About the Episode

Today we’re going to talk about optimizing the patient experience by looking at ways to optimize the operational efficiency—and in this case the operating room efficiency—at hospitals, by using machine learning tools. We’ll also talk about several other use cases for AI and ML in healthcare.

To help me discuss this topic, I’d like to welcome Michael Burke (CEO) and Josh Byrd (CMO), Copient Health.

About Michael Burke

A serial entrepreneur in the healthcare technology space, Mike has over 20 years of experience founding and growing healthcare technology companies.

In 1997, Mike founded Dialog Medical, which became the leading provider of Informed Consent systems to hospitals and physicians. He sold the company in 2011 to Standard Register (NYSE: SR), which was later acquired by Taylor Communications.

Mike founded Clockwise.MD in 2013 and grew the company to become the leading provider of healthcare queue management solutions. The company was acquired by the private equity firm Warburg Pincus in 2017 as part of a roll-up in the on-demand care segment and merged into their portfolio company, DocuTAP.

Mike was a founding member of Tapestry Charter School in Atlanta, an inclusive environment designed for middle and high school students on the autism spectrum and their typical peers.

Mike is currently a co-founder of Copient Health, a startup in the Operating Room optimization space.

Mike serves as a mentor for aspiring entrepreneurs at Georgia Tech’s Advanced Technology Development Center (ATDC) and at the Atlanta Tech Village.

Mike and his wife Tracy have three daughters, and are both avid cyclists.

Resources

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Synopsis

Artificial Intelligence (AI) and machine learning tools have the potential to revolutionize operational efficiency in healthcare, particularly in areas like the operating room. As discussed in the podcast episode, these technologies can be utilized to predict and identify operating room time that is likely to go unused, allowing hospitals to optimize their resources effectively. By leveraging machine learning algorithms, hospitals can proactively allocate this unused time to surgeons who can utilize it, similar to an "open table for the operating room."

The financial implications of optimizing operational efficiency in the operating room are significant. The episode highlights that the operating room accounts for a substantial portion of a hospital's revenue and operating margins. When operating rooms sit idle, the costs incurred are not just the direct expenses but also the opportunity costs associated with missed surgical cases. By using AI and machine learning tools to maximize the utilization of operating room time, hospitals can enhance their financial performance and support the overall mission of the institution.

Moreover, the improved operational efficiency facilitated by AI and machine learning tools can lead to enhanced patient access to care. By streamlining processes such as patient scheduling and optimizing resource allocation, hospitals can ensure that patients receive timely and efficient care. This not only benefits the patients by reducing wait times and improving access to necessary procedures but also contributes to the overall patient experience within the healthcare system.

The integration of AI and machine learning in healthcare operations, particularly in the operating room, offers a dual advantage of financial benefits for hospitals and improved patient access to care. By harnessing the power of these technologies to optimize operational efficiency, healthcare institutions can drive cost savings, enhance revenue generation, and ultimately deliver better healthcare services to their patients.

In healthcare, AI is utilized for decision support by analyzing patient data to assist healthcare providers in making accurate diagnoses, treatment plans, and recommendations for preventive care. By leveraging AI, healthcare professionals can access valuable insights and recommendations based on data analysis, leading to more informed decision-making processes.

AI plays a crucial role in prescriptive analytics within healthcare systems. By analyzing vast amounts of population health data, AI can identify gaps in care, high-risk patients, medication adherence issues, and other opportunities to enhance patient outcomes. This proactive approach allows healthcare providers to intervene early, personalize treatment plans, and improve overall patient care.

AI technology enables process automation in healthcare, streamlining repetitive administrative tasks such as patient intake, billing, referrals, and scheduling. By automating these processes, healthcare organizations can enhance operational efficiency, reduce manual errors, and allocate resources more effectively. This automation not only saves time and resources but also improves the overall workflow within healthcare facilities.

AI-driven patient scheduling solutions, like the one offered by Copiant Health, optimize operating room efficiency by predicting and allocating unused operating room time to surgeons. By utilizing machine learning tools, hospitals can maximize their resources, reduce idle time in operating rooms, and ensure timely access to surgical care for patients. This streamlined scheduling process not only benefits operational efficiency but also enhances the patient experience by reducing wait times and improving access to necessary medical procedures.

Overall, the integration of AI in healthcare for decision support, prescriptive analytics, process automation, and patient scheduling contributes to improved patient outcomes, operational efficiency, and overall quality of care within healthcare systems.

AI and machine learning are poised to revolutionize the healthcare industry in the future, with implications as significant as the impact of the internet. The episode discusses how Copiant Health utilizes machine learning to optimize operational efficiency in hospitals, particularly in the operating room. By leveraging AI tools, Copiant Health can predict and allocate unused operating room time efficiently, leading to cost savings and improved revenue generation for hospitals. This not only benefits hospital operations but also has a direct impact on patient care by ensuring timely access to surgical services.

Furthermore, the episode highlights the broader applications of AI and machine learning in healthcare, such as clinical workflows, decision support, prescriptive analytics, and process automation. These technologies have the potential to streamline administrative tasks, improve patient outcomes, and enhance the overall efficiency of healthcare systems. By automating processes like patient scheduling and documentation, AI can free up healthcare professionals to focus more on patient care, ultimately leading to better outcomes for patients.

The guests on the show emphasize the importance of continuous innovation and adaptation in the healthcare industry. They discuss how AI-driven solutions like Copient Health's surgery optimization platform can not only improve operational efficiency but also have a positive impact on patient experience. By balancing the need for cutting-edge technology with patient-centric care, healthcare organizations can drive significant advancements in both patient care and hospital operations.

Looking ahead, the guests express optimism about the transformative possibilities of AI in healthcare. They believe that AI has the potential to address major issues in the healthcare system and drive positive change for the greater good. With the rapid advancements in AI technology and the increasing openness to innovation in healthcare, the future holds immense potential for AI and machine learning to revolutionize patient care and hospital operations, leading to significant advancements in the healthcare industry.