To make practices more efficient, providers should look to AI

October 21, 2020
Khalid Al-Maskari
Khalid Al-Maskari

Artificial intelligence (AI) can offer a much-needed solution for practices struggling with inefficient Electronic Health Record platforms

In an industry not known for its operational efficiency, artificial intelligence (AI) can offer a much-needed solution for practices struggling with inefficient Electronic Health Record platforms. As patient populations increase while provider availability stagnates, having an automated solution for data and patient management can make a significant difference in quality of care.

A study by the University of Wisconsin and the American Medical Association found that EHR data entry takes more than half the typical 11.4-hour workday for physicians—a hindrance that’s now even more unacceptable given the demanding treatment and volume of COVID-19. Fortunately, AI can help reduce the administrative inefficiencies that keep providers from treating the patients that need them the most.

AI for Telemedicine

Telemedicine has exploded since the onset of COVID-19, and providers now need a way to effectively manage virtual patients. What was once considered an add-on service has become a critical component of patient care delivery. Measured by revenue, the telehealth services industry in the U.S. was worth $3.2 billion in 2020 according to IBIS World and is projected to grow by 9.7%to $3.5 billionby the end of the year. This nearly double-digit growth has been a direct result of the coronavirus pandemic, but telemedicine isn’t going away once it’s fully controlled.

Telemedicine has done an excellent job of providing at-home care access, but one thing that telehealth still struggles to address is the administrative process. With the traditional triage process in an in-person setting, patients complete the necessary forms through a receptionist, nurse, and provider. Each person is responsible for their own data entry, but the process needs to change to fit the direct nature of telehealth. Rather than speaking with all parties, patients are directly transferred to their health care provider. Although this can be great for efficiency, it leaves room for error when it comes to gathering data.

To address a growing virtual patient population, robust AI is a must. AI, coupled with interoperable systems, can greatly reduce the number of nonessential tasks a health care provider needs to complete in a day. The AI can keep track of patient data, while an interoperable system can ensure data transfer across all technologies. If a health care provider meets with a patient through telehealth, that data can be collected and integrated into the clinic’s EHR and revenue cycle management system.

Managing Low Acuity Care

Telemedicine was initially developed to handle patients with seemingly minor symptoms from the comfort of their homes, but the technology didn’t fully take off until the onset of COVID-19. Although the technology existed long before the pandemic, managing low acuity patients at a distance has never been more important. While telemedicine is handling patients virtually, AI is also a key component of effective distanced care. AI can send automatic reminders to help patients stay on top of their treatment. The technology lets them know when to take their medication, and when to contact a live health care provider. Not only does telemedicine help handle low acuity patients, but it also helps address dwindling provider availability.

Telemedicine for low acuity care also offers an additional revenue stream for clinics. Now, telehealth makes patients more likely to engage with their primary providers for symptoms they may have otherwise ignored. This also comes at a major benefit to patients. Telemedicine is much more cost-effective than in-person care, averaging $79 while an office visit’s average cost is $146, a study by Health Affairs found. Additionally, patients can receive more convenient care at a time that works for them.

Revenue Cycle Solution

As patients and providers are being encouraged to use virtual health care platforms due to COVID-19, the amount of data being captured has greatly exceeded in-person data collection. When AI provides the EHR with more data to crunch, the technology can learn faster to improve operations and patient outcomes. A major benefit of this process is a more efficient revenue cycle. AI can help clinics better manage patient revenue data to ensure they get paid faster. Like telehealth, interoperability is essential for the revenue cycle to function in this way. When all technologies are able to connect with one another, data can transfer easily and effectively. This means that when a provider completes an appointment, the patient data can be automatically entered into the EHR. From there, the EHR can analyze the data and bill patients appropriately without reliance on administrative staff. This reduces the possibility of human error and allows clinics to get paid on time.

COVID-19 brought significant challenges to clinical revenue cycles, especially clinics that weren’t equipped with effective technology. 70% of primary care practices have experienced a significant decrease in volume, and just more than half had enough cash to stay open for another month, as reported by MGMA. This outlook was even worse for behavioral health care providers, with 62% of behavioral health clinics closing at least one program, according to Open Minds.

However, one behavioral health care organization with multiple clinics that fared well during the onset of the pandemic was Tucson, Arizona-based COPE Community Services.As other behavioral health care clinics reported dramatic revenue losses, COPE’s revenue actually increased.

“During the first months of the COVID pandemic, clinics were reporting 65-70% loss in billable hours, but COPE’s billing actually increased,” said Rod Cook, CEO of COPE. “COPE is one of the few organizations that can say that, and our EHR’s interoperability is one of the main reasons.”

COPE’s AI-driven EHR and its interoperable systems allowed it to implement a robust telehealth solution to address patients immediately. Although COPE had a telehealth solution pre-pandemic, its group of clinics needed to drastically ramp up services to provide more at-home care. COPE’s telehealth platform was previously only used to treat substance abuse patients in rural areas. With the onset of the pandemic, it needed the proper infrastructure to treat the majority of its patient population. Because all of its IT solutions were cloud-based, interoperable and relied on advanced systems, COPE was able to complete its telehealth integration in less than 48 hours without losing revenue.

Overcoming Adoption Challenges

The biggest obstacle when it comes to evaluating AI-enabled solutions is considering the elements that make one solution more effective than others. Is data integration automatic? Can it handle several clinics at once? Is it easy to use? These are some examples of questions that can determine a solution’s value to a clinic. Consider the clinic’s priorities, then decide whether the existing solution is sufficient. In addition, it’s necessary to consider patient adoption. If patients have trouble using the technology, it won’t produce optimal results.

Another key consideration is whether the technology provider can be trusted to be flexible and efficient when it comes to integration. If the answer is no, it could be time to consider other options. The technology clinics rely on is meant to make operations easier, not introduce a new set of challenges that could’ve been avoided. Interoperability is critical, but so is trust in the solution provider.

Lastly, it’s important to consider any future growth the clinic might experience. For example, if a chain of behavioral health clinics chooses to expand into primary care, it’s key to ensure the AI can easily be trained to address primary care as well. There are different methods of AI learning, and not all of them are created equally. AI that learns through Natural Language Processing (NLP) will learn faster than those that rely on brute force learning.

Clinics were largely ill-equipped for the AI revolution before COVID-19, but the adoption of AI in health care is advancing each day. Practices that are learning and adjusting are poised for more favorable long-term outcomes than clinics that aren’t positioned to manage the ongoing crisis. AI is a key driver of societal change, and the health care industry is one worth watching.

Khalid Al-Maskari is the CEO of Health Information Management Systems and has more than 20 years of experience in health care. He can be contacted at kmaskari@hmsfirst.com.