Reducing proximity bias in remote work can improve public health and wellbeing
COVID-19 prompted numerous companies to reconsider their approach to the future of work. Many leaders felt reluctant about maintaining hybrid and remote work options after vaccines became widely available. Yet the emergence of dangerous COVID variants such as Omicron has shown the folly of this mindset.
To mitigate the risks of new variants and other public health threats, as well as to satisfy the desires of a large majority of employees who express a strong desire in multiple surveys for a flexible hybrid or fully remote schedule, leaders are increasingly accepting that hybrid and remote options represent the future of work. No wonder that a February 2022 survey by the Federal Reserve Bank of Richmond showed that more and more firms are offering hybrid and fully-remote work options. The firms expect to have more remote workers next year and more geographically-distributed workers.
Although hybrid and remote work mitigates public health risks, it poses another set of health concerns relevant to employee wellbeing, due to the threat of proximity bias. This term refers to the negative impact on work culture from the prospect of inequality among office-centric, hybrid, and fully remote employees.
The difference in time spent in the office leads to concerns ranging from decreased career mobility for those who spend less facetime with their supervisor to resentment building up against the staff who have the most flexibility in where to work. In fact, a January 2022 survey by the company Slack of over 10,000 knowledge workers and their leaders shows that proximity bias is the top concern – expressed by 41% of executives - about hybrid and remote work.
To address this problem requires using best practices based on cognitive science for creating a culture of “Excellence From Anywhere.” This solution is based on guidance that I developed for leaders at 17 pioneering organizations for a company culture fit for the future of work.
Protect from proximity bias via the "Excellence From Anywhere" strategy
So why haven’t firms addressed the obvious problem of proximity bias? Any reasonable external observer could predict the issues arising from differences of time spent in the office.
Unfortunately, leaders often fail to see the clear threat in front of their nose. You might have heard of black swans: low-probability, high-impact threats. Well, the opposite kind of threats are called gray rhinos: obvious dangers that we fail to see because of our mental blindspots. The scientific name for these blindspots is cognitive biases, which cause leaders to resist best practices in transitioning to a hybrid-first model.
The core idea is to get all of your workforce to pull together to achieve business outcomes: the location doesn’t matter.
Leaders can address this by focusing on a shared culture of “Excellence From Anywhere.” This term refers to a flexible organizational culture that takes into account the nature of an employee's work and promotes evaluating employees based on task completion, allowing remote work whenever possible.
Addressing Resentments Due to Proximity Bias
The “Excellence From Anywhere” strategy addresses concerns about treatment of remote workers by focusing on deliverables, regardless of where you work. Doing so also involves adopting best practices for hybrid and remote collaboration and innovation.
By valuing deliverables, collaboration, and innovation through a focus on a shared work culture of “Excellence From Anywhere,” you can instill in your employees a focus on deliverables. The core idea is to get all of your workforce to pull together to achieve business outcomes: the location doesn’t matter.
This work culture addresses concerns about fairness by reframing the conversation to focus on accomplishing shared goals, rather than the method of doing so. After all, no one wants their colleagues to have to commute out of spite.
This technique appeals to the tribal aspect of our brains. We are evolutionarily adapted to living in small tribal groups of 50-150 people. Spending different amounts of time in the office splits apart the work tribe into different tribes. However, cultivating a shared focus on business outcomes helps mitigate such divisions and create a greater sense of unity, alleviating frustrations and resentments. Doing so helps improve employee emotional wellbeing and facilitates good collaboration.
Solving the facetime concerns of proximity bias
But what about facetime with the boss? To address this problem necessitates shifting from the traditional, high-stakes, large-scale quarterly or even annual performance evaluations to much more frequent weekly or biweekly, low-stakes, brief performance evaluation through one-on-one in-person or videoconference check-ins.
Supervisees agree with their supervisor on three to five weekly or biweekly performance goals. Then, 72 hours before their check-in meeting, they send a brief report, under a page, to their boss of how they did on these goals, what challenges they faced and how they overcame them, a quantitative self-evaluation, and proposed goals for next week. Twenty-four hours before the meeting, the supervisor responds in a paragraph-long response with their initial impressions of the report.
It’s hard to tell how much any employee should worry about not being able to chat by the watercooler with their boss: knowing exactly where they stand is the key concern for employees, and they can take proactive action if they see their standing suffer.
At the one-on-one, the supervisor reinforces positive aspects of performance and coaches the supervisee on how to solve challenges better, agrees or revises the goals for next time, and affirms or revises the performance evaluation. That performance evaluation gets fed into a constant performance and promotion review system, which can replace or complement a more thorough annual evaluation.
This type of brief and frequent performance evaluation meeting ensures that the employee’s work is integrated with efforts by the supervisor’s other employees, thereby ensuring more unity in achieving business outcomes. It also mitigates concerns about facetime, since all get at least some personalized attention from their team leader. But more importantly, it addresses the underlying concerns about career mobility by giving all staff a clear indication of where they stand at all times. After all, it’s hard to tell how much any employee should worry about not being able to chat by the watercooler with their boss: knowing exactly where they stand is the key concern for employees, and they can take proactive action if they see their standing suffer.
Such best practices help integrate employees into a work culture fit for the future of work while fostering good relationships with managers. Research shows supervisor-supervisee relationships are the most critical ones for employee wellbeing, engagement, and retention.
Conclusion
You don’t have to be the CEO to implement these techniques. Lower-level leaders of small rank-and-file teams can implement these shifts within their own teams, adapting their culture and performance evaluations. And if you are a staff member rather than a leader, send this article to your supervisor and other employees at your company: start a conversation about the benefits of addressing proximity bias using such research-based best practices.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.