You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#H1-1-ZOI190463}
============

A disproportionate share of medical spending is provided to patients in their last year of life.^[@zoi190463r1],[@zoi190463r2],[@zoi190463r3]^ Much of that difference is no doubt because of unavoidable costs of serious illness.^[@zoi190463r4]^ However, for patients with cancer, it is often possible to predict when intensive medical services have lost much of their potential benefit.^[@zoi190463r4]^ For that reason, the National Academy of Medicine and the American Society of Clinical Oncology (ASCO) recommend a reduction in use of intensive medical services at the end of life, noting it is at odds with the focus on palliation and reduction in patient suffering that should characterize health care at this time.^[@zoi190463r5],[@zoi190463r6]^

Despite such recommendations, patients continue to receive intensive medical services in the last month of life.^[@zoi190463r7],[@zoi190463r8]^ Intensive services at the end of life are not linked to better outcomes,^[@zoi190463r9],[@zoi190463r10],[@zoi190463r11]^ are associated with poorer patient quality of life,^[@zoi190463r12],[@zoi190463r13]^ and are considered undesirable by many patients.^[@zoi190463r14],[@zoi190463r15]^ An ancillary consequence of poor-quality end-of-life care is its cost, both to the health care system and to patients, who often bear nontrivial cost-sharing. Medical care is the leading cause of personal bankruptcy in the United States, and insurance is not sufficiently protective against patient financial consequences; most persons experiencing medical bankruptcy were insured at the time of their illness.^[@zoi190463r16]^

Few studies have tried to quantify the financial consequences associated with these end-of-life intensive medical services. In the present study, we evaluate the additional costs incurred for patients who receive intensive medical services at the end of life, from both a health system and a beneficiary perspective. Although the medical community is no doubt aware that costs increase as use of health services increases, our goals in the present study are (1) to quantify the magnitude of that association, including for specific intensive medical services, and (2) to shed light on patient financial responsibility for medically intensive end-of-life services.

We focus the present study on patients dying of cancer, for 5 reasons. First, almost 40% of people in the United States will develop cancer at some point in their lives^[@zoi190463r17]^; and cancer accounts for nearly 1 in 4 US deaths.^[@zoi190463r18]^ Second, patient financial consequences for cancer care are especially high,^[@zoi190463r19]^ with almost half of patients reporting cancer care--related financial strain,^[@zoi190463r20],[@zoi190463r21]^ and many patients forgoing or discontinuing cancer treatment partly for financial reasons.^[@zoi190463r21],[@zoi190463r22]^ Third, literature indicates death from solid tumor may be easier to prognosticate than death from other chronic illness.^[@zoi190463r4]^ Fourth, the National Quality Forum (NQF)-endorsed ASCO guidelines for appropriate end-of-life care are premised on the assumption that death from cancer is able to be anticipated; therefore, there is a growing consensus that cancer-related death can often be forecast and that care decisions can be made based on that understanding. Fifth, in recognition of the high costs and limited health benefit of some cancer care, ASCO has recommended physicians discuss the value---the costs and likely outcomes---of treatment strategies with their patients. Yet physicians often have limited knowledge of the costs of care,^[@zoi190463r23],[@zoi190463r24]^ making informed discussions challenging if not impossible. The present study quantifies the financial consequences of medically intensive end-of-life services and provides physicians with reference estimates that may be of use.

Methods {#H1-2-ZOI190463}
=======

Consistent with ASCO/NQF measures, we evaluated total costs of care in the last month of life for patients who did or who did not receive the following medically intensive services: 2 or more emergency department visits; chemotherapy; a hospital admission without an intensive care unit (ICU) stay; an ICU stay; or hospice for fewer than 3 days in the last month of life. We evaluated both oral and intravenous chemotherapy. Of note, ASCO/NQF include metrics for chemotherapy use in the last 14 days of life. Others have included chemotherapy use in the last 30 days of life to evaluate end-of-life care quality.^[@zoi190463r25],[@zoi190463r26],[@zoi190463r27]^ Recognizing that the specific definition of "end of life" is subjective, and with a goal of being more inclusive, we similarly chose to evaluate chemotherapy in the last 30 days of life. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ([STROBE](http://www.equator-network.org/reporting-guidelines/strobe/)) reporting guideline for cohort studies. This study was approved by and received a waiver of informed consent from the Stanford University Institutional Review Board because the research involved no more than minimal risk to the participants and the waiver would not adversely affect the rights and welfare of the participants.

Study Population {#H2-1-ZOI190463}
----------------

Cohort members were veterans who died of cancer in fiscal years 2010 to 2014 and were identified using Surveillance, Epidemiology, and End Results--Medicare *International Statistical Classification of Diseases and Related Health Problems, Tenth Revision* (*ICD-10*) codes for underlying cause of death from National Death Index death certificate data.^[@zoi190463r28],[@zoi190463r29]^ Underlying cause of death data have more than 89% sensitivity and more than 90% specificity for multiple solid tumor types.^[@zoi190463r30],[@zoi190463r31],[@zoi190463r32]^ We made 2 adjustments to ASCO/NQF metrics to increase the likelihood that physicians were better able to anticipate their patients' deaths: we limited the cohort to persons dying of solid tumor and to those who had an *ICD-9* code for cancer for at least 6 months in administrative data. These criteria have the effect of providing more conservative estimates of intensive medical services at the end of life than would strict ASCO/NQF criteria.

Cohort members were aged 66 years or older and were continuously enrolled in fee-for-service Medicare in the 12 months prior to death; the latter criterion allowed for full capture of use. Veterans aged 65 years or older are eligible to enroll in Medicare, and most dually enrolled veterans use both the Veterans Health Administration (VA) and Medicare for services.^[@zoi190463r33],[@zoi190463r34],[@zoi190463r35],[@zoi190463r36],[@zoi190463r37]^ Medicare data were included in this analysis because other work indicates excluding Medicare substantially underestimates total costs of veteran care.^[@zoi190463r38]^ We excluded patients who were also enrolled in Medicaid owing to the unavailability of those cost data; our cohort was limited to patients for whom we had complete capture of costs and utilization.

Costs {#H2-2-ZOI190463}
-----

The health system costs of intensive medical services in the VA were obtained from VA Managerial Cost Accounting data that we linked to inpatient, outpatient, and pharmacy administrative utilization data. Beneficiary costs in the VA are not directly available and were assigned using VA national office guidance, based on patients' utilization of outpatient primary care, specialty care, and days of inpatient service use and enrollment priority.^[@zoi190463r39]^ For example, patients who have an enrollment priority of 7 or 8 are eligible for copayments in VA. Patients in priority group 8 have the highest copayments; if these patients received inpatient care, their cost-sharing was \$10 per day plus approximately \$1200 for the first 90 days, and \$600 for the next 90 days. Medicare health system and beneficiary costs and utilization were obtained from the MEDPAR, Outpatient, Carrier, Durable Medical Equipment, Hospice, Home Health and Part D administrative files. Beneficiary costs are a separate variable in the claims data present in these files. We also included care that was delivered in the community and paid for by the VA, known as Fee-Basis care. Taken together, these represent the entirety of costs incurred by patients in their last month of life. Medicare data are reimbursements for care provided, whereas VA data are cost estimates based on activity-based cost accounting. Patient costs in both systems are beneficiary expectations of payment, rather than patient copayments because the latter are not available in the Medicare or VA research data. They therefore represent the starting point for patient copayments, rather than final patient copayments; yet they remain the most comprehensive data available for research purposes. For simplicity, we refer to all as costs. Costs were inflation adjusted to 2014 dollars using the personal consumption expenditures index,^[@zoi190463r40]^ as recommended by the Second Panel on Cost-Effectiveness in Health and Medicine.^[@zoi190463r41]^

Statistical Analysis {#H2-3-ZOI190463}
--------------------

We estimated the association between receipt of medically intensive services and costs in the last month of life using a generalized linear model. A modified Park test and a Box-Cox regression recommended a gamma distribution with a log link function.^[@zoi190463r42]^ Models were adjusted for age as a categorical variable, race, the cancer category representing the underlying cause of death, and Elixhauser comorbidities.^[@zoi190463r43]^ Models were conditioned on geographic area (hospital referral region) to account for both geographic differences in the practice of end-of-life care^[@zoi190463r9]^ and geographic variation in wages; this method allows for interpretation of the cost differences between medically intensive and nonmedically intensive services within geographic regions.

Models were used to estimate the additional costs associated with medically intensive services, with bias-corrected 95% CIs generated through bootstrapping with 1000 replications. We adjusted for multiple hypothesis testing by constraining the familywise error rate to no more than 0.05 (2-tailed level of significance) across the 5 outcomes studied.^[@zoi190463r44]^ All analyses were conducted in Stata/MP, version 15.1 (StataCorp), from February to August 2019.

Results {#H1-3-ZOI190463}
=======

The study cohort consisted of 48 937 veterans who received care through the VA and Medicare. Our cohort was majority white (90.8%), male (98.9%), and had a mean (SD) of 8.8 (3.9) comorbidities ([Table 1](#zoi190463t1){ref-type="table"}). Cohort members were most likely to die of lung and bronchus cancer (31.1%) or prostate cancer (20.8%). Cohort members had a cancer diagnosis for a median (interquartile range) of 34.6 (16.0-54.8) months before death. In unadjusted analyses, patients who received a medically intensive service had a longer time from cancer diagnosis to death than patients who did not (*P* \< .001 using a Wilcoxon rank sum test) ([Table 1](#zoi190463t1){ref-type="table"}).

###### Demographic Characteristics and Comorbidities

  Characteristic                                                  No. (%) of Patients   *P* Value                             
  --------------------------------------------------------------- --------------------- ------------------ ------------------ --------
  Total, No.                                                      48 937                20 102             28 835             
  Age, y                                                                                                                      
  66 to \<71                                                      9870 (20.2)           3675 (18.3)        6195 (21.5)        \<.001
  71 to \<76                                                      7302 (14.9)           2736 (13.6)        4566 (15.8)        
  76 to \<81                                                      9990 (20.4)           4015 (20.0)        5975 (20.7)        
  81 to \<86                                                      10 421 (21.3)         4435 (22.1)        5986 (20.8)        
  86 to \<91                                                      8391 (17.2)           3857 (19.2)        4534 (15.7)        
  ≥91                                                             2963 (6.0)            1384 (6.9)         1579 (5.5)         
  Male                                                            48 378 (98.9)         28 557 (99.0)      19 821 (98.6)      \<.001
  Eligible for copayments in VA                                   15 283 (31.2)         6520 (32.4)        8763 (30.4)        \<.001
  Race                                                                                                                        \<.001
  White                                                           25 116 (51.3)         18 242 (90.8)      25 116 (87.1)      
  Black                                                           4365 (8.9)            1408 (7.0)         2957 (10.2)        
  Asian/Hawaiian/Pacific Islander                                 437 (0.9)             148 (0.7)          289 (1.0)          
  Missing                                                         346 (0.7)             151 (0.8)          195 (0.7)          
  Mixed                                                           286 (0.6)             103 (0.5)          183 (0.6)          
  Native American                                                 145 (0.3)             50 (0.2)           95 (0.3)           
  Rural status                                                                                                                
  Highly urban                                                    20 091 (41.0)         7811 (38.9)        12 280 (42.6)      \<.001
  Rural                                                           21 608 (44.2)         9194 (45.7)        12 414 (43.0)      
  Urban                                                           7166 (14.6)           3067 (15.3)        4099 (14.2)        
  Missing                                                         72 (0.2)              30 (0.2)           42 (0.2)           
  Underlying cause of death                                                                                                   
  Bladder                                                         2832 (5.8)            1121 (5.6)         1711 (5.9)         \<.001
  Brain and nervous system                                        655 (1.3)             309 (1.5)          346 (1.2)          
  Colorectal                                                      4158 (8.5)            1870 (9.3)         2288 (7.9)         
  Gastroesophageal                                                2458 (5.0)            987 (4.9)          1471 (5.1)         
  Head and neck                                                   1491 (3.1)            517 (2.6)          974 (3.4)          
  Hepatobiliary                                                   1703 (3.5)            710 (3.5)          993 (3.4)          
  Kidney                                                          1499 (3.1)            663 (3.3)          836 (2.9)          
  Lung and bronchus                                               15 241 (31.1)         6141 (30.6)        9100 (31.6)        
  Melanoma                                                        1040 (2.1)            490 (2.4)          550 (1.9)          
  Other                                                           5460 (11.2)           2040 (10.2)        3420 (11.9)        
  Pancreas                                                        2214 (4.6)            930 (4.6)          1284 (4.4)         
  Prostate                                                        10 186 (20.8)         4324 (21.5)        5862 (20.3)        
  Comorbidities                                                                                                               
  Chronic obstructive pulmonary disorder                          25 008 (51.1)         9845 (49.0)        15 163 (52.6)      \<.001
  Renal disease                                                   14 497 (29.6)         5536 (27.5)        8961 (31.1)        \<.001
  Diabetes without chronic complications                          18 325 (37.5)         7066 (35.2)        11 259 (39.1)      \<.001
  Diabetes with chronic complications                             6414 (13.1)           2406 (12.0)        4008 (13.9)        \<.001
  HIV/AIDS                                                        81 (0.2)              21 (0.1)           60 (0.2)           .01
  Peripheral vascular disease                                     15 367 (31.4)         6181 (30.8)        9186 (31.9)        .01
  Moderate or severe liver disease                                869 (1.8)             339 (1.7)          530 (1.8)          .21
  Mild liver disease                                              7993 (16.3)           3226 (16.0)        4767 (16.5)        .15
  Hemiplegia or paraplegia                                        1666 (3.4)            682 (3.4)          984 (3.4)          .91
  Acute myocardial infarction                                     6826 (14.0)           2653 (13.2)        4173 (14.5)        \<.001
  Cerebrovascular disease                                         12 632 (25.8)         5133 (25.5)        7499 (26.0)        .24
  Dementia                                                        3297 (6.7)            1528 (7.6)         1769 (6.1)         \<.001
  Rheumatologic disease                                           1586 (3.2)            593 (3.0)          993 (3.4)          .01
  Peptic ulcer disease                                            2369 (4.8)            882 (4.4)          1487 (5.2)         \<.001
  Congestive heart failure                                        13 876 (28.4)         5296 (26.4)        8580 (29.8)        \<.001
  Months between first cancer diagnosis and death, median (IQR)   34.6 (16.0-54.8)      33.3 (15.7-54.3)   35.6 (16.3-55.1)   \<.001

Abbreviations: IQR, interquartile range; VA, Veterans Health Administration.

More than half of the cohort (58.9%) received at least 1 medically intensive service in the last month of life ([Table 2](#zoi190463t2){ref-type="table"}). Patients who received medically intensive services were significantly more likely to have comorbidities, although the difference was small ([Table 1](#zoi190463t1){ref-type="table"}). The most frequently occurring intensive service was insufficient hospice exposure (36.6%), followed by a hospital stay without an ICU admission (30.3%); the least frequently occurring intensive service was chemotherapy (11.0%) ([Table 2](#zoi190463t2){ref-type="table"}). Patients were most likely to receive 1 (28.6%) or 2 (22.1%) intensive services in the last month of life; few patients had 3 or more intensive services.

###### Receipt of Medically Intensive Services in the Last 30 Days of Life

  Medically Intensive Service           No. (%) of Patients
  ------------------------------------- ---------------------
  Type                                  
  Any                                   28 835 (58.9)
  \>2 ED visits                         6159 (12.6)
  Chemotherapy                          5392 (11.0)
  Hospital admission without ICU stay   14 810 (30.3)
  Hospital admission with ICU stay      7038 (14.4)
  Hospice for ≤3 d                      17 886 (36.6)
  No.                                   
  0                                     20.102 (41.4)
  1                                     12 527 (28.6)
  2                                     10 826 (22.1)
  3                                     4822 (9.9)
  4                                     660 (1.4)
  5                                     0

Abbreviations: ED, emergency department; ICU, intensive care unit.

Receipt of medically intensive services was associated with higher costs of care, for both the health system and the beneficiary. Patients with at least 1 medically intensive service had \$15 952 (95% bias-corrected CI, \$15 676-\$16 206; *P* \< .001) greater health system costs in the last month of life compared with patients who did not have any medically intensive service (mean \[SD\], \$23 612 \[\$5528\] vs \$7660 \[\$1793\]). They also had \$1123 (95% CI, \$1115-\$1143) greater expected beneficiary costs ([Table 3](#zoi190463t3){ref-type="table"}).

###### Estimated Costs of Medically Intensive Care for the Health System and the Beneficiary in the Last Month of Life

  Service                               Cost for No Medically Intensive Service, \$   Cost for Receipt of Medically Intensive Service, \$[^a^](#zoi190463t3n1){ref-type="table-fn"}
  ------------------------------------- --------------------------------------------- -----------------------------------------------------------------------------------------------
  Any intensive service                                                               
  Health system                         7660                                          23 612
  Patient                               133                                           1257
  \>2 ED visits                                                                       
  Health system                         15 868                                        27 007
  Patient                               675                                           1553
  Chemotherapy                                                                        
  Health system                         16 919                                        20 379
  Patient                               680                                           1621
  Hospital admission without ICU stay                                                 
  Health system                         14 699                                        23 289
  Patient                               557                                           1327
  Hospital admission with ICU stay                                                    
  Health system                         14 142                                        35 235
  Patient                               608                                           1830
  Hospice for ≤3 d                                                                    
  Health system                         12 303                                        25 438
  Patient                               488                                           1300

Abbreviations: ED, emergency department; ICU, intensive care unit.

All differences between no medically intensive service and receipt of such service are statistically significant after adjusting for multiple hypothesis testing (all *P* \< .001).

Costs varied substantially by type of intensive service provided, with ICU stays associated with highest additional financial consequence and chemotherapy associated with lowest additional financial consequence for the health system ([Figure 1](#zoi190463f1){ref-type="fig"} and [Figure 2](#zoi190463f2){ref-type="fig"}). Patients with an ICU stay in the last month of life had \$21 093 (95% CI, \$20 364-\$21 689) higher health system costs and \$1222 (95% CI, \$1178-\$1238) higher expected beneficiary costs than those who did not have an ICU stay. Patients with a non-ICU hospital stay had \$8590 (95% CI, \$8224-\$8772) higher health system costs and \$771 (95% CI, \$749-\$778) higher expected beneficiary costs than those who did not have a non-ICU hospital stay. Patients with 2 or more emergency department visits in the last month of life had \$11 140 (95% CI, \$10 623-\$11 495) higher health system costs and \$879 (95% CI, \$853-\$901) higher expected beneficiary costs. Patients who received 3 or fewer days of hospice had \$13 134 (95% CI, \$12 713-\$13 501) higher health system costs and \$811 (95% CI, \$778-\$825) higher expected beneficiary costs than those who received hospice for the greater (recommended) number of days. Patients who received chemotherapy had \$3460 (95% CI, \$2927-\$3880) higher health system costs and \$942 (95% CI, \$888-\$969) higher expected beneficiary costs than those who did not receive any chemotherapy in the last month of life.

![Health System Costs in the Last Month of Life due to Medically Intensive Services\
ED indicates emergency department; ICU, intensive care unit.](jamanetwopen-2-e1912161-g001){#zoi190463f1}

![Beneficiary Costs in the Last Month of Life Due to Medically Intensive Services\
ED indicates emergency department; ICU, intensive care unit.](jamanetwopen-2-e1912161-g002){#zoi190463f2}

The total estimated mean (SD) health care system costs for patients with intensive services were as follows: \$23 612 (\$5528) for any intensive service; \$35 235 (\$10 067) for hospital admission with ICU stay; \$27 007 (\$8629) for 2 or more emergency department visits; \$25 438 (\$6729) for hospice lasting 3 or fewer days; \$23 289 (\$7495) for hospital admission without ICU stay; and \$20 379 (\$6646) for chemotherapy.

The total estimated mean (SD) expected beneficiary costs for patients with intensive services were as follows: \$1257 (\$468) for any intensive service; \$1830 (\$652) for hospital admission with ICU stay; \$1621 (\$591) for chemotherapy; \$1553 (\$585) for 2 or more emergency department visits; \$1327 (\$515) for hospital admission without ICU stay; and \$1300 (\$474) for hospice lasting 3 or fewer days ([Table 3](#zoi190463t3){ref-type="table"}).

In models evaluating the association between the number of intensive services provided and costs, (mean \[SD\]) health care systems costs were similar for patients with 2 (\$27 979 \[\$6432\]), 3 (\$28 641 \[\$6589\]), or 4 (\$28 854 \[\$6638\]) intensive services (all *P* \> .05). The greatest differences in health system costs were between 0 and 1 intensive services (mean \[SD\], \$7617 \[\$1752\] vs \$17 814 \[\$4099\]); *P* \< .001). There were also significant differences in costs between 2 services vs 0 services and 2 services vs 1 service (both *P* \< .001). However, the differences in health system costs between 2 services and 3 or 4 services was not significant (*P* = .13 for 2 vs 3; *P* = .39 for 2 vs 4; *P* = .85 for 3 vs 4). By contrast, expected beneficiary costs increased significantly with each intensive service provided (mean \[SD\]: \$133 \[50\] for no intensive services; \$820 \[\$309\] for 1 intensive service; \$1463 \[\$552\] for 2 intensive services; \$1830 \[\$691\] for 3 intensive services; and \$2230 \[\$842\] for 4 intensive services). For expected beneficiary costs, all *P* values were .01 or less for each number of service vs other numbers of services (2 services vs 0 services, 2 services vs 1 service, etc).

Discussion {#H1-4-ZOI190463}
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Despite recommendations to the contrary, more than half of cancer decedents receive medically intensive services in the last month of life. We found the costs associated with nonrecommended intensive services added a mean of almost \$16 000 to health care system costs and more than \$1100 to expected beneficiary costs in the last month of life alone, bringing total spending in the last month of life to a mean of \$24 000 for the health system and \$1300 for the beneficiary. Although it is not surprising that intensive medical services cost more, quantifying the magnitude of these costs can help spur efforts to reduce it.

Prior studies have estimated overall health system costs in the last 6 months of life for Medicare beneficiaries to range from means of \$41 712 to \$74 212^[@zoi190463r45],[@zoi190463r46]^ and medians around \$22 000.^[@zoi190463r47],[@zoi190463r48]^ To our knowledge, no other study has quantified the additional costs associated with intensive medical services at the end of life, and there is no other published evaluation of beneficiary expectations of payment or patient costs associated with intensive medical services at the end of life. In this work, we found ICU stays were associated with the highest excess financial consequence to the health system, of more than \$21 000. Chemotherapy was associated with the lowest excess financial consequence to the health system of approximately \$3500. The low excess financial consequence of chemotherapy is partially because patients not receiving chemotherapy were still receiving other intensive services in the last month of life, contributing to their total costs.

Many factors influence the patient-clinician decision to pursue medically intensive services. These may include patient desire for such care, clinician belief that it will provide significant medical or palliative benefit, patient denial of limited prognosis, or miscommunication about prognosis. However, when querying patients about what they would hypothetically prefer, they often assert that intensive services at the end of life are undesirable.^[@zoi190463r49],[@zoi190463r50],[@zoi190463r51]^ Evidence also indicates that patients and clinicians may not always share the same understanding about prognosis; in 1 large sample study, more than two-thirds of patients with metastatic solid tumor were unaware that their chemotherapy had no curative intent.^[@zoi190463r52]^ Thus, miscommunication or misunderstanding of prognosis is of particular concern.

Ideally, patient-clinician decisions to pursue medically intensive services should involve discussions of the likelihood of benefit, risks, and side effects, including potential financial consequences, of these interventions. The present study provides reference cost estimates that may help inform those discussions. The present analysis indicates that patients experience approximately \$1250 out-of-pocket health costs in the last month of life due to medically intensive services. To place this number in context, the median annual household income of a Medicare beneficiary in 2014, the last year of this analysis, was \$24 150,^[@zoi190463r53]^ or \$2013 a month. Using these figures, expected beneficiary spending on medical services that have a low likelihood of helping them and could harm them may represent 62% of the household income of the typical Medicare enrollee in the last month of his life. Indeed, analyses of Medicare-only beneficiaries find that beneficiaries with a new cancer diagnosis have out-of-pocket costs that are a mean of 24% of their household income.^[@zoi190463r54]^ The present study indicates that as cancer progresses, expected beneficiary responsibility for intensive medical services represents a higher proportion of household income, rising to almost two-thirds of household income in the last month of life. This study also identified costs in the last month of life only, and almost 90% of our cohort received no chemotherapy in this time frame. Thus, analyses looking farther back from death would yield much higher estimates of drug costs. This, coupled with the high cost of chemotherapies and immunotherapies that have come to market after fiscal year 2014 (the last year of our data),^[@zoi190463r55],[@zoi190463r56]^ indicate that the financial consequences due to cancer care will only grow.

The purchasing of medical care, including chemotherapy and end-of-life care, is largely guided by physicians; recent work finds that physician beliefs, rather than patient beliefs, explain much of oft-noted geographic variation in end-of-life spending.^[@zoi190463r57]^ Thus, physicians have a strong opportunity to limit medically intensive interventions at the end of life in ways that can avoid patient financial consequences. For example, early goals-of-care conversations with patients may help to ensure that patients receive the care they want and need. The present study also highlighted that given the low likelihood of benefit and the potential for financial consequences, as well as patient concerns about the cost of cancer care,^[@zoi190463r21],[@zoi190463r22]^ it may be worthwhile for physicians to discuss this openly with patients before proceeding with medically intensive interventions.

Limitations {#H2-4-ZOI190463}
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The present study was subject to certain limitations. First, this study was a retrospective evaluation of cancer decedents, and although we examined care for patients provided in the month prior to death, patients and their treating oncologists may have been less certain that the patients were in their final month of life. However, oncologists regularly use many clinical indicators to help prognosticate life expectancy, including understanding the expected trajectory of a specific solid tumor as it metastasizes, recognizing the rapidity of a patient's personal disease progression, and identifying symptoms that death is becoming imminent. Several tools have been developed to aid in this prognostication.^[@zoi190463r58],[@zoi190463r59],[@zoi190463r60]^ In fact, ASCO has created specific metrics regarding care in the last 30 days of life, indicating the importance the oncology community places on attempting to anticipate when patients are approaching death.

Second, as noted above, this study evaluates the beneficiary expectation of payment, rather than the actual costs paid by patients, as the latter are not available in Medicare and VA research databases. On the Medicare side, the majority of enrollees in traditional Medicare have supplemental insurance,^[@zoi190463r61]^ often referred to as Medigap insurance, that covers much of their cost-sharing. However, while supplemental insurance reduces patients' out-of-pocket costs, it does not eliminate them. Survey analyses found 1-year out-of-pocket payments by patients with cancer and Medigap insurance were \$5670 vs \$8115 for patients without Medigap insurance.^[@zoi190463r54]^ When factoring in premiums for Medigap insurance and Part D plans (the former conservatively estimated to be \$1440 per year),^[@zoi190463r62]^ the differences in patient total cost-sharing between patients with cancer and with or without Medigap insurance narrows further (estimated to be \$8115 vs \$7110 for patients with and without Medigap, not including the cost of Part D premiums). Part D coverage for oral medications, including oral chemotherapy, also does not fully protect patients from cost-sharing. Even after closure of the Medicare Part D "coverage gap," Part D patient out-of-pocket costs for oral chemotherapy are estimated to be \$5663 for an average course of treatment.^[@zoi190463r63]^ Taken together, our results as well as published information about Medicare cost-sharing indicate substantial patient financial responsibility for care provided in the last month of life, regardless of supplemental insurance status. In addition, the premiums patients pay for supplemental insurance are heavily influenced by Medigap expenditures for previous years. Finally, because VA care is not eligible for supplemental insurance, our VA-measured costs are a reasonable approximation or actual out-of-pocket payments, as is the cost-sharing associated with Medicare Part D services. Furthermore, 20% of the eligible Medicare population does not have supplemental insurance^[@zoi190463r61]^; beneficiary payments do equal out-of-pocket spending for that group, and given that their income distribution of that population tends to skew low,^[@zoi190463r61]^ the beneficiary costs we present are particularly relevant for that group. Given all of these factors, understanding the amount beneficiaries are expected to pay or must have reimbursed through secondary insurance they purchase remains important yet understudied.

Third, our work combined VA and Medicare data for a cohort of veterans. This produces conservative estimates of expected beneficiary responsibility for care because our beneficiary cost data include both VA and Medicare expenses, and patient cost-sharing in VA is nominal. Many veterans pay no copayments. In our cohort, approximately one-third of veterans were eligible for copayments in VA (although all were eligible for copayments in Medicare). Even those veterans who are eligible for cost-sharing in the VA have greater financial protection than veterans seeking care through Medicare. For example, veterans who are eligible for VA copayments pay \$15 per primary care visit, and \$50 per specialty care visit. By contrast, Medicare charges a 20% coinsurance for outpatient services, including for infused chemotherapies. In addition, unlike Medicare, VA has a drug formulary and negotiates drug prices. Thus, an analysis evaluating a nonveteran cohort would yield much higher figures for expected beneficiary costs in the last month of life. However, although the use of a veteran cohort limits the generalizability of our findings, it has the advantage of ensuring findings regarding intensive services are not driven by the fee-for-service incentives for overuse of services present in Medicare. Indeed, our results point to the need for further efforts to improve the value of end-of-life care in both integrated and fee-for-service environments.

Conclusions {#H1-5-ZOI190463}
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The present study is, to our knowledge, the first analysis of both health system costs and beneficiary costs associated with medically intensive end-of-life care. Our results indicated that receipt of medically intensive services in the last month of life was associated with substantial costs, a nontrivial portion of which was borne by beneficiaries and their decedents. Efforts to more appropriately use medical care may benefit by considering beneficiary financial consequences as well as health system costs associated with intensive medical interventions. Our results provide further support for the consideration of value in cancer care.