Modernizing Crisis Communication for Patient Safety
Arianne Smith, Princeton University
Since Covid-19’s inception, uncertainty for patients and families has become the new normal. When cases of the novel virus first appeared, it generated fear of the unknown. And rightly so. But our seasoned frontline healthcare professionals are trained for moments of this magnitude but what about our patients? How do patients respond in moments of crisis? Can we as healthcare providers and professionals work towards framework to create models of communication that work to achieve better outcomes in crisis? I believe we can.
Chinese officials who discovered the first cases of the coronavirus initially classified it as ‘viral pneumonia’. At this point, no one knew how the virus was spread, how often it could survive on a surface or the best way to slow transmission. Luckily, overtime, science has attempted to unmask the true nature of the coronavirus. For example, initially, the World Health Organization released a statement that those who were asymptomatic (showed no Covid-19 symptoms) could transmit the virus. On June 8th however, the WHO released another statement claiming that transmission of the virus from an asymptomatic individual was, in fact, very rare. Considering the discovery of new information, scientists then had to clarify a day later revealing that they were uncertain of “how frequently people with asymptomatic cases of Covid-19 pass the disease on to others.” (@DrewQJoseph et al., 2020). Despite the daily revelation of new information concerning the virus, there is always looming uncertainty for there seems to be so much we still do not know and misconceptions that need to be clarified. Science is just as perplexed as the rest of the global population. No one knows what the next few months will look like. No one knows if a new virus, we have no information about, will reveal itself in the near future. No one knows when we will resume in-person interactions, confident that we have conquered Covid-19. Uncertainty is the order of the day.
Though, many, like myself, have the comfort of staying home and not being forced to make any permanently life altering decisions in light of Covid-19, medical professionals working on the front-lines, are currently helping their Covid-19 patients make the best medical decisions despite the lack of information concerning the disease. Currently, there exists no recommended treatment for coronavirus nor does a vaccine or cure exist. Therefore, how are physicians meant to navigate this uncertainty? How are they expected to help patients make possible life-altering decisions when little evidence exists regarding the best ways to control symptoms? More specifically, how are medical professionals expected to help patients understand their treatment options when little literature evidence exists showing the advantages and disadvantages of a course of treatment? The million-dollar question truly is: How can shared-decision making be reimagined in low evidence situations where both the physician and patient feel empowered?
Shared Decision-Making
Shared Decision making is a collaborative process whereby clinicians “help patients to reach evidence-informed and value-congruent medical decisions” (Grad et al., 2017). Medical professionals become involved in guiding patients to make medical decisions concerning screening options or a course of treatment, for example, where there is a “close trade-off between the harms and benefits” of either option. (Grad et al., 2017). In this period of uncertainty Covid-19 has created, shared-decision making has become even more integral to the physician-patient interaction. Currently, there is so much information medical professionals do not know about this novel virus yet everyday they are expected to guide their patients to make possibly life altering decisions. Medical professionals everyday are faced with helping their patients decide if they should screen for the virus or for example take this new treatment that is supposedly said to alleviate symptoms. Patients are given the opportunity to weigh each option carefully when medical professionals outline the pros and cons and keep in mind each patient’s unique preferences and values.
Usually to facilitate effective shared decision making, clinicians often utilize patient decision aids which identify the decision that needs to be made, provide information about the options and outcomes based on the best evidence, and more importantly clarify personal values.” (Abbasgholizadeh Rahimi et al., 2017). In order to document the harms and benefits of a treatment course, patient decision aids have always drawn information from research such as existing scientific literature and evidence based in practice. This research is what informs and validates the information patient decisions aids present, hence giving clinicians more confidence in how to best guide their patients. However, the question is: how can medical professionals utilize decision aids and thus effective shared decision making when, as we are seeing in this time when coronavirus is wreaking havoc, little literature-based research exists to supplement these decision aids? As a result of their jobs, medical professionals are also expected to be experts in the medical field. However, in such an environment of uncertainty that Covid-19 has created, as a patient, I cannot expect that medical professionals will have confidence in their practice when they aren’t fully aware of what they are entirely faced with. In light of Covid-19, how can I, as a patient, trust that my physicians are even aware of all the harms and benefits of a course of treatment for Covid-19? Many, like myself, would be hesitant to place their lives in the hands of a medical professional who isn't entirely certain of what they are doing.
Knowledge base of Evidence Adaptive Clinical Decision Support System (CDSS)
The idea of Evidence Adaptive CDSS has sparked my interest and propelled me to consider how the concept can be altered and applicable to assist shared decision making in low-research evidence scenarios. What are Clinical decision support systems (CDSS)? They serve as decision aids in physician-patient interaction. According to the Journal of the American Medical Informatics, a CDSS is “software that is designed to be a direct aid to clinical decision-making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician or the patient for a decision” (Sim et al., 2001). Evidence- adaptive CDSS, more specifically, is a decision aid that is continually updated with new research findings and recommendations concerning a disease, virus, treatment method etc. In the previously cited article titled Clinical Decision Support Systems for the Practice of Evidence-based Medicine, proposals were made for a “transformation of text-based literature into a shared, machine-interpretable resource for evidence-adaptive CDSS.” This alteration calls for a knowledge base of the CDSS that can be automatically and easily updated with the most relevant and current information.
Evidence-adaptive Clinical Decision Support Systems and the automated version of the system primarily draws its knowledge base from literature-based evidence, one so heavily researched and its documented outcomes proven time and time again. However, how can Evidence-adaptive CDSS assist physicians in shared decision making and reinforcing trust with their patient when when little is known about a new and developing medical issue? How can this knowledge base be helpful in Covid-19 when as it is, it hasn't much to offer?
To translate into low-literature based evidence scenarios, I propose that future automated Evidence-adaptive CDSS be developed to allow for the inclusion of practice-based evidence to inform its knowledge base. Firstly, however, what is practice-based evidence? It is an evidence base that “is developed from multiple trials of experimenting with what works best” and tends to take into account culture, community context and co-existing disorders. (https://www.ncuih.org/krc/D_bigfoot_EBP_PBE). In simpler terms, it refers to evidence based on the outcomes of medical decisions that have already been made, outcomes that may have not already been proven by way of literature-based evidence (research findings). In lieu of Covid-19, the world has seen how important it is for countries and organizations to share newly discovered information. For example, if the Federal Drug Administration (FDA) opted to not share its findings that hydrochloroquine could exacerbate Covid-19 symptoms, many countries would have suffered greater Covid-19 losses. Having an Evidence -adaptive CDSS that allows clinicians to input practice-based evidence, for example, the results of a course of treatment that may or may not have worked for their patient, can benefit another clinician who may be faced with a similar issue but lacks the literature-based evidence to prove the efficacy. When the patient of Clinician A has a different gender, age and combination of underlying diseases from the patient of Clinician B, the Evidence- adaptive CDSS can still be useful. The implementation of a more practice-based evidence adaptive CDSS will allow for greater collaboration among clinicians especially when there exists little research on a developing medical issue and all that clinicians can depend on are the results of what has already been attempted in practice.
Further, if patients also had access to the knowledge base curated by the Evidence Adaptive systems, it would help in reinforcing trust with the clinician. As a patient, the majority of information one can find pertaining to a new and developing disease is normally from news sources and social media. Unfortunately, these sources of information are often inaccurate and, in the end, only create more confusion and panic when statements are retracted, misconceptions clarified etc. If patients were to have access to the knowledge base of the Evidence adaptive system that is kept up to date and verified, it is likely to strengthen the clinician-patient relationship. Patients are likely to feel more empowered, better understand that medical professionals have little to work with where the case may be and take greater initiative in the shared decision-making process. Some patients may even acknowledge how much they do need to trust in their medical professionals after recognizing that with the information that exists, they need guidance and cannot meander these unchartered waters on their own.
To utilize the knowledge based on the evidence adaptive system, the clinician, however, would need training in working in scenarios with low literature-based evidence where critical thinking and the skill of ‘putting things together’ becomes useful. The clinician would also need to be able to draw conclusions and adapt already documented treatment options to the uniqueness of their patient. A patient is not just a body but a human being with preferences, priorities and values that need to be considered in the shared decision-making process even if there is a lack of literature-based evidence.
Reimagining Healthcare Education
Even with the implementation of a modernized Evidence adaptive system to aid in shared-decision making, clinicians ought to be trained to function efficiently in settings with a lack of literature-based evidence. If medical professionals are not trained to work just as efficiently with a lack of literature-based evidence, they are likely to become overwhelmed and suffer from feelings of inadequacy and a lack of self-confidence. If a patient who is displaying severe symptoms of Covid-19 is approached by a medical professional who seems hesitant and unsure of the next steps to take or what recommendations to give to his/her patient, the patient cannot be expected to have confidence in this individual. Shared-decision making is a patient centered process and thus training clinicians to have the ability to make logical conclusions in situations similar to those presented by Covid-19, can allow for a restoration or strengthening of trust in clinician.
In order to train future clinicians to function in low literature-based evidence scenarios, I propose that in the first two years of medical school training, before professors begin teaching a new topic i.e. about a disease or a system etc., professors present open-ended case scenarios to their students to analyze. The key is simply ensuring that the theme of the case study is one that the majority of the class is unfamiliar with. Additionally, professors will also be expected to provide a few articles/ research papers pertaining to the case’s topic. These papers ought to provide little information nor the complete story of the disease or systems’ mechanism or whatever the theme of the case might be. The aim is to mimic what it is like to make decisions when there is a lack of literature-based evidence for a new and developing medical issue. The strategy will include small group discussions amongst student peers where students will be tasked with defining the problem, identifying what information is needed to arrive at a conclusion, assessing how existing literature (research findings the professor has presented) can inform the case and finally, assessing how the problem parallels other previously well-researched diseases, systems or medical issues for example. In these student groups, students will also be expected to weigh the possible pros and cons or the validity of possible conclusions. While medical students will eventually learn about the topic in a future lecture, this teaching strategy ought to increase creativity and resourcefulness, encourage collaboration and strengthen the skill of thinking outside the box. Lastly, students may make conclusions that contradict what is possible in science, and so professors have full discretion over how far they want their students to veer outside the possibility of science for the sake of increased creativity.
When future clinicians are actively trained to draw conclusions in scenarios where there is little literature-based evidence on a new and developing medical issue, it will be expected that these clinicians will be more comfortable and confident in the shared-decision making process in another similar future scenario. When a clinician with increased confidence and comfort in their conclusions approaches his/ her patient, I envision that the patient will feel a sense of security and comfort knowing that they are in good hands, While an automated Evidence Adaptive knowledge base is beneficial and a step in the right direction with modernizing the shared- decision making process, I believe it is no use to a clinician who does not know how to not only ‘put the puzzle pieces’ together but also tailor the Evidence Adaptive system to the uniqueness of his/ her patient.
Trained to Recognize Disempowerment
Shared-decision making is a collaborative process involving both the patient and physician. Therefore, how I see it that in a low-literature based evidence scenario, like Covid-19, shared-decision making is not only about ensuring the physician can decide the necessary screening or treatment options to share with his/her patient, it is also about ensuring that patients feel empowered in the decision-making process.
When a Covid-19 patient has made it clear that the cost of healthcare treatment is one of their biggest concerns and his/ her clinician shows a lack of concern for their finances and only compiles a list of possible treatment options that are all costly when cheaper alternatives exist, I expect that the patient will feel ignored and depersonalized in the shared decision making process. Such issues arise in cases where clinicians fail to clearly communicate treatment options to their patient or the clinicians fail to tailor the decision-making process to his/ her patient’s preferences and values. (Abbasgholizadeh Rahimi et al., 2017). Shared decision making is most effective when patients feel most empowered i.e. when patients feel they are actively taking part in the decision process and their concerns are being accounted for. As a result, clinicians need to be trained to recognize when patients do not feel empowered in shared decision making, even when the clinician is functioning in a setting with low literature-based evidence and the clinician may not feel as empowered themselves. Shared-decision making is truly a patient-centered process.
I propose that medical schools integrate scenario training where the physician-patient interaction is the focus. I envision that with this strategy, physicians will be paired with a volunteer, playing the role of a patient with a pre-assigned case, decided by the student’s professor. The clinician in training would then be tasked to engage in a shared-decision making process with the patient. The clinician would be expected to communicate the problem clearly, explain possible treatment options and their pros and cons, attempt to understand his/her patient’s preferences and values, assure the patient understands the decision they are making/ have made and a conclusion is made. This teaching strategy would, of course, also include diversifying the preferences and values of the volunteer in the patient role by varying age, gender, ethnicity, background etc. The goal here to mimic real-world experiences. At the end of the decision-making process, the clinician in training will be asked to self-evaluate their performance and compare it with feedback provided by both the volunteer patient and the professor. The primary objective of this training strategy is to ensure that clinicians in training recognize biases they may have, realize the impression of what they say or do has on a patient and what behavioral patterns tend to indicate that the patient feels disempowered in the shared decision-making process. Focusing on arming future clinicians with the capability to recognize when patients feel disempowered in the decision-making process, I believe will make effective shared-decision making possible even in the face of the unknown i.e. when there is a lack of literature-based evidence.
Conclusion
When clinicians engage in shared-decision making in scenarios with a lack of literature-based evidence, one of the main priorities ought to be ensuring that patients feel empowered and feel that they can trust their clinicians. I envision that with Evidence Adaptive Knowledge bases that can be easily updated by clinicians with both practice-based evidence and research findings concerning new unfamiliar medical issues, clinicians across the globe can collaborate and individually increase their self confidence in curating treatment options for every one of their patients. Through facilitated group discussions in medical school that strengthen critical thinking skills and creativity in analyzing unfamiliar medical issues and provided feedback from shared-decision making role plays, I also see a future of medical professionals who are capable of functioning efficiently in scenarios like Covid-19 where little research has been done on a pressing issue. My hope is that with the implementation of my suggestions to enhance medical professionals’ training, future clinicians will have the ability to gain or strengthen trust with their patients through their self-confidence and also actively make space for patients to feel empowered in making medical decisions.
References
1. @DrewQJoseph, A. J., Joseph, A., About the Author Reprints Andrew Joseph General Assignment Reporter Andrew is a general assignment reporter. andrew.joseph@statnews.com @DrewQJoseph, Andrew Joseph General Assignment Reporter Andrew is a general assignment reporter. andrew.joseph@statnews.com @DrewQJoseph, says: M. S. H., says: B. K., … says: V. (2020, June 9). WHO clarifies comments on asymptomatic spread of Covid-19. https://www.statnews.com/2020/06/09/who-comments-asymptomatic-spread-covid-19.
2. Grad, R., Légaré, F., Bell, N. R., Dickinson, J. A., Singh, H., Moore, A. E., … Kretschmer, K. L. (2017, September). Shared decision making in preventive health care: What it is; what it is not. Canadian family physician Medecin de famille canadien. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597010/.
3. Abbasgholizadeh Rahimi, S., Menear, M., Robitaille, H., & Légaré, F. (2017, June). Are mobile health applications useful for supporting shared decision making in diagnostic and treatment decisions? Global health action. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645724/.
4. Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001, November 1). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. OUP Academic. https://academic.oup.com/jamia/article/8/6/527/778603.