Waiting to be seen: Understanding the experience of waiting in hospital settings through a patient‐created digital story
Notice bibliographique
Résumé
Few healthcare experiences are as ubiquitous as waiting. Conversations at any social gathering will unearth stories of wait times and waiting rooms, waiting for referrals, appointments, test results, treatments, or medications. A common quip in patient advocacy circles is that the experience of waiting is why “people” become “patients” when they interact with the healthcare system.* After an accident at the age of 20 led to quadriplegia, Daniel is “good at waiting” as a recurrent user of the acute care system. His years of lived experience provided extensive material to draw upon as he created a short-film titled, Waiting to be Seen, on which this interpretive article is based (Click Here to View). In Canada, provincial healthcare systems across the country are infamous for their time-to-treatment metrics, with surgery and emergency department (ED) wait times being challenges in acute care.1 As a result, improving patient flow in hospital contexts is a focus of many quality improvement (QI) initiatives, including the “Know Your Data” Physician Audit and Feedback pilot project with Alberta Health Services. Audit and feedback (AF) is a method of providing performance data to physicians to help them translate knowledge into practice2 and this particular initiative aimed to help hospitalists and internal medicine physicians understand their practices using operational data. Led by the Hospital Medicine Section of the Medicine Strategic Clinical Network, the pilot project used physician AF literature3 and internal data and analytics to provide individual and service-level data to physicians and highlight data availability in the new province-wide Clinical Information System, called Connect Care. Data provision (e.g., acute length of stay:expected length of stay ratio, readmission rates, etc.) was coupled with QI approaches to cultivate change at the individual physician level, through self-reflection on their own data, and by reviewing data at an aggregated level in facilitated physician group discussions. It was noted during the initial phase of this project that these group conversations lacked context, specifically, the impact that patient flow had on the lives of patients and their families. In other words, why did the numbers matter? It was clear that lived experiences were needed to contextualize the AF group conversations, and five patient advisors were recruited to create their own short-film, called a Digital Story, about their acute care encounters. The remainder of this interpretive article will explore three ideas that emerge from the story Daniel created for the Know Your Data project. I remember the doctor saying, “I am really sorry. This shouldn't have happened.” Despite the long wait, I wasn't upset… It made me feel like he saw me as a person, not a number. The imagery emphasizes these words with a slowly dissolving image of Daniel in his wheelchair that reappears as the doctor apologizes and Daniel “feels seen.” By admitting a mistake and accepting responsibility, the ED physician demonstrated that he understood “how that long wait had been hard for me,” and his compassion made Daniel feel less upset. Compassion is a key contributor to patient-reported quality care in the ED,4 and an apology is one way to show it. However, apologizing to every patient who has endured a long wait could trivialize the act, place an undue burden on healthcare providers, and, if said in a flippant manner (e.g., “sorry for the wait”) could produce the opposite effect to what Daniel experienced. The difficult waiting experience may need to “be seen” and acknowledged in a different way, and perhaps the etymology of the word “wait” could provide insight. Interestingly, the word “wait” originates from the PIE root *weg, meaning “to be strong, be lively.”5 This seems contradictory to our current usage, but through Daniel's story, we see that waiting is certainly not passive or inactive. He says, “sometimes I pull the curtains back and watch like it is live TV,” taking a lively interest in the activities of the ED. Daniel also had to be strong mentally, emotionally, and physically when waiting hours in the hospital or months for a referral. Waiting requires fortitude and vigilance, and acknowledging the strength demonstrated by patients and caregivers could help salve the emotional rawness that can accompany a long wait. By saying something like, “I appreciate the emotional, mental, and physical strength it took for you to wait to see me today,” an ED physician could shift the waiting room narrative in a salutogenic direction, without apologizing for a flawed healthcare system or minimizing the toll waiting can take on a patient's health. Initiatives like the Know Your Data project are essential, as the strength patients and families utilize in waiting could be better spent healing, but perhaps at the clinician level, beginning consults by acknowledging the strength required to wait could be a positive and productive use of a ubiquitous healthcare experience. Sometimes, they offer a manual transfer to the wheelchair. When you have been waiting for so long, I usually say yes, but I don't want to. I don't want them to hurt their back. I know how that can change your life. The second “staircase” in Daniel's story occurs when a medical error extends a single acute care interaction into numerous tests, appointments, and a full week of IV antibiotic treatments. This readmission resulted from poor communication in the ED and much waiting happens due to communication breakdowns and lost data. During the Know Your Data pilot project, physician teams proposed improvement projects aimed at improving the quality of information shared between hospitals and with a patient's primary care physicians to reduce readmissions. Daniel's story helped contextualize these important conversations and led to meaningful discussions about how to ensure patients with complex conditions are safely transitioned into the community. In the climax of Daniel's story, the infectious disease physician says, “soon all the hospitals will be able to talk to each other through Connect Care, so (I) will keep an eye on (you)…” As this is said, a black hole on the screen slowly fades to a clear image of Daniel's smiling face. It is one final representation of what it means to “be seen” in acute care: easy access to a patient's complete health data. Few healthcare experiences are more disillusioning than regurgitating a health history for the 20th time or realizing that your healthcare “team” lacks an easy method of communication with each other. Troves of data are collected from patients like Daniel, and in Canada, data linkage is more important than ever as patients and families believe healthcare providers have access to their medical history,8 and healthcare providers affirm that it would lead to a better patient experience and safer, higher-quality care.9 In an indirect way, Daniel's story emphasizes how “knowing your data” could not only help physicians improve patient flow and decrease wait times, it could allow patients to see how their data is being used to support both their own health and the health of others. At the end of Daniel's digital story, you see the title page of a book he self-published called My Toolbox of Short Stories. This metaphor is a profound one, because it is exactly what patient stories are: tools that can help healthcare professionals understand their patients, the healthcare system, and their own clinical practice, in the same way as operational data. Importantly, both a good story and useful operational data cannot tell clinicians exactly what needs to be done to improve patient flow and decrease wait times in acute care, as every clinical context is different, but they can both provide a deeper understanding that could lead to solutions. A tool does not tell you how to build something, but gives you the ability to build something, and we hope that Daniel's story and the accompanying interpretations will provide hospitalists with additional tools of understanding to move beyond current approaches to the waiting experience in acute care settings. Michael Lang is an adjunct faculty member with the Faculty of Nursing at the University of Calgary and also runs two businesses related to Digital Storytelling and filmmaking: Mike Lang Stories (mikelangstories.com) and Common Language Digital Storytelling (commonlanguagedst.org). Michael was paid by Alberta Health Services as a consultant through Mike Lang Stories to help develop the Digital Stories for the Know Your Data project, including Daniel Piller's story. He received no renumeration for the writing and submission of this article. The remaining authors declare no conflict of interest.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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