Streamlining Hospital Operations, Optimizing Resource Allocation, and Improving Efficiency with AI Predictive Analytics and Machine Learning Algorithms

Although we all call it AI, the technology we have is not all the way there to be true independent and self-thinking artificial intelligence. Instead, the AI that we are working with is a very sophisticated technology that is trained to predict and analyze things based on what it has been coded and trained to recognize. So in that spirit, let’s take a moment to look at all of the work that AI-driven predictive analytics and machine learning algorithms are doing in the world of healthcare right now.

We reached out to our brilliant Healthcare IT Today Community and asked them how AI-driven predictive analytics and machine learning algorithms are being employed to streamline hospital operations, optimize resource allocations, and improve overall healthcare system efficiency. The following are their answers.

Varun Ganapathi, Co-Founder and Chief Technology Officer at AKASA
AI and ML have advanced dramatically recently. Now, with generative AI, artificial intelligence can play a significant role in understanding both clinical and payer-related documents. This advancement makes AI perfect for impacting the hospital revenue cycle. More specifically, within prior authorization — a critical component of operations and the second most time-consuming part of the revenue cycle. Prior authorization is a hot topic for health systems, providers, and legislators. It’s costly, inefficient, and responsible for patient care delays. It’s here where AI can have an immediate impact, be trained on a health system’s own data, and be course-corrected to ensure highly accurate outcomes. The revenue cycle touches every point of a patient’s journey in a hospital, from booking the first appointment to discharge. In this environment, AI can safely have a sizable impact on operations, empowering staff to complete administrative work more quickly, reducing burnout, and ensuring that patients get the timely care they need with a reduction in negative outcomes.

Etienne Boshoff, Managing Director at EHR Enhancify
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into healthcare is indeed revolutionizing the sector by enhancing hospital operations, optimizing resource allocation, and boosting overall system efficiency. These technologies harness the power of data analytics to predict future needs and trends, enabling healthcare delivery to become more proactive and personalized.

With regards to streamlining hospital operations, AI and ML help predict patient admission rates and identify peak times for various healthcare services. Such predictive capabilities enable hospitals to efficiently manage bed occupancy, ensuring they are neither overburdened nor underutilized. By accurately forecasting staffing needs, hospitals can reduce wait times and improve the quality of patient care. This optimization leads to a more responsive and effective healthcare environment, where resources are utilized in the most efficient manner possible.

AI-driven analytics also play a crucial role in predicting the demand for medical supplies and equipment with remarkable accuracy. This foresight is invaluable in avoiding shortages or excesses, thus reducing waste and ensuring that resources are directed precisely where they are most needed. Predictive models also assist in inventory management, guaranteeing that medications and other critical supplies are adequately stocked, especially during peak demand periods or disease outbreaks.

On a broader scale, AI and ML significantly contribute to enhancing the overall efficiency of healthcare systems. Predictive analytics are employed to identify patients at higher risk of developing certain conditions, enabling preventive measures or early interventions that can halt the progression of these conditions. Additionally, AI algorithms facilitate the quicker and more accurate diagnosis of diseases by analyzing vast datasets, thereby accelerating treatment initiation and improving patient flow through the healthcare system.

As AI and ML technologies continue to evolve, their impact on the healthcare sector is poised to grow even further, heralding a future where enhanced patient care and operational excellence are the norms. The promise of AI and ML in healthcare lies not just in their current applications but in their potential to reshape healthcare delivery in ways we are only beginning to imagine.

Piotr Orzechowski, CEO at Infermedica
AI-driven predictive analytics and machine learning are streamlining hospital operations, ensuring resources like staff and equipment are used where they’re needed most. A key innovation is AI-powered care navigation, which directs patients to the appropriate care setting based on their needs, easing the burden on emergency departments. This not only improves patient outcomes by ensuring timely, acuity-appropriate care but also helps prevent healthcare worker burnout by reducing unnecessary stress and workload, creating a more sustainable healthcare environment.

John Showalter, MD, Chief Strategy Officer at Linus Health
So much of medicine is based on drawing conclusions from observation. Over the centuries, technology—from the microscope to the MRI—has given us the ability to look more closely and in-depth. AI and machine learning are the next evolution in observation because they allow us to ‘see’ patterns in enormous data samples more easily than ever before and draw conclusions that can be applied to patient care. AI and machine learning are enabling healthcare to improve and modernize older technologies to make them more powerful and precise, as well as easier to use. For example, standard pen-and-paper tests to screen for mild cognitive impairment are being replaced with AI-powered digital tools that are more sensitive and accurate. Putting enhanced tools like these in the hands of primary care providers will allow them to provide better care sooner and more easily, which will benefit patients enormously. AI-enhanced technologies that can be used by different levels of providers will help clinicians operate at the top of their licenses.

Ashley Walsh, SVP Client Services, iQueue for Operating Rooms & Inpatient Flow at LeanTaaS
Amid the ongoing challenges of tight margins, labor shortages, and rising patient demand, hospitals and health systems are increasingly pressured to optimize the resources and staff they already have in place rather than buying, building, or hiring more. Too often though, operational roadblocks lead to ORs sitting empty, patients sitting in the waiting room rather than receiving treatment, or nurses missing lunch when appointments bleed into each other.

These challenges can be solved with AI-powered solutions. AI/ML-driven predictive and prescriptive analytics evaluate historical data and recent pattern shifts to forecast future demand far more accurately than humans and give hospital staff access to valuable real-time insights that reflect the actual state of their operations across the board. This means that hospitals can know what’s coming and make proactive decisions. Operational bottlenecks are eliminated before they even happen and key processes like scheduling OR time, managing patient discharges, and allocating staff are streamlined.

However, sustainable success and true operational transformation require more than just AI – data hygiene, workflow automation, and change management services are equally important factors in the magic equation to providing efficient and excellent care delivery. By combining these key pieces with the power of AI, hospitals and health systems can ensure they have the best technology, cleanest data, systemwide governance, and support for ongoing success.

The hospital of the future is one where nurses enjoy lunch breaks, patients receive treatments on time, surgeons easily access OR time, staff are freed from manual data entry, and no one waits in the ED because an inpatient bed is always available. Embracing AI-driven solutions is the first step in getting there.

Sachin Patel, CEO at Apixio
AI and machine learning algorithms will prove to be crucial to improving the efficiency of the healthcare system. AI’s ability to surface actionable insights is already reducing tedious administrative tasks and the burden of work for healthcare professionals. Two of the most successful use cases for AI/ML are surfacing insights from disparate data sources for physicians and supporting payment integrity tasks for health plans.

Most clinicians spend less and less time with patients as their overhead time has increased. During their visit, physicians are expected to listen to symptoms, assess the situation, and recommend testing or treatment that accounts for the patient’s current condition, past family history, social determinants of health, and medical history. Further complicating the physician’s typical workday is the challenge of accessing additional information beyond the patient record information in their EMR, which health plans are looking to deliver ahead of patient visits. High-quality AI models that have been developed thoughtfully with the right breadth and depth of training data can synthesize a patient’s medical history within seconds, provide clinicians with the most relevant takeaways, and help them complete documentation in the medical record. These opportunities help improve the accuracy and speed of healthcare treatment and support better healthcare outcomes.

An additional use for AI and ML is improving payment integrity. Up to 80% of medical bills contain errors, leading to systemic waste. Finding these mistakes requires sorting through pages of guidelines and medical bills. If an error occurs, multiple parties must review the documentation before the payments can be corrected. Employing machine learning can support the completion of these reviews in seconds and alert health plans, as algorithms work alongside human reviewers. Using artificial intelligence for payment reviews allows reviewers to prioritize bills with potential errors, helping deliver accurate payments faster.

AI and ML are already having a meaningful impact and have the potential to transform U.S. healthcare by enabling healthcare professionals with better data access and insights, giving them more time and resources to shift to an outcomes-based healthcare system.

Derek Streat, Co-Founder and CEO at DexCare
As we look over the horizon, the proliferation of AI is poised to relieve the severe supply and demand imbalance that causes bottlenecks to simply see a doctor. With a major shortfall of physicians anticipated in the next decade, resource allocation – how, when, and where care is delivered – is paramount. Today’s patchwork of datasets, from legacy EMRs to practice management systems, has created operational blind spots, preventing health systems from precisely managing the distribution of care. But data harmony has arrived. In a blink, generative AI and machine learning models can streamline complex data, like usage, capacity, and costs, to enhance predictive capabilities for operating a real-time health system. In practice, it’s the digital awareness to coordinate between doctors, nurses, and clinics to minimize delays and to maintain a steady flow of care. And it’s securing the data-linkages to manage an entire portfolio of care to deliver on access, capacity, and future growth. Practical applications of AI are already transformational. The real hurdle is how long health systems will grip onto archaic models.

Vivek Desai, Chief Technology Officer of North America at RLDatix
In healthcare, we can use AI and large language models (LLM) to empower our frontline staff and reduce the cognitive load, so their time can be spent taking care of patients. LLMs are proficient at removing ambiguity. For example, you might describe a color as burgundy, but someone else might think it’s more of a cherry red color. LLMs can remove this personal bias to standardize information inputs. If a frontline administrative staff member starts a task, and someone takes over while they are on lunch or during a different shift, the LLM can control for the difference in perspective between two people. AI can streamline a lot of use cases to do certain tasks predictively and at scale faster than humans can. Additionally, machine learning can be used to inform staff scheduling by incorporating publicly available information, such as local sporting events, the weather and holidays, to identify when additional support may be required to handle an anticipated patient volume increase.

Patrick Tarnowski, Chief Commercial Officer (CCO) at OneStep
AI-driven predictive analytics and machine learning algorithms are transforming health care operations by creating capabilities to forecast trends and optimize staffing. The ability to analyze historical data to anticipate patient needs, when deployed in a manner that is meaningful to providers and administrators, can enable the allocation of resources efficiently to improve access to care. Additionally, by automating repetitive tasks and identifying operational inefficiencies, AI can improve workflows, positively impacting both patient outcomes and provider satisfaction.

Matthew Cohen, Director, AI at Loyal Health
Following the pandemic’s virtual care surge, doctors were inundated with written inquiries, adding significant hours to their workloads, a contributing factor to industry-wide burnout. Leveraging GenAI’s abilities in text processing, patient communication can be streamlined, minimizing the time doctors spend crafting and editing responses. On top of that, GenAI’s predictive capabilities play a role in resource allocation. By forecasting downtrends in appointments and empowering providers to make informed decisions around initiating targeted outreach campaigns, patients are getting the care they need, when they need it. For instance, providers could launch a campaign dedicated to colon cancer and identify ideal candidates for screenings, ensuring appointments align with patient needs.

Jessica Curran, VP, Data Science and Analytics at Health Catalyst
We have been using AI and machine learning for many years to forecast patient volumes for acute care, critical care, ambulatory, emergency department, and procedures in order to help health systems optimize staffing, equipment and physical space. We saw a steep increase in the number of requests for this type of work at the start of the COVID-19 pandemic which accelerated development and adoption. We also frequently generate predictions at the patient level for who is most likely to require a procedure, have a bad outcome, cancel an upcoming appointment, need help paying their bill, etc so that interventions can be tailored to prevent or be better prepared for these situations. These 2 types of algorithms, when used together and with input from clinicians and administrators, can have a significant positive impact on the timeliness, efficiency, cost and quality of care provided for patients.

Vijay Adapala, EVP Global Supply Partnerships at Doceree
AI-driven modeling can assist hospitals in any task that requires learning from patterns within data. This can include practical applications such as predicting staffing needs and inventory management. By tapping into the data within electronic health records, health systems can also look at patterns in patient health, treatment efficacy, common errors, and immunization statuses across geographies. This data can be used to improve the quality of care by enhancing a provider’s perspective, indicating early signs of disease, and predicting risks.

Gary Shorter, Head of AI and Data Science at IQVIA
AI answers the call to a diverse amount of industry needs through various algorithms and machine learning capabilities. It is leveraged to assist healthcare professionals in the review of documents and images to make informed decisions about patient treatment and care. AI is also used to assist in regulatory and safety processes by monitoring for indicators of drug safety events, practicing quality assurance and streamlining regulatory reviews. Additionally, AI can speed up predictive scenarios which frequently lead to earlier patient diagnoses. Overall, the automation of tasks helps decrease administrative burden, for example through the form of virtual assistants and chatbots, answering simple questions for patients.

Stephen Sofoul, Senior Vice President, Data & Decision Science Services at MultiPlan
The potentials around AI capabilities are now driving advanced analytic solutions to optimize costs and equip patients, providers, and payors with the information needed to inform decision-making. This allows the industry to shift its focus towards personalized care journeys. Through machine learning algorithms, millions of data points can be analyzed faster than humanly possible to predict medical costs, identify billing errors, and enable price transparency, bringing cost-saving benefits to all parties in the healthcare ecosystem.

Machine learning algorithms enable the industry to take a data-driven approach and, through predictive analytics, identify future risks or care needs so providers can recommend preventative measures for patients. Patients and providers are given the tools and information to make informed decisions about their care quality and associated costs, bypassing potential cost and health issues down the road. As machine learning tools and the potentials of AI progress, we’ll continue to move the industry towards lower costs and personalized care to improve patient outcomes.

David Bates, PhD, Co-Founder and CEO at Linus Health
Healthcare has been struggling with the problem of generating huge amounts of data without the capability of sufficiently analyzing and acting on that information. AI and machine learning show great promise in analyzing that data, drawing conclusions from it, and presenting ways to use it for the good of the patient. For example, in the case of a new digital screening tool for mild cognitive impairment (MCI), it not only collects more data, but analyzes it and generates a treatment plan.

Josh Klein, CEO at Emerest Connect
One of the ways I have seen AI programs streamline healthcare processes is in the way they assist healthcare workers by automating what is otherwise mundane, time-consuming tasks that add to burnout, frustration, and long hours. Whether its generating invoices, tracking inventory, or managing schedules, advanced technologies carry the burden of administrative office work so caregivers can focus on their most impactful work – providing personalized care and ensuring the best health outcomes for their patients. This also means that nurses and doctors can treat more patients without sacrificing quality of care, because they are utilizing AI not only as an extra set of hands for administrative tasks, but also as another source of brain power in the form of real-time data and analytics for their patients.

Carolina Haefliger, Head of Transitional Medicine at Debiopharm
AI is fundamentally transforming healthcare from reactive treatment to proactive prevention. By analyzing vast datasets with machine learning (ML), AI expedites drug discovery by pinpointing promising targets and rapidly screening compounds. Furthermore, AI empowers healthcare providers to predict and prevent illnesses, enabling personalized medicine. This not only improves patient outcomes but also minimizes healthcare costs by reducing the need for extensive evaluations, treatments, or hospitalizations. Through ML algorithms, AI efficiently sorts through compounds, flagging those that exhibit promising characteristics for further investigation. Its speed and effectiveness streamline the screening process and enable researchers to focus their efforts on the most viable candidates, expediting the overall drug discovery timeline and conserving valuable resources that would otherwise be expended in exhaustive manual screenings.

Niraj Katwala, Vice President, Engineering at Edifecs
AI-driven predictive analytics and machine learning algorithms are being employed to streamline hospital operations, optimize resource allocation, and improve overall healthcare system efficiency through a multitude of different use cases including but not limited to:

• Medical Coding – both HCC and FFS coding have benefited from AI. AI brings more clinical accuracy to the entire coding process which means that the health of the patient is more accurately defined. Properly trained AI can identify and bring forth to the medical coder – codes and conditions which may be difficult to visualize and glean because it requires the human to stitch a multitude of evidence which many times is done much better by an AI system.
• Clinical Decision Support – AI can look at the longitudinal patient chart across millions of patients as training data. This helps Clinicians identify patients similar in demographics, comorbidities, and patients who have been through a similar treatment regimen. AI-driven CDS systems can come up with entire treatment regimens based on the current physical ailment of the patient. The patient’s condition can be used as a prompt for obtaining a treatment regimen. While such systems are in their early stages, they have the potential to completely change the way treatments are meted out to patients.

Similar to advertisement targeting, nowadays many organizations are using AI to target customers internally (consumers and businesses) alike by identifying their behavioral patterns and characteristics that include comorbidities, demographics, and past communications. The patient information data available and their various interactions are used as training data by AI to identify information and resources (physicians, medications, benefits, etc.) that can be valuable to health plan members, patients, and even organizations.

Kevin Keenahan, Chief Product Officer at Net Health
Artificial intelligence, machine learning, and predictive algorithms all show huge promise for improving healthcare, whether it’s providing deeper insights or better-informed courses of action for improved care delivery. For these models to be effective, providers should leverage technology partners that help generate high-quality data they can use to inform decision-making. Healthcare has previously struggled to understand and analyze large data sets, and although progress has been made in breaking down siloes, there’s still a need to standardize data models and improve data quality at the point of care. Without consistency in how data is recorded, results will be inaccurate and unable to provide the quality impact that AI is intended to deliver.

So many good insights to think about here! Huge thank you to everyone who took the time to submit a quote in for us and thank you to all of you for taking the time to read this article! We could not do this without all of your support. How do you think AI-driven predictive analytics and machine learning algorithms are being employed to streamline hospital operations, optimize resource allocations, and improve overall healthcare system efficiency? Let us know either in the comments down below or over on social media! We’d love to hear from all of you.