6 Ways to Leverage AI for Hospital Operations

Artificial intelligence (AI) is transforming every industry radically, and the ramifications are immense for healthcare. Clinical applications have been at the forefront of adoption: AI is used to predict and prevent readmissions, manage chronic diseases, and drive clinical decision support tools. How can AI improve hospital operations?

AI for Healthcare -- learn more at HealthTensor.com

Top companies are beginning to develop AI and machine learning tools to improve hospital operations, clinics, and practices. Innovative leaders can implement these next-generation tools to capture more value today and future-proof their business.

What is AI and Machine Learning?

Like many advanced and rapidly evolving technologies, artificial intelligence has many definitions. According to Merriam-Webster, artificial intelligence is the power of a machine to copy intelligent human behavior. In healthcare, AI refers to human-level or greater intelligence applied to specific tasks, such as knowledge management, speech recognition, natural language processing, computer vision, and more.

AI for Healthcare -- learn more at HealthTensor.com

One of the most essential aspects of AI is machine learning, a significant field within computer science, which aims to build computer systems that automatically improve with experience. When machine learning is applied to a task, it can learn how to perform it and improve its accuracy, output, or speed over time.

Why is machine learning important?

Imagine training an employee who never forgot, never retired, never took a break, and continuously learned on the job. Specific applications of AI and machine learning make this extraordinary productivity a reality across various operational tasks.

How can we use AI today to improve healthcare?

Executives and physicians ask: How have hospitals begun to adopt these technologies? What are the benefits? And how can my hospital leverage AI? The following applications for operations explain how hospitals can use AI today to reduce costs, improve patient outcomes, and transform healthcare.

6 Ways AI tools improve access to patients

A key driver of providers’ success is patients’ ability to see their caregivers promptly. Patient access is a significant priority for leading hospitals and practices nationwide. If your patients cannot get an appointment with primary care, specialists, or surgeons promptly, they will go to other providers. Referred patients who cannot be seen quickly will reduce referral rates, decrease revenues, and lower patient satisfaction scores.

By employing new AI-based technologies, your organization can significantly improve patient access. These artificially intelligent systems utilize predictive analytics to help your teams make better decisions.

1. Smart scheduling for outpatient appointments

Scheduling is laborious, often mismatched to actual needs, and seen as a chore for many care teams. Today, schedules are made around set time blocks, but the needs of each patient rarely fit into pre-defined 15 or 30-minute time slots. More importantly, scheduling tools cannot help predict no-shows and cancellations nor guide appropriate follow-up actions.

Reports show that a single no-show can cost your practice $200, and the total cost of no-shows for the US Healthcare system is a $150B problem. It is essential to understand how long each patient will need, determine who will be a no-show or cancellation, and improve the patient flow through your office. Your schedule is the intake to your entire practice, and when investments are made in better access, your patients get scheduled faster and spend less time waiting in the office.

AI for Healthcare -- learn more at HealthTensor.com

By leveraging machine learning, you can understand patient demands, plan for the unexpected, and improve patient access. These predictive analytics are multifaceted and offer two-fold benefits:

  • Predict no-shows and cancellations: using past patient data, patient demographics, chief complaints, location, and environmental factors, algorithms can predict who will show up late, who will cancel, and who will not. These aberrations to your calendar mean lost revenue, patients missing critical care, and reduced access for new patients. Predictive analytics for your schedule improves patient throughput and alerts your staff to trouble. By flagging likely no-shows, your team can proactively reach out to ensure a patient who needs care can come in. When necessary, it can also recommend double bookings to keep your schedule optimized.
  • Determine the appropriate appointment length: using the patient record, patient history, and physician habits, machine learning tools help your staff set the correct appointment length. Some of the best schedulers may have a deeper understanding of their patient population and know the size required of the appointment given the particular patient. With predictive analytics, these factors are known and estimated for every patient accurately. By right-sizing your appointment times, you can see more patients, have a more predictable schedule, reduce wait times, and increase satisfaction.

2. Smart scheduling for the OR, GI, and Cath labs

The operating rooms, GI labs, and Cath labs are some of your hospital’s most important service areas. These are the profit drivers of your hospital, but they can also be significant cost sinks if not carefully managed. Stanford studies show that the average cost of ORs can be as much as $60-80 per minute.

Procedures that run long can lead to costly overtime wages dissatisfied staff and may bump planned operations out of the schedule. Under-utilization of these rooms leaves revenue on the table and keeps patients and surgeons waiting. Unnecessary gaps in the program also lead to additional delays with staff taking breaks outside the operating theaters.

If your team better understands and predicts the needs of the OR, GI, and Cath lab, you can increase throughput, improve revenues, reduce costs, and improve physician and patient access.

AI for Healthcare -- learn more at HealthTensor.com

Novel machine learning techniques can ingest all your operating room scheduling data to generate insights. Tracking time-stamped data for arrivals, preparation, and procedures by patients and physicians can predict accurate operation and turn-around times. This tracking will enable your schedulers to allocate the right time for each system based on the patient and physician.

AI systems can suggest the ideal schedule and remove gap time from your hospital’s most valuable assets. Further, they can specifically optimize block time or help your team move off blocks entirely while still prioritizing key value drivers. An intelligent schedule powered by AI will drive efficiency in your OR and critical procedure rooms.

AI tools to support finance

Your practice or hospital’s financial well-being is paramount to keeping its doors open. It is time to arm your finance team with the same firepower that payers have used to limit and control payments. The cat-and-mouse game between providers and payers has led to shrinking margins and difficulty getting the due money. AI is ideally suited to help your finance team work through the myriad hoops and data requests payers burden on providers.

3. Automated Preauthorization

Preauthorization can take an army of trained clerks to process clinical notes, answer questions for payers, track dozens of contracts, and not let anything slip through the cracks. According to studies by the American Medical Association and the Journal of the American Board of Family Medicine, today’s preauthorization methods can take 20 hours of labor per week per physician and lead to a total annual cost of $600-700M in the US. Preauthorization is a significant financial burden.

Many for-profit hospitals won’t schedule imaging or procedural appointments without preauthorization, and delays in the process might turn away patients to other facilities. Some nonprofit hospitals that don’t wait for preauthorization put reimbursement at risk. Worse, after critical clinical documentation is captured by clerks for preauthorizations, they are not retained in claims.

AI for Healthcare -- learn more at HealthTensor.com

Fortunately, AI can help overhaul and automate the entire process. AI technologies automatically identify and collect critical clinical information from the patient record using advanced natural language processing, contextual understanding, and extensive training sets. These tools prepare information summaries for the clerks to help them answer questions faster and more accurately. These same engines can automate all but the most complex cases to quickly and efficiently get preauthorizations.

Leveraging AI to help tackle the bulk of preauthorizations enables your team to focus on the smaller pool of complex cases and frees it up for more critical administrative tasks. Further, with an automated engine to track and collect data, your team will have better documentation for claims processing downstream and decrease the time it takes for you to get paid.

4. Predictive denial management

Denial management is a critical component of the revenue cycle. According to the American Medical Association, Medicare denial rate averages can be as high as 5%. Rates vary by payer, but each payer requires different information, and the overwhelming number of charges and claims make tracking denials impossible without comprehensive software.

While many solutions are on the market today, many only show reports and “hot spots” for denials. These tools are not intelligent enough to predict which claims will be denied and how to change them before submission. More worrisome, payers partly develop and own many of these tools. There is little incentive to ramp up their effectiveness.

AI for Healthcare -- learn more at HealthTensor.com

Denial management is ripe for machine learning to help providers get reimbursed for what they are owed. A predictive system can highlight problems before they are submitted. An intelligent denial management system can flag problem areas and suggest interventions by tracking the entire patient billing record from authorization through claims. Moreover, it can automatically augment pieces of the claim to reduce the burden for your team.

Like other machine learning tools, predictive denial management software will also learn and adapt from the feedback of insurers. As rules and requirements change with new contracts, the system will automatically start to track and help your team react to those changes. Predictive denial management can drastically improve reimbursements, focus your team’s work on the most valuable areas, and reduce total workload.

AI tools to track Performance

Understanding performance and variation in your hospital’s clinical care is critical to driving change. With better data, your administration can help negotiate better deals, guide clinical care decisions, and optimize care. Today’s machine learning tools can help your organization achieve change, improve care, and reduce costs.

5. Identifying medication variation

Your EHR holds hundreds of data points that can illuminate your care team’s clinical decisions, including critical information about expenses. Medications are one of the fastest-growing costs in hospitals; according to a report by the American Hospital Association, drug costs increase an average of over 23% per year. Understandably, the pharmacy is often a significant source of financial pressure for your hospital.

Many medications have equivalent effectiveness but vary widely in price. One example is oral Tylenol versus intravenous Tylenol. Creating and administering an IV for Tylenol can cost $15-35 per unit, whereas oral Tylenol costs pennies. Multiple studies show they are clinically equivalent (Journal of the Society for Academic Emergency Medicine, Journal of Hospital Pharmacy, Clinical Neurology).

AI for Healthcare -- learn more at HealthTensor.com

So, how can you track variations in medication, determine levers of savings, and implement them in your organization?

Machine learning, again, can do the heavy lifting for your team. By analyzing patient profiles, prescribing physicians, medications, costs, and outcomes, these advanced systems can help your team make evidence-based decisions and drive down costs.

First, AI identifies what medications drive costs and which physicians prescribe them. This analysis enables your team to shift behavior to more price-effective drugs. Advocating and driving behavior change requires precise, insightful data. Ultimately, these tools will help your team save money and improve patient outcomes. An AI-powered medication variation software can deliver all this in one integrated solution.

6. Physician selection for capitation contracts

With the transition to value-based care, your provider group must negotiate contracts with payers. The shift is already happening, and value-based care will continue to grow dramatically over the next several years. CMS has stated they plan to move 50% of payments to value-based care by 2018.

To prepare for these opportunities, your group must understand how well you manage patient types and who is best suited to engage with and keep those patients healthy. If there is not sufficient information, you will not be able to succeed in negotiating or managing costs under these contracts.

AI for Healthcare -- learn more at HealthTensor.com

Fortunately, AI can arm your team with the data and insights you need to succeed in these changing environments. If you understand physician performance and variation by patient type, you can select the best physicians to handle at-risk patients. The complexity of patient profiles makes this a complex process without the tools to automatically understand, sort, and compare outcomes by physicians.

Moreover, these tools can help you guide behavior within your group to identify cost areas that can be managed. You will understand the best physicians to deal with certain patients and the best treatment plan that helps them heal quickly and cost-effectively.

The following two tabs change content below.
HealthTensor develops artificially intelligent systems to automate documentation for physicians. Our technology mines the medical record for pertinent information, assists in diagnosing key conditions, and generates proper documentation.

Latest posts by Nate Wilson (see all)

Request a Demo

See how ReferralMD delivers a better experience for providers, staff, and patients.

Learn More