Medical decision-making in the ICU is shaped by patient factors and the intensivist’s anticipated clinical course for the patient. 1 , 2 Patient factors, including physiologic characteristics, values, and goals, and their impact on the anticipated clinical trajectory can be difficult to discern accurately. 3 – 9 These factors make it challenging for intensivists to prognosticate patients’ clinical courses, specifically the risk of persistent critical illness (PerCI) developing. 10 – 12
PerCI is defined as remaining in the ICU for at least 11 days, and often occurs in the setting of new shock or acute hypoxic respiratory failure (AHRF) developing. 12 – 14 Although only a small portion of patients admitted to the ICU experience PerCI, 15 , 16 they account for a disproportional share of ICU bed-days. 15 – 17 Furthermore, nearly one-half of patients with PerCI die in the subsequent year, and survivors often experience new cognitive or physical impairments, or both. 18 – 20
Identifying patients at risk of PerCI developing is important for creating interventions to mitigate or prevent its development, or both. However, patients at high risk of PerCI developing are not readily identifiable by current risk prediction models, perhaps because they represents a middle ground of patients who neither die of critical illness nor recover enough to leave the ICU. 11 , 14 , 17 , 21 , 22
Given the lack of a high-quality tool for identifying patients at risk of progressing to PerCI, we sought to understand better expert clinicians’ evolving prognostication of patients in the ICU. Intensivists use illness scripts—mental frameworks for common disease processes that include risk factors, clinical presentation, and expected trajectory—to prognosticate risk of mortality quickly. 23 , 24 However, how these illness scripts are applied to prognosticating the risk of PerCI developing remains unknown, making it difficult to create an intervention to mitigate its progression. In light of this gap, we aimed to examine how intensivists formulate predictions about individual patient risk of new shock or AHRF and PerCI developing.
Study Design and Methods
Study Design and Sample
This exploratory qualitative study is a substudy of a prospective longitudinal study on intensivist prognostication for within-ICU events, specifically, new shock or AHRF and PerCI. 21 Using a purposive sample frame, all attending physicians who make rounds in the medical ICU at a quaternary academic medical center with transplant capabilities participated in the quantitative portion of this study. Participants who departed the university or did not complete the surveys were excluded from the qualitative interviews. The University of Michigan institutional review board approved both studies (Identifiers: HUM00182000 and HUM00192815). Additional methodologic details and the Consolidated Criteria for Reporting Qualitative Studies are included in e-Appendixes 1 - 4 .
In the parent study, intensivists received surveys for every new admission during the study period and, if the patient remained in the ICU under their care, on ICU day 3. The surveys focused on intensivist prognostication of late-onset shock or AHRF and PerCI. The intensivists were asked to grade the likelihood that the patient would experience each of these outcomes using a 4-point Likert scale. Intensivists also rated their confidence in each prognostication. 21 For this substudy, semistructured qualitative interviews were conducted with intensivists between July 2022 and January 2023 via Zoom (Zoom Video Communications Inc) within 4 weeks of an ICU service block. 21 Each interview lasted between 30 and 60 minutes and focused on 2 unique patient cases selected from patients included in the completed surveys. For each interview, 1 patients was chosen for whom the prognostication changed between ICU day 1 and day 3 for 1 of the 2 outcomes (eg, prognostication changed from no to yes regarding the likelihood that PerCI would develop). The second chosen patient was one for whom the prognostication stayed the same. Participants received a $50 gift card after completing the interview.
We asked intensivists to describe how the patient presented to the ICU and what happened each day they remained on service with the patient. Interviews used chart-stimulated recall to gain deeper insight into each intensivist’s medical decision-making and reasoning than could be gleaned from reviewing the medical record. 25 – 27 During each interview, we probed if the intensivist received any new information or if any events occurred each day that influenced their prognostication of the patient’s risk of new shock or AHRF and PerCI developing.
The interview guide was pilot tested with 2 intensivists who were not in the parent study ( e-Appendix 3 ). An experienced qualitative researcher (K. M. B.) with no prior relationship with the interviewees conducted interviews while another team member (E. M. V.) took notes. Recorded interviews were transcribed via Zoom, were reviewed for accuracy, and were deidentified by a team member (K. M. B.).
Data Analysis
We performed content analysis using both inductive and deductive codes. 28 Two researchers (K. M. B. and E. L. V.) developed an initial codebook of deductive codes based on prior research on prognostication and PerCI 4 , 11 , 14 , 22 and independently coded 4 transcripts, then met to discuss line-by-line codes. Subsequently, inductive codes related to the processes and challenges of prognostication were identified. Using MAXQDA (VERBI Software), 6 coders (K. M. B., K. T. L., A. T., M. M. L., J. M. K., and E. M. V.) from multidisciplinary backgrounds (sociology, public health, internal medicine, and pulmonary and critical care medicine) independently read and coded the same transcript. Meetings occurred for every transcript, where line-byline codes were compared until group consensus via discussion was reached and codes were added and continually refined. This process continued for all interviews. A third round of coding occurred with 3 coders (K. M. B., K. T. L., and E. M. V.) applying the emergent codes to previously coded transcripts and refining the existing codes using an axial coding approach to achieve thematic saturation. 29 – 31 Code reports with data aggregated by code were generated, allowing for comparison of the coded data for both accuracy and consistency of the code application.
Results
Participant Characteristics
Of the 21 intensivists who participated in the parent study, 17 participated in the qualitative interviews, yielding discussion of 34 total patient cases. Four intensivists were ineligible to participate in interviews because of departure or incomplete surveys. Among the 17 intensivists, most (n = 14) were male individuals who completed fellowship training within the past 10 years (n = 12) and worked in the ICU at least 5 weeks per academic year (n = 13) ( Table 1 ).
Major Themes
We identified 3 major themes that shape and challenge intensivists’ ability to prognosticate new shock or AHRF and PerCI: the contextualization of patient factors, the type of illness script, and the impact of clinical setbacks and consultant input on the patient’s clinical trajectory. We show the influence of each of these on prognostication across the clinical course ( Fig 1 ).
Contextualization of Patient Factors:
The patient’s presentation—which encompasses patient physiologic features, comorbidities, values, and goals—shaped the intensivist’s initial prognostication on ICU admission ( Table 2 ). The physician contextualized the patient factors based on their prior knowledge of similar patient presentations. In some cases, the patient’s presentation was familiar and well within the intensivist’s realm of expertise: “She came in and was quite obviously in . . . acute on chronic liver failure. This is not a circumstance where we need experts to tell us [she’s] at high risk of dying. Her [Model for End-Stage Liver Disease] score, which we used to prognosticate here, was through the roof . . . she was almost certain to die from this” (intensivist C). In this case, the ability to use an organ-specific prognostication tool made it easier for the intensivist to prognosticate.
In other cases, the patient’s presenting problem was outside of the intensivist’s realm of expertise, but still contextualizable based on other recent patients: “There was this stretch of time in the ICU when we had 3 or 4 patients admitted with almost identical histories. Elderly people with liver disease who just suddenly had a cardiac arrest out of the blue. She was one such person, although hers stood out to me . . . because [ventricular tachycardia] arrest is not a real common precursor for admission to our ICU” (intensivist G). This patient’s presentation fit within the patterns the intensivist was familiar with, although the cause of the arrest was outside of their expertise.
In other cases, the patient’s presentation did not fit the patterns the intensivist was accustomed to: “When I first saw him, I didn’t think he looked that sick. His BPs were soft but tolerable” (intensivist P). In this case, the patient did not seem sick enough to need ICU-level care based on the BP thresholds that this intensivist was accustomed to seeing. In other cases, the patient’s problem list was shorter than usual: “She’s 55. Looks like she had a bilateral lung transplant several years ago. Taking immunosuppressing medication, a number of other comorbidities, and came in for renal failure . . . that’s a very short problem list for our medical ICU patients” (intensivist F). In this case, the patient stood out because she had a shorter problem list compared with most patients in the ICU.
Although epidemiologic studies are unable to use patient characteristics to distinguish reliably which patients will experience PerCI, 12 , 32 , 33 intensivists continue to use this information to shape their prognostication. These factors also shape subsequent conversations with specialists and surrogate decision-makers around available treatment options.
Types of Illness Scripts:
The illness scripts, the mental models of disease processes 23 , 24 used to prognosticate patients, fell into 3 main categories ( Table 3 ). The first was the general illness script, within the realm of their specialty’s expertise that guided the working treatment plan. For example, importance was placed on finding the source of septic shock: “So I do feel good that we’ve got a source. We have a definite treatment for it, and he’s continued to improve” (intensivist R). However, when the source was unidentifiable, one physician explained, “I mean he was a classic, but very unfortunate, example of somebody with evolving progressive septic shock from a source of which we had no control over . . . he unfortunately did nothing but get worse” (intensivist H). These working illness scripts guided initial treatments and left the prognostication solely to the intensivist.
In the second type of illness script, the intensivists relied on consultants’ expertise to complement their knowledge: “This is a guy who’s got a lot of chronic illnesses. Now he seems to have a recurrent episode of septic shock, in a guy who’s immunocompromised” (intensivist R). Immunocompromised patients presented challenges for prognostication, but their presenting illness, that is, septic shock, remained within the intensivists’ realm of expertise. Although common in the medical ICU, these patients have a separate set of challenges impacting their possible trajectories and the intensivists’ ability to prognosticate new shock or AHRF and PerCI.
In contrast, the third type of illness script is outside of their expertise, leading the intensivists to be more reserved in prognosticating, especially for patients being evaluated for transplantation because of their inability to determine the patient’s transplant candidacy: “[The hepatologist] was pretty clear . . . there are a lot of obstacles to this guy being a suitable transplant candidate. So that was in the back of my mind . . . if we get him through this immediate house fire . . . then what? I did not have that direct conversation with the family, because I knew that if I did, they would have questions that I couldn’t answer. Those are questions really only the transplant team can answer” (intensivist G). The prognostication for these patients was intertwined with decisions around transplant candidacy that intensivists believed were beyond their control. Although the intensivist is the primary clinician for the patient’s ICU stay, some of the driving factors for the patient’s illness are beyond their control, forcing them to rely on information from consultants to shape subsequent treatment options and overall prognosis.
Clinical Setbacks and Consultant Input:
Within the ICU, clinical setbacks, ranging from worsening lab results to more severe deterioration events, are common and prompt the intensivist to revise their prognostication ( Table 4 ). Although these types of events diverged from the initial illness script, their impact on the intensivist’s prognostication varied. In some cases, they were not incorporated, because they were seen as being unrelated to the primary working illness script: “He was getting better. . . . The other thing that happened . . . was he clotted off his left leg . . . and had lost pulses and had to go to surgery that day. So overall he was doing better” (intensivist P). This intensivist continued to prognosticate that the patient was doing well, despite clotting off a leg, because the specific medical condition prompting initial ICU admission was improving. In these cases, the unexpected events were managed appropriately, but were not incorporated into the overall prognostication for PerCI or the development of new shock or AHRF.
In other cases, the patient seemed to be improving clinically, until new laboratory results became available showing that the initial problem was not improving: “His labs, you know, continued to show positive bacteria in the blood, and I think that was a surprising finding for us, because we thought that the source was his indwelling Foley [catheter], and we had removed it and exchanged it and given him broad spectrum antibiotics, and fluid resuscitated him” (attending F). In this case, the discordance between the clinical improvement and laboratory results (eg, positive blood culture findings), resulted in clinical uncertainty and questioning the prognosis.
Clinical setbacks also occur in the context of conflicting input from consultants, leading the intensivist to alter their prognosis. In some cases, this is because the possible treatment options are outside of the intensivist’s expertise, for example: “We already had a diagnosis of [Epstein-Barr virus] viremia, resulting in secondary [hemophagocytic lymphohistiocytosis]. . . . I had to ask the [hematology] consultant, ‘Let’s hypothetically bring up a few scenarios here. You keep saying that as a consequence of these abscesses he can’t get therapy. You have infectious disease telling us that it’s not abscess, it’s secondary to [hemophagocytic lymphohistiocytosis].’ So I pinned the team down, and I said, ‘Hypothetically speaking, if he is not infected, would you offer him therapy?’ And they said . . . [that] there is no treatment option” (intensivist J). In this case, the patient’s prognosis depended on knowing if treatment options were available from both sets of consultants. As soon as this was clarified, regardless of the intensivist’s initial prognostication, the patient’s prognosis was poor.
In other cases, the intensivist was waiting for the consultants to share their prognosis with the patients either because of their expertise and the patient’s desire to hear from the experts or because of the consultant’s long-standing relationship with the patient. One intensivist explained: “We finally got the clarity from the oncologist . . . in terms of saying what we needed to hear, which is [that] it’s not like there’s a miracle cure waiting upon discharge. . . . That was very helpful both for us and the patient and her family. . . . It was a dramatic shift in our goals of care, with an ultimate goal of getting her home with hospice” (attending C). It was not until the oncologist was able to provide clarity on possible treatments that hospice was discussed as a viable option. Intensivists constantly process multiple sources of information for each patient, from vital signs and test results to how patients are responding to different treatments and what consultants are recommending. 34 Ideally, all of this information is pointing the intensivist in the same direction, but when discrepancies occur, it makes prognostication more challenging.
Discussion
In this exploratory qualitative study, we identified 3 major themes exemplifying the processes intensivists use when prognosticating a patient’s risk of PerCI and new shock or AHRF developing: the contextualization of patient factors, the type of illness scripts, and the impact of any clinical setbacks and consultant input. The continuously evolving information across each day of the patient’s ICU stay shapes the challenges faced by intensivists when predicting which patients are likely to demonstrate new organ failures as well as their broader prognostication. Although intensivists are responsible for conveying the patient’s prognosis to the surrogate decision-makers, they are often prognosticating as part of a larger team, with consultants, and may lack expertise over key aspects of the presenting illness.
As shown in Figure 1 , each point across a patient’s ICU stay presents the opportunity for more deliberate medical decision-making to occur. Intensivists are incorporating information from clinical interactions, working through differential diagnoses and their subsequent range of treatment options, while attempting to align care with patient’s goals and navigating consultant recommendations or constraints. Each decision point is subject to possible biases on the part of the clinician(s) and the patient’s surrogate decision-makers. 6 , 35 , 36
The challenges physicians encounter in medical decision-making and prognostication are well described across medical specialties. 4 , 37 – 40 However, the ICU adds another layer to prognosticating and engaging in shared decision-making with patients and their surrogate decision-makers. 41 – 43 Patients seek treatment in the medical ICU with similar diagnoses, such as sepsis, yet their subsequent pathway is less predictable because of the interactions among the patient’s underlying physiologic features, their comorbidities, clinical setbacks, and their goals and values.
In light of the clinical and prognostic uncertainty inherent in the ICU, sharing this uncertainty with patients and surrogate decision-makers may allow for the opportunity to ensure that goal-concordant care is being pursued. Time-limited trials (TLTs) are 1 way to cope with the challenges inherent in complex clinical courses, especially when uncertainty exists. 40 A TLT is a collaborative plan between the medical team and the patient, their surrogate decision-makers, or both to provide life-sustaining therapy for an agreed-on period, with the patient’s response to the therapy informing the subsequent decision to continue recovery-directed care, to transition to comfort care, or to extend the trial. 44 Importantly, TLTs prespecify the anticipated time for the interventions to work within the illness script, yet are flexible enough to consider and alter expectations around clinical setbacks. TLTs create moments for intentional deliberation, offering an alternative to the default of clinical momentum, that should be explored further when prognostic uncertainty exists. 6 , 45
Study Limitations
Our study has several limitations. First, this study occurred after the onset of the COVID-19 pandemic, which may have influenced the intensivists’ comfort levels in expressing their challenges with prognostication because of unexpected events. 46 Second, we relied on intensivists’ recall and chart reviews, which may be subjected to recall or social desirability bias. However, we did use a chart-based recall approach to try to minimize recall bias, and a nonclinician performed the interviews. 25 – 27 Third, this study was conducted at a quaternary academic medical center with transplant capabilities, which might have influenced the physician’s illness scripts. However, patients who have undergone transplants may be seen first and stabilized at nonquaternary ICUs, and these findings may still be relevant. Finally, our focus on the relationship between new shock or AHRF and PerCI may not align with clinicians’ conceptualizations of their daily practice, given the rarity of these conditions. However, new shock or AHRF and PerCI, although infrequent, impact patients meaningfully, and encouraging clinicians to incorporate PerCI into their working scripts may improve patient care by increasing their willingness to use TLTs.
Interpretation
Our study highlights the challenges intensivists face when prognosticating the risk of patients experiencing PerCI and new shock or AHRF across the clinical course. Although the intent of our study was to understand intensivist prognostication of PerCI, these results likely are generalizable to the prognostication of all critically ill patients. Intensivists rely on the contextualization of patient factors and the type of illness script to prognosticate initially. However, within the ICU, clinical setbacks are common, and the complexity of critically ill patients often requires the expertise of consultants whose insights can impact the intensivist’s prognosis. Notably, when clinical or prognostic uncertainty, or both, arises, engaging in TLTs may be an appropriate strategy to navigate uncertainty while ensuring goal-concordant care.
Supplementary Material
Funding/Support
This work was supported by the National Heart, Lung, and Blood Institute, National Institutes of Health [Grants K23 HL157364 to E. M. V. and K23 HL146890 to J. M. K.]. This material is the result of work supported with resources and use of facilities at the Ann Arbor VA Medical Center.
Role of sponsors:
The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.
Disclaimer: This work reflects the views of the authors and does not necessarily represent the views of the US Government or Department of Veterans Affairs.
Additional information: The e-Appendixes are available online under “ Supplementary Data .”
Financial/Nonfinancial Disclosures
None declared.
ABBREVIATIONS:
acute hypoxic respiratory failure
persistent critical illness
time-limited trial
References
1. Seidlein AH , Hannich A , Nowak A , Grundling M , Salloch S . Ethical aspects of time in intensive care decision making . J Med Ethics . 2020 ; 47 ( 12 ): e24 . 10.1136/medethics-2019-105752 32332151
2. James FR , Power N , Laha S . Decision-making in intensive care medicine: a review . J Intensive Care Soc . 2018 ; 19 ( 3 ): 247 – 258 . 30159017 10.1177/1751143717746566 PMC6110024
3. Neville TH , Tarn DM , Yamamoto M , Garber BJ , Wegner NS . Understanding factors contributing to inappropriate critical care: a mixed-methods analysis of medical record documentation . J Palliat Med . 2017 ; 20 ( 11 ): 1260 – 1266 . 28967808 10.1089/jpm.2017.0023 PMC5672617
4. Christakis NA . Death Foretold: Propecy and Prognosis in Medical Care . University of Chicago Press ; 2001 .
5. Kruser JM , Benjamin B , Gordon EJ , Patient and family engagement during treatment decisions in an ICU: a discourse analysis of the electronic health record . Crit Care Med . 2019 ; 47 ( 6 ): 784 – 791 . 30896465 10.1097/CCM.0000000000003711 PMC6522290
6. Kruser JM , Cox CE , Schwarze ML . Clinical momentum in the intensive care unit. A latent contributor to unwanted care . Ann Am Thorac Soc . 2017 ; 14 ( 3 ): 426 – 431 . 27997808 10.1513/AnnalsATS.201611-931OI PMC5427724
7. Melvin RG , Kruser JM , Shin S , Razak F , Verma AA , Detsky M . Characterizing physician recommendations within code status documentation: a multicentre cohort study and qualitative discourse analysis . J Gen Intern Med . 2025 ; 41 ( 2 ): 355 – 363 . 39979705 10.1007/s11606-025-09402-z PMC12894568
8. You JJ , Downar J , Fowler RA , Barriers to goals of care discussion with seriously ill hospitalized patients and their families: a multicenter survey of clinicians . JAMA Intern Med . 2015 ; 175 ( 4 ): 549 – 556 . 25642797 10.1001/jamainternmed.2014.7732
9. Fritsch J , Peteronio S , Helft P , Making decisions for hospitalized older adults: ethical factors considered by family surrogates . J Clin Ethics . 2013 ; 24 ( 2 ): 125 – 134 . 23923811 PMC3740391
10. Detsky ME , Harhay MO , Bayard DF , Discriminative accuracy of physician and nurse predictions for survival and functional outcomes 6 months after an ICU admission . JAMA . 2017 ; 317 ( 21 ): 2187 – 2195 . 28528347 10.1001/jama.2017.4078 PMC5710341
11. Iwashyna TJ , Viglianti EM . Patient and population-level approaches to persistent critical illness and prolonged intensive care unit stays . Crit Care Clin . 2018 ; 34 ( 4 ): 493 – 500 . 30223989 10.1016/j.ccc.2018.06.001 PMC6146412
12. Viglianti EM , Kramer R , Admon AJ , Late organ failures in patients with prolonged intensive care unit stays . J Crit Care . 2018 ; 46 : 55 – 57 . 29684773 10.1016/j.jcrc.2018.03.029 PMC6014899
13. Viglianti EM , Bagshaw SM , Bellomo R , Late vasopressor administration in ICU patients: a retrospective cohort study . Chest . 2020 ; 158 ( 2 ): 571 – 578 . 32278780 10.1016/j.chest.2020.02.071 PMC7417379
14. Jeffcote T , Foong M , Gold G , Patient characteristics, ICU-specific supports, complications, and outcomes of persistent critical illness . J Crit Care . 2019 ; 54 : 250 – 255 . 31630075 10.1016/j.jcrc.2019.08.023
15. Iwashyna TJ , Hodgson CL , Pilcher D , Bailey M , Bellomo R . Persistent critical illness characterised by Australian and New Zealand ICU clinicians . Crit Care Resusc . 2015 ; 17 ( 3 ): 153 – 158 . 26282252
16. Viglianti E , Bagshaw SM , Bellemo R , Hospital-level variation in the development of persistent critical illness . Intensive Care Med . 2020 ; 46 ( 8 ): 1567 – 1575 . 32500182 10.1007/s00134-020-06129-9 PMC7444658
17. Bagshaw SM , Stelfox HT , Iwashyna TJ , Bellomo R , Zuege D , Wang X . Timing of onset of persistent critical illness: a multicentre retrospective cohort study . Intensive Care Med . 2018 ; 44 ( 12 ): 2134 – 2144 . 30421256 10.1007/s00134-018-5440-1
18. Nelson JE , Meier DE , Litke A , Natale DA , Siegel RE , Morrison RS . The symptom burden of chronic critical illness . Crit Care Med . 2004 ; 32 ( 7 ): 1527 – 1534 . 15241097 10.1097/01.ccm.0000129485.08835.5a
19. Nelson JE , Tandon N , Mercado AF , Camhi SL , Ely EW , Morrison RS . Brain dysfunction: another burden for the chronically critically ill . Arch Intern Med . 2006 ; 166 ( 18 ): 1993 – 1999 . 17030833 10.1001/archinte.166.18.1993
20. Nelson JE , Cox CE , Hope AA , Carson SS . Chronic critical illness . Am J Respir Crit Care Med . 2010 ; 182 ( 4 ): 446 – 454 . 20448093 10.1164/rccm.201002-0210CI PMC2937238
21. Viglianti EM , Thenappan A , Admon AJ , Accuracy of intensivist prognostications of within-ICU deterioration and development of persistent critical illness: a prospective cohort study . Ann Am Thorac Soc . 2025 ; 22 ( 8 ): 1210 – 1216 . 40198113 10.1513/AnnalsATS.202411-1174OC PMC12329326
22. Darvall JN , Boonstra T , Norman J , Persistent critical illness: baseline characteristics, intensive care course, and cause of death . Crit Care Resusc . 2019 ; 21 ( 2 ): 110 – 118 . 31142241
23. Charlin B , Boshuizen H , Custers E , Feltovich P . Scripts and clinical reasoning . Med Educ . 2007 ; 41 : 1178 – 1184 . 18045370 10.1111/j.1365-2923.2007.02924.x
24. Keemink Y , Custers E , van Djik S , Cate O . Illness script development in pre-clinical education through care-based clinical reasoning training . Int J Med Educ . 2018 ; 9 : 35 . 29428911 10.5116/ijme.5a5b.24a9 PMC5834830
25. Sinnott C , Hugh SM , Boyce MB , Bradley CP . What to give the patient who has everything? A qualitative study of prescribing for multimorbidity in primary care . Br J Gen Pract . 2015 ; 65 ( 632 ): e184 – e191 . 25733440 10.3399/bjgp15X684001 PMC4337307
26. Sinnott C , Kelly MA , Bradley CP . A scoping review of the potential for chart stimulated recall as a clinical research method . BMC Health Serv Res . 2017 ; 17 ( 1 ): 583 . 28830405 10.1186/s12913-017-2539-y PMC5567630
27. Rencic J , Durning SJ , Holmboe E , Gruppen LD . Understanding the assessment of clinical reasoning . In: Wimmers P , Mentkowski M , eds. Understanding the Assessment of Clinical Reasoning . Cham : Springer ; 2016 : 209 – 235 . chap 11.
28. Elo S , Kyngas H . The qualitative content analysis process . J Adv Nurs . 2008 ; 62 ( 1 ): 107 – 115 . 18352969 10.1111/j.1365-2648.2007.04569.x
29. Strauss A , Corbin J . Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 2nd ed . Sage ; 1998 .
30. Saldana J The Coding Manual for Qualitative Researchers . 4th ed . Thousand Oaks, CA : Sage Publications ; 2021 .
31. Small ML . ‘How many cases do I need’: on science and the logic of case selection in field-based research . Ethnography . 2009 ; 10 ( 1 ): 5 – 38 .
32. Viglianti EM , Zajic P , Iwashyna TJ , Amrein K . Neither vitamin D levels nor supplementation are associated with the development of persistent critical illness: a retrospective cohort analysis . Crit Care Resusc . 2019 ; 21 ( 1 ): 39 – 44 . 30857511 PMC6696918
33. Viglianti EM , Kruser JM , Iwashyna T . The heterogeneity of prolonged ICU hospitalisations . Thorax . 2019 ; 74 ( 11 ): 1015 – 1017 . 31534030 10.1136/thoraxjnl-2019-213779 PMC7001884
34. Simpkin AL , Armstrong KA . Communicating uncertainty: a narrative review and framework for future research . J Gen Intern Med . 2019 ; 34 ( 11 ). 25896 – 25891 . 10.1007/s11606-019-04860-8 PMC6848305 31197729
35. Bornstein BH , Emler AC . Rationality in medical decision making: a review of the literature on doctors’ decision-making biases . J Eval Clin Pract . 2021 ; 7 ( 2 ): 97 – 107 . 10.1046/j.1365-2753.2001.00284.x 11489035
36. Moskowitz AJ , Kuipers BJ , Kassirer JP . Dealing with uncertainty, risks, and tradeoffs in clinical decisions: a cognitive science approach . Ann Intern Med . 1988 ; 108 : 435 – 449 . 3277516 10.7326/0003-4819-108-3-435
37. Bodemer N , Hanoch Y , Katsikopoulos KV . Heuristics: foundations for a novel approach to medical decision making . Intern Emerg Med . 2015 ; 10 : 195 – 203 . 25348561 10.1007/s11739-014-1143-y
38. Zaza SI , Arnold RM , Schwarze ML . Innovations in surgical communication 4—present the downsides of surgery, not just the risk . JAMA Surg . 2023 ; 158 ( 10 ): 998 – 1000 . 37610756 10.1001/jamasurg.2023.3650
39. Kruser JM , Pecanac KE , Brasel KJ , Steffens NM , McKneally MF , Schwarze ML . “And I think that we can fix it”: mental models used in high-risk surgical deicion making . Ann Surg . 2015 ; 261 ( 4 ): 678 – 684 . 25749396 10.1097/SLA.0000000000000714 PMC4356019
40. Hendershot KA , Elias MN , Taylor BL , Wahlster S , Creutzfeldt CJ . An update on palliative care in neurocritical care: providing goal-concordant care in the face of prognostic uncertainty . Curr Treat Options Neurol . 2023 ; 25 : 517 – 529 . 39055121 10.1007/s11940-023-00778-z PMC11271663
41. Ganz FD , Engelberg R , Torres N , Curtis JR . Development of a model of interpersonal shared clinical deicison making in the ICU: a mixed -methods study . Crit Care Med . 2016 ; 44 ( 4 ): 680 – 689 . 26565629 10.1097/CCM.0000000000001467
42. Curtis JR , White D . Practical guidance for evidence-based ICU family conferences . Chest . 2008 ; 134 ( 4 ): 835 – 843 . 18842916 10.1378/chest.08-0235 PMC2628462
43. White DB , Engelberg RA , Wenrich MD , Lo B , Curtis JR . Prognostication during physician-family discussions about limiting life support in intensive care units . Crit Care Med . 2007 ; 35 ( 2 ): 442 – 448 . 17205000 10.1097/01.CCM.0000254723.28270.14
44. Kruser JM , Ashana DC , Courtright KR , Defining the time-limited trial for patients with critical illness: an official american thoracic society workshop report . Ann Am Thorac Soc . 2024 ; 21 ( 2 ): 187 – 199 . 38063572 10.1513/AnnalsATS.202310-925ST PMC10848901
45. Viglianti E , Ervin J , Newton C , Kruser JM , Iwashyna T , Valley TS . Time-limited trials in the ICU: a mixed-methods sequential explanatory study of intensivists at two academic centres . BMJ Open . 2022 ; 12 ( 4 ):e059325. 10.1136/bmjopen-2021-059325 PMC8981404 35379644
46. Appel H , Sanatkar S . Systematic search and scoping review of physicians’ intolerance of uncertainty and medical decision-making uncertainties during the COVID-19 pandemic: a summary of the literature and directions for future research . J Clin Psychol Med Settings . 2023 ; 31 . 338 – 158 . 37932520 10.1007/s10880-023-09974-0 PMC11102404