Who will solve all our problems?

/ MIT Collaborative, Research Blog / By

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[nectar_dropcap color=”#3452ff”]W [/nectar_dropcap]e’ve been talking to a wide variety of stakeholders who agree that the clinical trials system needs to get better. There might be different answers on how it needs to get better, but the consensus is that there is something wrong that needs to be fixed. When asked for a solution though, it’s clear that no one has come up with systems-level recommendations that are implementable. “I’ve been thinking about your question for a while and have no answers.” Those were the words of Curt Meinert, who we’ve heard referred to as a legend in the clinical trials world, and has dedicated his life to this topic.[/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]So who has society has entrusted to think about solutions to these grand problems? The obvious answer is government, but we know that every part of the government has specific mandates they need to fulfill. Each stakeholder has their own goals, their own interests, and their own ideas.

MIT Collaborative has turned to academics in this and previous projects as well. It makes sense, academia has long been the place where society has expected the neutral ideas and solutions to emerge. But the academic world has it’s own problems.

Like other groups, academia is moving towards a model of consistent, positive results. There are metrics for innovation, impact, and even connections. With these metrics, the value of research has taken on a new tone, and the large-scale, systems-level problems have a low value. For academic researchers to work on large-scale problems, they have to forego publication for long periods of times, sometimes years. Producing PhDs is very difficult when the scale of the problems you are working on can fit multiple academic careers. Finally, working on one project, especially in engineering, is very risky in the current academic climate. Multiple projects that leverage each other, produce consistent papers, and can be used for future funding is the way to go. It doesn’t matter how smart and hardworking you are, in a system that doesn’t allow you to think broadly and deeply at the same time will not let you tackle problems that no one else wants to think about.

In many ways, it mirrors the clinical trials system. The drive for consistent, positive results has lead to a lot of new discoveries, but a lot of waste as well. We are trying to produce solutions and products before we have even defined the problem. In many cases, the problem is big, and requires thought instead of quick action such as the example from stroke. Multiple trials for the same molecule happen at the same time, hoping one of them will succeed. We blindly do research hoping for that one great discovery that will drive our profits. Finally, clinical trials have also been criticized for lacking implementation and translation research. We produce drugs, but no one regulates how they fit into the current population. Academics are often panned for producing research that has no relevance.[/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]We have heard that academics need to step up if the clinical trial system is to be improved. But it’s unfair to ask them of such a tall task without providing incentives in a system that discourages such thought. So what keeps our team going? I am reminded of the example Dean Ed Schlesinger of the Johns Hopkins Whiting School of Engineering provided: “Abel Wolman was a professor who standardized the chlorination of water to kill certain bacteria and microbes that prevented the spread of many diseases. His research has saved thousands of lives. And you know what, no one even cares what his h-index is.”[/vc_column_text][/vc_column][/vc_row]

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.