Appointment Scheduling

Appointment scheduling and templates are built to maximize provider’s productivity as well as utilize staffing appropriately. Currently, the third next available appointment reports have long wait times and the patients no show rates are consistently high for adult primary care clinics located in the Sarasota County Health Department. These high no show rates reduce the productivity of providers and reduce potential revenue. The long wait times are hindering the ability to meet performance goals that could be generating payment incentives for the chronic disease and the complex high acuity patients. In addition, we know that access to care is important for overall quality health delivery as well as disease prevention, detection, and screening.

Background
Expectations from Medicaid, Medicare or other insurances are for the provider to improve managed care and deliver quality outcomes. This requires patients have access to care for providers to maintain patient care. Managing these outcomes is now directly related to reimbursement rates. There is no current data or existing research studies that identify any patterns or times that could be contributing to these high no show rates at Sarasota County DOH Ringling office. My August 2017 field experience report found monthly rates to be the lowest at 11.76% for provider A, while being the highest at 24.61% for provider B. Then the data in September was completely opposite, with provider A at 37.5% and provider B at 18.46%. There did not seem to be any obvious identifiable reasons, determinants or variables for the providers monthly no show rate variability. This particular health department needs to be generating revenue for its increased expenses as the DOH is in a two-year transition releasing its Federally Qualified Health Center (FQHC) status to a private nonprofit. It is critical that the DOH maintain its revenue, quality of care and staffing productivity and utilization during this transition.
Statement of Purpose
The purpose of this study is to identify days and times for additional appointment times to be built into the templates. The primary objective will be to analyze data to distinguish if there is a pattern of days or times that are consistent or identifiable in six adult health provider clinics. This data may be critical to adjusting immediate scheduling needs and increasing provider productivity and patient access to appointments. Now more than ever, time is of the essence and a deeper analysis of no shows root causes cannot be evaluated quickly.
Costs of No Shows
Patients not showing for their appointment have many costs. “Patients who frequently miss medical appointments have poorer health outcomes and are less likely to use preventive services” (Crutchfield & Kistler, 2017, p2). In addition, these patients can overburden the whole health system by using the emergency room for non-urgent matters when they miss their scheduled appointments. According to The Washington Post (2011) Medicaid recipients used the ER three times as much as people with private insurance due to difficulty finding providers.
Since the 1990’s, the National Committee for Quality Assurance (NCQA) developed a standardized tool to measure health care performance (NQCA, n.d.). These Healthcare Effectiveness Data and Information Set (HEDIS) measurements are a way to improve the healthcare system and a majority of insurance companies are expecting providers to manage the patient to meet these HEDIS measurements for reimbursement for services. The shift has become based on the patient outcomes and value-based care management versus fee for service (HealthPayer Intelligence, 2017). These reportable measures make it possible for customers to compare providers and health plans. These measures become a problem for providers and their ability to meet HEDIS measures if their patients are not showing up. It also scores the health plans poorly is the providers are not meeting standards of care. This situation, in turn, reduces the payment amounts to providers and could “assess penalties against them, up to and including shutting the organization down with poor performance for three years” (CareNational, 2017, para 7). Primary care should be managing their patients and long wait time for appointments could contribute to the poor performance scoring of providers thus reducing payments on the patients that are showing. Receiving a low rating could also detour new patients from seeking care from these providers and impact future business.
FQHC designation puts this practice into a fixed prospective payment system. This payment system varies for a FQHC, based on geographical location factor and patient visit type. However, the 2017 rate is set at $163.49 (Nordian, 2017). If this figure is used and applied to the 25% of a schedule that is not showing up for care, the impact of 6 providers daily having 25% no show rates (5 pts. per day ave.) could be 30 wasted appointments times daily. With 6 providers having 5 no shows, it is approximately $4,900 daily loss of revenue. If the practice is open 5 days a week that brings the total to about $24,500 weekly loss of potential revenue. If even 20 patients could be accommodated for overbooking appointments or urgent needs, it would help divert care from the nearby hospital ER rooms.
Factors associated with no shows
Fear and emotions play a part in appointment keeping.

Not wanting to hear bad news was one of the three reasons found by Lacy, Paulman, Reuter & Lovejoy (2004). Not understanding the scheduling process and feeling disrespected in the health care system were the other two reasons. The article reflected patients having to wait for appointments, wait to be seen and waiting in the exam rooms for providers were also perceived to be disrespectful. A retrospective study in primary care by Kaplan-Lewis and Percac-Lima (2103) found that no show rates are higher in underserved populations and that the patients simply forgetting was the most frequent reason for the no show. Kheirkhah, Feng, Travis, Tavakoli-Tabasi and Sharafkhaneh conducted a retrospective cohort study spanning from 1997-2008 comparing 10 clinics from primary care to subspecialty clinics. The goal was to evaluate economic consequences, predictors and prevalence. The authors presented a mean rate of 18.8% for no shows, the average cost of $196 for 2008. The article concluded with the data reflecting a major financial burden on the healthcare systems and women’s clinics with higher no show

rates.
Demographics and no shows
The findings and factors vary from clinic to clinic.

Fiorillo, Hughes, I‐Chen, Westgate, Gal, Bush, & Comer (2018) found that there was an association and a 24% no show rate for new patient visit types in pediatric otolaryngology. While adult patients not showing for otolaryngology appointments tended to be younger aged females who had to wait longer for appointment dates (Cohen, Kaplan, Kraus, Rubinshtein & Vardy (2007). Menendez and Ring (2014) reported that an outpatient hand surgery clinic study found that their no-shows data were in the beginning of the week. Monday and Tuesday were the days with a higher prevalence of missing the appointment. While a pediatric otolaryngology practice found morning appointments and June to be the highest times for no shows (Huang, Ashraf, Gordish-Dressmand, Mudd (2017). Butterfield points out that younger men who are unmarried, and those patients living 19-60 miles away from the practice had higher no show rates (Butterfield, 2009). Dantas, Fleck, Cyrino & Hamacher (2018) reviewed over 105 in a systematic review of literature available and found that certain patient characteristics were identifiable overall. Younger age, lower income, no private insurance and living farther from the clinic has a more frequent no show association. Mehra, A., Hoogendoorn, C., Haggerty, G., Engelthaler, J., Gooden, S., Joseph, M., Carroll, S., Guiney, P. set out to when their patients were not showing and to apply interventions to reduce no

shows. They also concluded in their conclusion that four factors impacted no show rates. The younger patients, a lower socioeconomic status, previous no shows and not understanding the purpose of their appointment were identifiable factors.
Scheduling and no shows
The no shows rates can increase appointment wait times. Drewek, Mirea and Adelson (2017) reviewed pediatric patient no shows. The theory of appointment lead times was tested for association with no shows in outpatient pediatric specialty clinics. Overall those scheduled less than 30 days resulted in 23% no shows while those over 30 days were at a 47% rate. In addition, new patients were 30% of those while follow-ups were 21% if scheduled within the 30 days. Over 30 days resulted in similar rates with new patients at 46% and follow-ups 48%. The authors concluded there was a risk factor for no shows in the time distance to an appointment. Giunta, D., Briatore, A., Baun, A., Luna, D., Waisman, G. and de Quiros, F., also found a strong association between the time of the request for the appointment and the date given for the appointment.
Insurance and no shows
Cheeseman, Trivedi, Peterseim, Blice, and Unkrich complied a retrospective study from July 2014 through June 2015. The evaluated the patient insurance carriers of no shows at a university pediatric based practice. Their study found that the Medicaid insurance population had the highest rates of same day cancelations as well as now shows. Norris, Kumar, Chand, Moskowitz, Shade, & Willis’s (2014) empirical investigation also found four factors of the highest association. The greatest was the lead time between the call for an appointment and the appointment interval the type of insurance payer, the patient age, and the previous no show history of the patient.
Overbooking and no shows
Parente, Salvatore, Gallo, & Cipollini (2018) reported work from their quasi-experiment on developing a model to overbook appointments where they predicted a no show. The variables they use to predict were age group, previous no show record percentage, returning patient or new and insurance status. These March 2018 results concluded that further studies were needed, but it was positive evidence that an overbooking strategy could improve managing clinics and health centers.
Methods
The data for this retrospective study was generated from Sarasota County Health Departments Health Management System (HMS) electric medical records system. Six adult health primary care providers were selected that work specifically at the Sarasota County Health Departments Ringling location. (Dr. Santun, Dr. Siegal, Dr Nygen, Dr. Brand, C. Finney ARNP and K. Ferris ARNP ) Patient no shows were defined as the appointment times that patients failed to make appointments and did not call to cancel. No show reports were modified to report only appointment time and date, generated from HMS for a period of one year (February 2017- through February 2018). No patient identifying data was included for this study. Days and appointment times were logged into Excel sheets. The total number of no shows was counted for each appointment time, day and month for each provider. Data was then put into graph format for each provider and a total for all six providers to see complete totals for this study.