Doing More with the Same: Comparing Public and Private Hospital Palliative Care within California

Abstract

Background:

Public and private hospitals treat different patient populations, which may impact resources to deliver palliative care (PC).

Objectives:

Compare public and private hospital PC service structures, processes, and treatment outcomes.

Design:

Retrospective data analysis of the Palliative Care Quality Network between 2018 and 2019.

Settings/Subjects:

Six public and 40 private California hospitals provided PC consultations to 4244 and 38,354 adults, respectively.

Measurements:

PC team and patient characteristics, care processes, and treatment outcomes.

Results:

Public and private hospital PC services had similar full-time equivalent/100 beds (1.2 vs. 1.4, p = 0.4). Public hospital patients were younger (65.2 vs. 73.5, p < 0.001), less likely to be non-Hispanic Caucasian (22.5% vs. 57.5%, p < 0.001), or English speaking (51.1% vs. 79.9%, p < 0.001). Public hospital patients had more moderate/severe pain (21.3% vs. 19.3, p < 0.03), anxiety (12.4% vs. 9.2%, p < 0.001), nausea (6.5% vs. 4.7%, p < 0.001), and dyspnea (11.0% vs. 8.6%, p < 0.001). Both hospitals equally improved pain (70.9% vs. 70.5%, p = 0.83) and nausea (82.0% vs. 87.6%, p = 0.09), but public hospitals were less effective at improving anxiety (67.3% vs. 78.4%, p = 0.002) and dyspnea (58.4% vs. 67.9%, p = 0.05). Although there was no difference in hospital length of stay (public = 10.2 days vs. private = 9.5 days, p = 0.07), public hospitals conducted more patient visits (2.6 vs. 1.8, p < 0.001). They also more often clarified code status (87.7% vs. 84.4%, p < 0.001) and surrogate decision maker (94.9% vs. 89.9%, p < 0.001).

Conclusions:

Public hospital PC teams treat a more diverse symptomatic population. Yet, they achieved comparable outcomes with similar staffing to private hospitals. These findings have important ramifications for policy makers and public institution leaders.

Introduction

Palliative care (PC) is specialized medical care for people at every stage of serious illness focusing on improving quality of life for patients and their families.¹ It is most effectively delivered by a multidisciplinary team that commonly includes physicians, nurses, social workers, and chaplains working alongside the patient's other providers.¹ PC teams contribute expert communication, symptom management, and planning skills to the care of complex patients. They also manage delicate goals of care conversations, so that solicited preferences can be matched to concordant treatments.

There is abundant evidence that PC team involvement leads to improved patient, provider, and health system outcomes. Inpatient PC consultation reduces symptom burden^2,3 and improves satisfaction and quality of life for patients and their caregivers.^4–7 After PC consultation, treatment can be better matched with patient goals, reducing unwanted and nonbeneficial care. It also reduces workload and improves satisfaction for other hospital-based providers by assisting colleagues with difficult and time-consuming tasks.³ Finally, PC teams reduce the cost^8–15 and length of active admissions,^3,8,11 facilitate fewer readmissions,^10,16–19 and increase appropriate referrals to hospice.^19–22

Inpatient PC teams now operate in most U.S. hospitals with >50 beds.²³ In California, there are 17 public (safety net) hospitals that disproportionately care for low-income and uninsured patients. As of 2020, 15 of these hospitals had active PC teams.²⁴ Little is known about how public and private hospital PC teams compare in terms of resources and outcomes. This analysis compares the staffing and performance of California public and private hospital PC teams using data from the Palliative Care Quality Network (PCQN), a national learning collaborative. We compared the characteristics of the California public and private hospital PC teams and patient populations, then examined processes of care and treatment outcomes achieved at both types of hospitals. We also explored how patient characteristics impacted the effort required to deliver PC.

Methods

Study population

The PCQN is a national collaborative of specialty PC teams from across the United States that collects standardized data on processes of care and patient-level outcomes.²⁵ As of December 2019, there were 46 California PCQN teams, including 6 from public hospitals, which were included in this study.

Dataset

The PCQN dataset has been described in detail in previous reports.²⁵ Teams in the PCQN collect a standardized set of 23 data elements for all patients seen. Data are collected during the hospital admission and are then submitted to the PCQN's online database. Elements describing patient characteristics include gender, age, race/ethnicity, primary language, primary diagnosis, referral location, and reason(s) for referral. Data on processes of care include date of initial PC consultation, number of family meetings, and number of visits by the PC team.

Initial PC team assessments address presence of advance care planning (ACP) documentation including either a Physician's Orders for Life-Sustaining Treatment (POLST) form or a non-POLST advance directive (AD) such as a living will. Patient report of symptom severity is documented at initial assessment and subsequent visits. Items documented at discharge include code status (Full Code, Do Not Resuscitate/Do Not Intubate, or Partial Code), if code status was clarified during the hospital stay, ACP documentation and whether surrogate decision maker was clarified, vital status at discharge, and postdischarge services.

In addition to the patient-level data, we used responses to the PCQN structure survey that is completed by member organizations annually. Survey information included hospital location, number of hospital beds, presence of a residency program, PC team availability, PC team composition, and PC team clinical full-time equivalents (FTEs).

Procedure

De-identified data for this retrospective cohort study were extracted on February 18, 2020 and include information for 42,613 patients who received their first PC consultation within the 46 study hospitals between January 1, 2018, and December 31, 2019. Hospital and PC team characteristics were extracted from the 2019 PCQN Structure Survey and were merged with the patient-level data. The study was reviewed and approved by the University of California, San Francisco Institutional Review Board (No. 16-18596).

Statistical analysis

Continuous variables were calculated using means (95% confidence intervals [CI]) and medians (with range). Frequencies were calculated for categorical variables. We used chi-squared tests (χ²) to examine bivariate associations between categorical variables and analysis of variance to examine associations between categorical and continuous variables. We conducted generalized linear mixed models that included “PC teams” as a random effect to account for intra-team correlation of patient measures. We also included hospital size, region, and presence of a residency program, as a fixed effect. Results of these analyses were described using yearly adjusted mean proportions (or percentages) of the measures.

There was no adjustment or imputation for missing data. Analyses were performed only for patients for whom data were available for each specific data element, resulting in different n values for each analysis. An alpha of ≤0.05 was used to determine statistical significance. The Statistical Package for the Social Sciences (SPSS, Inc., Chicago, IL) for Mac (version 27) was used to conduct all analyses.

Results

Characteristics of hospitals and PC teams

There were no differences in public and private hospitals in terms of geographic location or number of hospital beds (Table 1). All public hospitals had residency programs, compared with only 42.5% of private hospitals. Public and private hospital PC teams were equally available during weekdays, weekends, and after business hours. Public hospital teams included a wider range of clinical disciplines, defined as physicians, nurses (all training levels), social workers, and chaplains. All public hospital teams included at least three clinical disciplines and averaged 3.8 disciplines in their interdisciplinary teams (IDTs) versus 3.0 disciplines for private hospitals (p = 0.03). Public hospital (PH) teams were more likely to have chaplains (83.3% vs. 50%, p = 0.03). There was no difference in clinical FTE per 100 hospital beds.

Table 1.

Characteristics of Studied Hospitals and Palliative Care Teams

Hospital characteristics	Public hospital			p
Hospital characteristics	No	Yes		p
Region of California, % (n)	N = 40	N = 6
Central	10.0 (4)	16.7 (1)	χ² = 2.7	0.3
North	37.5 (15)	66.7 (4)
South	52.5 (21)	16.7 (1)
Bed size categories, % (n)	N = 31	N = 6
100–199	29.0 (9)	33.3 (2)	χ² = 0.86	0.65
200–299	35.5 (11)	16.7 (1)
300+	35.5 (11)	50.0 (3)
	N = 40	N = 6
Residency program (yes), % (n)	42.5 (17)	100 (6)	χ² = 6.9	0.009
Availability (yes), % (n)
Weekdays	N = 40	N = 6
9:00 am–5:00 pm	100 (40)	100 (6)	—	—
After hours	57.5 (23)	33.3 (2)	χ² = 1.2	0.3
Weekend
9:00 am–5:00 pm	67.5 (27)	50.0 (3)	χ² = 0.7	0.4
After hours	52.5 (21)	33.3 (2)	χ² = 0.8	0.4
No. of clinical disciplines on IDT^a, % (n)	N = 39	N = 6
1	2.6 (1)	0 (0)	χ² = 5.6	0.13
2	25.6 (10)	0 (0)
3	38.5 (15)	16.7 (1)
4	33.3 (4)	71.4 (5)
Mean number clinical disciplines on IDT^a, mean (95% CI)	3.0 (2.8–3.3)	3.8 (3.4–4.3)	F = 5.2	0.03
Clinical full-time equivalent/100 beds	1.4 (1.2–1.6)	1.2 (0.7–1.7)	F = 0.6	0.4

Clinical disciplines = physician, nurse, social worker, chaplain.

CI, confidence interval; IDT, interdisciplinary team.

Patient characteristics

Compared with private hospitals, public hospital patients were more racially diverse, were less likely to speak English (51.5% vs. 79.9%, p < 0.001), and were more likely to have advanced cancer (39.2% vs. 27.6%, p < 0.001) (Table 2). Public hospital teams were more likely to receive referrals from medical/surgical units (57.2% vs. 34.8%, p < 0.001) and from the emergency department (7.5% vs. 4.5%, p < 0.001). The most common reason for referral for both types of hospitals was to clarify goals of care/support ACP; however, public hospitals received significantly more referrals for management of pain (22.7% vs. 11.7%, p < 0.001) or other symptoms (19.4 vs. 11.5%, p < 0.001).

Table 2.

Characteristics of Patients Receiving Palliative Care Consultation

	Public hospital			p
	No	Yes		p
	N = 38,354	N = 4244
Gender (female), % (n)	49.6 (19,011)	43.5 (1848)	χ² = 55.5	<0.001

Age (years), mean (95% CI)	73.5 (73.3–73.6)	65.2 (64.7–65.7)	F = 1042.1	<0.001

Race/ethnicity (optional), % (n)	N = 12,073	N = 3167
Non-Hispanic Caucasian	57.5 (6944)	22.5 (711)	χ² = 1640.7	<0.001
Hispanic	22.0 (2653)	44.3 (1403)
African American	3.5 (420)	10.9 (345)
Native American	0.1 (18)	0.7 (21)
Asian	9.2 (1115)	17.7 (560)
Native Hawaiian/Pacific Islander	0.8 (101)	1.5 (49)
Other	6.8 (822)	2.5 (78)
	N = 11,771	N = 3175
Primary language English, % (n)	79.9 (9402)	51.1 (1623)	χ² = 1068.5	<0.001
Primary diagnosis, % (n)	N = 36,338	N = 4239
Cancer	27.6 (10,032)	39.2 (1663)	χ² = 533.0	<0.001
Cardiovascular	14.5 (5276)	8.0 (339)
Pulmonary	12.2 (4426)	4.7 (199)
Neurological/stroke	11.4 (4154)	8.8 (372)
Other	34.3 (12,450)	39.3 (1666)
Referral location, % (n)	N = 37,790	N = 4199
Medical/surgical unit	34.8 (13,159)	57.2 (2400)	χ² = 1183.7	<0.001
Critical care	24.0 (9059)	21.6 (905)
Telemetry/step down	22.4 (8457)	10.0 (421)
Emergency department	4.5 (1695)	7.5 (316)
Other	14.3 (5420)	3.7 (157)
Reason for referral, % (n)	N = 37,496	N = 4241
Goals of care/advance care planning	78.6 (29,454)	74.7 (3170)	χ² = 33.5	<0.001
Pain management	11.7 (4365)	22.7 (962)	χ² = 416.2	<0.001
Other symptom management	11.5 (4293)	19.4 (822)	χ² = 222.3	<0.001
Withdrawal of interventions	2.4 (904)	4.5 (190)	χ² = 63.7	<0.001
Hospice referral	12.3 (4605)	16.4 (695)	χ² = 57.6	<0.001
Support for patient/family	31.9 (11,936)	26.0 (1101)	χ² = 61.7	<0.001

Timing and duration of PC services

There was no difference in hospital length of stay for public and private hospital PC patients: mean 10.2 days (95% CI 9.5–10.8) versus 9.5 days (95% CI 9.3–9.8), respectively (p = 0.07). Although public hospital PC teams followed patients for fewer days (mean = 5.5, 95% CI 5.1–5.8 vs. 5.9, 95% CI 5.8–6.1; p = 0.02) they averaged more visits per patient (mean 2.6 visits [95% CI 2.5–2.7] vs.1.8 visits [95% CI 1.7–1.8], p < 0.001.) Requests for PC consultation were generated within 24 hours of hospital admission for 48.7% (95% CI 46.9–50.5) of public hospital patients compared with 42.7% (95% CI 42.0–43.4) of private hospital patients (p < 0.001).

Initial symptom burden and symptom improvement

Compared with private hospitals, public hospital patients were significantly more likely to report moderate or severe symptoms at initial PC consultation (Table 3). There was no difference in percentage of PC patients who reported improvement in pain (70.9% vs. 70.5%, p = 0.83) and nausea (82.0% vs. 87.6%, p = 0.09) between the first and second PC team assessments. However, significantly fewer public hospital patients reported improvement with their anxiety (67.3% vs. 78.4%, p < 0.002) and dyspnea (58.4% vs. 67.9%, p < 0.05).

Table 3.

Initial Symptom Burden and Symptom Improvement

	Public hospital adjusted percentages^a, % (95% CI)		p
	No	Yes	p
Patients reporting moderate or severe symptoms at time of initial consult
Pain	19.3 (18.5–20.2)	21.3 (19.7–23.0)	0.03
Anxiety	9.2 (8.5–9.9)	12.4 (11.0–14.0)	<0.001
Nausea	4.7 (4.3–5.2)	6.5 (5.6–7.7)	<0.001
Dyspnea	8.6 (8.0–9.2)	11.0 (9.7–12.5)	0.001
Symptom improvement from first to second PC team assessment^b
Pain	70.5 (67.4–73.3)	70.9 (66.5–75.0)	0.83
Anxiety	78.4 (73.9–82.4)	67.3 (58.9–74.7)	0.002
Nausea	87.6 (79.0–93.0)	82.0 (68.3–90.6)	0.09
Dyspnea	67.9 (62.2–73.0)	58.4 (49.1–67.2)	0.05

Models adjust for fixed effects (hospital size, region, and residency program) and a random effect (PC teams).

Of patients with moderate/severe symptoms.

PC, palliative care.

ACP and code status

Compared with private hospital patients, significantly fewer public hospital patients had a POLST form or AD available at initial PC consultation, and significantly fewer completed these documents while being followed by the PC team (Table 4). Fewer public hospital PC patients identified an available surrogate decision maker during the PC consultation period (75.8% vs. 85.4%, p < 0.001). The surrogate decision maker situation was, however, clarified in the medical record for 87.7% of patients with either an identified surrogate, or verification that no surrogate was available or desired by the patient. Significantly more public hospital PC patients elected Full Code status at the time of discharge (37.5% vs. 31.2%, p < 0.001).

Table 4.

Advance Care Planning and Code Status

	Public hospital adjusted proportions^a, % (95% CI)		p
	No	Yes	p
POLST at time of initial PC consult (yes)	16.4 (15.9–17.0)	13.4 (12.1–14.7)	<0.001
AD at initial PC consult (yes)	23.2 (22.5–23.8)	6.6 (5.8–7.6)	<0.001
Code status at discharge
Full	31.2 (31.0–32.5)	37.5 (35.7–39.4)	<0.001
Partial	5.1 (4.8–5.5)	5.1 (4.3–6.0)	0.97
Do not resuscitate/do not intubate	64.4 (63.6–65.1)	58.6 (56.7–60.5)	<0.001
POLST completed during PC consultation	20.8 (20.2–21.4)	14.0 (12.9–15.2)	<0.001
AD completed during PC consultation	4.6 (4.3–4.9)	3.7 (3.1–4.5)	0.04
Surrogate decision maker identified during PC consultation (yes)	85.4 (84.7–86.2)	75.8 (74.1–77.4)	<0.001
Code status clarified during PC consultation	84.4 (83.7–85.0)	87.7 (86.6–88.8)	<0.001
Surrogate decision maker clarified during PC consultation (yes)	89.9 (89.3–90.4)	94.9 (94.3–95.5)	<0.001

Models adjust for fixed effects (hospital size, region, and residency program) and a random effect (PC teams).

AD, advance directive; POLST, physician's orders for life-sustaining treatment.

Discharge status and services

Significantly more public hospital PC patients were discharged alive (81.1% vs. 75.8%, p = 0.001). Public hospital PC patients were less likely to be discharged to a nursing home (10.7% vs. 15.5%, p < 0.001), a clinic-based PC service (6.4% vs. 6.6%, p = 0.66), a home-based PC service (3.5% vs. 6.3%, p < 0.001), or to hospice (22.9% vs. 26.3%, p < 0.001).

Discussion

This study provides new insights into the resources available to serve public hospital PC patients, the patient population receiving PC in public and private hospitals in California, and process and treatment outcomes achieved by PC teams operating in both settings.

PC team structures

Although there was no difference in clinical FTE/100 beds for PC teams at public and private hospitals, public hospital PC teams had better representation of the four core disciplines (physician, nurse, social worker, and chaplain). Interdisciplinary collaboration defines PC and is the first item addressed in the National Consensus Project Clinical Practice Guidelines for Quality PC.¹ An interdisciplinary team is also a requirement for teams seeking advanced PC certification from the Joint Commission.²⁶ Thus, the prevalence of robustly interdisciplinary teams alone speaks to the quality of California public hospital PC services. Over the past decade, the California Health Care Foundation provided technical expertise and financial support to expand PC access in the California safety net.²⁴ That support helped ensure the creation of interdisciplinary PC teams, especially regarding chaplaincy representation, in California's public hospitals and highlights opportunities for other philanthropies that fund initiatives in serious illness care.

Patient characteristics

In our study, more PC consults were requested for pain and other symptom management at public hospitals, and public hospital patients more often reported moderate to severe anxiety, nausea, dyspnea, and pain at initial PC assessment. It is well documented that patients who are underinsured or Medicaid recipients carry higher symptom burdens than their insured counterparts.^27–29 The frequency of colleagues seeking expert help in addressing symptoms points to the critical role public hospital PC teams play in supporting other hospital-based providers.

Public hospital PC patients were much less likely to have previously documented goals of care. Supporting ACP is a key element of PC, and improves care concordance and reduces costs.^30,31 Whether any ACP occurred before PC consultation impacts the effort required to deliver optimal care, as additional work will be required if ACP is new to a patient and their loved ones.

Public hospital patients in our study were younger and substantially more racially, ethnically, and linguistically diverse, with just >50% of patients speaking English as their primary language. The characteristics of the patients served in public hospitals have profound implications for PC delivery. Younger patients are more likely still in the workforce and contributing to household finances. Being younger, they are more likely to have minor children. Both factors increase the strain placed on families and the need for postdischarge support—tasks that impact the workload of PC team social workers.

The ethnic and racial diversity of patients cared for in public hospitals has several implications. Recent immigrants often have family in other countries and may have little local support, increasing the need for postdischarge services. Issues of repatriation to a country of origin also frequently arise at the end of life.³² The associated linguistic diversity also substantially impacts care delivery and can contribute to health care disparities. Prior studies suggest that even with professional interpretation services, there can be key messages lost in translation or alterations that interfere with communication.^33,34 Translated conversations also take longer to perform.

Psychiatric illness or substance use disorders are often comorbid conditions experienced by patients disproportionately served in public hospitals and can complicate medical care. Finally, lack of health insurance or being underinsured often impacts access to postdischarge services. Collectively, our findings add to the growing literature showing that public hospitals serve a more complex^35–37 and symptomatic population compared with private hospitals, such that delivering PC in public hospitals is comparatively more complicated, stressful, and time consuming.

Care processes and treatment outcomes

Once consulted, public hospital PC teams accomplished a great deal. They provided more visits per hospital stay, likely due to symptom intensity and patient complexity. Public hospital PC teams were equally successful at treating pain and nausea, but less effective in improving anxiety or dyspnea. Further research is required to better understand these differences in treatment outcomes. However, public hospital PC patients could have higher baseline anxiety in part due to entrenched psychosocial issues such as poverty, housing insecurity, or food insecurity. These issues are difficult to address adequately during a hospital stay.

More public hospital patients remained Full Code after consultation despite having similarly advanced disease as patients seen by private hospital PC teams. These findings corroborate prior studies that populations that are disproportionately cared for in safety net settings prefer more aggressive medical care.^38,39 AD and POLST completion occurred less often in public hospital settings and may be a marker of cultural factors that impact ACP^40–42—collective decision-making styles, apprehension around signing legal documents given immigration status, or general mistrust of the health care system. Importantly, this finding may indicate that quality metrics tracking ACP documentation completion might underestimate the contributions of a public hospital PC team. Even without formal documents, the public hospital PC teams clarified and documented goals of care and surrogate decision makers in the medical record for nearly all patients.

Despite the many systemic barriers to securing postdischarge services, the public hospital teams arranged home- or clinic-based PC or hospice care for nearly one-third of their patients who were discharged alive. It is notable that all of the PH study sites also have PC clinics, which were launched with support from California Health Care Foundation (CHCF). These referrals both support patients and families, and help contain future preventable health care costs.

Policy implications

These findings have important implications for policy makers and leaders of public institutions. Nationwide, the development of PC services in PH systems has lagged behind that of private hospitals.²³ A paucity of literature exists describing the unique features, patient population, or the challenges associated with the provision of PC in these essential health systems. These findings also demonstrate a replicable model of private philanthropy working in partnership with PH systems to develop robust PC services within profoundly resource constrained environments. During economic downturns, public hospitals become even more vulnerable as states, counties, and cities have fewer resources to distribute.

This can lead to staffing reductions just when more patients turn to public hospitals for care due to rising unemployment and lost health benefits.⁴³ Our study indicates that public hospital PC teams are already being asked to do more work with the same resources that are available to private hospital PC teams, and are not positioned to absorb staffing reductions. Administrators and policy makers should focus on preserving or enhancing the public hospital PC workforce. Furthermore, assessments of the staffing levels needed to provide PC should account for the complexity of the population being served.

Limitations

The data for our study were collected by clinicians during usual patient care, for primarily quality improvement purposes. This approach has advantages, as the data were prospectively collected and directly reflect the work done by treating clinicians. However, this approach requires a concise dataset; the PCQN dataset includes only those data elements determined to be useful for real-time clinical care as well as quality reporting and improvement. Furthermore, despite the recommendation that clinicians complete the entire 23-item core dataset for all patients they see, some clinicians recorded only the aspects of care that they engaged in during their usual clinical care, so there are some patients for whom we do not have pain assessments or other data. Given the large sample size in our study, we are aware that statistically significant findings are not necessarily clinically meaningful, and we, therefore, used caution in interpreting results. Finally, PCQN data are collected and submitted by individual PC teams, which may introduce bias.

A strength of this study is that it compares information from dozens of California public and private hospitals of different sizes in all parts of the state. However, these findings may not be generalizable to other regions of the United States. California has aggressively expanded Medicaid access, and substantial philanthropic support bolstered public hospital PC development. However, this study demonstrates a replicable model of philanthropy working in partnership with PH systems caring for similar populations with similar challenges to develop robust PC services in safety net systems elsewhere. To address health care quality and the impact of health care disparities, future research should assess trends in the quality of PC provided to Medicaid enrollees and other populations served disproportionately in public hospitals. Future investigations should also examine differences between states where variation in Medicaid expansion and access to PC exist.

Conclusions

Given the paucity of literature regarding PC in safety net settings, this study provides new insights into how public and private inpatient PC teams compare in resources and outcomes. Public hospital PC teams had the same clinical FTE as private hospitals, yet were more interdisciplinary than private hospitals. Public hospital PC teams cared for a younger, more diverse, and symptomatic population with more psychosocial issues and less ACP work addressed before hospitalization. Nonetheless, public hospital teams achieved comparable outcomes. Staffing levels should account for the volume of patients seen and the complexity of the population being served.

Footnotes

Authors' Contributions

All listed authors contributed to this article. Dr. van Zyl contributed to study design, data interpretation, and drafting/revising the article. Dr. O'Riordan contributed to study design and concept, data acquisition and analysis, and drafting/revising the article. Ms. Kerr was contributed to the study design and concept, data acquisition and analysis, and drafting/revising the article. Dr. Harris contributed to study design and concept, data interpretation, and drafting/revising the article.

Acknowledgments

The PQCN data analyzed in this article were collected by PC teams at the 46 hospitals featured in this study. We are grateful for their contributions to the PCQN database and for their work to advance the care of seriously ill patients and their families.

Funding Information

Funding of this study was provided by the California Health Care Foundation.

Author Disclosure Statement

No competing financial interests exist.

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