Outpatient regulation system in health management: economic benefits of technological innovations

2.1 Appointment systems in outpatient departments

The regulation of the appointments system is very important for health management to fulfill its democratic role of providing health services equally and indistinctly to all citizens. From a managerial point of view, the appointment system ensures efficiency in the allocation of health resources to promptly meet the demands of the population with quality and fulfilling adequately medical treatments and needs (Cayirli & Veral, 2003; Guy et al., 2012; Ho & Lau, 1992; Murray & Berwick, 2003; Rocha, 2015).

There is a high demand from citizens to specialized health services and each citizen, when looking for some sort of health attendance, may require different services that can create a sequential and iterative flow of health care, consultations, examinations, hospitalization, among others (e. g., Cayirli & Veral, 2003; Cayirli, Veral, & Rosen, 2006; Ho & Lau, 1992; Kaandorp & Koole, 2007; Kemper, Klaassen, & Mandjes, 2014; Klassen & Rohleder, 1996). Such health services may be available at the same outpatient department or hospital at any given time, but the offer is not entirely homogeneous and some procedures, examinations, consultations and/or hospitalizations may not be available all at the same time in the same clinic or hospital. Thus, the appointment system plays a fundamental role in allocating the patient – according to his/her medical urgency – to the most appropriate health center available within a reasonable period of time (Liu, 2016; Rocha, 2015; Souza, 2017).

The health appointment system is an essential instrument to ensure the effectiveness of health management, which administers a large structure divided into three levels of hierarchical health care (primary, secondary and tertiary care) – the conglomerate of health basic units, outpatient departments, hospitals, emergency rooms and large hospitals correspond to the tertiary level, i.e. facilities that deal with more complex cases. One of the most important functions of the appointment system is to instantly guarantee and prioritize access to health services for citizens who are in need of medical care. The appointment system must be effective in addition to operating efficiently; that is, it must establish the most adequate appointment to each patient considering his/her medical and health needs while considering other aspects, namely, social, demographic and logistical (Cayirli & Veral, 2003; Cayirli et al., 2006; Denton & Gupta, 2003; Kemper et al., 2014; Klassen & Rohleder, 1996; Liu, 2016; Murray & Berwick, 2003; Robinson & Chen, 2003; Souza, 2017).

The appointment system favors the organization and the control of health services, i.e. it brings clinics, health basic units, outpatient departments, hospitals and emergency rooms together, in an integrated, articulated and structured way, providing high levels of health-care quality to citizens, increasing efficiency and reducing resource waste. The appointment system integrates all health units and provides online information, in real time, of the capacities and availability of appointments for consultations and health exams, procedures and hospital beds – in addition, the system also integrates urgencies (prioritizations) and medical protocols (Deyo & Inui, 1980; Kemper et al., 2014; Liu, 2016; Murray & Berwick, 2003; Souza, 2017; Vissers, 1979).

Appointment systems should use heuristic and allocation criteria that guide the patient toward the most appropriate health unit to optimize the conditions of the health care, to provide the appropriate resolubility of the medical treatment and to consider the mitigation of possible risks involved. Appointment systems can also implement various technological innovations for scheduling appointments and provide clinical sequencing to maximize the utilization of the installed capacity of health units or minimize patient waiting time. To turn the operation of the health system or of an outpatient department into more effective, agile and intelligent actions, the appointment system can use some technological innovations, as machine learning algorithms, along with process optimization techniques and resource allocation. Some examples of these techniques are linear and nonlinear programming, mixed-integer programing, balancing and sequencing procedures, queueing theory, Monte Carlo simulation and Markov chains (used in reinforcement learning) (Bailey, 1952; Cayirli & Veral, 2003; Cayirli et al., 2006; Denton & Gupta, 2003; Ho & Lau, 1992; Kemper et al., 2014; Klassen & Rohleder, 1996; Liu, 2016; Robinson & Chen, 2003).

In addition, implementing technological innovations, such as machine learning algorithms, integration and optimization procedures, will enable a more modern and effective higher-quality management; such management tends to be faster and to provide more assertive responses to the demands of citizens and the appointment system will also allow a more efficient management of the resources involved in the provision of health-care services and also a deeper analysis of the restructuring capacity of the entire health system. Together with the primary functions of health-care regulation, the appointment system can also enable the monitoring of general indicators of production, productivity of the resources used, equipment, employees, maintenance expenses and equipment failures, absenteeism of patients, as well as the generation of occupancy rates in outpatient departments, health basic units, hospitals, hospital beds and health examinations (Cayirli et al., 2006; Deyo & Inui, 1980; Kaandorp & Koole, 2007; Kemper et al., 2014; Klassen & Rohleder, 1996; Zacharias & Pinedo, 2014).

The appointment system also considers, when regulating the access to outpatient departments, the characteristics and needs of the patient. The appointment system should allocate the most appropriate and specialized medical appointment considering some aspects, like personal needs, demographic profile, proximity to the health-care unit and convenience. The appointment system, which considers the principle of priority of patients’ treatment and important health protocols related to the complexity of the medical condition and consequent urgency, seeks to allocate – with the support of machine learning algorithms – the best outpatient consultation appointment to a patient, to improve the chance of his/her attendance, i.e. the attendance of patient to the appointment, minimizing no-show rates (Cayirli & Veral, 2003; Cayirli et al., 2006; Kemper et al., 2014; Liu, 2016; Srinivas & Ravindran, 2018).

2.2 Problem of patient no-show in outpatient departments

Absenteeism of patients is a serious problem for the management of any health system, for the patient him/herself and for others involved. In addition to being harmful to the patient’s own health condition, the absenteeism causes other problems, as it presents difficulties in managing the outpatient queue, hinders the access to consultations in a determined outpatient facility (considering that the queue is increased), besides creating an idle appointment schedule, which implies loss of time and resources of the management of the health unit and/or of the entire health system (Bender et al., 2010; Cavalcanti et al., 2013; Cohen et al., 2008; Deyo & Inui, 1980; Dreiher et al., 2008; Guy et al., 2012; Izecksohn & Ferreira, 2014; Moore et al., 2001; Murray & Berwick, 2003; Neves, 2017; Silveira et al., 2018).

Deyo and Inui (1980), when developing a literature review on absenteeism in medical consultations, found a few studies that reported no-show rates between 15% and 30% in outpatient departments. The authors also cite papers that report lower rates, between 5% and 11%, in family health clinics and mentioned other studies that reported no-show rates of 15%–18% in prepaid health clinics. Guy et al. (2012), when carrying out their literature review, found several studies with reports of patient no-show rates in outpatient departments ranging from 10% up to 60%.

Moore et al. (2001) report that some studies indicate no-show rates of 6%, 26.1% and even of 50% in some family health clinics. The authors conducted their study based on 4,055 visits to The Family Practice Center (FPC) of Palmetto Richland Memorial Hospital of the University of South Carolina and found a 24.4% no-show rate.

Cohen et al. (2008) conducted a research on absenteeism at a pediatric dermatology clinic of the Clalit Health Service (CHS), an Israeli state-mandated health service organization. The authors analyzed a sample composed of 52,604 appointments over a 44-month period and found a 27.6% no-show rate. Dreiher et al. (2008) also conducted a survey on the absenteeism of patients in obstetrics and gynecology clinics of the Clalit Heath Service (CHS); they analyzed 8,883 appointments in 2003 and indicated a no-show rate of 30.1%.

Considering this specific matter, one finds several examples of no-show rates in Brazilian medical institutions. Bender et al. (2010) conducted a research on the Saco Grande Health Center (CSSG) in Florianópolis from January to July 2008 and analyzed 828 appointments of various outpatient specialties and discovered a no-show rate of 34.4%. Cavalcanti et al. (2013) studied patient absenteeism levels in outpatient departments in the city of João Pessoa between 2009 and 2010, and they found no-show rates between 32.9% and 39.8%.

Izecksohn and Ferreira (2014) carried out an analysis of patient absenteeism, from July to December 2012, in family health clinics of the Manguinhos School of Health Center in the city of Rio de Janeiro, which was later incorporated into the School of Health Center Germano Sinval Faria. The authors analyzed 2,272 appointments and found a no-show rate of 48.9%. Neves (2017) studied the patient absenteeism in outpatient departments at the Antonio Pedro University Hospital (HUAP) in the city of Niterói; this outpatient department belongs to the Fluminense Federal University (UFF). The author analyzed 357,251 appointments made in 2015 and 2016 and found a patient no-show rate of 28.2%.

Silveira et al. (2018) conducted a study on patient absenteeism in a basic health unit supported by the Federal University of Pelotas from July 2016 to April 2017. The authors analyzed 3,131 appointments for various outpatient specialties and found a no-show rate of 19.2%.

Table 1 summarizes some of the main characteristics of the studies that analyze patient no-show rates in outpatient departments in Brazil.

2.3 Technological innovation and reduction of no-show rates in outpatient departments

Patient absenteeism is a serious issue for medical treatment, for other patients waiting in the hospital queue and for the efficient functioning of outpatient departments; thus, such matter has been the focus of several regulation appointment systems. Literature points out some strategies that can be implemented through the employment of technological innovation by outpatient departments to reduce the patient no-show rates.

Among the main strategies, we mention the reduction of waiting time; that is, the time interval between the appointment and the consultation itself. Some studies found that no-show rates increase considerably when the patient faces longer waiting times. Technological innovations, as machine learning algorithms, optimization procedures, dynamic sequencing, Markov chains and others, have been implemented in appointment systems to automatically detect patients’ characteristics, patterns and needs to turn the appointment process more assertive and effective and, consequently, reducing patient no-show rates (Benjamin-Bauman, Reiss, & Bailey, 1984; Denton & Gupta, 2003; Deyo & Inui, 1980; Kaandorp & Koole, 2007; Kemper et al., 2014; Klassen & Rohleder, 1996; Liu, 2016; Norris et al., 2014; Srinivas & Ravindran, 2018; Wang & Gupta, 2011).

Some appointment systems in outpatient departments use innovation to automatically create and to send alerts through short message service (SMS), voice mail or e-mails a few days prior to the consultation to remind the patients of the medical appointment, reducing, then, no-show rates (Almog et al., 2003; Deyo & Inui, 1980; Downer, Meara, Da Costa, & Sethuraman, 2005; Parikh et al., 2010; Robotham, Satkunanathan, Reynolds, Stahl, & Wykes, 2016; Teo, Forsberg, Marsh, Saha, & Dobscha, 2017).

Another strategy for managing the problem of patient absenteeism in medical consultations is medical appointment overbooking (LaGanga & Lawrence, 2007; Muthuraman & Lawley, 2008; Vissers, 1979; Zacharias & Pinedo, 2014; Zeng, Turkcan, Lin, & Lawley, 2010). Overbooking is not an ideal solution considering it does not solve the problem definitively because it does not reduce the no-show rate itself; it is, however, a risk management that allows a greater utilization of resources, increasing the productivity of the health system. More recently, other technological innovations, as business intelligence management techniques that use data mining and artificial intelligence algorithms, make it possible to better understand the demographic and behavioral characteristics of patients who represent the majority in no-show rates; the use of these innovations aim at better understanding patient scheduling and better assisting patients to schedule their appointments, reducing no-show rates (Bean & Talaga, 1992; Defife, Conklin, Smith, & Poole, 2010; Lee, Heim, Srikandarajah, & Zhu, 2018; Liu, 2016; Nancarrow, Bradbury, & Avila, 2014; Samorani & LaGanga, 2015).

4.1 Estimation of patient no-show in outpatient departments

The Regulation Center of Supply of Health Services (CROSS – Central de Regulação de Oferta de Serviços de Saúde, 2019) manages the health appointments through appointment systems in the outpatient, hospital bed and emergency spheres in the state of São Paulo. It works in an integrated way with all public health units, i.e. all municipal and state-owned health units at the three levels of hierarchical health care (primary, secondary and tertiary care; the latter deals with complex care units and involves health basic units, outpatient departments, outpatient departments, hospitals, emergency rooms and large hospitals). With a request for consultation, the appointment system in outpatient departments presents an optimal scheduling algorithm that pursues and identifies the best appointment for the patient taking into consideration the region where the patient lives and the location of the outpatient facility.

The appointment system that operates in the state of São Paulo has, as main priority, the fulfillment of requests, medical protocols and meets, by regulating the access to appointments, the needs of the patient regarding appointment scheduling and, at the same time, pursues the most adequate appointment so that the patient attends the medical consultation. To minimize the chances of patient no-show, the appointment system of the state of São Paulo uses heuristic and artificial intelligence algorithms that take into account the accumulated knowledge of no-show ratios considering some aspects, like personal needs, demographic profile, proximity to the health-care unit and convenience to regulate the appointment schedule, minimizing the chances of no-show.

In addition to the employment of these technological innovations, scheduling heuristics and artificial intelligence algorithms – which identify and designate the most appropriate medical appointment from a medical protocol perspective while minimize patient no-show – the appointment system that operates in the state of São Paulo also presents internal modules responsible for the automatic alerts sent (e-mail, SMS and phone calls) to patients some days prior to the consultation to remind them of the appointment, thus, reducing the chances of patient no-show. If the patient cannot attend the medical consultation appointment, the system proceeds immediately by trying to schedule the appointment to another patient in the queue, hence, avoiding resource waste in the health system.

The outpatient regulation platform of the state of São Paulo schedules appointments, keeps all the information safely stored and relies on disaster recovery as a risk management strategy. The appointment system also provides – in addition to data referring to scheduled appointments – an integrated registration of users, which takes into account data related to the capacity of the health system and the availability of resources of every health unit. Such technological innovations have provided a reduction in patient no-show rates in the operation of the outpatient regulation system of the state of São Paulo, especially when comparing these rates to the ones found in other regions of Brazil, as we can see in Table 1 (in the literature review section) and Table 2, which demonstrates no-show rates across some Brazilian regions and, more specifically, in the state of São Paulo.

Tables 1 and 2 show absenteeism rates with averages of 33.4% and 18.2% in outpatient departments in medical facilities from, respectively, various locations in Brazil and in the state of São Paulo. Table 2 indicates that the appointment system in the state of São Paulo also presents greater robustness regarding the response to the increasing growth in the demand for regulation over the past few years. The appointment system in outpatient departments has performed very efficiently, reducing no-show rates even when there was a strong growth in demand for appointments because of the high and continuous inclusion of citizens in the system over recent years, considering that the beginning of the operation of the appointment system in outpatient departments in the state of São Paulo was in 2011. Table 2 and Figure 1 show that, even with a stronger demand for appointments, the CROSS system was able to continuously reduce patient no-show rates until reaching the percentage of 17.39% at the end of 2019; in the same year, more than 11.5 million appointments were scheduled, which represents the double of the appointments scheduled in 2014.

This increase in the number of outpatient appointments, which we observe in Table 2, is the result of the continuous entry – in addition to the regulatory system in the state of São Paulo – of new outpatient departments, health basic units, clinics and hospitals, in addition to others that were continuously being included in the system. Simultaneously, there was an increase in new registrations of citizens in outpatient departments, which reinforces the need for regulation in the state of São Paulo.

Table 3 and Figure 2 show the continuous increase in the number of appointments scheduled in outpatient departments, the population growth and the variation in population growth over the years. Thus, we can observe that the appointment system in outpatient facilities, in the state of São Paulo, performed efficiently and with robustness, reducing no-show rates, dealing with the increasing and continuous number of citizens registered for health case and managing the higher number of health units incorporated into the system.

4.2 Economic evaluation of the benefits of an appointment system with technological innovation and low patient no-show rate

To estimate the value of a medical consultation, we must resort to data provided by the Transparency Portal of the Health Department of the state of São Paulo. These data bring additional information on the budget provided by the state government for consultations in specialized medical outpatient departments (AME, in Portuguese). Table 4 shows the total budget appropriations, the total number of consultations and the expenses per consultation in AMEs in the state of São Paulo during 2018. This knowledge provides us with the average expense consultation in outpatient departments; approximately R$141.88 (in current Brazilian currency).

Table 5 summarizes the main values obtained in our study that are important for assessing the economic value of the benefits stemming from the management and operation of the appointment regulation system in the state of São Paulo, that is, the amount saved (not wasted) because of a lower patient no-show rates. The average difference between no-show rates in the State of São Paulo and those in other regions of Brazil is 15.18%. Considering the total number of almost 11.6 million appointments scheduled in the State of São Paulo in 2019, we estimate that almost 1.76 million consultations were not lost; that is, these appointments were attended by patients who benefited from the appointment system of the state of São Paulo.

Considering the average expense (in Table 5) of a medical consultation in an outpatient department (AME) – R$141.88 in 2018 – we estimate an economy of around R$250m annually because of the appointment regulation system, in the state of São Paulo, which reduced patient no-show rates.

To evaluate the economic benefits, we used the BCR, which is widely used for economic analysis of many public projects. Our intention was to verify if the benefits (when the BCR is greater than 1) overcome the investments and costs involved in the project. Thus, considering the annual costs of the operation of the regulation appointments system (R$8,151,378.00, in current Brazilian currency), we have a BCR of 30.6, a very high value that indicates that the benefits of the operation involved in the appointment system in outpatient departments in the state of São Paulo presents a high economic and social impact.

We can also analyze the economic value of the appointment system through the payback method, which evaluates a project by measuring the time it will take to recover the initial investment and costs. Thus, payback represents the time required to accumulate revenues or benefits, in current values, which must be sufficient to exceed the accumulation of expenses, also in current values.

When we crosscheck the annual costs of the operation of the appointment system with the benefits, the resulting payback is very small (12-days payback period) considering the annual basis of the analysis. It means that the economy (benefits generated by the operation of the appointment system, with lower rates of patient no-show in the state of São Paulo) in the first 12 days of the year would already be enough to recover the costs of the entire year involved in the operation of the appointment system.