The golden age of implementation

/ MIT Collaborative, Research Blog / By

[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text][nectar_dropcap color=”#3452ff”]A [/nectar_dropcap]t the last Systems Symposium organized at Johns Hopkins University, Guru Madhavan from the National Academies spoke about how engineers can influence policy and economics. In particular, he told the story about Penicillin, which I think has important lessons for us today.[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column centered_text=”true” column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”7076″ alignment=”” animation=”Fade In” img_link_large=”yes” border_radius=”none” box_shadow=”none” max_width=”100%”][vc_column_text]Alexander Fleming (Source: Wikimedia)[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]Almost everyone knows that Alexander Fleming discovered Penicillin in 1928. He shared the Nobel Prize in 1945 with Howard Florey and Ernst Chain, two other researchers who looked at further development of the drug to be better used in the population. Penicillin had been discovered, the researchers had already received recognition, and for purposes of history, the story was over.

But we had no idea how to distribute or produce Penicillin for maximum impact. As late as 1942, half the Penicillin supply of the country was used up to treat one patient with sepsis. If research and development on Penicillin had stopped then, we would not have this cure available to us today.

Thankfully, Margaret Hutchinson Rousseau used deep-tank fermentation for producing Penicillin,[/vc_column_text][/vc_column][/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”30″ overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]

and we finally had a method that could implement the great discovery that happened over 15 years earlier. Her impact often gets a footnote, or at most a paragraph, in the history of Penicillin. But she arguably had as much of an impact as Fleming and the others on us having access to Penicillin today.

Penicillin offers us a variety of lessons. Our society has had a complicated history with antibiotics like Penicillin, with issues of misuse, resistance, and diversity. The Clinical Trials Transformation Initiative (CTTI), has a number of research areas open on how to encourage trials for antibiotics, even for specific conditions such as pneumonia.

Margaret Hutchinson Rousseau (Source: Wikimedia)

But this offers us an even more nuanced lesson. It took 17 years from the discovery of a new drug to the actual production and dissemination. This was before the days of a lengthy clinical trial system, yet it took a while before we even knew how to use the drug in a large population. It has also taken us even longer to realize what impact the spread of antibiotics will have on our population. We knew Penicillin would work wonders, and thanks to Margaret Hutchinson Rousseau it cured millions, yet it would have been great to go back to 1928 and let them all know about the issues of today.[/vc_column_text][vc_column_text]The time from 1950 to 1960 has been called the Golden Age of Antibiotic Discovery, almost half the drugs used today were discovered in that period. Time and time again, we have heard the current system promotes drug discovery. There is funding, incentives, momentum, and fame for discovering new drugs. This is truly one of the strengths of the system.

But what about implementation? For every dollar spent on research, we spend 99 cents on the discovery of new drugs. Conversations around prevention, public health, and personalized medicine all center around what impact cures have on the population and the individual. Let alone funding, there are no incentives or reasons for researchers to perform research on implementation. Again and again, we hear this in different ways. “How do we even know big data will work in the CT System.” – Curt Meinert. “We need more work on evidence generation for new drugs.” – Robert Califf. “The system needs to move towards research on implementation.” – Ayesha Kamal.

We are discovering new cures nonstop, and we are still in a crisis with the CT system. The problem isn’t discovering new drugs. The problem is we don’t know the questions we want to answer. That last sentence was roughly quoting Clay Christensen. And that is what we as a society should be focusing on. To complement the Golden Age of Discovery, we need to usher in a Golden Age of Implementation.

So how do we encourage more Margaret Hutchinson Rousseaus? She was a chemical engineer, looking to solve a medical problem. Engineering is designed to project and test the best way to bring inventions into society. Bringing in these lessons and methods from engineering offers us a great opportunity. Bayesian and machine learning, as opposed to frequentist and regression approaches. Integrated device and continuous monitoring. A culture shift towards making the patient the consumer, designing the product that will be ideal. It’s the Margaret Hutchinson Rousseau way. Dare I say it, if she was alive today, she would call herself a systems engineer.[/vc_column_text][/vc_column][/vc_row]

In radiation therapy with continuous dose delivery for Gamma Knife® Perfexion™, the dose is delivered while the radiation machine is in movement, as oppose to the conventional step-and-shoot approach which requires the unit to stop before any radiation is delivered. Continuous delivery can increase dose homogeneity and decrease treatment time. To design inverse plans, we first find a path inside the tumor volume, along which the radiation is delivered, and then find the beam durations and shapes using a mixed-integer programming optimization (MIP) model. The MIP model considers various machine-constraints as well as clinical guidelines and constraints.

Radiation therapy is frequently used in diagnosing patients with cancer. Currently, the planning of such treatments is typically done manually which is time-consuming and prone to human error. The new advancements in computational powers and treating units now allow for designing treatment plans automatically.

To design a high-quality treatment, we select the beams sizes, positions, and shapes using optimization models and approximation algorithms. The optimization models are designed to deliver an appropriate amount of dose to the tumor volume while simultaneously avoiding sensitive healthy tissues. In this project, we work on finding the best beam positions for the radiation focal points for Gamma Knife® Perfexion™, using quadratic programming and algorithms such as grassfire and sphere-packing.

Many outpatient facilities with expensive resources, such as infusion and imaging centers, experience surge in their patient arrival at times and are under-utilization at other times. This pattern results in patient safety concerns, patient and staff dissatisfaction, and limitation in growth, among others. Scheduling practices is found to be one of the main contributors to this problem.

We developed a real-time scheduling framework to address the problem, specifically for infusion clinics. The algorithm assumes no knowledge of future appointments and does not change past appointments. Operational constraints are taken into account, and the algorithm can offer multiple choices to patients.

We generalize this framework to a new scheduling model and analyze its performance through competitive ratio. The resource utilization of the real-time algorithm is compared with an optimal algorithm, which knows the entire future. It can be proved that the competitive ratio of the scheduling algorithm is between 3/2 and 5/3 of an optimal algorithm.

This work was performed with the MIT/MGH Collaboration.

In many healthcare services, care is provided continuously, however, the care providers, e.g., doctors and nurses, work in shifts that are discrete. Hence, hand-offs between care providers is inevitable. Hand-offs are generally thought to effect patient care, although it is often hard to quantify the effects due to reverse causal effects between patients’ duration of stay and the number of hand-off events. We use a natural randomized control experiment, induced by physicians’ schedules, in teaching general medicine teams. We employ statistical tools to show that between the two randomly assigned groups of patients, a subset who experiences hand-off experience a different length of stay compared to the other group.

This work was performed with the MIT/MGH Collaboration.

Primary care is an important piece in the healthcare system that affects the downstream medical care of patients heavily. There are specific challenges in primary care as healthcare shifts from fee-for-service to population health management and medical home, focuses on cost savings and integrates quality measures. We consider the primary care unit at a large academic center that is facing similar challenges. In this work we focus on the imbalance in workload, which is a growing regulatory burden and directly concerns any staff in primary care. It can result in missed opportunities to deliver better patient care or providing a good work-environment for the physicians and the staff. We consider the primary care unit at the large academic center and focus on their challenge in balancing staff time with quality of care through a redesign of their system. We employ optimization models to reschedule providers’ sessions to improve the patient flow, and through that, a more balanced work-level for the support staff. 

This work was performed with the MIT/MGH Collaboration.

Perioperative services are one of the vital components of hospitals and any disruption in their operations can leave a downstream effect in the rest of the hospital. A large body of evidence links inefficiencies in perioperative throughput with adverse clinical outcomes. A regular delay in the operating room (OR), may lead to overcrowding in post-surgical units, and consequently, more overnight patients in the hospital. Conversely, an underutilization of OR is not only a waste of an expensive and high-demand resource, but it also means that other services who have a demand are not able to utilize OR. This mismatch in demand and utilization may, in turn, lead to hold-ups in the OR and cause further downstream utilization. We investigate the utilization of operating rooms by each service. The null hypothesis of this work is that the predicted utilization of the OR, i.e., the current block schedule, matches completely with the actual utilization of the service. We test this hypothesis for different utilization definitions, including physical and operational utilization and reject the null hypothesis. We further analyze why a mismatch may exist and how to optimize the schedule to improve patient flow in the hospital.