Sage Journals: Discover world-class research

Abstract

Introduction

Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology.

Methods

We conducted a retrospective study of electronic health record data from 55 367 patient encounters between June 2019 to April 2020, using generalized estimating equations to model the likelihood of a serious illness conversation across clinic time.

Results

Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios for Serious illness conversations documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted odds ratios .91 [95% CI, .84-.97], P = .006 for overall linear trend).

Conclusions

Serious illness conversations between oncologists and patients decrease considerably through the clinic day, and proactive strategies to avoid missed conversations should be investigated.

Keywords

Palliative care quality of life oncology health care decision making

Introduction

Early serious illness communication in oncology increases goal-concordant care, decreases clinician and caregiver moral distress, and increases hospice near the end of life.^1,2 Serious illness conversations (SIC) are discussions between clinicians and patients about the progression of an advanced health condition that could adversely strain the patient’s quality of life or their caregivers’. These conversations typically inquire the patient’s knowledge of their illness and prognosis, deliver new prognostic information, and explore next steps of medical care consistent with the patient’s goals and priorities.¹ Organizations including the National Coalition for Hospice and Palliative Care and the National Comprehensive Cancer Network recommend that oncologists initiate serious illness discussions for patients early in the cancer care continuum.³ In addition, a majority of surveyed patients and caregivers in oncology prefer earlier and more in-depth goals of care conversations.⁴ However, serious illness communication generally does not occur until approximately 1 month before death – if at all.³

Time pressures and decision fatigue during a busy clinic day may be contributing reasons that prevent clinicians from engaging in necessary conversations.^5,6 Aspects of care that are not immediately urgent may be omitted when clinicians fall behind in their schedule throughout the day. Furthermore, as the day progresses, physicians experience a progressive inability to continue making difficult decisions after having made many already, referred to as decision fatigue.⁶ Decision fatigue is well characterized in primary care settings. For example, studies have reported that time of day is associated with lower rates of flu vaccinations, cancer screening referrals, and statin prescriptions, as well as greater unwarranted antibiotic and opioid orders by primary care physicians.^7-11 However, decision fatigue in serious illness communication has not been described.

Given prior evidence of suboptimal clinician decision-making in non-oncology settings in latter parts of a clinic day or session, we investigated the association between appointment time and likelihood of serious illness conversations. Our work expands knowledge on the frequency and nature of serious illness conversations; and informs interventions that promote proactive communication and goal-concordant cancer care delivery.

Methods

This work presents a secondary post-hoc analysis of a randomized clinical trial (NLM, NCT03984773) among 75 clinicians and 17 696 patients with cancer.¹² Results from the trial were previously published.¹³ The trial was conducted between July 2019 to April 2020 and investigated the use of machine-generated mortality predictions and behavioral nudges to clinicians to promote serious illness conversations. The trial protocol and overall study was granted approval by The University of Pennsylvania Institutional Review Board with a waiver of written informed consent. We merged billing and institutional electronic health record (EHR) data from Clarity, an EPIC® reporting database, to identify a cohort of medical oncology encounters from 1 of 9 medical oncology clinics (8 disease-specific clinics within a tertiary practice, 1 general oncology clinic) within a large academic healthcare system. We studied return patient encounters with a clinician (medical oncology physician, nurse practitioner, or physician assistant) from June 17, 2019 to April 17, 2020. New patient encounters, encounters with clinicians with <40 total appointments during the study period, and encounters after the first documented SIC within the study period (Supplementary Figure S1).

While SIC conversations were not audio recorded, SIC documentation was used as a surrogate for serious illness communication, as SIC documentation is a quality metric used by many organizations including the American Society for Clinical Oncology’s Quality Oncology Practice Initiative and the Centers for Medicare and Medicaid Services Oncology Care Model; and has been used to define SIC conversations in prior work.^2,14 We ascertained the presence of a SIC from either (1) a specific SIC note type in the EHR, or (2) an SIC smart phrase in clinical progress notes. A smart phrase is a pre-built template or shortcut for entering commonly used phrases, sentences, or paragraphs into a patient’s EHR record. Smart phrases are designed to save time and increase efficiency in documenting patient encounters. The ACP smart phrase in the EHR is customized to pull a pre-defined SIC template, created by the Ariadne Labs, into the note.¹⁴

Appointment times between 8am and 4pm were separated by the hour. For example, all appointments between 8am and 8:59am were assigned to 8am. Visits before 8am and after 4pm were grouped with the 8am and 4pm timepoints, respectively. Oncology clinicians (ie medical oncology physician, nurse practitioner, or physician assistant) in eligible clinics practiced in either a morning (8am to 11am) or afternoon (12pm to 4pm) session and could alternate between morning and afternoon sessions on different days. Time was indicated by grouping appointment times in the order they occur in a session (eg 8am and 12pm were grouped as hour 1). Advanced practice providers (APPs), including physician assistants and nurse practitioners, were consistently assigned to oncology physicians, with a ratio of oncologists to APPs ranging from 1:1 to 2:1.

We use the generalized estimating equation approach, clustering by individual clinician, to estimate the probability of SIC documentation.¹⁵ Session hour (1-5) was included as a categorical variable to calculate the relative odds of documentation for each hour of a session after the first and as a continuous variable for assessing overall linear time trend. We adjusted for patient age, race, ethnicity, and gender, insurance, tumor type and stage, Charlson comorbidity count, and appointment month/year. As the study period coincided with a quality improvement effort to prompt conversations among patients at risk of short-term mortality, we additionally adjusted for whether a patient’s clinician received a conversation prompt for a specific encounter.¹³ To evaluate potential bias from the morning to afternoon session transition, we performed a sensitivity analysis using a restricted sample that excluded encounters from 12pm. Our final model includes 11 independent variables and is trained on 55367 observations, which exceeds the minimum recommended sample size for an observational multivariable regression analysis of at least 650 (n = 100 + 50i, i = 11).¹⁶ Two-sided Wald tests were used to test all hypotheses with P < .05 indicating statistical significance. Analyses were performed between December 2021 and February 2023 using R, version 4.0.3. The reporting of the study conforms to STROBE guidelines.¹⁷

Results

The sample consisted of 75 oncology clinicians and 55 367 encounters with 17 696 patients. Patients’ mean (SD) age was 61.9 (14.0) years; 53.6% were female and 70.6% were non-Hispanic White. Compared to patients without SICs, patients with SICs were more likely to have gastrointestinal (14.1% vs 26.8%) and thoracic (12.6% vs 24.7%) malignancies and to carry Medicare insurance (47.0% vs 57.4%); other demographic characteristics were similar (Table 1). Unadjusted SIC documentation rates in the morning and afternoon sessions are displayed in Figure 1. Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios (ORs) for SIC documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted OR .91 [95% CI, .84-.97], P = .006 for overall linear trend) (Table 2, Table 3). Results were consistent in the sensitivity analysis (adjusted OR .90 [95% CI, .83-.97], P = .007 for overall linear trend).

Table 1.

Descriptive statistics of patient population.

	SIC Documentation		Overall (N = 55 367)
	No (N = 54 635)	Yes (N = 732)	Overall (N = 55 367)
Age (years)
Mean (SD)	61.8 (14.0)	66.0 (12.4)	61.9 (14.0)
Median [Min, Max]	63.6 [16.1, 101]	67.0 [19.1, 96.1]	63.7 [16.1, 101]
Sex
Female	29 373 (53.8%)	364 (49.7%)	29 737 (53.7%)
Male	25 262 (46.2%)	368 (50.3%)	25 630 (46.3%)
Race
Asian	1 748 (3.2%)	27 (3.7%)	1 775 (3.2%)
Non-Hispanic Black	11 061 (20.2%)	152 (20.8%)	11 213 (20.3%)
Hispanic	1 150 (2.1%)	16 (2.2%)	1 166 (2.1%)
Unknown	2 097 (3.8%)	23 (3.1%)	2 120 (3.8%)
Non-Hispanic White	38 579 (70.6%)	514 (70.2%)	39 093 (70.6%)
Tumor type
Breast	7 852 (14.4%)	38 (5.2%)	7 890 (14.3%)
Central Nervous system + Melanoma	3 908 (7.2%)	64 (8.7%)	3 972 (7.2%)
Gastrointestinal	7 696 (14.1%)	196 (26.8%)	7 892 (14.3%)
General oncology	11 765 (21.5%)	133 (18.2%)	11 898 (21.5%)
Genitourinary	44 97 (8.2%)	31 (4.2%)	4 528 (8.2%)
Lymphoma	54 37 (10.0%)	36 (4.9%)	5 473 (9.9%)
Myeloma	6 608 (12.1%)	53 (7.2%)	6 661 (12.0%)
Thoracic	6 872 (12.6%)	181 (24.7%)	7 053 (12.7%)
Insurance
Commercial	25 155 (46.0%)	263 (35.9%)	25 418 (45.9%)
Medicaid	3 812 (7.0%)	49 (6.7%)	3 861 (7.0%)
Medicare	25 668 (47.0%)	420 (57.4%)	26 088 (47.1%)
CCI
Mean (SD)	4.54 (3.18)	6.58 (3.21)	4.57 (3.19)
Median [Min, Max]	3.00 [0, 18.0]	8.00 [0, 16.0]	3.00 [0, 18.0]
Conversation prompt
False	49 730 (91.0%)	479 (65.4%)	50 209 (90.7%)
True	4 883 (8.9%)	230 (31.4%)	5 113 (9.2%)
Unknown	22 (.0%)	23 (3.1%)	45 (.1%)
Tumor stage
Stage IV	3 621 (6.6%)	107 (14.6%)	3 728 (6.7%)
Other/Unknown	51 014 (93.4%)	625 (85.4%)	51 639 (93.3%)

Figure 1.

SIC rates by appointment time. Legend: Absolute serious illness communication rates by oncology clinic appointment hour. 8 – 11 represents typical morning session; 12 – 16 represents typical afternoon session.

Table 2.

Adjusted odds of serious illness communication by session hour.

^aMain Model			^bSensitivity Analysis (excluding 12pm)
Hour	Adjusted OR (95% CI)	P Value	Hours	Adjusted OR	P Value
1	1.00 (Reference)		1	1.00 (Reference)
2	.79 (.65-.93)	.03	2	.87 (.70-1.07)	.21
3	.80 (.66-.95)	.05	3	.80 (.64-1.00)	.05
4	.67 (.58-.86)	.001	4	.72 (.57-.92)	.01
5	.86 (.85-.96)	.60
Overall time trend	.91 (.84-.97)	.006	Overall time trend	.90 (.83-.97)	.007

Note: OR = Odds ratio. Hour represents hour within a morning (8-11am) or afternoon (12-4pm) session.

^aModels were adjusted for patient age, race, ethnicity, gender, insurance, tumor type and stage, Charlson comorbidity count, appointment month and year, and whether a conversation prompt was given for the encounter.

^bModel does not include 12pm.

Table 3.

Multivariable logistic regression for serious illness conversation outcome.

	Coefficient estimate	Odds ratio (95% CI)	P-Value
Appointment hour
2	−.23	.79 (.63-.97)	.03
3	−.21	.8 (.64-.99)	.05
4	−.39	.67 (.53-.84)	.001
5	−.14	.86 (.49-1.48)	0.6
Race
Black	−.08	.92 (.59-1.39)	0.7
Hispanic	.07	1.07 (.55-2.01)	.82
Unknown	−.18	.84 (.47-1.48)	.53
White	−0.2	.82 (.54-1.21)	.32
Age	.02	1.02 (1.01-1.03)	<.001
Sex	−.05	.95 (.80-1.12)	.55
Insurance
Medicaid	.17	1.19 (.84-1.65)	.31
Medicare	1 (.81-1.23)	.96
CCI	.12	1.13 (1.10-1.16)	<.001
Conversation prompt (True)	1.27	3.56 (2.96-4.23)	<.001
Stage IV	.42	1.52 (1.23-1.96)	<.001
Tumor Body Site
CNS + Melanoma	1.29	3.63 (2.30-5.40)	<.001
Gastrointestinal	1.44	4.22 (2.84-5.89)	<.001
General Oncology	1.02	2.77 (1.94-4.03)	<.001
Genitourinary	0.2	1.22 (.74-2.04)	.44
Lymphoma	.68	1.97 (1.26-3.19)	.004
Myeloma	.61	1.84 (1.20-2.86)	.006
Thoracic	1.42	4.14 (2.73-5.67)	<.001
Year	−.14	.87 (.57-1.29)	.49
Month	−.01	.99 (.95-1.03)	.76

Discussion

Oncology clinicians’ likelihood of having and documenting serious illness conversations decreases as a clinic session progresses. Falling behind schedule and decision fatigue could be contributing reasons for this effect.^5-11 Serious illness conversations involve coordinated efforts between the patients, their healthcare team, and their families to discuss disease trajectory and goals of care. These conversations can require significant time commitments and can be hard to prioritize if delays interfere with clinician schedules. Furthermore, SICs in oncology often cover emotionally charged topics, like unfavorable treatment outcomes, and can discourage clinicians from engaging in such hard conversations towards the end of a clinic session as they experience decision fatigue. Ultimately, lower rates of discussions about goals of care later in a session could result in more aggressive treatment regimens and ICU admissions near end-of-life, as well as fewer hospice referrals.²

This work expands the literature on how time of day affects clinician decision-making; and mechanisms for delayed serious illness conversations in oncology which could reason the paucity of similar discussions in adjacent fields.¹⁸ We acknowledge some methodological limitations that should be considered in the interpretation of our results. While we only examined return visits and adjusted for important metrics of patient severity, we could not account for unmeasured confounders such as patient and family wishes in this observational study. However, by clustering at the level of the oncologist, we accounted for clinician-specific variation in scheduling and patient risk. Furthermore, the cohort in this observational study constitutes clinicians and patients from a single academic institution where the clinicians had been trained a priori on specific SIC EHR documentation practices, which may restrict the generalizability of our results. We also acknowledge that physicians and patients may have had serious illness conversations without documentation or were not documented using the serious illness conversation template in the EHR, and the frequency of these conversations may be underreported. Finally, a low baseline rate of SICs could limit the conclusions drawn from the study. Although, our findings were similar to rates measured in prior studies (1.9% - 4.9%).¹³ The low rate could be explained by the inclusion of all patients, not just decedents, in our analyses; and by the nature of SICs, which are more in-depth and can take longer than traditional advance care planning conversations.

Conclusion

Efforts to improve the quality of care should recognize the time pressure on patients and physicians, the effects of behavioral interventions, and the time costs of improving patient-physician communication. Several straightforward practice changes could address these time pressures. Proactive scheduling of high-risk patients earlier in a clinic session or scheduling separate visits for serious illness communication could facilitate necessary conversations and should be further studied. Alternatively, democratizing serious illness communication to other members of the health care team – including lay health workers – may offload clinicians who are under time pressures from potentially low-quality or missed serious illness communication.¹⁹ Future work should study the downstream effects of time-based decisions for serious illness conversations on end-of-life outcomes (ie chemotherapy treatments in the last 14 days of life, ICU admissions in the last 30 days, and late or non-referrals to hospice). In conclusion, oncologist-patient serious illness communication decreases considerably through the clinic day, reflecting potential time pressures and decision fatigue that warrant proactive strategies to avoid missed conversations.

Supplemental Material

Supplemental material - Time of Clinic Appointment and Serious Illness Communication in Oncology

Supplemental material for Supplemental material - Time of Clinic Appointment and Serious Illness Communication in Oncology by Likhitha Kolla, Jinbo Chen and Ravi B. Parikh in Cancer Control Journal

Footnotes

Acknowledgments

We thank Corey Chivers, PhD, Abigail Doucette, MPH, and Peter E. Gabriel, MD, MSE for assistance with database curation; and Kyunga Ko, BS, Penn Medicine, for providing guidance on analysis. None of these individuals received financial compensation for their contributions.

Author Contributions

Ms. Kolla had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: All authors. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Kolla. Obtained funding: Chen, Parikh. Supervision: Chen, Parikh.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the NIH Medical Scientist Training Program (to Ms Kolla) and grants NIH R01-HL138306 (to Dr Chen) and NIH K08-CA-263541 (to Dr Parikh). Dr Parikh reports receiving grants from Humana, the National Institutes of Health, Prostate Cancer Foundation, National Palliative Care Research Center, Conquer Cancer Foundation, and Veterans Administration; personal fees and equity from GNS Healthcare, Inc. And Onc.AI; personal fees from Biofourmis, Cancer Study Group, Thyme Care, Humana, and Nanology; honorarium from Flatiron, Inc. And Medscape; and serving on the board (unpaid) of the Coalition to Transform Advanced Care, all outside the submitted work.

Role of the Funder/Sponsor

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Ethical Approval

The trial protocol and overall study was granted approval by The University of Pennsylvania Institutional Review Board (IRB # 833178, approved on 05/09/2019, Philadelphia, PA, USA) with a waiver of informed consent.

Statement of Human and Animal Rights

This article is a secondary analysis of a randomized control trial (NLM, NCT03984773). All initiatives in the study involving human subjects were conducted in accordance with the Institutional Review Board guidelines of the University of Pennsylvania in Philadelphia, PA, USA. Patient EHR information was de-identified.

Statement of Informed Consent

Study protocol was approved by the University of Pennsylvania Institutional Review Board, with a waiver of informed consent due to minimal risk of the initiative. Verbal consent was obtained from each clinician who participated in the serious illness conversation interviews. Clinicians were not required to participate in the study and could opt out at any point.

ORCID iD

Likhitha Kolla

Supplemental Material

Supplemental material for this article is available online.

References

Jacobsen

Bernacki

Paladino

. Shifting to serious illness communication. JAMA. 2022;327(4):321-322.

Bestvina

Polite

. Implementation of advance care planning in oncology: A review of the literature. J Oncol Pract. 2017;13(10):657-662.

Knutzen

Sacks

Brody-Bizar

Murray

Jain

Holdcroft

, et al. Actual and missed opportunities for end-of-life care discussions with oncology patients: A qualitative study. JAMA Netw Open 2021;4(6):e2113193.

Toguri

Grant-Nunn

Urquhart

. Views of advanced cancer patients, families, and oncologists on initiating and engaging in advance care planning: A qualitative study. BMC Palliat Care. 2020;19(1):150.

Tai-Seale

McGuire

Zhang

. Time allocation in primary care office visits. Health Serv Res. 2007;42(5):1871-1894.

Liss

Linder

. Decision fatigue, running late, and population health management—Screening out of time. JAMA Netw Open. 2019;2(5):e193402.

Linder

Doctor

Friedberg

Nieva

Birks

Meeker

, et al. Time of day and the decision to prescribe antibiotics. JAMA Intern Med 2014;174(12):2029-2031.

Neprash

Barnett

. Association of primary care clinic appointment time with opioid prescribing. JAMA Netw Open. 2019;2(8):e1910373.

Hare

Adusumalli

Park

Patel

. Assessment of primary care appointment times and appropriate prescribing of statins for at-risk patients. JAMA Netw Open. 2021;4(5):e219050.

10.

Hsiang

Mehta

Small

Rareshide

Snider

Day

, et al. Association of primary care clinic appointment time with clinician ordering and patient completion of breast and colorectal cancer screening. JAMA Netw Open 2019;2(5):e193403.

11.

Kim

Day

Small

Snider

Rareshide

CAL

Patel

. Variations in influenza vaccination by clinic appointment time and an active choice intervention in the electronic health record to increase influenza vaccination. JAMA Netw Open. 2018;1(5):e181770.

12.

Patel

. Machine-generated mortality estimates and nudges to promote advance care planning discussion among cancer patients. Identifier NCT03984773 2019. Available at: https://clinicaltrials.gov/ct2/show/NCT03984773.

13.

Manz

Parikh

Small

Evans

Chivers

Regli

, et al. Effect of integrating machine learning mortality estimates with behavioral nudges to clinicians on serious illness conversations among patients with cancer: A stepped-wedge cluster randomized clinical trial. JAMA Oncol 2020;6(12):e204759.

14.

Bernacki

Paladino

Neville

Hutchings

Kavanagh

Geerse

, et al. Effect of the serious illness care program in outpatient oncology: A cluster randomized clinical trial. JAMA Intern Med. 2019;179(6):751-759. doi: 10.1001/jamainternmed.2019.0077

15.

Liang

Zeger

. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13-22.

16.

Bujang

Sa’at

Sidik

TMITAB

Joo

. Sample size guidelines for logistic regression from observational studies with large population: Emphasis on the accuracy between statistics and parameters based on real life clinical data. Malays J Med Sci. 2018;25(4):122-130. doi: 10.21315/mjms2018.25.4.12

17.

von Elm

Altman

Egger

Pocock

Gotzsche

Vandenbroucke

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann Intern Med 2007;147:573-577.

18.

Wang

Lebsack

Sudore

Lai

. Low Rates of Advance Care Planning (ACP) discussions despite readiness to engage in acp among liver transplant candidates. Dig Dis Sci. 2021;66(5):1446-1451.

19.

Patel

Kapphahn

Dewland

Aguilar

Sanchez

Sisay

, et al. Effect of a community health worker intervention on acute care use, advance care planning, and patient-reported outcomes among adults with advanced stages of cancer: A randomized clinical trial. JAMA Oncol 2022;8(8):1139-1148.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB