What Should Healthcare Systems Consider When Modernizing Call Centers? Early Considerations From the Veterans Health Administration.

GOAL: Growing numbers of hospitals and payers are using call centers to answer patients' clinical and administrative questions, schedule appointments, address billing issues, and offer supplementary care during public health emergencies and national disasters. In 2020, the Veterans Health Administration (VA) implemented VA Health Connect, an enterprise-wide initiative to modernize call centers. VA Health Connect is designed to improve the care experience with the convenience, flexibility, and simplicity of a single toll-free number connected to a range of 24/7 virtual services. The services are organized into four areas: administrative guidance for scheduling and general inquiries; pharmacy support for medication matters; clinical triage for evaluation of symptoms and recommended care; and virtual visits with providers for urgent and episodic care. Through a qualitative evaluation of VA Health Connect, we sought to identify the factors that affected the development of this program and to compile considerations to support the implementation of other enterprise-wide initiatives. METHODS: The evaluation team interviewed 29 clinical and administrative leads from across the VA. These leads were responsible for the modernization of their local service networks. PhD-level qualitative methodologists conducted the interviews, asking participants to reflect on barriers and facilitators to modernization and implementation. The team employed a rapid qualitative analytic approach commonly used in healthcare research to distill robust results. PRINCIPAL FINDINGS: A review of the early implementation of VA Health Connect found: (1) deadlines proved challenging but provided momentum for the initiative; (2) a balance between standardized processes and local adaptations facilitated implementation; (3) attention to staffing, hiring, and training of call center staff before implementation expedited workflows; (4) establishing national and local leadership commitment to the innovation from the onset increased team cohesion and efficacy; and (5) anticipating information technology infrastructure needs prevented delays to modernization and implementation. PRACTICAL APPLICATIONS: Our findings suggest that healthcare systems would benefit from anticipating likely obstacles (e.g., delays in software implementations and negotiations with unions), thus providing ample time to secure leadership buy-in and identify local champions, communicating early and often, and supporting flexible implementation to meet local needs. VA leadership can use this evaluation to refine implementation, and it could also have important implications for regulators, federal health exchanges, insurers, and other healthcare systems when determining resource levels for call centers.

Why Veterans Choose VA Versus non-VA Emergency Care: A Qualitative Study.

INTRODUCTION: In ensuring the timely delivery of emergency care to Veterans, Veterans Affairs (VA) offers both emergency care services in its own facilities and, increasingly, purchases care for Veterans in non-VA (community) emergency department (ED) settings. Although in recent years emergency care coverage has become the single largest contributor to VA community care spending, no study to date has examined Veteran decision-making as it relates to ED setting choice. The purpose of this study is to identify and describe reasons why Veterans choose VA versus non-VA emergency care settings. MATERIALS AND METHODS: Veterans Health Administration data were used to identify geographically diverse Veterans who recently used emergency care. We conducted semi-structured telephone interviews from December 2018 through March 2020 with 50 Veterans to understand the factors Veterans consider when deciding where to obtain ED care. Interviews were audio-recorded and transcribed verbatim. We conducted a directed content analysis of interview transcripts and developed a matrix to summarize and categorize each Veteran's decision-making process to compare participants and to identify common patterns. RESULTS: When choosing between VA and non-VA-EDs, Veterans described 3 distinct patterns of decision-making: (1) choosing the closest ED (often community) for acute conditions; (2) traveling farther for VA care due to preference and financial coverage; and (3) selecting VA when both types of ED care were equidistant. Perceptions of community resources, condition-specific needs, financial considerations, and personal preferences dominated the decision-making. For example, most Veterans (74%) rated their acuity as high, and self-perceived severity/urgency of their condition was the most cited factor influencing where Veterans decided to go for ED care. CONCLUSIONS: Our qualitative results help provide insight into how and why Veterans choose to seek emergency care. As the number of Veterans treated in non-VA EDs continues to rise, VA and non-VA ED providers as well as policy makers may benefit from understanding the challenges Veterans face when making this decision.

Access to and accessibility of care for rural Veterans with disabilities: A qualitative evaluation of VA healthcare experiences.

BACKGROUND: Persons with disabilities experience significant physical, attitudinal, and communication-based barriers to accessing care. These challenges are exacerbated for rural-dwelling persons with disabilities. Although US Veterans experience disabilities at a higher rate than non-Veterans and are also more likely to dwell in rural locations, research examining the accessibility of VA care for rural Veterans with disabilities is limited. OBJECTIVES: With a focus on access and accessibility, we sought to explore the experiences of rural Veterans with disabilities who receive care at VA. METHODS: We conducted 30 qualitative interviews with rural-dwelling Veterans who experience at least one of three types of disabilities: hearing loss, vision loss, and mobility loss. Using a descriptive qualitative approach, we focused on creating a taxonomy of potential access barriers experienced among this population. RESULTS: Participants reported experiencing access barriers in five main areas, including policies and operational processes at VA clinics; navigating VA campuses and clinics; limited transportation and parking options; communicating with healthcare personnel and occasional negative interactions; and challenges due to pandemic-related changes in policies and procedures. CONCLUSION: These findings suggest that Veterans with disabilities may experience a host of challenges and access barriers while navigating the VA Healthcare system. While these challenges have been reported among individuals with disabilities receiving care in other healthcare settings, they have not been assessed in VA specifically. Given its focus on caring for Veterans with service-aggravated conditions and its commitment to equity and inclusion, addressing access barriers among Veterans with disabilities should be a high priority for VA.

A Comparison of Web-Based Cancer Risk Calculators That Inform Shared Decision-making for Lung Cancer Screening.

INTRODUCTION: To align patient preferences and understanding with harm-benefit perception, the Centers for Medicare & Medicaid Services (CMS) mandates that providers engage patients in a collaborative shared decision-making (SDM) visit before LDCT. Nonetheless, patients and providers often turn instead to the web for help making decisions. Several web-based lung cancer risk calculators (LCRCs) provide risk predictions and screening recommendations; however, the accuracy, consistency, and subsequent user interpretation of these predictions between LCRCs is ambiguous. We conducted a systematic review to assess this variability. DESIGN: Through a systematic Internet search, we identified 10 publicly available LCRCs and categorized their input variables: demographic factors, cancer history, smoking status, and personal/environmental factors. To assess variance in LCRC risk prediction outputs, we developed 16 hypothetical patients along a risk continuum, illustrated by randomly assigned input variables, and individually compared them to each LCRC against the empirically validated "gold-standard" PLCO risk model in order to evaluate the accuracy of the LCRCs within identical time-windows. RESULTS: From the inclusion criteria, 11 calculators were initially identified. The analyzed calculators also vary in output characteristics and risk depiction for hypothetical patients. There were 13 total instances across ten hypothetical patients in which the sample standard error exceeded the mean risk percentage across all general samples and set standard calculations. The largest measured difference is 16.49% for patient 8, and the smallest difference is 0.01% for patient 2. The largest measured difference is 16.49% for patient 8, and the smallest difference is 0.01% for patient 2. CONCLUSION: Substantial variability in the depiction of lung cancer risk for hypothetical patients exists across the web-based LCRCs due to their respective inputs and risk prediction models. To foster informed decision-making in the SDM-LDCT context, the input variables, risk prediction models, risk depiction, and screening recommendations must be standardized to best practice.