Article, Oncology

An ED pilot intervention to facilitate outpatient acute care for cancer patients

a b s t r a c t

Introduction: Unplanned hospitalizations are common in patients with cancer, and most hospitalizations origi- nate in the emergency department (ED).

Methods: We implemented an ED-based pilot intervention designed to reduce hospitalizations among patients with solid tumors. The intervention, piloted at a single academic medical center, involved a medical oncologist embedded in the ED during evening hours. We used a quasiexperimental preimplementation/ postimplementation study design to evaluate the proportion of ED visits that resulted in Inpatient hospital admission, before and after pilot implementation. General estimating equations were used to evaluate the association between the intervention and hospital admission.

Results: There were 390 ED visits by eligible cancer patients in the preintervention period and 418 visits in the intervention period. During the intervention period, 158 (38%) of 418 ED visits were identified by the embedded oncologist during the evening intervention shift. The proportion of ED visits leading to hospitalization was 70% vs 69% in the preintervention and intervention periods (odds ratio, 0.93 [95% confidence interval, 0.69-1.24]; P= .62). There were no differences between periods in ED length of stay or subsequent use of acute care. Among patients with initial ED presentation during the operating hours of the intervention, the proportion of ED visits leading to hospitalization was 77% vs 67% in the preintervention and intervention periods (odds ratio, 0.62 [0.36-1.08]; P= .08).

Conclusion: Embedding an oncologist in the ED of an academic medical center did not significantly reduce hospital admissions. Novel approaches are needed to strengthen outpatient acute care for patients with cancer.

(C) 2016

Introduction

Unplanned hospitalizations in patients with cancer are common [1] and costly [2]. Approximately 1 in 5 of these hospitalizations may be avoidable, as evaluated by various criteria [3-5]. Most unplanned hospitalizations in cancer patients transit through the emergency department (ED), a clinical environment where high patient acuity, inconsistent access to outpatient resources, and the absence of

? This research was supported by an intramural grant to GAB from the Dana-Farber Can- cer Institute Department of Medical Oncology. MAM acknowledges support from an insti- tutional research training grant from the National Cancer Institute of the National Institutes of Health awarded to the Dana-Farber Cancer Institute Department of Medical Oncology (T32-CA009172).

* Corresponding author at: Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215. Tel.: +1 617 632 6713; fax: +1 617 582 7450.

E-mail addresses: [email protected], [email protected] (G.A. Brooks).

longitudinal patient relationships all create barriers to outpatient dis- charge plans. In this context, population-based studies report that 63% to 72% of ED visits in cancer patients result in hospital admission [6,7]. Enhancing the capacity of emergency physicians to reduce the propor- tion of ED visits resulting in hospital admission is a promising strategy to enhance the Value of ED care generally and is of considerable rele- vance to cancer care [8].

To improve the effectiveness and efficiency of acute care for patients with cancer, we designed and pilot tested an intervention to reduce the Hospital admission rate among cancer patients presenting to the ED affiliated with our institution. We reasoned that placing a medical on- cologist in the ED to consult directly with ED care providers and patients would improve communication and Care coordination, thus facilitating outpatient discharges for cancer patients. Because we lacked resources to post an oncologist in the ED at all hours, we chose to staff our inter- vention between 5 PM and 11 PM–hours with high volumes of oncology patients in the ED and with restrictED capacity for acute care in the out- patient clinics. Here, we describe the findings of the 5-week pilot imple- mentation, compared with the preceding 5-week control period.

http://dx.doi.org/10.1016/j.ajem.2016.06.076

0735-6757/(C) 2016

Methods

Setting and patients

The intervention setting is an urban, academic tertiary care hospital that serves as the ED and inpatient affiliate of an NCI-designated com- prehensive cancer center. The ED is a 39-bed facility with more than 60 000 patient visits annually. The target population for our interven- tion included patients under active outpatient management for solid tumor malignancy at the affiliated cancer center, as this population is at increased risk for seeking acute care and has established access to follow-up with an outpatient care team. Active management was defined as 2 or more outpatient oncology clinic visits in the 6-month period pre- ceding an ED visit.

Intervention

The 5-week pilot intervention involved embedding a medical oncol- ogist in the ED between 5 PM and 11 PM, 6 nights per week (Sunday through Friday). The intervention oncologist was stationed inside the ED and identified patients in the target population through active re- view of the electronic ED patient list and regular communication with on-duty emergency physicians. When the intervention oncologist iden- tified an appropriate patient, he or she discussed the patient with the at- tending emergency physician. When an outpatient discharge was considered potentially feasible, the oncologist assisted with clinical evaluation and discharge planning, including coordination of outpatient follow-up and communication with the patient’s primary medical on- cology team. All final management and Disposition decisions were made by the responsible emergency physician. The oncologist was not involved in the care of patients when the emergency physician declined his or her involvement or once a decision for inpatient admission was made. Oncologists who participated in the intervention also completed a log of all solid tumor malignancy patients identified in the ED during intervention shifts, including subjective assessments of the feasibility of alternative management approaches. Six board-certified or board- eligible medical oncologists participated in the intervention.

Evaluation

The intervention was evaluated using a quasiexperimental preintervention/postintervention design. The preintervention period was the 5-week interval immediately preceding the intervention peri- od. The primary study outcome was the proportion of eligible oncology patients admitted to the hospital within 2 calendar days of ED presenta- tion. Patients who were managed on the ED observation service but never admitted to inpatient status were not considered to be admitted to the hospital. The key secondary outcome and safety outcome was the proportion of nonadmitted patients who received additional acute care (inpatient hospital admission or a second ED visit) within 5 calen- dar days of the index ED presentation. All objective study data were col- lected from clinical and administrative records and were assessed from the same data sources in both study periods. In addition to the study outcomes defined above, additional descriptive characteristics and out- comes reported include age, primary Cancer diagnosis, timing of ED and oncology clinic visits, ED principal visit diagnosis (categorized using the Healthcare Cost and Utilization Project’s single-level clinical classifica- tion system [9], with the authors’ modifications for relevance to oncol- ogy care), ED disposition, ED and hospital length of stay, 30-day mortality after the index ED visit, and survey response data from the participating medical oncologists’ intervention log (regarding the avoidability of ED or hospital care).

For both the primary and secondary outcomes, we used generalized estimating equations to evaluate the association between study period and outcome. This approach accounts for clustering of outcomes by pa- tient, as some patients had multiple ED visits during the study. For other

categorical outcomes, the association between study period and out- come was evaluated using ?2 tests. Right-skewed outcomes (eg, length of stay) were compared using Wilcoxon tests. The study was designed to have 80% power to detect a 10% reduction in the primary outcome (hospitalization within 2 days of ED presentation), with a 2-tailed type I error rate of 5%. This study was reviewed and approved by the ap- plicable institutional review board.

Results

Descriptive findings

Among all visits to the ED between March 15 and May 24, 2015, we identified 11,515 live discharges (inclusive of home discharges, hospital admissions, and admissions to the ED observation service). There were 808 eligible ED discharges in patients with solid tumor malignancy (7.0% of all ED discharges), for a mean of 11.5 ED visits per day among actively managed solid tumor oncology patients. Characteristics of visits and patients are shown in Table 1. A plurality of the ED visits occurred during weekday daytime hours, 8 AM to 4 PM (37%). There were 390 qualifying ED visits in the first 5 weeks of the study (preintervention pe- riod) and 418 visits during the following 5 weeks (intervention period). Of the 418 ED visits occurring during the intervention period, 158 (38%) were identified by study staff (including 99 of 128 patients [77%] who presented to the ED between 4 PM and 11 PM, Sunday through Friday). The intervention oncologist screened the medical records of all 158 identified patients, discussed 114 patients (71%) with the attending emergency physician, directly evaluated 29 patients (18%), and contacted the primary medical oncologist (by telephone or e-mail) for

30 patients (18%).

Association of the intervention with key outcomes

Among all patients, we found no association between the study in- tervention and the primary outcome of hospital admission within 2 days of ED evaluation, with 70% of ED visits leading to hospital admis- sion in the preintervention period vs 69% in the intervention period (P= .62). Similarly, we found no change in the proportion of nonadmitted patients who received acute care in the ED or hospital within 5 days of disposition from the ED (21% vs 23%; P= .65). Emer- gency department disposition patterns, ED and inpatient length of stay, and 30-day mortality were all unchanged across study periods (see Table 2). Hospital admission rates for the most frequently encoun- tered ED diagnosis categories are shown in Table 3. Admission rates ranged from 96% for pneumonia (24 of 25 ED visits) to 41% for nonspe- cific chest pain (9 of 22 ED visits).

We performed a secondary, exploratory analysis of ED disposition

outcomes in the subset of 252 ED visits with presentation to the ED be- tween 4 PM and 11 PM on Sunday through Friday–the visit subset corre- sponding to those patients most likely to be present in the ED during the operating hours of the intervention (31% of all ED visits; see Table 2). The proportion of all patients hospitalized within 2 days of ED presenta- tion in the exploratory analysis population was 77% in preintervention period vs 67% in the intervention period (P= .08). The proportion of nonadmitted patients receiving additional acute care within 5 days of ED disposition was 33% in the preintervention period vs 15% in the in- tervention period (P= .07).

Subjective assessments of intervention participants

Oncologists participating in the study intervention judged that the complaints leading to 42 (27%) of 158 identifiED patient visits could “probably or definitely” have been effectively managed in the oncology clinic, without ED evaluation and/or management. Among 98 patients who were evaluated by the intervention oncologist in the ED and were subsequently admitted to the hospital, the oncologist identified

Table 1

Visit and patient characteristics of solid tumor oncology patients with ED visits

Period, n (%)

that embedding a medical oncologist in the ED would increase outpa- tient discharges in cancer patients by (1) enhancing coordination of care between the ED and the outpatient oncology clinics and (2) sharing clinical expertise between oncology and ED care teams.

In the primary analysis, our intervention was not associated with any significant change in hospital admissions (primary outcome) or short-term acute care utilization (secondary outcome) among patients under active management for solid tumor malignancies. Concurrently, the intervention was not associated with any differences in ED or inpa- tient length of stay. In an exploratory analysis focusing only on patients presenting to the ED during or immediately preceding the operating

Characteristic

All

Preintervention

Intervention

visit characteristics

ED visits, n

808

390

418

ED arrival time

Weekday daytime (8 AM-4 PM,

295 (37)

139 (36)

156 (37)

Mon-Fri)

Intervention hours (4 PM-11 PM,

Sun-Fri)a

252 (31)

124 (32)

128 (31)

Weekday overnight (11 PM-8 AM,

78 (10)

36 (9)

42 (10)

Sun-Thu)

Weekend (Fri 11 PM-Sun 4 PM)

183 (23)

91 (23)

92 (22)

hours of the intervention, we found that the study intervention was as- sociated with a nonsignificant 10% reduction in the proportion of ED

Oncology clinic visit within prior 30 d

685 (85)

344 (88)

341 (82)

visits leading to hospital admission (77% in the preintervention period

Chemotherapy receipt within prior 30 db

ED principal visit diagnosis

236 (29)

114 (29)

122 (29)

vs 67% in the intervention period; P= .08) as well as a nonsignificant re- duction in the proportion of outpatient ED discharges leading to further

Abdominal pain

55 (7)

27 (7)

28 (7)

acute care within 5 days (33% vs 15%; P= .07).

Fever of unknown origin

34 (4)

16 (4)

18 (4)

Although the intervention failed to show a significant association

Respiratory complaint (eg, dyspnea)

Hypovolemia and hypotension

Cancer (primary site code)

33 (4)

28 (3)

27 (3)

18 (5)

14 (4)

12 (3)

15 (4)

14 (3)

15 (4)

with key study outcomes, the results observed in the exploratory anal-

ysis population (the population of patients most directly exposed to the intervention) leave open the possibility that the intervention may have

Pneumonia and empyema

25 (3)

14 (4)

11 (3)

been modestly effective. The finding from the exploratory analysis of a

Nausea and vomiting

25 (3)

11 (3)

14 (3)

greater than 50% reduction in the receipt of additional acute care over

Thromboembolism (including PE) Nonspecific chest pain

intestinal obstruction

Other

Patient characteristics

Unique patients, n

Sex

663

343

379

Female

389 (59)

200 (58)

143 (42)

care. This finding emphasizes the relevance of care coordination for can- cer patients after an initial ED presentation.

25 (3)

22 (3)

21 (3)

7 (2)

8 (2)

10 (3)

18 (4)

14 (3)

11 (3)

the 5 days after ED presentation is of particular interest. Although not statistically significant, this finding suggests that early oncologist in-

513 (63)

253 (65)

260 (62)

volvement may have led to Improved care coordination after ED dis-

charge, at times obviating the need for subsequent unplanned acute

Male

Age (y), median (IQR)

274 (41)

189 (59)

131 (41)

What lessons can we learn from this pilot implementation study?

First, oncologists participating in the intervention perceived that the

Median (IQR) (y)

62 (53,

62 (53, 71)

62 (53, 70)

goal of safely reducing acute care intensity for patients with cancer

18-49

50-59

70)

118 (18)

170 (26)

67 (20)

79 (23)

62 (16)

106 (28)

was feasible. Oncologist participants viewed 19% of ED visits leading to hospital admission as potentially avoidable–a figure that is well-

60-69

195 (29)

103 (30)

112 (30)

aligned with previously reported estimates of the prevalence of poten-

70-95

180 (27)

94 (27)

99 (26)

tially avoidable hospitalizations in cancer patients [3,4]. Additional evi-

dence to support the feasibility of preventing hospital admissions comes from a considerably larger quasiexperimental study, where investiga-

Cancer diagnosis

Lung

105 (13)

51 (13)

54 (13)

Breast

103 (13)

48 (12)

55 (13)

Ovary

92 (11)

47 (12)

45 (11)

tors at Memorial-Sloan Kettering showed that an observation care

Colorectal

40 (5)

22 (6)

18 (4)

model was associated with a modest but statistically significant reduc-

Prostate

39 (5)

19 (5)

20 (4)

tion in the hospital admission rate among cancer patients presenting

Pancreas

Other solid tumors ED visits per patient 1 visit

34 (4)

395 (49)

544 (82)

14 (4)

189 (48)

300 (87)

20 (5)

196 (48)

345 (91)

to an urgent care center [10].

Second, our pilot intervention provided substantial experience in understanding the obstacles to and opportunities for improving outpa-

2 visits

94 (14)

39 (11)

29 (8)

tient acute care for cancer patients. Our intervention design in this im-

3 visitsc

25 (4)

4 (1)

5 (1)

plementation followed a consultative model and relied on the

Abbreviation: IQR, interquartile range.

development of effective collaboration between emergency physicians

a Patients registering in the ED between 4 PM and 11 PM (Sunday through Friday) were considered to be exposed to the intervention. Intervention hours were 5 PM to 11 PM.

b Intravenous chemotherapy, oral chemotherapies not captured.

c One patient had 4 visits across both study periods.

19 admissions (19%) as potentially avoidable. The intervention oncolo- gist also identified 29 admissions (30%) that could have been safely co- ordinated as direct admissions from the outpatient oncology clinic, rather than transiting through the ED. Among these admissions, 8 of 29 admitted patients had been referred to the ED after initial evaluation in clinic, and an additional 9 patients registered in the ED before 4 PM on a weekday, without initial oncology clinic evaluation.

Discussion

Based on prior research indicating that approximately 1 in 5 hospital admissions among patients with cancer may be avoidable, [3-5] we cre- ated and pilot tested a pragmatic intervention to reduce hospital admis- sions among patients with solid tumor malignancies. We hypothesized

and ED-embedded medical oncologists. Because of the rapid pace of ED evaluation and management, we observed that this consultative ap- proach often resulted in the oncologist becoming involved in patient care only after the initial evaluation and laboratory testing had been completed by the ED care team. A more explicit plan for integration of the oncologist in the ED care team workflow (eg, through joint initial evaluation by both the emergency physician and oncologist) may have strengthened the evaluation by facilitating earlier, deeper involvement of the oncologist in management deliberations and decision making.

In addition, our approach did not involve standard use of acute care protocols or pathways, largely because of a lack of tested approaches that are applicable to cancer patients. An example of an oncology acute care situation where protocolized management may be appropri- ate is febrile neutropenia, where high-quality evidence demonstrates that risk algorithms accurately identify patients who can be safely man- aged as outpatients [11,12]. A recent National Institutes of Health work- shop on emergency care in cancer patients identified ED management of febrile neutropenia as a priority area for further study [13]. Further re- search is also needed to identify other oncology acute care populations

Table 2

Outcomes of ED management of cancer patients, before and after intervention implementation

Outcome measure

Preintervention period

Intervention period

OR (95% CI)

P

All ED visits (primary analysis), n

390

418

Hospital admission within 2 d of ED evaluationa

274 (70%)

287 (69%)

0.93 (0.69-1.24)

.62

Acute care within 5 d after ED evaluationb

29/138 (21%)

35/154 (23%)

1.15 (0.64-2.06)

.65

Death within 30 d of ED visitc

47 (12%)

45 (11%)

0.88 (0.57-1.36)

.57

ED disposition

.82

Inpatient admission

252 (65%)

264 (63%)

ED observation care

41 (11%)

42 (10%)

Discharged

97 (25%)

112 (27%)

ED length of stay (h:min), median (IQR)

5:37 (4:09, 7:31)

5:15 (4:01, 7:03)

.07

Inpatient length of stay (d), median (IQR)

4 (2, 7)

4 (2, 7)

.21

ED visits with presentation 4 PM-11 PM, Sun-Fri (exploratory analysis), n

124

128

Hospital admission within 2 d of ED evaluation

95 (77%)

86 (67%)

0.62 (0.36-1.08)

.08

Acute care within 5 d after ED evaluationb

13/40 (33%)

7/46 (15%)

0.37 (0.13-1.06)

.07

Death within 30 d of ED visit

13 (10%)

14 (11%)

1.05 (0.47-2.33)

.43

ED disposition

.49

Inpatient admission

84 (68%)

82 (65%)

ED observation care

12 (10%)

10 (8%)

Discharged

28 (23%)

36 (28%)

ED length of stay (h:min), median (IQR)

5:11 (3:55, 6:45)

4:55 (3:53, 6:27)

.20

Inpatient length of stay (d), median (IQR)

4 (2, 8)

4 (3, 8]

.31

a Primary outcome.

b Hospitalization or repeat ED evaluation, assessed among patients with an ED disposition other than hospital admission.

c Thirty-day follow-up complete for 98% of ED visits.

where outpatient management is safe and effective or where early spe- cialized management can otherwise contribute to improved outcomes of care. Candidate areas for study may include pneumonia [14], throm- boembolism, or nausea and vomiting–all common acute diagnoses where severity of illness varies widely.

Third, the optimal setting for an intervention to reduce hospitaliza- tions in cancer patients is uncertain. We chose to implement our pilot intervention in the ED because it is the most proximal care setting be- fore hospital admission for a majority of patients. Alternatively, reaching patients before they present to the ED may be a preferable approach to reducing hospital admissions and enhancing outpatient management. Exponents of the oncology patient-centered medical home have de- scribed their experience in reducing acute care utilization [15,16] which is largely based on availability of high-functioning telephone tri- age services and timely outpatient clinical evaluation for acute com- plaints [17]. Although promising, the generalizability of these types of interventions remains unproven. Enhancing the capability of outpatient oncology clinics to manage acute complaints (including expanded clinic access in the later afternoon and early evening and greater capacity for same-day outpatient palliative procedures, such as paracentesis or thoracentesis) is another promising approach for reducing hospital ad- missions. alternative payment models may provide the needed stimu- lus for this kind of care redesign [18-20], as current fee-for-service

Table 3

Hospital admission rates, stratified by ED principal diagnosis category

payment systems provide little incentive for institutions to make sys- tematic investments in outpatient acute care [21].

The principal strength of this study is its interventional, quasiexperimental design. Although prior observational studies have suggested that a substantial proportion of hospitalizations in cancer pa- tients may be avoidable, experimental and quasiexperimental ap- proaches provide the strongest lens for identifying and examining avoidable hospitalizations. As a pilot evaluation, our study was limited by its Short duration (5-week intervention period) and by certain fea- tures of the evaluation design. Our intervention was staffed during eve- ning hours, Sunday through Friday; however, the prospectively defined analysis population included all patients presenting to the ED at any time during the preintervention and intervention periods. We planned the analysis in this way because it allowed for a clean definition of the preintervention and intervention populations and because we hypothe- sized that the effectiveness our intervention might spill over into nonin- tervention hours. However, this decision meant that many patients in the analysis population were not effectively exposed to the study intervention.

In addition, we decided prospectively to limit our intervention to pa- tients with solid tumor malignancies. This decision was made due to ini- tial concerns that emergency presentations in patients with Hematologic malignancy might differ substantially from those in pa- tients with solid tumors. During the course of conducting this study, however, the intervention oncologist frequently participated in “curb- side” discussions regarding patients with hematologic malignancy,

ED principal diagnosis category Admissionsa, ED

visits

Admission rate (%)

and emergency presentations in this population often shared many sim- ilarities with presentations in solid tumor malignancy patients. Inclu-

Pneumonia (including empyema) 24 of 25 96

Cancer (primary site code) 24 of 27 89

Thromboembolism (including PE) 20 of 25 80

Hypovolemia and hypotension 22 of 28 79

Respiratory complaint (eg, dyspnea) 23 of 33 70

Fever of unknown origin 23 of 34 68

Nausea and vomiting 14 of 25 56

Other gastrointestinal disorders 11 of 20 55

Abdominal pain 28 of 55 51

sion of hematologic malignancy patients in our study population would have increased both the reach of our study and the power of the analysis, and future acute care interventions targeting cancer pa- tients need not necessarily segregate hematologic and solid tumor ma- lignancy patients. Finally, our intervention was labor intensive and would likely be challenging for many hospitals and EDs to implement. In summary, our intervention–consisting of a medical oncologist embedded in the ED to participate in team-based acute care of cancer

Complications of surgical/medical

care

10 of 20 50

patients–did not demonstrate effectiveness in reducing inpatient hos-

pital admissions. An analysis of patients who were most directly ex-

Nonspecific chest pain 9 of 22 41

Otherb 353 of 494 71

All 561 of 808 69

a Hospital admission within 2 days of ED evaluation.

b Includes all principal diagnosis categories with less than 20 ED visits.

posed to the intervention does not exclude the possibility of a modest reduction in hospitalizations and subsequent acute care; however, this analysis was underpowered and was not part of the original research plan. We conclude that future ED-based interventions to reduce

hospital admissions in cancer patients should include deeper, more systematic collaborations among emergency physicians and oncolo- gists, perhaps leveraging health informatics-based approaches to identify patients who can be safely managed without acute hospital admission. Alternatively, we recommend designing acute care interven- tions that can be offered in extended access clinic settings, outside of the ED environment, as another promising area for innovation in oncology acute care delivery. The recently established Comprehensive Oncologic Emergencies Research Network (supported by the National Cancer Institute) provides a new forum to facilitate multisite studies of acute care interventions in cancer and presents a promising setting to further explore approaches for preventing potentially avoidable hospitaliza- tions in patients with cancer [22].

References

  1. Manzano J-GM, Luo R, Elting LS, George M, Suarez-Almazor ME. Patterns and predic- tors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer. J Clin Oncol 2014;55:3527-33.
  2. Brooks GA, Li L, Uno H, Hassett MJ, Landon BE, Schrag D. Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced can- cer. Health Aff 2014;33:1793-800.
  3. Brooks GA, Abrams TA, Meyerhardt JA, Enzinger PC, Sommer K, Dalby CK, et al. Iden- tification of potentially avoidable hospitalizations in patients with GI cancer. J Clin Oncol 2014;32:496-503.
  4. Brooks GA, Jacobson JO, Schrag D. Clinician perspectives on potentially avoidable hospitalizations in patients with cancer. JAMA Oncol 2015;1:109-10.
  5. Manzano J-GM, Gadiraju S, Hiremath A, Lin HY, Farroni J, Halm J. Unplanned 30-day readmissions in a general internal medicine hospitalist service at a comprehensive cancer center. J Oncol Pract 2015;11:410-5.
  6. Mayer DK, Travers D, Wyss A, Leak A, Waller A. Why do patients with cancer visit emergency departments? Results of a 2008 population study in North Carolina. J Clin Oncol 2011;29:2683-8.
  7. Barbera L, Taylor C, Dudgeon D. Why do patients with cancer visit the emergency department near the end of life? CMAJ 2010;182:563-8.
  8. Smulowitz PB, Honigman L, Landon BE. A novel approach to identifying targets for cost reduction in the emergency department. Ann Emerg Med 2013;61:293-300.
  9. HCUP CCS. Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality. Accessed December 1, 2015, at https://www.hcup-us.ahrq. gov/toolssoftware/ccs/ccsfactsheet.jsp.
  10. Lipitz-Snyderman A, Klotz A, Atoria CL, Martin S, Groeger J. Impact of observation status on hospital use for patients with cancer. J Oncol Pract 2015;11:73-7.
  11. Klastersky J, Paesmans M, Georgala A, Muanza F, Plehiers B, Dubreucq L, et al. Outpa- tient oral antibiotics for febrile neutropenic cancer patients using a score predictive for complications. J Clin Oncol 2006;24:4129-34.
  12. Carmona-Bayonas A, Jimenez-Fonseca P, Virizuela Echaburu J, Antonio M, Font C, Biosca M, et al. Prediction of serious complications in patients with seemingly stable febrile neutropenia: validation of the clinical index of stable febrile neutropenia in a prospective cohort of patients from the FINITE study. J Clin Oncol 2015;33:1-7.
  13. Brown J, Grudzen C, Kyriacou DN, Obermeyer Z, Quest T, Rivera D, et al. The emer- gency care of patients with cancer: setting the research agenda. Ann Emerg Med 2016. http://dx.doi.org/10.1016/j.annemergmed.2016.01.021.
  14. Gonzalez C, Johnson T, Rolston K, Merriman K, Warneke C, Evans S. Predicting pneu- monia mortality using CURB-65, PSI, and patient characteristics in patients present- ing to the emergency department of a comprehensive cancer center. Cancer Med 2014;3:962-70.
  15. Sprandio JD. Oncology patient-centered medical home. J Oncol Pract 2012;8:47s-9s.
  16. Hoverman JR, Klein I, Harrison DW, Hayes JE, Garey JS, Harrell R, et al. Opening the black box: the impact of an oncology management program consisting of level I pathways and an outbound nurse call system. J Oncol Pract 2014;10:63-7.
  17. Waters TM, Webster JA, Stevens LA, Li T, Kaplan CM, Graetz I, et al. Community oncology medical homes: physician-driven change to improve patient care and reduce costs. J Oncol Pract 2015;11:462-7.
  18. Colla CH, Lewis VA, Gottlieb DJ, Fisher ES. Cancer spending and accountable care organiza- tions: evidence from the physician group practice demonstration. Healthcare 2013;1:100-7.
  19. Kline RM, Bazell C, Smith E, Schumacher H, Rajkumar R, Conway PH. Centers for Medicare and Medicaid Services: using an episode-based payment model to im- prove oncology care. J Oncol Pract 2015;11:114-6.
  20. Newcomer LN, Gould B, Page RD, Donelan SA, Perkins M. Changing physician incen- tives for affordable, quality cancer care: results of an episode payment model. J Oncol Pract 2014;10:322-6.
  21. Newcomer LN. Changing physician incentives for cancer care to reward better patient outcomes instead of use of more costly drugs. Health Aff 2012;31:780-5.
  22. Comprehensive Oncologic Emergencies Research Network (CONCERN). National Cancer Institute, Division of Cancer Control & Population Sciences. Accessed May 31, 2016, at http://www.epi.grants.cancer.gov/CONCERN.