Productivity in Audiology and Speech-Language Pathology

Abstract

You have accessThe ASHA LeaderFeature1 Jun 2011Productivity in Audiology and Speech-Language Pathology Kyle Dennis andPhD, CCC-A Stephen A. GonzenbachEdD, CCC-A/SLP Kyle Dennis Google Scholar More articles by this author , PhD, CCC-A and Stephen A. Gonzenbach Google Scholar More articles by this author , EdD, CCC-A/SLP https://doi.org/10.1044/leader.FTR2.16062011.16 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In http://www.asha.org/Publications/leader/2011/110517/Productivity-in-Audiology-and-Speech-Language-Pathology.htm As health care dollars grow increasingly tight, health care organizations must focus on being more concerned about accountability and efficiency. Accordingly, in member surveys ASHA has tracked the requirements for productivity—defined as the number of hours in direct patient care divided by the number of hours worked. In the ASHA 2009 SLP Health Care Survey, for example, 59% of respondents (n=1915) reported their facility had a productivity requirement. The ASHA Care Survey Report: Workforce and Work Conditions Trends, 2005–2007, showed that in 2007, as in 2005, most respondents indicated that their facility had a productivity requirement (60% and 61%, respectively). Productivity models are an effective way for audiology and speech-language pathology programs to examine how efficiently they provide services (not merely counting the number of services they perform). Some basic principles of productivity are particularly applicable to hospital settings. Productivity Factors Several factors influence productivity, including: Complexity. Complexity of services affects productivity because of the amount of time it takes to perform services. In addition, the type of service performed (e.g., diagnostic services, treatment services, or aftercare services), age of patients, and type and severity of communicative disorders also affect the complexity of clinical services, and consequently the number of services that can be performed and the time required to perform them. Staffing. The availability of professional and support staff greatly affects productivity. Although large clinical staffs tend to generate more patient visits, they are not necessarily more efficient in service delivery. For example, a clinic with clerks and assistants may be more productive than a clinic without such support because clerical and technical activities have to be performed by the professional staff. Assistants free the clinician to concentrate on more complex procedures, which typically require more time. Clinic Space and Equipment. A clinic can perform procedures only when equipment or space is available. The availability of space (e.g., exam rooms, sound suites, and special procedure rooms) and equipment limits the patient flow through the clinic. On-duty Hours, Leave, and Non-clinical Duties. On-duty hours and leave (e.g., vacation, sick days, personal days) affect time available for direct patient care. For example, a clinician could be assigned duties other than direct patient care (e.g., administration, mentoring, or research). This time is productive from the standpoint of clinic operations, but it does not generate direct patient care hours. Coding Systems. Coding systems affect productivity if they do not capture all of the services the clinic provides. Ideally, there should be a procedure code for every service performed. In reality, code systems such as CPT (Common Procedural Terminology, © American Medical Association) do not capture all audiology and speech-language pathology services. For example, audiologists and speech-language pathologists may not be able to capture professional services such as decision making, care planning, coordination of care, counseling, team management, and device handling. Coding systems designed for reimbursement (billing) also may not capture non-covered services. As a result, these systems may actually undercount productivity because some professional services will not be included. Labor Mapping. Because the work day typically involves time dedicated to direct patient care and other professional activities that are important to the function of the clinic but do not generate direct patient care hours, labor mapping is a useful way to allocate time during the work day. Labor can be allocated to the following areas: Direct patient care—Time devoted to prepare for, provide for, and follow-up on patients’ clinical care needs. It includes time spent rendering care to patients (pre-, intra-, and post-service), care coordination, documentation, continuing education, and staff meetings focused on patient care. It may include time spent supervising or mentoring trainees participating in direct patient care. Administration—Time devoted to program management, staff supervision, or managerial functions. Administration also may include time working on department and hospital committees or serving on state or national committees, advisory boards, or professional societies. Education—Time devoted to formal didactic education and teaching at a university. It may include managing a training program, but does not include time spent receiving continuing education or training or supervision or mentoring of students. Research—Time devoted to performing formal, approved health care research, or in activities in direct support of approved research. Research can be laboratory, clinical, or health services research. Examples include working in a research lab, serving on hospital or university research committees, supervising research, writing for publications or grants, attending research meetings, presenting at meetings, and preparing presentations or publications. Time spent in clinical research that produces clinical workload may be allocated to direct patient care. Labor mapping assigns labor cost and hours to the work unit in which the work occurred. If a clinician works in more than one unit, labor time is allocated to each unit. If an SLP works 40 hours in Clinic A and 40 hours in Clinic B, that clinician's labor time would be distributed as 50% in each unit. If the employee were mapped 100% to Clinic A, even though he or she worked there only half of the time, Clinic A would appear to be less productive because of the additional labor that would be mapped to that unit without any direct patient care production. Conversely, the productivity of Clinic B would be overstated because the employee's labor was not mapped to that unit although the provider contributed patient care hours to that unit. Productivity Methods Productivity can be measured in many ways, such as counting the number of patients seen, the number of visits or encounters per clinician, or the number of billable hours, or by calculating the percentage of on-duty hours spent in direct patient care (Table 1 [PDF]). Three common productivity methods are based on workload, capacity, and relative value units (RVUs). Workload-Based Method Perhaps the simplest productivity statistics to compile are workload-based systems. They typically include the number of visits, procedures, encounters, or patients. They provide an easily understood indication of the volume of work. The major disadvantage of workload-based methods, however, is that they usually do not take into account the complexity of services—all services are counted equally. Capacity-Based Method Capacity measures also are easy to construct. They are based on typical appointment length and are usually adjusted for clinician availability (e.g., the model adjusts to scheduled hours in the clinic, vacation time, and projected no-shows or other planned down time). These models are usually prospective (i.e., they show how many encounters a clinician should generate). When compared with workload models, the capacity model can demonstrate how well clinicians meet productivity goals (e.g., number of patients seen). Like workload-based methods, capacity methods value each encounter equally, regardless of complexity. Capacity models also do not provide information on how efficiently services are provided. The capacity models shown in Table 2 [PDF] illustrate how adjustment of time allocated to each appointment slot will affect the expected productivity. The models were constructed with the following assumptions: 260 possible work days (52 weeks/year, five days/week) reduced by scheduled holidays, leave, mandatory continuing education, and related professional assignments, leaving 202 possible work days per year. Assuming a typical work day of eight hours, minus one hour for lunch and breaks, there are seven hours of direct patient care per day or 1,414 DPC hours per year. The key element that determines productivity is the duration of the appointment slot. In Model C, for example, the typical appointment takes 45 minutes; a full-time employee should generate 1,885 patient visits per year. Patient no-shows—scheduled time that does not generate direct patient care time (wasted capacity)—also must be factored in the capacity model. This model, also called panel size, indicates the number of patients for which an audiologist or SLP is responsible, given scheduled availability. This model can be particularly useful in projecting how many clinicians a facility will need to hire for an expected demand. The danger in using this kind of model is the temptation to increase capacity by reducing appointment length. This scenario creates an ethical dilemma by reducing patient care time and possibly affecting the quality of patient care. It may be unsustainable because other factors—such as time needed to complete documentation, analyze results, coordinate care, attend team meetings, process orders, or teach students—are not included in the model. Driving capacity upward by reducing the time allocated to each patient may have undesirable consequences such as poor quality, errors, low morale, and low patient satisfaction. In this model, it is better to overestimate the appointment time to account for time that is not face-to-face direct patient care but is nevertheless essential to delivering quality patient care. RVU–Based Method Workload- or capacity-based models count each episode of care equally—that is, they do not account for the complexity of services. The relative value unit (RVU) productivity method has the advantage of weighting procedures by their complexity. There are three methods for establishing an RVU: Time Studies. A clinic can conduct a time study to determine how much time each procedure takes. This time becomes the RVU. An alternative method is to appoint an expert panel to arrive at a consensus on procedure times, a method that reflects actual local practice. Conversely, these RVUs cannot be standardized across health care facilities. Resource-Based Relative Scale. Another option is the Resource-Based Relative Value System (RBRVS) of the Centers for Medicare and Medicaid Services, which receives substantial input from the American Medical Association (AMA). RVUs in this methodology are dimensionless. As the name implies, they are based on a relative value scale that weights all CPT procedure codes. The AMA, with input from specialty societies, assigns a relative value to each CPT code. The RVU has three components: professional work (time, technical skill, physical effort, stress, and professional judgment); practice expense (overhead costs and non-physician labor); and professional liability (malpractice costs). RVUs are available in the Physician Fee Schedule published by the Centers for Medicare and Medicare Services (CMS). These RVUs have two major advantages: They account for complexity on a relative scale and they can be benchmarked to the productivity of other facilities. The major disadvantage of using RVUs as a productivity measure is that not all audiology and speech-language pathology services are captured by CPT codes or covered by Medicare. This method also may not capture professional time involved for case histories, decision making, floor time, counseling, coordination of care, documentation, data analysis, or chart review (evaluation and management services). Labor Time. Fortunately, the RVUs include time-based clinical labor components. CMS maintains a file of direct labor times for each procedure. These time values can be applied to time-based productivity calculations. Table 3 [PDF] shows examples of direct labor values for two audiology procedures (92553 and 92633) and two speech-language pathology codes (92506 and 92507) extracted from the CMS practice expense labor file. Some audiology and speech-language pathology procedures have professional work values. This component also contains a direct labor time value. Table A [PDF] shows examples of procedures from the physician labor file. Some audiology and speech-language pathology services have both professional work and practice expense (technical) components. Table B [PDF] shows examples of procedures that have both professional (modifier 26) components and technical (modifier TC) components. If the clinician administers and interprets the test, the combined value (known as the global) is the RVU time. Using RVU-Based Productivity Simply defined, time-based productivity is the ratio of labor output (time needed to generate clinical procedures) to labor input (worked hours). For example, a clinical procedure takes 10 minutes to perform (the RVU). The clinician generates 1,000 of these procedures (the clinical volume). Therefore, it takes 10,000 minutes (or 166.66 hours)—the labor output—to perform these procedures. This labor output (also called specified hours) becomes the numerator of the productivity ratio. Computing productivity requires knowing how much time the clinician worked. Payroll shows 180 worked hours. This time becomes the denominator of the productivity ratio. The productivity is calculated as: 166.66/180 = 92.58%. That is, 92.58% (154.3 hours) of the clinician's possible on-duty hours were spent in direct patient care. Conversely, 7.42% (13.34 hours) were not specified (i.e., not associated with production). This level is a very strong degree of individual productivity. It has been long established in social science research that productivity ratios at or above 75%–78% are good levels of productivity. Table 4 [PDF], which shows application to the clinic level, depicts a productivity report for a clinic with 7.8 employees. The report shows work as hours and full-time employee equivalents (FTEs). FTE is calculated by dividing the total hours by the number of possible work hours in a year: 2,080 hours (52 weeks at 40 hours per week). There were 16,224 total paid hours (7.8 FTE x 2080 hours). Clinicians used 2,438 hours of vacation, sick leave, and holiday time, or 1.17 FTE, resulting in 13,786 possible on-duty work hours or 6.66 FTE. The clinic generated 11,063 direct patient care hours or 5.32 FTE. Direct patient care (specified) hours are the accumulated RVUs associated with procedures multiplied by the clinical volume of the procedures performed. The productivity ratio is calculated by dividing the total direct patient care hours by the worked hours. In this example, the productivity (specified percent worked) was 80.24%. Managers need to pay particular attention to unspecified hours (difference between the possible worked hours and direct patient care hours). In this example, there were 2,723 unspecified hours (1.31 FTE). Unspecified hours do not necessarily mean non-productive hours, and may include direct patient care activities such as analyzing data, making decisions, planning and coordinating care, documentation, ordering and handling devices, and inter-disciplinary team meetings that are not associated with specific procedure codes. Comparing Productivity Methods Workload-based statistics (e.g., number of visits, encounters, or caseload) give information about the volume of work, but not how efficiently the work is being delivered. For example, Clinic A produced 10,000 visits and Clinic B produced 5,000 visits. Based on workload-based statistics, Clinic A is more productive than Clinic B. If both clinics have the same staff size (3.0 FTE), Clinic A generated 3,333 visits per FTE and Clinic B generated 1,667 visits per FTE. Using a per-FTE workload-based metric, we would again say that Clinic A is more productive because it generates more visits. Looking at the complexity of the procedure performed by the two clinics reveals a different picture. Clinic A generated 10,000 procedures with RVU=10 minutes. Clinic B produced 5,000 procedures with RVU=60 minutes. Under an RVU-based analysis, Clinic A generated 100,000 RVU minutes (1,667 RVU hours) and Clinic B produced 300,000 RVU minutes (5,000 RVU hours). Both clinics had 5,520 possible work hours (3.0 FTE x 1,840 on-duty work hours). Clinic A has a productivity of 30.2% and Clinic B has a productivity of 90.6%. A clinic can look very productive in terms of visits but actually be quite inefficient when the complexity of procedures is considered. A Bigger Picture RVU-based productivity models provide a simple and informative alternative to traditional workload-based or capacity-based methods. These RVU methods are powerful and flexible. For example, Medicare RVU data provide a standardized way to weight procedures by complexity and can be used to calculate billable productivity (percent of on-duty hours that generate billable hours). Time-based productivity methods allow easy calculations of the percentage of on-duty hours associated with direct patient care. Finally, these models can be used to create models to predict how much staff will be needed to meet expected demand. Simply measuring productivity, however, is not an effective solution to cost management. Clinicians in hospital settings also need to consider other factors including long-term sustainability, staff morale, outcomes, quality, and constraints that limit patient flow through the clinic. The opinions expressed herein are those of the authors and do not necessarily reflect the opinions or official positions of the Department of Veterans Affairs or the U.S. Government. References American Speech-Language-Hearing Association (2009). ASHA SLP Health Care Survey Report: Workforce and Work Conditions Trends, 2005–2009. Google Scholar American Speech-Language-Hearing Association (2009). ASHA SLP Health Care Survey 2009: Workforce and Work Conditions. Google Scholar American Speech-Language-Hearing Association (2009). Productivity. Available from www.asha.org/slp/productivity.htm. (Members only). Google Scholar Centers for Medicare and Medicare Services. Physician Fee Schedule. www.cms.gov/PhysicianFeeSched/PFSFRN/itemdetail.asp?filterType=none&filterByDID=99&sortByDID=4&sortOrder=descending&itemID=CMS1223902&intNumPerPage=10 Google Scholar Author Notes is an audiologist at the National Audiology and Speech Pathology National Program Office for the Department of Veterans Affairs. Contact him at [email protected]. is chief of the Audiology and Speech Pathology Service at the VA New York Harbor Health Care System. Contact him at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetailsCited ByPerspectives of the ASHA Special Interest Groups7:4 (1120-1136)15 Aug 2022Impact of Clinical Education of Student Clinicians on Speech-Language Pathologists' Productivity in Medical SettingsJennifer St. Clair, Karen J. Mainess, Paige Shaughnessy and Benjamin BecerraAmerican Journal of Audiology28:3 (628-659)13 Sep 2019Pediatric Audiology Productivity: Results From a Multicenter SurveyWendy Steuerwald, Lisa L. Hunter and Roanne Karzon Volume 16Issue 6June 2011 Get Permissions Add to your Mendeley library History Published in print: Jun 1, 2011 Metrics Downloaded 2,841 times Topicsasha-topicsleader_do_tagleader-topicsasha-article-typesCopyright & Permissions© 2011 American Speech-Language-Hearing AssociationLoading ...