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Home > News and Publications > JHM Publications > Promise and Progress > Promise & Progress - A Spectrum of Achievements
Promise and Progress - Better Models of Care
Promise & Progress - A Spectrum of Achievements
Better Models of Care
Date: January 15, 2015
The Era of personalized cancer medicine is driven by data, and many experts believe that the solutions to a lot of the remaining cancer mysteries may be hidden within this data. Radiation oncologist Todd McNutt is among them.
It is not always a sophisticated new device or clever invention that propels science and medicine forward. Sometimes monumental advances are the result of ingenuity and imaginative perspective—a different way of using the data or technology we already have or a better way of structuring or delivering care.
Big Data: The Next Medical Frontier
The era of personalized cancer medicine is driven by data, and many experts believe that the solutions to a lot of the remaining cancer mysteries may be hidden within this data. Radiation oncology physicist Todd McNutt is among them. Within a sea of data, the challenge is figuring out what information has the value to advance patient care and how to extract it.
“There is so much more data collected than is ever used,” says McNutt. To put some of this unused data to work in radiation therapy, he built—from the ground up—a complex, computerized data mining system. It is called Oncospace, and it scrutinizes and analyzes data from prior patients who received radiation treatment to improve the treatment of new patients. It evaluates the therapies that worked best for a particular cancer as well as those that resulted in less than favorable outcomes, and it generates an optimal treatment plan.
Creating this complex, interactive system has been a laborious, 10-year process for McNutt and colleagues, but it is rapidly gaining traction in the research and clinical setting. “The practice of cancer medicine naturally creates data,” he says, “but for the first time in history, we have the technology to sift and sort through this data in completely new ways."
Beginning with astronomy professor Alexander Szalay, who developed a computerized system to survey large swaths of the night sky and to store, measure and analyze properties of 300 million galaxies, Johns Hopkins University scientists have been out front pioneering technologies that analyze data sets too large for the human mind to manage unassisted. Johns Hopkins University president Ronald Daniels recognized what could be accomplished in science, medicine, and public health with the ability to decode the immense amount of data collected every day at the university and made big data analysis an institutional priority.
The success of individualized medicine—the ability, among other things, to determine which patients will benefit from a particular drug or treatment and which ones will not—rested on the ability to conquer big data. In the Kimmel Cancer Center, McNutt’s work was one of the first practical demonstrations of this promise. “Todd has proven that large data warehouses of patient information collected from previously treated patients can be used to individualize treatment decisions for new patients,” says Theodore DeWeese, Director of Radiation Oncology and Molecular Radiation Sciences.
Radiation oncology is a data intensive treatment, and DeWeese believed his department provided fertile ground for such an innovative, data-driven project. As did Scott Zeger, a Bloomberg School of Public Health biostatistician and proponent of individualized health who was following McNutt’s work with Oncospace and was one of its biggest fans. Oncospace was another example of Johns Hopkins’ leadership in informatics, and Zeger worked with President Daniels, McNutt, DeWeese, radiation oncology physicist John Wong, and computer science professor Russ Taylor to secure early funding for it from the Commonwealth Foundation, Maritz Foundation, Philips Corporation, and Elekta Corporation. More recently, they earned a grant from information technology giant Toshiba to incorporate imaging into the data collected.
Oncospace does more than collect and store data. It takes informatics to the critical next level with the capability to perform interactive analysis that informs clinical decision- making. Radiation oncologist and head and neck cancer expert Harry Quon provided the critical link. He could put the system McNutt designed to the test in clinical practice. This was the moment McNutt had staked his career on. It was what drove him from the corporate setting where he was designing radiation treatment planning systems to the Kimmel Cancer Center. “I could tell you very accurately where the radiation dose goes,” says McNutt. “The important question in treating patients is where should it go and where shouldn’t it go?” That was the central question that Oncospace could answer, but unlike McNutt’s other inventions, this one could only be tested in the clinical setting through direct collaboration with physicians.
In working with radiation, the line between healing and harming is almost as narrow as the beam itself. Quon understands the consequences of crossing that line. His job is to develop the treatment plans that use radiation to destroy cancers in the head and neck without causing permanent damage to the dense anatomy surrounding the cancer. Patients want their disease cured, but they do not want to be left unable to speak or eat—some of the toxic effects radiation treatment of head and neck cancers can cause.
It was the reason McNutt saw these cancers as the ideal choices to put Oncospace to the test. Head and neck cancers are among the most difficult cancers for radiation physicists and oncologists to plan, often requiring as many as 20 treatment revisions as they work to design a treatment that hits the cancer with radiation but does not do damage to vital organs and glands, such as the voice box and salivary glands.
McNutt’s system could provide the guidance that would allow Quon and other clinicians to maximize the healing and minimize harm. It scours all of the data on head and neck cancer patients treated in the Kimmel Cancer Center: charts radiation dose distributions, toxicity, and other data in vividly colored computerized maps and graphs; and reveals the optimal plan. At the same time, it takes into account and connects all of the variables—age, underlying health conditions, and other treatments patients are receiving—and figures out how all of these variables relate and influence toxicities and response to treatment. “We can build predictive models of toxicities and other side effects based on data we have collected from prior patients, including indicators that a patient may be at higher risk for certain treatment toxicities and use this information to adjust the treatment plan,” explains McNutt.
“There is knowledge in the variations in toxicities and response that occur from patient to patient,” says Quon. “That type of analysis is not possible without the analytic capabilities of Oncospace. It does what no other tool can do and allows us to see unique relationships that otherwise would be hidden.” He was sold, but head and neck cancer treatment involved more than one specialty, and he recognized that getting the entire team of clinical specialties on board was paramount to achieving the full value Oncospace could offer.
As important then as the data it stores and analyzes is the interface it uses to gather the data. McNutt worked closely with Quon and other members of the clinical care team, including nurses, speech pathologists, and nutritionists—all of the specialists involved in the treatment of head and neck cancer patients—to develop Web-based assessment forms so that all of the information collected by caregivers could be easily integrated into the clinical workflow and ultimately into the Oncospace database. “It required some changes in habits and doing things a little bit differently than we were used to, but the reward gets people on board,” says Quon. “We have a tool that no one else has. As a result we’ve improved our patient care and doubled our head and neck practice.”
McNutt and Quon have proven that Oncospace improves treatment plan quality and reduces toxicities. Now they are using it to track and improve treatment outcomes and to advance research. McNutt says it is imperative that the data be tied to outcome, and he is among the first to take on the challenge.
This is where the Toshiba grant is playing a major role, joining Phillips and Elekta in providing funding and scientific expertise to help McNutt and team adapt the Oncospace system to incorporate data on disease response and status: Is the cancer stable? Has it progressed? Did it recur? Toshiba has developed a sensor system for computers that generates millions of data points on temperature, usage, and other factors to provide predictive models for hard drive failure. McNutt is hopeful that this data mining expertise can be applied to cancer medicine through Oncospace.
Some of the new data he hopes to use to enrich Oncospace is in computerized tomography (CT) imaging scans done in treatment simulation to guide how patients are positioned. These images are not currently used beyond that purpose, but inherent in these scans is information that shows how tumors are responding to treatment. If the scans could be incorporated automatically into Oncospace, it would allow them to track the history of the tumor during treatment. “Using Oncospace to analyze and quantify these daily images, we could potentially tell early on in the course of the treatment if the tumor is responding and change the treatment plan if necessary,” says McNutt.
He says it is the radiation oncology version of the work being done in molecular genetics using genetic biomarkers to track and monitor the response of cancers to targeted drug therapies. “It is real-time, in-treatment monitoring,” says McNutt. “The same way we used the system to relate the dose of radiation to the parotid salivary gland to the loss of gland function, we can use it to relate treatment plans to treatment responses.”
It is a lofty goal, and McNutt acknowledges its complexity. The CT images are just part of the information he hopes to gather. Much of the data he needs is contained within the treating physicians’ notes in text, and text is not the language of computers. As a result, he says, many data mining systems are missing this critical clinical piece. “Physicians are trained to document records for communication, but not for data collection,” says McNutt. To incorporate patient outcomes in Oncospace, he worked with clinicians to develop a new interface designed to extrapolate clinical information through a numerical ranking system caregivers use each time they see a patient.
As McNutt continues to expand the capabilities of the pioneering system he built, its success in head and neck cancer has made it the model for use in other cancer types, including lung, pancreatic, and prostate cancers. He is also planning to extend the use of Oncospace to other cancer centers in a novel endeavor that has never before been tried but offers to even more extensively realize the power of data. If the answers are in the data, than more data analyzed should lead to more rapid discovery of better roadmaps for care. Partner institutions would be given access to Oncospace technology and would share their results with all of the others participating centers. McNutt says sharing the technology with other institutions will also allow many cancer types to be studied simultaneously.
The Resident’s Model
The Kimmel Cancer Center’s multidisciplinary clinics are viewed as the model for modern and efficient cancer care. In the Multi-D, as these clinics are called, a new cancer patient’s case is reviewed and discussed by all of the experts involved in care, and a recommended treatment plan is offered in a one-day visit.
This was the concept of radiation oncologist Joseph Herman. He was a member of the pancreatic cancer clinical team who was faced with growing numbers of patients from across the country seeking appointments because of a promising vaccine therapy developed by Kimmel Cancer Center scientists.
“The fight against cancer is not only a biological one,” says Herman. “It also requires getting patients involved in treatment decisions and making care convenient, safe, and affordable for patients, and this is where I felt like we could improve.”
Herman overcame many challenges in getting the clinic off the ground, not the least of which was the reluctance of the other Johns Hopkins specialists involved in treating pancreatic cancer patients. It wasn’t that they thought Herman’s idea was bad; they simply wondered if it was feasible to get all of the experts—more than a dozen people—together in one room, one day each week, to review cases and decide on a course of treatment.
He persevered, and the pancreatic cancer Multi-D clinic became a reality. The clinic revolutionized and optimized how pancreatic cancer patients were cared for at the Kimmel Cancer Center. In fact, it became the model for how all cancer types are managed in the Cancer Center.
Still, Herman thought it could be even better. “The care we were providing was great, but we were limited by the number of patients we could see,” he says. The opportunity to remedy this problem came from a talented and forward-thinking young resident, an M.D./ M.B.A., who was coming to train in the Department of Radiation Oncology and Molecular Radiation Sciences.
The resident, Shereef Elnahal, had developed an operations management system that he wanted to test out in the pancreatic cancer Multi-D clinic. Elnahal is among a growing class of physicians coalescing the practice of quality medicine and the business of quality medicine.
Historically, physicians focused on providing medical care and left the business decisions to administrators. In today’s climate of physician shortages, rising healthcare costs, and large managed healthcare systems, more and more physicians, like Elnahal, are pairing their medical degrees with business degrees and taking an active role in problem-solving. With serious issues making news headlines, like the mishandling of patients at Veterans Administration hospitals across the U.S., managed healthcare—or what could more aptly be described as mismanaged healthcare—had captured the public’s attention. Elnahal believed he had the solution, and the Kimmel Cancer Center was the ideal place to test it.
A dual M.D.,/M.B.A. graduate from Harvard, Elnahal was already an accomplished doctor, and had published research on this topic while still a medical student. He would likely have been a top residency choice for any of the country’s best medical institutions. Ted DeWeese, the Department of Radiation Oncology and Molecular Radiation Sciences Director, says Elnahal was one of the finest candidates he had ever interviewed, so he was thrilled that Johns Hopkins was his first choice.
Elnahal was drawn to Johns Hopkins because of Peter Pronovost’s internationally recognized patient safety models and Herman’s pioneering efforts to establish the pancreatic cancer Multi-D clinic. In his dual medicine/business studies, Elnahal had first planned to major in health policy, but was frustrated with the lengthy time lag between ideas and actually effecting a policy change. Instead, he chose to focus on business efficiency models. “At the organizational level, one can pilot change and influence people to change behaviors, and they can obtain effects in a very short time,” says Elnahal. “If a model works, you can scale it up throughout hospital systems and influence policy on a national level.”
Elnahal believed the Kimmel Cancer Center was the perfect testing ground for his medical adaptation of a business model. His goal was to improve the efficiency of its cancer clinics, starting with the pancreatic cancer Multi-D clinic. Almost every other industry had proven that efficiency models could result in near-perfect quality, says Elnahal. He points to the airline industry as an example. “Planes don’t crash very often, and that’s because the airline industry knows how to organize operations to prevent mistakes,” he says. He felt strongly that the same philosophy could be applied to medicine with similar results.
With DeWeese and Herman on board, Elnahal put his plan into action. Within a year, the pancreatic cancer Multi-D clinic was seeing results. Before Elnahal’s model was put into place, the pancreatic cancer clinic could accommodate four to five patients per week. With Elnahal’s model in place, 12 to 15 patients were being seen in the clinic each week.
How did he do it? Elnahal says he applied an amalgamation of two business models, lean methodology and the Military Acuity Model, to healthcare. These models are used in the corporate world to prioritize tasks, drilling down to a core set of essential actions that, if missed, would result in a compromised product, safety, or function. In the pancreatic cancer clinic, Elnahal worked with the clinical team to pare down a task list of 20 to 30 actions that were routinely performed for each patient to just six that they determined were essential to quality patient care. If one of these six tasks were missed, it could predict clinic delays and potentially compromised care. “We found that many of the things that had become standard practice in the clinic—things people thought had to be done—were duplicative or not as relevant to clinical outcomes as they thought,” says Elnahal.
Essential tasks included having patients’ scans and diagnostic test results available to physicians on the clinic day or sooner, gaining a general understanding of patients’ presumed disease stage, assessing pain and other health problems that could impact the treatment plan, and evaluating social risk factors that could derail treatment.
The key to the system’s success is care coordinators, says Elnahal. The clinic coordinators are knowledgeable about patient care and the flow of the clinic, and those things that tend to interrupt flow. Often “those things” are missing scans or insurance issues.
“A patient thinks they have received preauthorization for a CT scan. They get to Johns Hopkins and find out they are unable to get the CT,” explains May Hodgin, the pancreatic cancer clinical care coordinator. In the past, residents and fellows might be scrambling to solve the imaging problems, and the patient’s appointment would be delayed, often disrupting other patient appointments for the remainder of the clinic. In Elnahal’s model, Hodgin works with administrative support staff member Lindsay Parish to ensure everything needed for the appointment is in hand before the patient arrives. If there is an additional test or scan required, they handle the insurance preauthorization and bring the patient in the day before so all of the information the clinic team needs to make treatment decisions is there for their review before they meet with the patient.
“We are learning everything we can about the patient before they walk in our door,” says Herman. “In the past, we would schedule people in clinic, look at their records, and then figure out what they need. It makes much more sense to properly triage people in advance, and that’s what Dr. Elnahal’s system does so well.”
The past habits often left clinical staff working frantically to get tests scheduled or to locate additional records. “It is in these types of situations that caregivers are most likely to become overwhelmed,” Elnahal says, “and that is when things get missed.” Elnahal’s model frees up doctors and nurses to make clinical decisions and safely reallocates other tasks to administrative staff members. “This model absolutely works,” says Hodgin. “We are a well-oiled machine now. We rock and roll.” She says the patients like it because they have a constant point of contact and their appointments run smoothly. When clinics run smoothly, the physicians and nurses also like it better.
The system is structured to fluidly assess workloads to prevent any person from becoming overloaded with assignments. If that is in danger, the clinical care coordinator is triggered to redistribute tasks. As a result, all members of the team report feeling less burdened. In fact, administrative staff members express a higher job satisfaction because they are now directly engaged in the clinical mission of the Kimmel Cancer Center. Doctors report feeling less stressed; even though they are seeing more patients, they are less bogged down with administrative tasks are and able to focus fully on what they were trained to do—practice medicine.
Much like personalized cancer medicine gets the right treatments to the right patients at the right time, Elnahal’s system gets the right task assigned to the right person at the right time. “The model allows us to make excellent use of the faculty and staff we have,” says Elnahal. “If we had to hire more people, it wouldn’t be economically feasible, but the value of this is that it improves efficiency, quality of care, and employee satisfaction using the talent we already have.”
To convince the skeptics who question whether efficiency necessarily equates with quality care, Elnahal reviewed key indicators, such as increased phone calls from patients following appointments with questions and a rise in emergency department visits. After his model was implemented, there was a significant decrease in patient phone calls and a slight decrease in emergency visits, despite a greater volume of patients being seen. While not a scientific survey, he says it is evidence nonetheless that the model is providing the value they were after.
“We’re getting at least the same quality of care, and probably slightly better,” says Elnahal. Moreover, the model has increased patient volumes while lowering costs because more patients are being cared for with the same level of resources.
With these promising results, Elnahal and team are now working to tailor the business model more specifically to cancer care and deploying it throughout the radiation oncology department. He has added more individualized care coordination specific to each phase of cancer management, including diagnosis, treatment, and survivorship as well as a coordinator to guide patients as they navigate each of these phases.
“Cancer is not like other diseases,” says Elnahal. “We recognized that there are several distinct phases and that many patients may transition between phases more than once; patients in the survivorship phase, may experience a recurrence and find themselves back in the diagnostic and treatment phase. Distinct care coordination for each phase and each transition improves the efficiency and quality of care.”
Lauren Rosati, a research assistant and clinical care coordinator on the path to medical school, is working with Elnahal to secure continued funding for the efficiency project, helping ensure that the success of the pancreatic cancer Multi-D can be put to use in other multi-D clinics. Rosati experienced the pancreatic cancer Multi-D when a close family member began treatment. “At the end of the day, the patient and family is our main priority,” says Rosati. “We must focus on what is best for them and how we can make their cancer diagnosis and treatment most bearable for everyone involved. Most often, the factor most likely to improve overall patient experience is care coordination and communication—and this is what our model targets.”
Elnahal is also working with Kimmel Cancer Center chief administrative officer Terry Langbaum to extend use of the method to all of the center’s outpatient clinics. The Veteran’s Administration has also expressed interest in using the model to improve its outpatient care.
“One of the problems in medicine is that we tend to do things the same as practitioners before us have done them. We don’t stop to think about ways to do it better,” says Herman. “Shereef has caused us to look at things in a new way,” he says. “Now we are the trail blazers, excited to expand this model throughout Hopkins and beyond.”
Fixing the Target
Breathing is something we don’t give much thought to unless we have a disease or injury that impairs it or unless we deliberately choose to hold our breath. Otherwise, it is another exquisite process of the human body that is controlled by our brain without any conscious effort required by us.
This paradigm shifts when it comes to radiation treatment of lung cancer. Breathing makes tumors a moving target. With every inhale and exhale, the tumor moves. If the radiation beam is not adjusted to this movement, it misses the cancer and hits normal tissue. As a result, radiation physicists and oncologists use a few techniques and technologies to incorporate movement into treatment planning, but radiation oncologist and lung cancer expert Russell Hales says there have been no studies that compare the technologies head to head to determine the best way to predict tumor movement.
With the explosion of new technologies, each with unique strengths and weaknesses, the key is to match the tool to the job.
One technology, developed by Kimmel Cancer Center physicist John Wong, takes advantage of our ability to consciously control breathing—to safely hold our breath for brief periods. Active breathing control (ABC) technology is based upon this but incorporates additional guidance and controls. The patient has a plastic tube in his or her mouth and is given instructions off and on throughout treatment to take a deep breath and hold it. ABC locks the breath in position so that the patient’s tumor does not move. The technology works well to stabilize tumor movement and has become a staple in radiation therapy.
The negative outcome is that it increases the time it takes to treat patients. Patients are treated in 20-second intervals. ABC assists them in holding their breath for a comfortable 20 seconds while the radiation is delivered. Then a short break is taken so they can breathe normally, and the process begins again and repeats until treatment is completed. The entire treatment takes much longer because it is given in 20-second intervals rather than in one continuous action.
To address these timing limitations, two new imaging technologies, 4-D CT and 4-D MRI, are being used to help clinicians combat tumor movement influenced by breathing without extending the time it takes to administer radiation treatment. The fourth dimension is the capture of movement. Hales says 4-D CT imaging captures a few seconds of patient’s breathing. These data are used to project how and where the tumor will move throughout the actual treatment. “Wherever the tumor moves in those few seconds is where we anticipate it will move throughout a 30-minute radiation treatment,” says Hales.
It is a reasonable approach, but it may not be realistic, Hales says, because it does not take into account spontaneous irregularities that occur in normal breathing, such as sighs, yawns, and coughs. He says 4-D MRI, which uses a magnetic field and radio waves instead of X-rays to capture images, can be safely used to capture longer periods of breathing—as much as 30 minutes—and may offer a more accurate picture of breathing-related tumor movement. Plus, it provides the clearest image of tumors. “When you get a 4-D MRI of the chest, you see nothing but the tumor,” says Hales. “It doesn’t do a good job of imaging the lungs, but it provides a beautiful picture of how the tumor moves.”
Then there is the increasingly popular Cyberknife, robotic radiosurgery. It has become a desirable option for early lung cancers, particularly in the community setting, because of its low toxicity and rapid recovery times. This technology incorporates infrared sensors, much like the motion detectors used in interactive gaming systems, positioned on the patient to track chest wall movement. This process assumes that tumor movement tracks with chest wall movement, but Hales says the movement of the chest wall as a proxy for tumor movement has not been fully validated.
Hales currently has a project funded by the National Institutes of Health to study 4D MRI as a way of confirming or refuting the accuracy of image-based projections of tumor movement. Hales says the problem is that no one has done studies to prove if any of these technologies accurately capture tumor movement or if one technology is better than another. “They may or may not be. We can’t be sure because no one has done the comparisons,” says Hales.
Yet it is critically important to make these determinations because in radiation treatment, precision is paramount. “We have to make sure radiation is hitting the
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