEs). Technology has been used to create many checks-and-balances within hospitals, which requires a double-check and scan of a barcode for each patient and medication to validate the drug was prescribed by a physician. There is continued work needed to capture the full patient history as these solutions are hospital system specific, do not include interoperability with the PBM data, and do not share with other hospital systems. Ultimately, a more complete patient system of record may be necessary to ensure that each system connects to each other to share data.

One of the areas where AI in healthcare has shown the most promise is in diagnostics, which can ultimately be leveraged in operating and emergency room settings. Right now, early diagnosis is one of the most important factors in the ultimate outcome of a patient’s care. AI deep-learning algorithms are being used to shave down the time it takes to diagnose serious illnesses. Our PulmoAi X-ray solution is an example of a tool that amplifies the work of radiologists, who leverage AI to triage cases as emergency rooms and ICUs overflow.
AI is being used within healthcare for evidence-based recommendations. AI algorithms ingest collected vitals, lab results, medication orders and comorbidities and produce smarter triage tools.

We have seen growth in digital applications for mental health and virtual assistants to answer patient questions. As telehealth grows, I would not be surprised if the virtual assistants handle increasingly large volumes of questions, significantly greater than live operators. These bots are becoming much more important as the front-end to a telehealth call.

AI and Robotics for laser eye surgery and orthopedic surgeries are growing. AI-based visualizations are exploding in the market. AI is attempting to enter every facet of healthcare.

MTI: What factors should technology developers consider when designing AI solutions for hospitals?

McGowan: There are a number of important factors: Regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

Healthcare is a highly regulated industry. HIPAA balances portability with privacy. This is for a very good reason, but has a lot of side effects, like complicating marketing efforts. You can’t send an email to a patient telling her it’s okay to get the hip surgery she canceled when COVID-19 struck, because you can’t guarantee someone else won’t read it. If you send someone a reminder about their diabetes medication and are too specific in the email, what happens when that email is opened by someone other than the specific patient? Solutions that require you to log into a website to view the information was the evolution during the 2010’s and continued to evolve with the growth in depth and sophistication of the mobile app solutions. Inappropriate sharing of data, even within a family, can create legal liability that hampers more specific and appropriate messaging.

When building solutions, AI can enable a very quick solution to the above concerns. Tools like robotic process automation (RPA) and chat bots have allowed providers to quickly create solutions that gather patient information and respond with an appropriate response, even in the patient’s preferred language. These more natural language conversations guide the patient to a choice without being overly and overtly intrusive.
Most importantly, AI and ML people really have to deeply understand their craft if they want to influence medical decisions of any kind. Data science is not just technology development. It requires deep understanding of the problem domain being addressed, as well as statistics, inference, and logic. And data science without exceptional data engineering is useless. There is no magic inside the algorithms. If the data is bad, the results will be bad. We’ve seen data systems where almost half the data is inaccurate. Let that sink in. Would you go to a doctor if half the facts in their medical books were wrong? AI solutions start with great data engineering.

Jim MGowan, ElectrifAI
Jim McGowan, ElectrifAI

I’d like to talk directly to the C-Suite in the hospitals for a moment.

Let’s discuss the elephant in the room: many hospitals are poorly run businesses, with razor thin margins and inadequate spending controls. These are not financially healthy organizations.

This year we saw 42 hospitals file for bankruptcy—so far. All have two things in common: They all had revenue capture solutions, and they all couldn’t pay their bills.

First, revenue capture doesn’t address your problem: you need elective surgeries. Revenue Capture fixes leaks in your billing process. Hospitals don’t go bankrupt because their billing process is too leaky. The revenue isn’t coming in. The elective surgeries aren’t there.

Second, the revenue capture programs you do have use rules-based systems, and those don’t work when the rules change. COVID-19 changed the rules. You needed a machine-learning based solution. Rules-based systems have been around since the 1950s. The world has moved on. We have a machine learning based revenue capture solution, and not one hospital using it has gone bankrupt. And still, that should not be your priority right now—that’s just a part of getting healthy.

You need to restart elective surgeries. You need to manage your finances.

Customer engagement isn’t optional for any other business, and it isn’t optional for yours. Machine learning can help.

You also need to get control of your spending. Spend analytics is critical. Again, this is not optional for any business, hospital or not. Machine learning can help.

AI—especially machine learning—helps improve the health of the patient, the financial health of the hospital, and ultimately the health of the community. The pandemic should not be a reason to push off these technologies—it’s the reason you should embrace them today.


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Maria Fontanazza, MedTech Intelligence