The PACT Act-Expanding Coverage and Access for Veterans.

This Viewpoint examines the implementation of the Sergeant First Class Heath Robinson Honoring Our Promise to Address Comprehensive Toxics Act, known as the PACT Act, which expanded health care for millions of veterans from any era exposed to toxic hazards.

Cancer pain: a review of epidemiology, clinical quality and value impact.

Cancer-related pain, reported by more than 70% of patients, is one of the most common and troublesome symptoms affecting patients with cancer. Despite the availability of effective treatments, cancer-related pain may be inadequately controlled in up to 50% of patients. With the growing focus on 'value' (healthcare outcomes achieved per dollar spent) in healthcare, the management of cancer-related pain has assumed novel significance in recent years. Data from initiatives that assess the quality of pain management in clinical practice have shown that effective management of cancer-related pain improves patient-perceived value of cancer treatment. As a result, assessment and effective management of cancer-related pain are now recognized as important measures of value in cancer care.

Changes in Homelessness Among US Veterans After Implementation of the Ending Veteran Homelessness Initiative.

Importance: Homelessness is a persistent and growing problem. What role health systems should play and how that role is incorporated into larger strategic efforts are not well-defined. Objective: To compare homelessness among veterans with that in the general population during a 16-year study period before and after implementation of the Ending Veteran Homelessness Initiative, a program to rehouse veterans experiencing homelessness. Design, Setting, and Participants: This national retrospective cohort study using a mixed-methods approach examined annualized administrative (January 1, 2007, to December 31, 2022) and population data prior to (2007-2009) and during (2010-2022) the Ending Veteran Homelessness initiative. Participants included unhoused adults in the US between 2007 and 2022. Exposure: Enrollment in Veterans Health Administration (VHA) Homeless Program Office components providing housing, case management, and wrap-around clinical and supportive services. Main Outcomes and Measures: Point-in-time (PIT) count data for unhoused veterans and nonveterans during the study period, number of Section 8 housing vouchers provided by Housing and Urban Development-Veterans Administration Supportive Housing, number of community grants awarded by Supportive Services for Veterans and Families, and total number of veterans housed each year. Semistructured interviews with VHA leadership were performed to gain insight into the strategy. Results: In 2022, 33 129 veterans were identified in the PIT count. They were predominantly male (88.7%), and 40.9% were unsheltered. During the active years of the Ending Veteran Homelessness initiative, veteran homelessness decreased 55.3% compared with 8.6% for the general population. The proportion of veterans in this cohort also declined from 11.6% to 5.3%. This change occurred during a shift to "housing first" as agency policy to create low-barrier housing availability. It was also coupled with substantial growth in housing vouchers, grants to community partner agencies, and growth in VHA clinical and social programming to provide homeless-tailored wrap-around services and support once participants were housed. Key respondent interviews consistently cited the shift to housing first, the engagement with community partners, and use of real-time data as critical. Conclusions and Relevance: In this cohort study of the federal Ending Veteran Homelessness initiative, after program implementation, there was a substantially greater decrease in homelessness among veterans than in the general population. These findings suggest an important role for health systems in addressing complex social determinants of health. While some conditions unique to the VHA facilitated the change in homelessness, lessons learned here are applicable to other health systems.

Comparison of Wait Times for New Patients Between the Private Sector and United States Department of Veterans Affairs Medical Centers.

Importance: Concerns have been raised about the adequacy of health care access among patients cared for within the United States Department of Veterans Affairs (VA) health care system. Objectives: To determine wait times for new patients receiving care at VA medical centers and compare wait times in the VA medical centers with wait times in the private sector (PS). Design, Setting, and Participants: A retrospective, repeated cross-sectional study was conducted of new appointment wait times for primary care, dermatology, cardiology, or orthopedics at VA medical centers in 15 major metropolitan areas in 2014 and 2017. Comparison data from the PS came from a published survey that used a secret shopper survey approach. Secondary analyses evaluated the change in overall and unique patients seen in the entire VA system and patient satisfaction survey measures of care access between 2014 and 2017. Main Outcomes and Measures: The outcome of interest was patient wait time. Wait times in the VA were determined directly from patient scheduling. Wait times in the PS were as reported in Merritt Hawkins surveys using the secret shopper method. Results: Compared with the PS, overall mean VA wait times for new appointments in 2014 were similar (mean [SD] wait time, 18.7 [7.9] days PS vs 22.5 [7.3] days VA; P = .20). Department of Veterans Affairs wait times in 2014 were similar to those in the PS across specialties and regions. In 2017, overall wait times for new appointments in the VA were shorter than in the PS (mean [SD], 17.7 [5.9] vs 29.8 [16.6] days; P < .001). This was true in primary care (mean [SD], 20.0 [10.4] vs 40.7 [35.0] days; P = .005), dermatology (mean [SD], 15.6 [12.2] vs 32.6 [16.5] days; P < .001), and cardiology (mean [SD], 15.3 [12.6] vs 22.8 [10.1] days; P = .04). Wait times for orthopedics remained longer in the VA than the PS (mean [SD], 20.9 [13.3] vs 12.4 [5.5] days; P = .01), although wait time improved significantly between 2014 and 2017 in the VA for orthopedics while wait times in the PS did not change (change in mean wait times, increased 1.5 days vs decreased 5.4 days; P = .02). Secondary analysis demonstrated an increase in the number of unique patients seen and appointment encounters in the VA between 2014 and 2017 (4 996 564 to 5 118 446, and 16 476 461 to 17 331 538, respectively), and patient satisfaction measures of access also improved (satisfaction scores increased by 1.4%, 3.0%, and 4.0% for specialty care, routine primary care, and urgent primary care, P < .05). Conclusions and Relevance: Although wait times in the VA and PS appeared to be similar in 2014, there have been interval improvements in VA wait times since then, while wait times in the PS appear to be static. These findings suggest that access to care within the VA has improved over time.

Using a real-time location system to measure patient flow in a radiation oncology outpatient clinic.

PURPOSE: Common performance metrics for outpatient clinics define the time between patient arrival and entry into an examination room as "waiting time." Time spent in the room is considered processing time. This characterization systematically ignores time spent in the examination room waiting for service. If these definitions are used, performance will consistently understate total waiting times and overstate processing times. Correcting such errors will provide a better understanding of system behavior. METHODS AND MATERIALS: In a radiation oncology service in an urban academic clinic, we collected data from a patient management system for 84 patients with 4 distinct types of visits: consultations, follow-ups, on-treatment visits, and nurse visits. Examination room entry and exit times were collected with a real-time location system for relevant care team members. Novel metrics of clinic performance were created, including the ratio of face time (ie, time during which the patient is with a practitioner) to total cycle time, which we label face-time efficiency. Attending physician interruptions occurred when the attending is called out of the room during a patient visit, and coordination-related delays are defined as waits for another team member. RESULTS: Face-time efficiency levels for consults, follow-ups, on-treatment visits, and nurse visits were 30.1%, 22.9%, 33.0%, and 25.6%, respectively. Attending physician interruptions averaged 6.7 minutes per patient. If these interruptions were eliminated, face-time efficiencies would rise to 33.2%, 29.2%, 34.4%, and 25.6%, respectively. Eliminating all coordination-related delays would increase these values to 41.3%, 38.9%, 54.7%, and 38.7%, respectively. CONCLUSIONS: A real-time location system can be used to augment a patient management system and automate data collection to provide improved descriptions of clinic performance.

Risk factors for near-miss events and safety incidents in pediatric radiation therapy.

BACKGROUND AND PURPOSE: Factors contributing to safety- or quality-related incidents (e.g. variances) in children are unknown. We identified clinical and RT treatment variables associated with risk for variances in a pediatric cohort. MATERIALS AND METHODS: Using our institution's incident learning system, 81 patients age ≤21 years old who experienced variances were compared to 191 pediatric patients without variances. Clinical and RT treatment variables were evaluated as potential predictors for variances using univariate and multivariate analyses. RESULTS: Variances were primarily documentation errors (n = 46, 57%) and were most commonly detected during treatment planning (n = 14, 21%). Treatment planning errors constituted the majority (n = 16 out of 29, 55%) of near-misses and safety incidents (NMSI), which excludes workflow incidents. Therapists reported the majority of variances (n = 50, 62%). Physician cross-coverage (OR = 2.1, 95% CI = 1.04-4.38) and 3D conformal RT (OR = 2.3, 95% CI = 1.11-4.69) increased variance risk. Conversely, age >14 years (OR = 0.5, 95% CI = 0.28-0.88) and diagnosis of abdominal tumor (OR = 0.2, 95% CI = 0.04-0.59) decreased variance risk. CONCLUSIONS: Variances in children occurred in early treatment phases, but were detected at later workflow stages. Quality measures should be implemented during early treatment phases with a focus on younger children and those cared for by cross-covering physicians.

Changes to physician processing times in response to clinic congestion and patient punctuality: a retrospective study.

OBJECTIVES: We examine interactions among 3 factors that affect patient waits and use of overtime in outpatient clinics: clinic congestion, patient punctuality and physician processing rates. We hypothesise that the first 2 factors affect physician processing rates, and this adaptive physician behaviour serves to reduce waiting times and the use of overtime. SETTING: 2 urban academic clinics and an affiliated suburban clinic in metropolitan Baltimore, Maryland, USA. PARTICIPANTS: Appointment times, patient arrival times, start of service and physician processing times were collected for 105 visits at a low-volume suburban clinic 1, 264 visits at a medium-volume academic clinic 2 and 22 266 visits at a high-volume academic clinic 3 over 3 distinct spans of time. INTERVENTION: Data from the first clinic were previously used to document an intervention to influence patient punctuality. This included a policy that tardy patients were rescheduled. PRIMARY AND SECONDARY OUTCOME MEASURES: Clinicians' processing times were gathered, conditioned on whether the patient or clinician was tardy to test the first hypothesis. Probability distributions of patient unpunctuality were developed preintervention and postintervention for the clinic in which the intervention took place and these data were used to seed a discrete-event simulation. RESULTS: Average physician processing times differ conditioned on tardiness at clinic 1 with p=0.03, at clinic 2 with p=10-5 and at clinic 3 with p=10-7. Within the simulation, the adaptive physician behaviour degrades system performance by increasing waiting times, probability of overtime and the average amount of overtime used. Each of these changes is significant at the p<0.01 level. CONCLUSIONS: Processing times differed for patients in different states in all 3 settings studied. When present, this can be verified using data commonly collected. Ignoring these behaviours leads to faulty conclusions about the efficacy of efforts to improve clinic flow.

Identifying Predictive Factors for Incident Reports in Patients Receiving Radiation Therapy.

PURPOSE: To describe radiation therapy cases during which voluntary incident reporting occurred; and identify patient- or treatment-specific factors that place patients at higher risk for incidents. METHODS AND MATERIALS: We used our institution's incident learning system to build a database of patients with incident reports filed between January 2011 and December 2013. Patient- and treatment-specific data were reviewed for all patients with reported incidents, which were classified by step in the process and root cause. A control group of patients without events was generated for comparison. Summary statistics, likelihood ratios, and mixed-effect logistic regression models were used for group comparisons. RESULTS: The incident and control groups comprised 794 and 499 patients, respectively. Common root causes included documentation errors (26.5%), communication (22.5%), technical treatment planning (37.5%), and technical treatment delivery (13.5%). Incidents were more frequently reported in minors (age <18 years) than in adult patients (37.7% vs 0.4%, P<.001). Patients with head and neck (16% vs 8%, P<.001) and breast (20% vs 15%, P=.03) primaries more frequently had incidents, whereas brain (18% vs 24%, P=.008) primaries were less frequent. Larger tumors (17% vs 10% had T4 lesions, P=.02), and cases on protocol (9% vs 5%, P=.005) or with intensity modulated radiation therapy/image guided intensity modulated radiation therapy (52% vs 43%, P=.001) were more likely to have incidents. CONCLUSIONS: We found several treatment- and patient-specific variables associated with incidents. These factors should be considered by treatment teams at the time of peer review to identify patients at higher risk. Larger datasets are required to recommend changes in care process standards, to minimize safety risks.

Patients and Physicians Can Discuss Costs of Cancer Treatment in the Clinic.

PURPOSE: As one solution to reducing costs and medical bankruptcies, experts have suggested that patients and physicians should discuss the cost of care up front. Whether these discussions are possible in an oncology setting and what their effects on the doctor-patient relationship are is not known. METHODS: We used the National Comprehensive Cancer Network (NCCN) Guidelines and the eviti Advisor platform to show patients with metastatic breast, lung, or colorectal cancer the costs associated with their chemotherapy and/or targeted therapy options during an oncology consultation. We measured provider attitudes and assessed patient satisfaction when consultations included discussion of costs. RESULTS: We approached 107 patients; 96 (90%) enrolled onto the study, three (3%) asked if they could be interviewed at a later date, and eight (7%) did not want to participate. Only five of 18 oncologists (28%) felt comfortable discussing costs, and only one of 18 (6%) regularly asked patients about financial difficulties. The majority of patients (80%) wanted cost information, and 84% reported that these conversations would be even more important if their co-pays were to increase. In total, 72% of patients responded that no health care professional has ever discussed costs with them. The majority of patients (80%) had no negative feelings about hearing cost information. CONCLUSION: In an era of rising co-pays, patients with cancer want cost-of-treatment discussions, and these conversations do not lead to negative feelings in the majority of patients. Additional training to prepare clinicians for how to discuss costs with their patients is needed.

Organizing a multidisciplinary clinic.

Multidisciplinary cancer care models have become increasingly more popular in recent years. This comprehensive approach to care delivery has evolved to address numerous medical and social aspects critical to all patients diagnosed with cancer. Because of the sheer number of specialists involved in the care trajectory of patients with cancer, multidisciplinary care models add significant value in facilitating communication between specialists and in coordinating care. Multidisciplinary models also allow for new and innovative therapies to be incorporated more quickly than is seen in single provider care. Two principal modes of multidisciplinary care have been described-the tumor board and the multidisciplinary clinic. While tumor boards are well established in oncology literature and practice, there is growing support favoring the use of multidisciplinary clinics, bringing providers together at the point of care. In this section, we describe recent data demonstrating the value of the multidisciplinary approach, with particular focus on the creation and potential benefits of a formal multidisciplinary clinic.

Proton beam therapy and accountable care: the challenges ahead.

PURPOSE: Proton beam therapy (PBT) centers have drawn increasing public scrutiny for their high cost. The behavior of such facilities is likely to change under the Affordable Care Act. We modeled how accountable care reform may affect the financial standing of PBT centers and their incentives to treat complex patient cases. METHODS AND MATERIALS: We used operational data and publicly listed Medicare rates to model the relationship between financial metrics for PBT center performance and case mix (defined as the percentage of complex cases, such as pediatric central nervous system tumors). Financial metrics included total daily revenues and debt coverage (daily revenues - daily debt payments). Fee-for-service (FFS) and accountable care (ACO) reimbursement scenarios were modeled. Sensitivity analyses were performed around the room time required to treat noncomplex cases: simple (30 minutes), prostate (24 minutes), and short prostate (15 minutes). Sensitivity analyses were also performed for total machine operating time (14, 16, and 18 h/d). RESULTS: Reimbursement under ACOs could reduce daily revenues in PBT centers by up to 32%. The incremental revenue gained by replacing 1 complex case with noncomplex cases was lowest for simple cases and highest for short prostate cases. ACO rates reduced this incremental incentive by 53.2% for simple cases and 41.7% for short prostate cases. To cover daily debt payments after ACO rates were imposed, 26% fewer complex patients were allowable at varying capital costs and interest rates. Only facilities with total machine operating times of 18 hours per day would cover debt payments in all scenarios. CONCLUSIONS: Debt-financed PBT centers will face steep challenges to remain financially viable after ACO implementation. Paradoxically, reduced reimbursement for noncomplex cases will require PBT centers to treat more such cases over cases for which PBT has demonstrated superior outcomes. Relative losses will be highest for those facilities focused primarily on treating noncomplex cases.

Prevalence and significance of subcentimeter hepatic lesions in patients with localized pancreatic adenocarcinoma.

PURPOSE: To determine the prevalence and significance of incidental, subcentimeter hepatic lesions in patients with a new diagnosis of pancreatic cancer. MATERIALS AND METHODS: This Institutional Review Board-approved retrospective study included 101 patients [45% men, median age 63 years (34-85)] treated for localized pancreatic adenocarcinoma at Brigham and Women's Hospital and Dana Farber Cancer Institute from January 1999 to December 2007. Initial staging and follow-up computed tomographic scans were reviewed to determine the frequency of liver lesions that were initially too small to characterize and later proved to be metastases. Clinical variables known to be prognostic for patients with pancreatic cancer were also recorded. Using Cox regression, we calculated adjusted hazard ratios to determine the association between presence of liver lesions and overall survival. RESULTS: A total of 31 patients (30.7%) had subcentimeter hepatic lesions on staging scans. Of these patients, 21 (20.7% of total, 67.7% of patients with lesions) had eventual metastases to the liver. Finally, of this group, 5 patients (5.0% of total, 16.1% of patients with lesions) eventually had a metastatic focus at the specific site of the original lesion. Liver lesions predicted the occurrence of metastatic disease to the liver compared with patients without lesions (67.7% with lesions vs 44.4% without, P = .034). The presence of subcentimeter liver lesions at diagnosis was significantly associated with reduced overall survival (hazard ratio 1.65; 95% confidence interval 1.03-2.64, P = .036). CONCLUSIONS: Subcentimeter lesions in the liver are common in patients with a new diagnosis of pancreatic cancer. Approximately 16% of these lesions represent metastases. The presence of indeterminate liver lesions may be associated with reduced overall survival.