Today’s healthcare landscape is complex. Health systems are highly focused on efficiency, productivity, and optimal patient outcomes; however, being efficient while maintaining high level of quality and impeccable patient satisfaction is not simple. Having success in the healthcare industry involves commitment, effort, and intricate coordination from highly skilled teams and leaders to drive efficiency and reach goals. Meeting goals and being efficient will also support the financial stability and growth endeavors of health systems. Even in the non-profit healthcare sector, there needs to be a financial margin to continue to support the needs of the community and provide exceptional care to those who need it. One way to meet these demands is through data analytics. Data analytics can assist in driving the direction of work within a healthcare system by telling a story. Data can assist leaders in making informed decisions, identifying trends, optimizing processes, benchmarking success, and ultimately driving change when needed.

Identifying areas of inefficiency and implementing performance improvement initiatives is a standard process within healthcare to improve outcomes. To be efficient, organizations need to use available resources effectively, while minimizing waste. Resources can be people, supplies, and even time. Every year, there is an estimated health spending loss due to inefficiency. Reducing health system inefficiencies can improve the availability of quality healthcare to the communities that need it, which in turn will yield better health outcomes overall. Operational efficiency can be monitored through many different data analytics platforms, in many different areas of a health system. It is imperative that data is available to teams to improve workflows and minimize waste. There should be a standard approach to measuring, analyzing, and utilizing data to engage teams to close gaps when opportunities exist. Some areas within health care that might use data analytics to improve workflows could be the: operating room (OR), emergency department (ED), or even hospital medicine to improve quality metrics or length of stay (LOS) standards.

Prioritizing the opportunities from data analysis should be a strategic step to understand how to drive success.

The OR is an area of intricate skill and complex coordination. OR efficiency is crucial for patient safety, employee satisfaction, and overall healthcare effectiveness. Often, a significant amount of hospital revenue comes from the OR. Hospitals facing economic challenges from shrinking reimbursement should consider maximizing resources within the OR to allow for continued financial success and data analytics can be a means to guide and focus workstreams. Some of the data-driven outcomes that assist with OR efficiency include turn over time (TOT), first case on time starts (FCOTS), room utilization, cancellation rates, and scheduling accuracy.

Additionally, data from supporting departments such as sterile processing can assist with care coordination and efficiency. Benchmarking this data is also important to show success rates based on like organizations/departments in the region/country. Many benchmarks will show that OR utilization between 75%-80% is acceptable and appropriate; however, what happens if utilization is under or over the benchmark? In an organization where the utilization rate is over 85%, a cascade of challenges can occur as the volume outpaces the space available. Data is imperative in these situations to assist with short-term and long-term strategies for success.

OR utilization refers to the percentage of time an operating room is being occupied over a certain period. It’s a key metric used to assess the efficiency of OR operations. Through deep dives with data analytics, care delivery can be transformed to provide better care and profitability for a health system. In ORs, leadership typically focus on room and block utilization. Room utilization focuses on how efficiently a physical single operating room is used. Block utilization looks at how surgeons use their allocated room throughout a period of time. Focusing on these outcome metrics can assist with developing an action plan and process metrics to yield success. Many action plans or process metrics will involve workflow redesign.

When working on OR workflow redesign, data analytics can support several areas. Optimizing patient preparation, minimizing delays, and enhancing team collaboration can create safer and more efficient surgical environments. Standardizing physical and workflow designs will also assist in workflow efficiency. Assessing the previously discussed data can allow teams to focus on areas of opportunity to improve workflow. Additionally, data analytics can assist teams in developing simulation platforms to work through barriers or obstacles in workflow and design a collaborative approach to parallel processing or other methods to assist with efficiency. Data analytics in the OR can ultimately enhance decision-making, assist with resource management, allow for surveillance of processes, and reduce healthcare costs. Data must be collected and shared routinely, but more importantly, data must be acted upon.

There are a multitude of platforms to receive data in the OR. The primary data source is the electronic health record (EHR); however, there are other platforms, such as patient surveys, employee surveys, wearable devices, and financial data. All this data can be analyzed through multiple tools to explore opportunities as well as existing areas of operational excellence. Prioritizing the opportunities from data analysis should be a strategic step to understand how to drive success. Focusing on standard pillars such as quality, growth and finance, patient experience, and research or scientific advancement may all yield opportunities of focus to be key drivers of optimal outcomes. It is up to each healthcare organization to strategically analyze their data and identify priorities. Prioritizing initiatives will enable organizations to develop strategy, play books, or other action plans to optimize efficiency, improve patient outcomes, positively impact financial performance, and ultimately allow for continued success to support community needs and healthcare sustainability. Data is the future of medicine and trends are moving toward machine learning (ML) and artificial intelligence (AI). Systems that are poised with data will be well positioned for success in the rapidly changing healthcare landscape.

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As the population ages, the imbalance between the demand for care and providers of care will continue to fuel the need for innovative solutions in our healthcare system. Digital technologies continue to be developed and applying these solutions across the continuum of care is increasingly popular. Surgeons, such as myself, who are directly involved in patient care have witnessed firsthand the evolution of technology from an electronic health record (EHR) to today’s AI-based clinical decision support (CDS) tools. However, their success is not solely determined by technological advancements and unique problem-solving; rather, effective change management plays a pivotal role in ensuring seamless integration into clinical practice. I will discuss the significance of change management in driving the adoption and success of digital health solutions.

The Promise of Digital Health

Digital health technologies impact patient care, offering unprecedented opportunities for enhanced diagnosis, treatment, and patient engagement. From EHRs to telemedicine platforms and CDS systems, these innovations have the potential to streamline workflows, expand access and improve outcomes.

Nevertheless, none of the current digital health and CDS tools we use today would be successful in health care practice if they had not been solutions to pressing challenges facing clinicians and hospital teams. At Houston Methodist, as with other healthcare systems, we had been using telemedicine programs prior to the COVID-19 pandemic.  Nonetheless, when COVID cases first hit our hospital system, our ability to scale our virtual platform and learn its most effective role was our first encounter using many digitally based innovations in direct patient care. This would not have been successful without aligning and synchronously engaging our formal change management strategy and the telemedicine integration.

The successful implementation of digital health tools requires more than just technological prowess; they demand a strategic approach to change management.

Change Management

The successful implementation of digital health tools requires more than just technological prowess; they demand a strategic approach to change management. Digital technologies are not commodities; they look to enhance real-time decision-making based on historical data. In other words, the digital tool tells you something, but it is up to the user to execute the information. The successful adoption of technologies tends to follow these three critical steps:

1) How big is the problem that the technology attempts to solve,

2) Ease in the first step of the change process, and

3) Positive feedback after the first step. 

If any of these steps faces significant challenges, then the likelihood of adoption tends to be less successful. This strategy encompasses a series of structured processes aimed at training and supporting clinical teams, administrative staff, and hospital operations personnel to navigate change successfully. Effective change management ensures that these individuals are capable of using new technologies and embracing them as integral components of their practice.

Resistance to change, workflow disruptions, and concerns about data security are just a few of the hurdles that must be addressed. Moreover, the fast-paced nature of health care delivery leaves little room for extensive training or experimentation with unfamiliar technologies. An analogy for implementing change in health care is like trying to change a tire on a moving car.  Thus, it becomes imperative to implement change management strategies that minimize disruptions while maximizing user acceptance and proficiency.

At Houston Methodist, we begin with a small pilot program to determine quickly whether an innovation can expand throughout our hospital system, building upon our “succeed fast, fail fast” agile model. With a recent pilot project, we co-developed an innovation to use ambient technology in the operating room (OR) to improve accuracy in our OR data analytics and reduce the reliance on nurse-driven data entry into the EHR. The objective was to focus on maximizing case duration accuracy to improve scheduling and staffing and reduce the data entry demands of the clinical staff. Educating clinical teams about the new ambient intelligence cameras in their ORs was and continues to be a delicate process for change management within our organization. Strategies around its application and engagement are iteratively evolving and are integral to evaluating the success of an executed technology.   

Disruptive Innovation

Startups that challenge the status quo and introduce groundbreaking solutions often find success in reshaping the health care landscape, but the iterative process is essential for new technologies to develop and integrate in an impactful manner in health care. Without this collaboration, the technology would still be unrealized because just creating great technology is not enough. Startups which are amenable and flexible to co-develop their products with industry partners will be more successful in the long run.

The concept of disruptive innovation is no stranger to the health care sector. It is typically touted as a key component to innovation. However, at this institution, we recognize all change; therefore, all change management is disruptive. Therefore, our goal is to create strategies for adopting these digital innovations that are as small a disruption as possible to the end user in regards to their daily activities. Disruption is not our goal; technological adoption, execution of new workflows, and improved alignment with digital technologies are the focus. Developing an effective process in this regard is as critical as our innovation.

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Many, if not all, operating room (OR) committees look at block allocation and utilization, but other than a few changes, most block schedules are static and legacy protected. 

It got me thinking… how do I really know if my block time is allocated properly? And what does properly really mean?

It always seemed odd that we make a ‘plan’ for clinical operations – our budget, and a ‘plan’ for surgical cases – our block schedule, but the two never were discussed together. We are also good at measuring budget vs. actual cases/minutes and block utilization percentages, but something still felt off.

Why is it that some services are always scrambling for additional time while others seem to coast along? For example, I had a urology group that would beg, borrow and steal any time they could to get into the OR. Looking into it further, I was surprised to see their case volume wasn’t growing exponentially over time, and it was actually kind of flat. So why the scramble for time?

I wondered what it would look like if we compared budgeted minutes/cases to allocated block minutes to actual minutes (with adjustment for after-hours and out-of-block cases). The premise is that we are setting our services up for success or failure with budgeted volumes that are adjusted annually. In contrast, the block schedule is only adjusted if someone drops below a subjective percentage threshold. 

Looking back at the urology group, they were budgeted around 80,000 minutes, allocated for 60,000 minutes, and used 90,000 minutes. In comparison, orthopedics was budgeted for around 90,000 minutes, allocated for 120,000 minutes and used 75,000 minutes. It was no wonder why urology was always picking up any time available!

To help address this perpetual scramble, we tried a different approach in that we reached out to our physicians and office and service line administrators to all get together and make “the mother of all boards” and list all of our surgeons by service and schedules on a wall to visualize both the clinic’s and the OR’s schedules together. We hoped that by getting together and visualizing this work, we could start linking the budget, block time and actual time to maximize efficiency in all of our areas.

We also incorporated the budget to block actual data into our Block Utilization Committee to provide an additional data point to help evaluate new requests. In addition to this data, we utilize a best-in-class software and service technology partner to utilize machine learning (ML) and artificial intelligence (AI) for additional insights.

The results have been pretty phenomenal.

We were able to:

• Increase primetime utilization by 24%
• Increase the number of cases per staffed OR by over 50%
• Improved Staffed OR utilization by 4%

Overall, this work has been a great example of bringing the right people together, empowering them to make a difference, and utilizing 21^st century technology to accelerate their success.

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One of the most challenging areas to support in our hospitals is the procedure areas. Whether they be the OR rooms, the cath lab rooms or the interventional radiology rooms, these rooms contain not only a high volume of equipment but also some of the most complex equipment in our medical centers. Not only does this equipment require a high level of support, but it is also some of the most expensive equipment as well. The reason for this is because of the delicate procedures that are happening in these rooms and the invasive nature of what is being done. Managing such a high demand area requires a special approach to not only ensure positive patient outcomes but also to stay within the business expectations of the medical center. Our team of clinical engineers and biomedical technicians play a vital role in ensuring these outcomes.

Of the many priorities we manage, the highest priority is patient safety. There must be a delicate balance between the various teams that interact with each other before, during and after each case. There is a beautiful relationship within these departments where everyone knows what to do and when, ensuring that the intervention proceeds without any issues. Communication is key. Some of the tools we use to communicate beyond our phones are cameras to check in on rooms and the progress of the case in that room. Other teams can also use the camera as they may be on the ready should a case be nearing its end and may need a turn-over very soon. 

Working with OR Leadership and brainstorming about how to best support the technology within these special areas is crucial to having an effective and efficient program.

Leadership plays an important role in setting the expectations for when and how things should happen as patients are being brought in, taken care of, and brought out. This includes careful planning of the types of cases that are to be performed at the facility and the equipment that will be needed for those cases. There must be thoughtful planning for the capital equipment, instruments, training and workflows that will ensure the best outcomes. Clinical engineering can assist in all phases of these plans to ensure the right technology is sourced, evaluated for compatibility in the institution and has the proper support once the device is acquired.

One of the biggest ‘gotchas’, especially in the OR space, is not considering the total cost of ownership of these costly devices such as navigation, robotics, and intra-operative imaging including the very highly specialized accessories that come with these devices, such as the flexible scopes for endoscopy cases. Facilities should be proactive in looking beyond the first year of warranty and determine how much will be the cost to keep that device working properly, what will be required to repair it in case of a breakdown and what will be the cost of any extended warranties.

To stay abreast of the latest and greatest technology, a hospital should have a capital equipment life cycle planning that may look at least a few years into the future to predict what equipment will be coming to the end of life and may require replacement. If done properly, this will avoid unwarranted failures and ensure the hospital is always current with its medical equipment.

At our facility, we have weekly tag-up meetings with our OR leadership to discuss hot topics regarding equipment maintenance and review medical equipment service agreements that are due to expire in the next quarter. This allows time to request renewal quotes, review them, make any adjustments and process them before the end of the term. Having continuity in service ensures that equipment is always working properly and available for use.

Some of our most pressing challenges regarding medical equipment include tracking the mean time between failures. If this number is too low, then this may be pointing to a larger issue. We had an example where we discovered high breakage volumes of scopes due to improper techniques by clinical users. At this point, we instituted user training to help the clinician not break the equipment. At other times, it was discovered that the breakages were happening as the equipment was being processed for cleaning and disinfection. This proved to be an opportunity to train the sterile processing teams on better handling techniques. Knowing how your equipment is failing is key to having high reliability and equipment that is ready to be used at any given time.

Another of our secret weapons to help support the technology in this very delicate space is the collaboration with other technical teams. Our clinical engineering team is already in scrubs working in the OR space, but if the need is for a different team to be present, this may cause delays in service due to long arrival times. A creative solution is to empower those technical folks that are already in the OR spaces with a little information so they may be effective in solving simple but high-volume calls. Allowing our team to be flexible in responding to minor calls that may fall outside of their normal workflow will empower them to increase uptime, improve customer satisfaction and reduce service delays. 

Working with OR Leadership and brainstorming about how to best support the technology within these special areas is crucial to having an effective and efficient program. Having positive relationships with the various levels of staff also helps improve communications so that information can be delivered promptly and accurately. There are many nuances to supporting the procedure spaces, but when done properly, everyone feels supported and work becomes just another day in the ORs.

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To create a sustainable healthcare system of the future, healthcare providers are turning to vendor partners who can offer integrated tech solutions in the OR, aggregating huge amounts of data from different sources and modalities to provide insight. Data analysis, decision support, patient pathway planning, and OR workflow are key areas of interest, with outputs aimed to make surgeries safer and smarter. Caresyntax has created a new category of technology to address this challenge. The Caresyntax Data-Driven Surgery Platform enables health systems, payor partners, and industry innovators to aggregate and distill data from the massive surgical ecosystem, leverage data for insights, and innovate in ways to create a sustainable and resilient health system of the future.

The Caresyntax vendor neutral, enterprise-grade surgical technology platform delivers actionable insights through AI-powered software, devices, and clinical services, analyzing large volumes of clinical, operational, and financial data to improve patient outcomes and operational efficiency.

Clinical Benefits: Leveraging Technology for Optimal Surgical Workflow Efficiency at Every Step

Caresyntax is revolutionizing the surgical space by harnessing the power of data to drive excellence in every phase of the surgical journey. From preoperative planning to postoperative analysis, the vendor-neutral platform captures and returns useful information through the entire surgical process from all modalities: Electronic Health Records (EHRs), inventory & supply chain systems, financial records, and high-definition surgical footage. Assembling this data into a High-Fidelity Surgical Record, the platform utilizes Intel® Edge computing technology to return useful outputs to improve patient safety and improving clinical outcomes. Whether through industry-leading and clinically validated procedural assessments and surgical guides, or through real time AI-driven patient risk models, Caresyntax is focused on redefining surgical quality through continuous education, development, and improvement. And, with available telepresence add-on capability, Caresyntax can create a surgical education environment where surgeons can learn advanced techniques directly from the experts in their fields, fostering a culture of continuous learning and improvement in surgical practices across geographic and socioeconomic barriers.

Safeguarding Data Collected for Enhancing Surgical Quality and Safety

Caresyntax, a recognized Patient Safety Organization (PSO) for Surgery, prioritizes the protection of surgical data collected across the care continuum. With robust anonymization and privacy controls, Caresyntax ensures the confidentiality of data used in surgical variation reduction, error reduction, and quality improvement programs.

Caresyntax’s commitment to data protection empowers healthcare providers to confidently pursue quality improvement initiatives, knowing their efforts and the generated data are safeguarded. Thus, Caresyntax is transforming healthcare from a culture of “blame and shame” to one of constant improvement and forward momentum.

Operational Improvements: Enhancing Operational Efficiency with Data-Driven Insights

The Caresyntax platform transforms perioperative efficiency by utilizing surgical data for real-time decision-making and remote collaboration. It enriches post-surgery analysis, enabling continuous improvement in surgical practices and resource management. This approach not only supports healthcare providers but also aids medical device manufacturers and insurers by offering insights derived from comprehensive risk assessments.

With the introduction of dynamic surgical scheduling solutions, Caresyntax is further optimizing operating room allocations, reducing scheduling conflicts, and improving patient flow.

Key features include:

• Empowerment of leaders with actionable insights for scheduling optimization, operational throughput, and cost efficiency.
• Data-driven optimization of OR team composition for enhanced procedure outcomes.

With Caresyntax’s data-driven insights, hospitals have experienced substantial improvements, such as:

• 39% increase in operation room scheduling block utilization
• 25% increase in billable minutes
• 40% decrease in after-hours cases

Maximizing Financial Outcomes: Expanding Surgical Services for Increased Volume, Reimbursement, and Profitability

Caresyntax significantly boosts financial performance in healthcare by leveraging surgical data to enhance growth, increase patient volume, improve reimbursements, and boost profitability. The Caresyntax platform offers a comprehensive suite of tools for hospitals and surgery centers, blending unstructured and structured data from various sources to provide a holistic view of surgical care. This enables proactive risk management and better negotiations for value-based contracts. Through advanced financial analytics and customizable tools, Caresyntax identifies cost-saving opportunities and supports service line expansion and marketing with real-world evidence. Additionally, it promotes the development of high-performing teams, facilitating the transition to value-based payment models and positioning organizations at the forefront of healthcare innovation.

Hospitals using Caresyntax have reported remarkable financial outcomes, including:

• Up to 1.5 more surgeries per day
• More than $300k additional revenue per OR
• More than $500 additional savings per OR case, based on better block time utilization.

AI-driven insights are transforming surgical care by enabling a personalized approach to patient treatment, enhancing safety, and care quality.

Pioneering Healthcare Innovation through with Insurers and Medtech Partners

The success of the Caresyntax platform is perhaps most well represented in its innovative partnership with Relyens, a European leader in insurance and risk management, to enhance surgical safety and efficiency through a category-defining Risk Management as a Service (RMaaS). RMaaS embodies a holistic approach to healthcare by combining risk management, Caresyntax technology, best practices, and insurance solutions, to improve patient safety and reduce costs and complications over the entire cycle of care.

And, with its newest offering, Clinical Data as a Service (CDaaS), Caresyntax now offers proven and scalable data collection and data science capabilities for industry partners to grow revenue, measure and improve value, and inform product development. CDaaS offers contextual data collection that is unmatched by current players, lower costs compared to traditional research organizations, and shortened timelines compared to in-house data collection teams.

About Caresyntax

Caresyntax is on a mission to make surgery safer and smarter by converging AI-powered software, devices, and clinical services to help customers improve surgical outcomes. Our vendor neutral, enterprise-grade surgical intelligence platform delivers actionable insights to improve patient outcomes by using proprietary software and artificial intelligence (AI) to analyze large volumes of video, audio, images, device data, clinical and operational data in and around the OR. This real-world evidence can be used by the care team live, during a procedure, and accessed by those outside the operating room via the platform’s dedicated telehealth link. After a procedure, the Caresyntax platform provides insights that help surgeons benchmark and improve their care, hospital administrators use surgical resources more efficiently, medical device companies advance better products, and insurance companies understand risk and devise more tailored policies. Headquartered in San Francisco in the US and internationally in Berlin, Caresyntax software is used in more than 3,000 operating rooms worldwide and supports surgical teams in more than three million procedures per year.

For more information on how Caresyntax can make surgery safer, smarter, and more profitable, visit www.caresyntax.com.

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The post-pandemic staffing crisis, combined with high healthcare costs, has intensified the focus on waste reduction. Waste significantly impacts patients, hospitals, and health systems. Leveraging technology is one way for surgery leaders and hospital administrators to reduce waste in the operating room (OR). There are three types of technologies that should be considered for maximizing efficiency in all hospitals and ambulatory surgery centers (ASCs).

Technology increases automated documentation

Any technology that decreases the amount of manual documentation required by the OR team will improve efficiency. One of the best systems is a radio-frequency identification (RFID) system. RFID is a technology that leverages tags to relay information to electronic readers. A tag is placed on a patient’s chart, which accompanies them throughout the operative journey. Tag readers are placed throughout the OR. As the patient/chart passes the tag readers, the patient’s journey becomes automatically timestamped. The time when a patient enters the OR and when a patient exits the OR are automatically captured and documented in the medical record. The timestamps are often manually entered into the medical record by the OR team, so the RFID system decreases the documentation and improves efficiency.

Years ago, RFID systems were not very accurate. However, advancements in this technology make it one of the top 3 types of technologies that hospitals should evaluate and consider in 2024.

Technology in the OR should be progressive. It should consider ways to maximize resources while maintaining excellent patient outcomes.

Technology enhances communication

Both team communication and patient/family communication are critical for high efficiency. Delays, or perceived delays, in the OR are often avoidable and dependent upon clear communication. Advancing technology in an OR to provide increased communication to patients, surgeons, and anesthesiologists will support efficiency and maximize satisfaction. Most hospitals and ASCs have patient trackers in the waiting room lobby. These HIPAA-compliant trackers are convenient and provide updates to family members on the patient journey. This is an example of a baseline technology platform. There have been enhancements to several of these platforms. One of these enhancements is a text message alert when case changes occur. For example, a recent technology being evaluated for Medical City Healthcare, a 20-hospital system in North Texas, sends a text message to the physician and anesthesiologist if the case will start sooner than scheduled or when there will be a delay. The text also notifies the team of any room changes. These push notifications via text message provide synergy and coordination, reducing waste. These push text notifications allow the team to plan their time proactively. It decreases unnecessary room set-up and tear down which wastes supplies. The notification also allows surgeons and anesthesiologists to prioritize their time versus sitting and waiting for a case or being late to a case that was bumped up.

Medical City Healthcare also has a patient/family texting feature integrated into our electronic infrastructure. This feature is available to opt-in during the registration process. Patients and families who decide to opt-in receive text message notifications throughout the surgical journey.

Technology leverages predictive modeling

Running an OR is similar to playing a game of Tetris. There are a lot of moving pieces that have to fit together perfectly. In order to make every piece fit together perfectly, predictive modeling technology is essential. Case length, or case duration, is one of the most challenging components of running a seamless OR. Often, there are varying opinions regarding how long a case is really going to take. When a case duration is inaccurate, it can throw the remainder of the day into delays and create significant operational challenges.

There are technologies that generate case duration based on several inputs versus traditional models of case duration. Traditionally, an OR will schedule case duration based on a surgeon’s case duration average, what the surgeon’s office recommends, or how long the surgeon thinks it will take to complete the case. Case duration can drastically change based on team composition. For example, adding a first assist or a resident to a case can significantly alter the expected case duration. Advancements in predictive modeling allow the OR schedulers and teams to optimize time by improving schedule duration accuracy.

Technology in the OR should be progressive. It should consider ways to maximize resources while maintaining excellent patient outcomes. Increasing automated documentation, enhancing communication, and leveraging predictive technology are the three considerations moving into 2024. Before incorporating new technology or adopting enhancements, questions that I always ask:

1. Will it benefit our patients and the community we serve?
2. Will it benefit our teams?
3. Does it integrate/interface with the technology that we already have?

If the answer is yes, it is worth your time.

The post Three Technology Considerations to Maximize Efficiency in the Operating Room (OR) appeared first on HealthTech Magazines.

The emerging COVID-19 pandemic has become a once-in-a-century challenge that has impacted society profoundly and has disrupted almost every facet of life for people around the world. The demands on the infrastructure of health information technology (Health IT) are numerous, as we learn to use the tools we have created to address the issues we are facing now.

I wanted to review three key areas where health IT is being leveraged to address several facets of the pandemic response:

1. Moving ambulatory care to an increasingly virtual environment.
2. Developing reliable regional registries to coordinate needed resources, such as testing, ventilator availability, personal protective equipment (PPE) supplies, etc.
3. And using Artificial Intelligence (AI) to analyze data already at hand to find best practices and treatments at the point of care.

Increasing use of virtual care environments

In most areas where work-from-home and social distancing have become prevalent, and especially in areas where shelter-in-place orders have been issued, the nature of ambulatory care has dramatically changed. In primary care practices, many have developed protocols that evolve day-by-day, but generally fall into the following workflow:

1. Reschedule all elective visits, such as annual wellness exams, to later in the year;
2. For those whose needs cannot be postponed, attempt to set up virtual visits;
3. For those who are unable to do virtual visits, then they are seen in the office.

Some practices, such as non-trauma orthopedics, are unable to find operating room availability, and their practices have ground to a halt. The result of this has been a dramatic and sudden decrease in office volume – 5 or 6 virtual visits in a day, plus one or two in-person visits, in the place where a practice used to be 20 visits per provider per day, is not economically sustainable, especially for small and independent practices.

Many electronic health records (EHR) systems have an added feature to do video visits, but not all do. Historically, the use of such technology has been only a trickle, primarily driven by payer coverage of such visits. In the recent times, this has changed rapidly, and payment for such visits on a par with in-office visits has removed the disincentive for practices to use such resources. Our practice participates in a network of a few thousand regional independent practices, and the use of video visits has spiked by two orders of magnitude in the first three weeks of March.

Practices that use EHRs which cannot do in-system video visits have to find separate stand-alone systems that can accomplish the task. Despite some casual comments about using Facetime and Skype for such visits, these methods are not HIPAA compliant and run the risk of HIPAA breach when used. There are other platforms, in-EHR and stand-alone, which are HIPAA compliant and should be the methods used.

With the rise in use of virtual ambulatory care, there is an increased bandwidth burden on the system. Coupled with increased bandwidth demands from households that are now stay-at-home, such as work-from-home, teleconferencing, entertainment streaming services, online shopping, etc., the bandwidth burden may result in internet slowdown. However, for ambulatory medical practices doing more video visits, there is an offset of reduced overall visit volume. The carrying capacity of the internet does not seem to have been exhausted as yet.

Regional networking to coordinate resources

In regional areas, such as the San Francisco Bay Area, efforts to coordinate resources have emerged, involving numerous stakeholders – CIOs and CMIOs at hospitals and delivery networks, and CMOs at technology companies.

Such initiatives have included developing real-time registries of availability of COVID-19 testing, given that these resources are changing day by day. Capacity issues exist, making obtaining testing materials difficult, and limited laboratory capacity in performing these tests means slowed response times for reporting results. But the supply availability and testing capacity are changing daily, so real-time, single-source-of-truth dashboards are needed, and many in technology are developing this. The needs are regional, not local, given the mix of services emerging – public health laboratories, emergency department hospitals, universities, commercial laboratories such as Quest and LabCorp, some physician offices, drive-through test options, and even the emergence of the collection at home. All these mixed efforts can be chaotic, not strategically, and efficiently deployed unless there is a regional coordinated effort. These are bottom-up initiatives, more than top-down, growing out of needs “in the trenches.”

Supply chain issues around personal protective equipment, availability of nursing resources for ventilators, the availability of ventilators in the first place, are all matters where resource coordination is similarly emerging. Local initiatives, such as converting operating rooms (since few elective surgeries are taking place) to Intensive Care Unit (ICU) beds with respiratory support, are ideas and experiences which are being shared through health IT networks.

Using AI to find best-practice treatment strategies

There is a tremendous opportunity for using AI to identify treatment options that work best. The standard of care in medicine has relied on the classic double-blind prospective study, where subjects are recruited to meet study criteria, then subjects are grouped to blindly receive either the treatment-under-study or placebo and the results followed over time. It’s a lengthy process. The spread of COVID-19, where the virus is infectious days before symptoms first appear, and which seems to be spread easily, given that it is novel and no one has immunity to it yet, makes such a process important but not fast enough.

AI looks at patterns in the data at hand. It is by nature, retrospective, and is not necessarily affected by study design biases. With sufficient data to analyze (the worldwide data is accumulating rapidly), AI can identify the risk of severe illness, and risk of death, based on observation of demographics, co-morbidities, and other medications taken. Anecdotal reports of successes can be tested – for instance, the report of the benefits of chloroquine (a malaria drug) for reducing respiratory failure risk has become a pop phenomenon. It has resulted in a run on chloroquine at local pharmacies, and an indefinite back-ordering due to supply chain and manufacturing capacity issues. Other remedies using influenza treatments or HIV treatments, alone or on a combination, have been tried, again with anecdotal reports. To make sense of all this, AI can help deliver sane, observational best-practices recommendations that can guide health care delivered at the point of care. And do it quickly.

Conclusions

The global impact of COVID-19 is unprecedented and has disrupted almost every facet of daily life. Healthcare delivery is significantly impacted in its response to this pandemic, and the health IT infrastructure is an integral part of this response. Many tools already in place, such as video visits, are ramping up in their usage at a dramatic pace. Regional registries of resources, updated on a real-time basis, are necessary for an environment that changes day by day, even hour by hour. Leveraging AI algorithms already available and applying it to data available from around the world may well result in creating science-based, consistent best practices recommendations. We have the tools, we are learning how best to use those tools, and need to ramp up quickly.

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Population health is one of the last frontiers in medicine. One can argue that genomics and other therapeutics advances are still ahead of us, but the reality is that all medical advances work to advance the goal of population health, or as defined, the overall health of a population.

Over the past several years, there has been an almost incomprehensible burst of scientific literature, lay press, and blogs on the wonders of artificial intelligence. I would argue that there has been an unmet promise. Artificial intelligence has not invaded the way clinicians practice day-to-day medicine. It has not revolutionized diagnostics. Moreover, it has not dramatically improved our health.

To me, the primary reason for this unmet promise has been a lack of attention on the fundamentals. Taken at its most basic core, artificial intelligence requires a data set to learn from – a data set that must be (mostly) reliable, consistent, and reproducible.

Without this data set, it harkens back to the adage of “garbage in, garbage out.”

However, how about all the terabytes of data being generated not only by our electronic medical records (EHR) in healthcare systems, hospitals, and clinics – not to mention all the data within standardized claims or from health monitoring devices from iPhones to Fitbits?

The issues rest in that this information, in multiple layers, are not yet standardized.

To start, there is no standardization of data collection. There are two layers to this problem. First, we do not consistently collect the same information. One hospital may collect procedural information (time outs, sterile processes, checklists) in a free text format; making data collection almost impossible without the use of natural language processing. Another hospital may collect this information in a checkbox format, making data extraction much more manageable. Each hospital defines if and how to collect this information, thereby creating significant collection bias. Second, the information we collect may look on the surface consistent, but due to operational differences, be vastly divergent. One operating room may collect start time as a function of when the patient enters the room. Another hospital’s operating room may collect start time as a function of when anesthesia starts their process. Although the time in the time field looks consistent, if any comparison of operational metrics is made between these two hospitals, there will be a paucity of actionable information.

Next, there are no standards regarding common terms. One typical example is the idea of the length of stay. Within a hospital, an administrator may think that a length of stay represented by 2.4 days indicate that length of stay must reflect the time when a patient arrives and leaves the room. Thus, this administrator may institute projects at earlier discharge. As a clinician, you are then pushed to do morning discharges versus late afternoon discharges. After several months, the length of stay metric barely shifts. Ultimately, one may discover that the length of stay is a function of claims submission. In other words, each day is counted as an independent integer. Discharge in the morning or afternoon of the same day will have no impact. Without clarity of standard terms, data again become hard to use.

Lastly, there are no standards regarding common metrics. With countless payers across the US, many medical societies, and numerous measurement organizations, it is hard to classify what is a quality outcome. For instance, good diabetes care can be categorized by the number of hospitalizations, highest blood glucose measurements of the past six months, the hemoglobin A1C of a patient, the blood pressure parameters, the body mass index, or even a combination of the above. Depending on the metric chosen, a population (without any changes) can be classified as either extremely well controlled or extremely poorly controlled. The clearest example of this is the divergence a hospital may have when it comes to star ratings from different organizations. Within the same year, a hospital can be classified as the top 100 hospitals by one measurement organization and a one-star hospital by another – how do we know what is the “truth”?

Without a foundation of consistency in the collection, a consistency in definition, and consistency in metrics, artificial intelligence within healthcare is a free-floating mass of inconsistent teaching material. No strength in computing or programming can overcome bad data. On the road to meeting the promise of AI in healthcare to improve population health, we must collectively work to ensure that our teaching data is clear, defined, and with visible outcome metrics, whether quality, efficiency, or even costs.

Without this foundation, AI will always be an unmet promise.

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