As background for the discussions in the report, this chapter defines some of the common terms and concepts from the area of health care access and scheduling, describes the scheduling practices most often seen in various health care settings, and identifies the basic factors that play a role in scheduling delays and variability.
In the U.S. health care system, the three most commonly used scheduling techniques for both inpatient and outpatient services are block scheduling, modified block scheduling, and individual scheduling (NAE/IOM, 2005). In block scheduling, patients are scheduled within specific times throughout the day, such as morning or afternoon, and then seen on a first-come, first-served basis within that time frame. Modified block scheduling assigns a smaller number of patients to smaller segments of time throughout the day, such as hourly. Individual scheduling, the most commonly used scheduling technique in the country, occurs when a single patient is scheduled for a specific point in time, with the timing of the appointments determined according to the supply of care providers (NAE/IOM, 2005).
Although delays in care delivery are common—and unpleasant—occurrences in both public and private health care systems, there are few reliable data with which to determine the prevalence, degree, or nature of the problem. Even defining when a delay in seeing a preferred health care provider is inappropriate is not always straightforward.
There are a number of approaches for categorizing scheduling and wait time delays. They include approaches, such as the third next available appointment (TNA) for ambulatory care, defined as the average length of time in days between when a patient requests an appointment and the third next available appointment; boundary approaches, such as the 4-hour wait time target for emergency departments used in England and Australia (Hughes, 2010; IHI, 2014a,b; Jones and Schimanski, 2010; Weber et al., 2012); and the “office visit cycle time,” defined as the time between a patient’s arrival and departure times at a medical office, which can be used to separate productive time from waiting time. Each of these scheduling tools is focused on a delay in a different part of the patient care continuum. For example, TNA captures the delay in getting an appointment or service, whereas cycle time measures the delay at the appointment or service. They are different methods designed to capture delays in different processes. Patient views of acceptable wait times are also poorly captured in available scheduling assessment tools, and the measurement of these factors becomes costly and is often imprecise (Paterson et al., 2006). Some of the terms commonly used in discussions of patient scheduling are listed in Box 2-1.
Concepts and Terms in Patient Scheduling
Access includes contact with the health care system, availability of appropriate services, and the delivery of the services necessary to meet patient needs.
Actual wait time, a measure currently used by the U.S. Department of Veterans Affairs, is a retrospective time stamp that uses the date the appointment was created in the scheduling system or the date that the patient desired as the start date for the wait-time computation. The time of the completed appointment is used as the end point.
Advanced open access scheduling, also referred to as advanced access, open access, or same-day scheduling, offers a patient calling for an appointment the opportunity to be seen on the same day, preferably although not necessarily by the patient’s customary clinician.
Block scheduling schedules patients within specific times throughout the day, such as morning or afternoon, and then they are seen on a first-come, first-served basis within that time frame.
Capacity, or supply is traditionally defined as the number of appointment slots available for a given period of time, such as 1 day, for all clinicians available during that period. Previous demand that has not yet been matched with appointments
shows itself as a backlog of work or a waiting list. The committee considers it important to view the term more broadly so that supply also entails resources that include labor, equipment, and any required physical environment for safe delivery. Demands can be met by supply elements that include face-to-face meetings, as well as other means, e.g., through a virtual care delivery infrastructure.
Demand traditionally refers to the total number of patient calls for appointments over a fixed period of time, such as 1 day, plus the number of walk-ins and the number of follow-up appointments generated by the physicians at a given practice site. Demand includes those patients that cannot be accommodated on a given day, as demand is independent of the limit of available appointments. The committee considers it important to view the term more broadly, so that demand not only covers the actual visits of patients but comprises all patients reporting problems daily.
Individual scheduling is the most commonly used scheduling technique in the United States, implemented through patient-by-patient scheduling for a specific point in time on a specific day, according to care provider availability in the care setting.
Modified block scheduling assigns a smaller number of patients to smaller segments of time throughout the day, such as hourly.
Office visit cycle time is a term applied to wait times that occur during an appointment. The office visit cycle time is generally measured from check-in to checkout for that appointment and can be broken down into various components of the visit. Each step in the cycle can be classified as either non-value-added time, such as time spent waiting for the next step in the visit, or value-added time, such as time spent with a care team member.
Supply–demand mismatch. An immediate cause of poor access to health care can be an imbalance between the demand for services and the available service capacity. Permanent imbalance, or mismatch, leads to a continued rise in delays until patients choose to seek medical care elsewhere. However, mismatch can also be impermanent, resulting from shifting variations in either supply or demand.
Third next available appointment (TNA) is a value determined by assessing appointment availability and is aimed at providing a reliable indication of the number of days that a patient has to wait to get an appointment (Murray and Berwick, 2003). Because the first and second available appointments are often the result of last-minute cancellations or other events, the third next available appointment best represents the performance of the appointment access system as a whole. TNA can serve as one metric to measure scheduling performance. It allows organizations to capture the TNA before and after an improvement is made.
Wait time to obtain an appointment within the health care system is a measurement of the access delay in the system and reflects the time differential between a patient’s call or request for an appointment and an opening in a provider’s schedule.
Health care scheduling practices vary by setting. Practices in the emergency room, for example, are different from those used by primary care physicians. This section provides an overview of the scheduling practices typically employed in various health care settings. It also discusses some of the issues that lead to delays and increased wait times.
Primary care providers typically serve a large and steady pool of regular patients, and relatively few new patients. The demand for primary care appointments usually has a predictable variation. There is higher demand for the first and last appointments of the day to accommodate work schedules and increased demand on Mondays and in the winter months. The variation in supply is less amenable to change, due to several factors, including competing priorities and responsibilities of the providers and workforce shortages. As a result of the recent Medicaid expansion and the number of patients who are now insured through state exchanges, a shortage has developed in the supply of primary care physicians in some areas of the country relative to the demand (Petterson et al., 2013). Although hiring additional physicians might seem to be the obvious solution to this shortage, given the financial constraints in today’s health care sector, this is not a viable option for many health care organizations, and thus they need to find ways to make better use of the existing provider capacity.
No matter which of the three major scheduling techniques is used—block, modified block, and individual scheduling—the majority of scheduling decisions are generally based on predictions of patient need. Priority-based scheduling assigns different wait times to different patients according to assumptions made concerning the level of acuity or need associated with various conditions. For example, an individual with a history of congestive heart failure may be scheduled for follow-up visits at a periodic interval based on patient trends, rather than being given a schedule that reflects his or her actual needs, preferences, or circumstances. Priority-based scheduling creates multiple queues, each associated with a different wait time.
Referrals and Transfers
The term specialty care describes any specialized practice that focuses on care for certain conditions or diagnostic or treatment approaches and primarily receives work as a consult, referral, or transfer (JHU, 2015).
Providing timely appointments for specialty care requires the same baseline measurements that are needed for primary care. Specialty care scheduling can be affected by a number of external factors that are not within the control of either the practice or the patient. These include delays caused by the requirement for insurance preauthorization, the need for additional diagnostic tests that are performed by third parties, and the referring provider not being co-located with the specialty care provider (Murray, 2002). For some conditions, it may be necessary for multiple specialists to coordinate their care, which introduces another level of variability that must be accommodated. An additional challenge for specialty care practices is responding to new patients with urgent needs while maintaining available appointments for returning patients.
Academic specialty practices experience a high degree of variability in providers’ availability because the providers tend to have competing education, research, and clinical responsibilities. Although the natural variation in demand in an academic specialty setting is similar to what is seen in other types of settings, the higher degree of variability in supply can lead to challenges. These challenges are complicated by the presence of resident physicians, who are found in specialty care practices as well as other settings. Residents can increase the capacity of a clinic as their experience and training progress, but they can have frequent absences from the practice and require a more flexible model, with additional senior physician oversight. It is a challenge to achieve the competing goals of having patients see their own physicians, minimizing delay, and offering an educational environment for resident physicians. Any scheduling system used in specialty care must not only accommodate a clear definition of a care team, variable caseloads, and clinical times, it must also accommodate providers with substantially different experience levels.
Specialty Care: Providing Mental Health Services
With the implementation of the Affordable Care Act and the expansion of Medicaid, an increasing number of people are gaining access to treatment for mental health and addiction services because of the increased use of public and private insurance coverage. Yet timely access to these services is already a challenge for many Americans, especially veterans. And, given that both public and private health systems require patients to engage with primary care providers before allowing access to mental health care, the total wait times for such services are even longer. Because of the requirement to first see a primary care clinician, mental health patients waiting for transfer to facilities outside of the local health care system were found in one study to experience waits that averaged 15 hours (Weiss et al., 2012).
Overcrowding, prolonged waiting times, patient care delays, and scarce resources are common in urban emergency medicine today (Yoon et al., 2003). Besides contributing to increased levels of patient frustration and anxiety, prolonged waiting times and protracted lengths of stay can also increase the proportion of patients who leave emergency departments without being seen by a physician (Johnson et al., 2009; Monzon et al., 2005). Emergency department wait times are often caused by hospital systems that require patients to remain in the emergency department while awaiting an opening elsewhere in the hospital (Hoot and Aronsky, 2008). Many hospitals in the United States have attempted to reduce emergency department wait times, but for various reasons their efforts often fail to produce sustainable results. One reason that many emergency department improvement programs do not produce long-lasting results is that the programs focus primarily on discrete processes, disregarding staff behaviors and overall system performance or organizational culture (Melon et al., 2013).
A factor considered as a critical contributor to emergency department overcrowding is patient boarding, or holding patients in the emergency department for observation, rather than discharging them or admitting them to the hospital (ACEP, 2008). Research has demonstrated a correlation between the length of stay in the emergency department and an increased risk of adverse events in patients who are subsequently admitted to the hospital (Guttmann et al., 2011). For example, as a relatively fixed resource for hospitals, bed availability becomes an increasing concern as occupancy increases. Using systems strategies, industrial models and optimization techniques, health care institutions can serve more patients treated in hospitals without increasing the number of actual beds, as is discussed in greater detail in Chapters 3 and 4.
Supply and demand are interconnected in a hospital process. There are entry points, exit points, and various steps or nodes involving patients within the system. Three types of delays can result: input delays, which are delays in access to a service, such as the delay for a bed, measured as the time between the decision to admit and the time the patient is actually admitted; throughput delay, or a delay that affects the length of time between a patient’s admission and the time he or she is ready to be discharged from in the hospital; and output delay, a delay in the amount of time it takes to get a patient discharged from the hospital, such as a delay caused by a lack of availability of beds in a rehabilitation or extended-care facility (Hall, 2013).
Optimizing performance requires measuring the demand, capacity, and flow into and out of each node within the system, and system-wide assessments and adjustments are required to improve the overall collection of steps, including such steps as consolidating or removing processes in order to streamline patient service flow (Lee et al., 2015). The typical hospital includes individual departments and providers who work to meet or exceed patient care standards for their particular discipline. Although this can be an admirable goal, it can also lead to unintended inefficiencies, and it is preferable to rely on a whole-system model rather than a unit- or provider-centric model, which emphasizes performance in specific areas, often at the expense of interdepartmental or system-wide cooperation and coordination (IHI, 2003).
Ideally, the movement of patients from admission through treatment and on to discharge should occur without significant delays. However, a department-centric or provider-centric environment focuses on the needs of individual areas, and one area’s needs are not necessarily compatible with another area’s priorities. For instance, nurses on a medical/surgical unit may not notify bed management that a bed has been vacated or may do so only after a substantial delay—because such notifications are not a high priority for the medical/surgery unit. This can lead to a situation in which there are vacant beds that could be occupied by patients who may be kept waiting somewhere else, including hallways or the emergency department.
The discharge planning and placement processes require coordination and communication among personnel from different departments. The processes also need to have an agreed-upon care plan, and attention to various logistical challenges to ensure a patient’s safety outside of the hospital setting, such as the arrangement of rehabilitative or in-home care. Ideally, discharge planning begins on the day of admission. Delayed discharges can cause problems because of their impact on hospital admissions and patient throughput. Delayed discharges may, for example, lead to a situation in which there are not enough available beds to meet incoming demand. Critical care units can find it difficult to move patients into step-down areas, which then directly affect admissions from the emergency department. Perioperative services can also experience backups while waiting for beds to become available in the post-anesthesia care unit (Jweinat et al., 2013).
Even under the best of circumstances, the discharge-planning process in hospitals is inherently complex. Patient-specific information (such as medical status and needs, patient and family preferences, and information about available community resources) must be gathered from many sources. Currently, Web-based discharge instructions have the potential to improve readmissions and transitional care (Bell et al., 2013). Poor-quality hospital discharge planning not only will affect the flow of patients within the hospital setting but also puts patients at risk for adverse events outside of the hospital, which in turn can lead to emergency department visits and hospital readmissions.
When returning to a home care setting is not an option, transfer to an inpatient rehabilitation facility (IRF), a skilled nursing facility (SNF), or a long-term care facility becomes necessary. The committee’s review of the literature found scant information regarding IRF and SNF access, although reports are common of poorly informed family preferences leading to transfers and increased health care costs (Lamb et al., 2011).
IRFs provide hospital-level treatment with a focus on rehabilitation and face many of the same challenges related to access and wait times as acute care hospitals do. As with acute care hospitals, insurers have an influence on access to these facilities. In determining demand, it is important to have accurate measurements of admission trends, patient characteristics, and costs. At this time, the best practices for access to inpatient rehabilitation hospitals and skilled nursing facilities remain largely undocumented or validated and will require further development and evaluation.
Some of the causes of prolonged wait times are inefficiencies in operation, in care coordination, and in health care organizational culture that result in flow disruption, the underuse of resources, and an imbalance between the demand of patients to be seen and the supply of providers, facilities, and alternative strategies to care for them at any given time (Mazzocato et al., 2010; Young and McClean, 2008). Organization-specific factors, including leadership and the resulting culture, can contribute to access difficulties and long wait times. The many complexities and process interdependencies of our health care system can complicate the challenge of balancing supply and demand.
Supply and Demand Issues
The most fundamental concept in scheduling is attention to the balance of supply and demand (Murray and Berwick, 2003). Unfortunately, most clinical settings do not take a broad enough view of the various options for either increasing supply or reducing demand, nor do they maintain the analytic capacity to observe and understand the dynamics involved (Murray and Berwick, 2003). As noted in Box 2-1, demand traditionally refers to the total number of patient calls for appointments over a fixed period of time, such as 1 day, plus the number of walk-ins and the number of follow-up appointments generated by the physicians at a given practice site. But many facilities define their supply simply in terms of the number of slots they have to fill on a given day or other period of time—that is, only in terms that relate to the availability of clinicians in that period of time. It is very unusual for a practice or clinic to keep a running record of the calls received, appointments made, wait-times, walk-ins, and no-shows, or to document how many queries could be handled by alternate clinicians, telemedicine, and electronic consults (Murray and Berwick, 2003).
Similarly, “supply” as traditionally defined in Box 2-1 is the number of appointment slots available for a given period of time, such as 1 day, for all clinicians available during that period. But often, for scheduling purposes, supply is viewed primarily as the slot availability for the clinician of record or requested by the caller, without consideration of (or the offering of) ways to augment the supply, such as other physicians and clinicians who are available; backup arrangements with other clinics for appropriate circumstances; and other sources, including digital and telephonic sources, that are available to meet callers’ needs for information, referral, or advice. Without information of this sort, patterns of variability will be unobserved, alternatives will go untapped, and a supply–demand mismatch—which is often unnecessary—will be inevitable and chronic.
The committee considers it important to view the terms of supply and demand more broadly. Daily patient “demand” covers not only the actual visits of patients but also all contacts from patients reporting problems that day—each query requiring contacts from health care system resources to accommodate properly. Supply entails resources that include labor, equipment, and any required physical environment for safe delivery. Demands can be met via face-to-face setting or virtually. By reframing and expanding the notions of supply and demand, the relationship between a given care team and a patient panel could be expanded and redefined (Murray et al., 2007). Experience from various systems, including Kaiser Permanente and Group Health, suggest that at least 25 percent of patients calling in on a given day will not require an in-person visit but can have their needs addressed using methods such as telehealth (Darkins et al., 2008; Hsu et al.,
2012; Pearl, 2014). Regardless of the use of in-person appointments or alternatives, the supply and demand associated with any strategy that is adopted is dynamic and will become mismatched if not continuously measured, monitored, and readjusted as necessary.
The Current Provider-Focused Approach
The U.S. health care system is influenced by many competing priorities. Health care providers focus on providing care with autonomy and on receiving payment for that care. Providers have incentives to deliver higher paid services that can be supplied at low costs. Consumers seek accessible services and low out-of-pocket costs. Payers desire to select risks and limit costs. Because of these differences, the needs and priorities of different stakeholder groups are not always aligned (IOM, 2001a). The health care system currently reflects mainly the priorities of providers and organizations, which has resulted in a focus on traditional scheduling systems that have not been engineered to engage or satisfy patients but that instead are designed to fit a staff schedule that may be poorly aligned with patient perspectives or circumstances.
One emerging consequence is that, faced with the challenges of navigating the scheduling process for primary care, people often turn to other settings for their health care, such as retail health clinics (Zamosky, 2014). A 2013 survey of retail clinic users found that 58.6 percent of these patients used retail clinics because the hours were more convenient, and 55.9 percent because they could get care without an appointment (Tu and Boukus, 2013).
Outmoded Workforce Models
The Association of American Medical Colleges estimates that without an increased use of non-physician clinicians and staff, by 2025 the United States will have a shortage of 46,000-90,000 physicians (AAMC, 2014). Due to growth and replacement needs, the Bureau of Labor Statistics’ Employment Projections 2012-2022 released in December 2013, projects 1.05 million job openings for registered nurses by 2022 (Bureau of Labor Statistics, 2013). The committee learned that efforts are under way, including within the VA/VHA, to identify and address the challenges of hiring and retaining core staff. For example, the LASI human resources workgroup’s recommendations focused on such “areas as student loan repayment, the credentialing process, the pay system, hiring time frames, and nonmonetary incentives” (VA, 2014g).
Despite expected problems with physician understaffing, prevailing practices continue prioritizing physicians over other providers, and not
using non-physician clinicians and other staff to their full capacity, such as in the provision of immunizations, pre-visit record screens, escorting patients to exam rooms (Gabow and Goodman, 2014; Toussaint and Berry, 2013), and by making use of other means of providing needed information and by offering remote site consultation. Such current workforce models will not be sufficient to meet future health care demands without other practice transformations (IOM, 2011).
As described in the IOM’s The Future of Nursing report, transforming the health care system from one that is centered on provider convenience to one that is patient-centered will require re-conceptualizing the roles of all health care professionals, including physicians, nurses, allied health professionals, social workers, pharmacists, and other staff (IOM, 2011). As patient demands shift away from a focus on acute care to greater needs for primary care and especially chronic care management, the roles of health care professionals in the primary care setting need to be reevaluated in particular (IOM, 2011). Improving the performance of the primary care workforce will require practice redesigns. Small changes include such strategies as divesting from physicians tasks and responsibilities that can be performed by other members of the care team, while greater transformations through the enhanced role of nurses may include using nurses to facilitate care coordination, implement and manage informatics systems, act as health coaches, and serve as primary care providers themselves (IOM, 2011). Improving primary care capacity will also require making use of other means of delivering needed information and consultation (e.g., phone and Web-based video consultations). To that end, non-physician clinicians have the opportunity to play a greater role in the development, redesign, implementation, and delivery of such technology-based services (IOM, 2011).
Priority-Based Queues (Acuity Model)
As noted above, priority-based scheduling assigns different wait times to different patients according to assumptions made about the predicted need associated with different categories of conditions. This not only tends to limit the services provided and to require additional visits for other primary care services, but it also creates multiple categories—groups or queues—each with a wait time threshold established through assumptions about predicted clinical urgency associated with a given classification. Visits presumed to be routine or less acute are put off until a future date.
These estimated wait times reflect the best clinical judgment of providers, and the scheduling model was originally developed to help ensure patient safety and fairness. However, little formal evidence exists for the estimates of risk and need that should guide protocols for the timing of
clinical appointments (Desalvo et al., 2000; Sirovich et al., 2008; Welch et al., 1999; Yasaitis et al., 2013). Furthermore, there are a number of challenges associated with the model. For example, urgent appointments placed through priority-based scheduling practices often address only one need per visit, which limits the opportunity for the care provider to meet multiple needs of the patient in a single visit. In addition, patients diverted to other settings for urgent care often want to follow up with their primary doctor later on, expanding a need for one visit into a need for multiple visits, and patients requiring visits deemed to be routine or less urgent can experience increased wait times (Murray and Berwick, 2003). Another challenge with the model is that—apart from truly immediate-need circumstances—the process of determining urgency in primary care using predictions of acuity that are based on a classification system is complex, difficult, and unreliable (Jennings, 2008).
Indeed, because of the limitations of the mathematical models used, priority-based scheduling models are likely to be unreliable any time that there is poor information on variation in demand or capacity. Because patients are sorted into multiple waiting queues, the provider supply is spread out, which introduces inefficiency and wasted time into the system. Queuing theory holds that the effect of variability on wait times will be more pronounced in a system with an increased number of queues (Saaty, 1961).
As a result of health care innovation and the development of new treatments, patients are living longer with complex, chronic diseases, which has resulted in an aging population with increasing medical needs, involving physical and emotional conditions that require different types and amounts of health and related services (Bodenheimer et al., 2009). Providing appropriate, cost-effective care for a patient with multiple conditions can require coordination with multiple subspecialists, which can further complicate scheduling challenges. In the current provider-centered health care model, this requires the patient or the family to schedule multiple appointments, often on different days and in different locations, creating multiple opportunities for scheduling failures. Provider efforts are consistently challenged and strained by care complexity because of the limits of individual provider capacity (IOM, 2012).
The ongoing changes in reimbursement have had a direct effect on patient access to health care. Medicaid patients, both adults and children,
are limited in their access to health care, by virtue of limited acceptance among physicians of Medicaid payments. They also often experience poorer health outcomes than privately insured patients (Bisgaier and Rhodes, 2011; Hwang et al., 2005; Merrick et al., 2001; Wang et al., 2004). As Medicaid reimbursement rates have decreased, the number of providers refusing to accept Medicaid patients has increased (Tanne, 2010). As a result, Medicaid patients have an increasingly limited choice of providers from which to receive primary and specialty care.
Also contributing to prolonged wait times is the requirement for pre-approvals imposed by payers. A preapproval is an authorization required by health insurance plans that patients must obtain before receiving certain services. Although intended as a cost-cutting measure to reduce unnecessary services, this requirement places an additional obstacle in the flow of care. A delay in any step of this process can lead to a prolonged wait time.
The Affordable Care Act has reduced the number of Americans without health insurance, but many in the United States still lack the financial means to pay for health care (KFF, 2015). In addition, as noted above, many practices, particularly specialty practices, do not accept patients who have public insurance. In one survey of wait times, the average rate of Medicaid acceptance by physicians across five specialties in 15 major metropolitan markets in 2013 was 45.7 percent, down from 55.4 percent in 2009, while in 2013 the average acceptance rate of Medicare patients was 76 percent (MerrittHawkins, 2014). Studies have also shown that children with Medicaid or Children’s Health Insurance Program (CHIP) coverage are more likely than those with private insurance to be made to wait more than 1 month, even for serious medical problems (Bisgaier and Rhodes, 2011; Rhodes et al., 2014).
The Veterans Access, Choice, and Accountability Act of 2014 offers a new national standard for geographic access for veterans and provides a choice to receive care in the private sector for those living more than 40 miles from the nearest VHA medical facility. The Department of Defense Military Health System has designated a standard of a 30-minute drive time for primary care appointments and a 60-minute drive time for specialty care appointments (DoD, 2014). For non-veterans receiving care in the private sector, access is typically determined by their insurance status, which requires patients to live within a specific geographic service area for
enrollment and varies with each payer program. Care provided outside of the insurer network typically has higher patient copayments.
The Centers for Medicare & Medicaid Services (CMS) has also developed its own criteria for geographic access for applicants for its Medicare Advantage program. In a sampling of geographic areas, CMS analyzed the percentage of beneficiaries with access to a specialty type and varied travel time and travel distance to improve the system, which resulted in maximum time and distance criteria that vary by specialty type and geographic area. Providers within Medicare Advantage must demonstrate that 90 percent of their provider network meets the established time and distance requirements (CMS, 2015d).
Underlying these geographic and physical barriers to access is the reliance of the U.S. health care system on the office visit as the default model of care. Telehealth, or telemedicine, and the use of electronic information and technologies to support long-distance health care can be an alternative to an office visit and is discussed later in more detail.
With all the different factors in play and with the lack of organizational attention to issues of prolonged wait times, the wide variation in the wait times is not surprising. As previously noted, according to access data publicly reported from VA facilities, statewide data from Massachusetts, and private-sector data from 15 metropolitan areas, there is significant national variability in wait times among care settings, among specialties, and over time (Council, 2014; MerrittHawkins, 2014; VA, 2014d). In addition to the significant variability in wait times among care settings, among specialties, and over time, there is a lack of national standards and benchmarks for appropriate wait times. Although references to timely care appear regularly in legislative proposals, a prevailing definition of timeliness has not yet emerged.
Instead, individual institutions are developing varied approaches and standards for appropriate wait times. For example, the Military Health System and the California State Department of Managed Health Care developed benchmarks for access and included the following (DoD, 2014):
- 30-minute drive time for primary care
- Specialty care appointments within 4 weeks
- Routine appointments within 1 week
- Urgent mental health care by a physician or non-physician clinician within 48 hours
- Non-urgent appointments with specialist physicians within 15 business days
- Non-urgent appointments with a non-physician clinician within 10 business days
- Urgent care appointments generally not to exceed 24 hours
- Emergency room access available 24 hours per day, 7 days per week
- 60-minute drive time for specialty care
- Office wait times not to exceed 30 minutes unless emergency care is being rendered to another patient
Benchmarks such as these have served as useful reference points at the practice level in various places. Yet, because they have not been validated for national use, they are of limited applicability. Though useful as examples, they can even carry the potential for unintended adverse consequences if applied arbitrarily and without consideration to local circumstances. The committee contends that although benchmarks can help an organization set a goal and move toward improvement, the benchmarks should be determined according to the unique capacity and demand of each organization and care site.