Computer-aided Patient Scheduling

Without a computerized scheduler, a practice has less than a 2% chance of earning the title of a “better-performing practice,” according to the Medical Group Management Association. Computerized scheduling helps decrease service costs, provide fairness in service delivery, increase patient satisfaction, and reduce waiting times (Zhang et al., 2019). A massive investment in scheduling features across a wide spectrum of billing products indicates the importance of computerized scheduling.

Convenience and front office efficiencies are two obvious benefits of a computerized scheduling system; without them, the only manual way to find out if a specific patient has a scheduled appointment is to flip through the appointment book page by page. Worse, manual scheduling hurts both patient satisfaction and practice financial performance because of scheduling inconsistencies and unbilled (and therefore unpaid) visits.

But the benefits of integrated computerized scheduling stretch far beyond convenience, front office efficiencies, and better charge follow-up of stand-alone, albeit computerized, scheduling. A well-designed and integrated scheduler allows preferential patient scheduling, which, along with improved controls, helps revenue optimization and practice compliance. Next, we review key aspects of computerized scheduling and demonstrate the important benefits of integrated scheduling, billing, and compliance management.

Scheduling Policies

Computerized schedulers allow a combination of single- or multiple-interval scheduling, with open-access scheduling subject to various priority constraints. Such priority-constraint-driven, open-access scheduling creates preferential appointments based on patient demographics or insurance coverage.

Typical time-slot-based appointment systems essentially divide a physician’s schedule into finite slots in a day, which can be allocated according to appointment requests. However, such systems are limited by the risks of schedule fragmentations in late shows or no-shows (Chen et al., 2019). Examples of time-slots-based scheduling include single-interval scheduling and multiple-interval scheduling.

Single-interval scheduling allocates appointments at regular intervals of 5 to 15 minutes, depending on the specialty. The downside of single-interval scheduling is that as soon as one appointment takes longer than the allocated slot, all subsequent patients must wait.

Multiple-interval scheduling also sets appointments at regular intervals; however, unlike single-interval scheduling, it allocates the appointment length depending on the chief complaint. Such scheduling requires up-front categorization of key appointment types and their projected lengths. For instance, an initial appointment might take 30 minutes, while a routine injection might take only 5 minutes.

According to the CAHPS survey database, about 12% of patients who called in did not get appointments for urgent care that they needed at the time. Forjuo et al., (2001) also showed that inadequate access to primary care providers was a leading cause of patient dissatisfaction. These challenges are mitigated by open-access scheduling. Open-access scheduling requires holding several appointments open every day. These open appointments are filled only within 48 hours of the appointment, catering to same-day or last-minute patient requests. Open-access scheduling improves access to the physician, reduces no-shows, and eliminates patient screening time. The downside of open-access scheduling is, of course, the potential for longer patient waiting lines or physician idle time because of the inability to maintain a predictable patient flow.

A novel scheduling variant is the overlapping appointment scheduling (OLAS) model (Huet et al., 2020). OLAS model refers to deciding the optimal overlapping periods between the patient appointment and allocated service times. The model is formulated as an optimization problem to minimize the total cost of patients waiting and doctors’ idle time.

One way to balance the practice workload is to schedule group, routine, or repeat appointments during slow hours. For instance, pediatric well-child visits or patients with a particular chronic disease—such as congestive heart failure or diabetes—could be scheduled for early mornings when there are typically fewer patients waiting in line. These scheduled visits include educational components and often involve multidisciplinary teams. It also helps save time since standard advice need not be repeated to individuals, improving on the efficiency of care delivery (Jones et al., 2019). Patients also benefit from the socialization aspect of group visits; members encourage one another, exercise together, and so forth. A good scheduler allows a repeat appointment schedule subject to total frequency and time slot constraints.

Compliance Process

An integrated scheduler verifies the filing of a signed patient consent form—and, in certain cases, a signed ABN form. An ABN (Advance Beneficiary Notice) serves three goals:

• To protect the beneficiaries from liability for services denied as not reasonable (depends on the frequency or duration) and necessary (depends on the diagnosis and the provider’s specialty)
• To protect the provider’s revenue by shifting financial liability for denied services to the patient
• To provide documentation for a Medicare audit

For more complex procedures, the scheduler warns the front office about the need to obtain all required diagnostic test results and clearances up front.

Billing Interface

The integrated scheduler avoids unbillable patient encounters and reconciles visits with patient balances. It checks outstanding patient balances and verifies coverage and eligibility at the point of scheduling before the appointment. AI-driven computerized coding and billing systems can accurately provide the code for a particular disease condition and help with appropriate automated billing (Venkatesh et al., 2023). In many cases, such a test discovers data entry errors too, reducing the payment cycle at later stages.

Additionally, the insurance company may require referrals or separate pre-authorization/certification for certain procedures, refusing the payment if the procedure was performed without a referral or preauthorization. The integrated scheduler can access medical records to supply necessary background and diagnosis information to obtain pre-authorization. Finally, without the ability to reconcile visits with payments, the practice owner cannot be sure that every visit resulted in a payment.

Practice Flow Interface

The integrated scheduler manages the entire patient flow, continuously updating arrival lists, checkoffs, and office/room tracking. Further, the scheduler tracks no-shows and follow-up actions. Detailed reports include daily schedules, load reports, missed appointments, free time, canceled appointments, etc.

With AI-integrated schedulers, different color codes and status flags can be used for different appointment types, whether emergencies or routine or based on the specialist to be seen by the patient. This offers a good visually appealing summary by just glancing over the appointments for a particular day or session. There can also be alarm systems such as a screen pop-up or a beeping sound for any appointment that has run beyond the set time. This helps reduce overtime and spill-overs, except when it is indispensable. A good scheduler will also have a waiting list, which ensures that for any canceled appointments, the slots can quickly be filled by simply reaching out to the next client on the waiting list (Unitek College, 2022).

Patient Interface

Patient self-scheduling through online platforms ensures round-the-clock access to medical services since they can access the calendar from their mobile devices. A retrospective study by North et al. on the impact of web-based self-scheduling for well-child clinics showed that about 30% of the appointments were made outside the official business hours, adding to the convenience of self-scheduling (North et al., 2021 ) However, data driven AI tools must be integrated to ensure automatic update of the calendar and availability to prevent double booking for the same time slots (Kasle, 2018). Patient self-scheduling also prevents patient leakage, the scenario in which patients abandon one provider for the next due to abandonment during scheduling. The abandonment can be in the form of prolonged call waiting times or no response from the call center. This can be very frustrating and may result in patients seeking services from a different provider (Interactions LLC, 2022). Another aspect of the patient interface that is useful during scheduling is the utility of conversational AI, where patients get real-time feedback from virtual assistants, such as FAQ sections and available calendar slots during scheduling (Lee, 2023).

Advanced schedulers include appointment reminders and provide online registration and online scheduling request forms. Missed appointments result in inefficiencies in care delivery, delays in care delivery, and timely diagnosis. A leading cause of missed appointments is forgetfulness (Gurol-Urganci et al., 2013). Advanced computerized schedulers enable patients to receive reminder alerts on their phones, minimizing the rates of missed appointments.

Online scheduling request forms must consider the risk of scheduling patients beyond the scope of the practice, which a live operator would have screened out. The concerns of patient privacy must also be addressed, and any online scheduler should meet the regulatory standards set by HIPAA and FDA (Hall et al., 2014)

Summary

Artificial intelligence and machine learning form the core of any efficient computerized integrated scheduling system since it can automatically provide predictive patterns for the future based on the data fed to it. An ideal computerized integrated scheduling system improves provider efficiency, reduces losses incurred, and improves patients’ satisfaction as well as shortens the billing cycles. It also makes it possible to implement a preferential scheduling policy to the patient’s satisfaction.

References

1. J. Zhang, L. Wang, and L. Xing, (2019). Large-scale medical examination scheduling technology based on intelligent optimization. Journal of Combinatorial Optimization, 37(1): 385–404.
2. CAHPS Database Online Reporting System (2016). Comparative data from the 2016 Clinician & Group Survey Database: https://www.ahrq.gov/cahps/cahps-database/cg-database/index.html
3. Forjuoh SN, Averitt WM, Cauthen DB, et al, (2001). Open-access appointment scheduling in family practice: comparison of a demand prediction grid with actual appointments. J Am Board Fam Pract, 14(4):259-65.
4. Hu M., Xu X., Li X., Che T (2020). Managing patients’ no-show behavior to improve the sustainability of hospital appointment systems: Exploring the conscious and unconscious determinants of no-show behavior. Journal of Cleaner Production. 269
5. Jones T., Darzi A., Egger G., et al, (2019). Process and systems: A systems approach to embedding group consultations in the NHS. Future Healthc. J. 6:8-16.
6. Venkatesh KP, Raza MM, Kvedar JC, (2023). Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med, 3;6(1):16.
7. Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, et al, (2013). Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2013 5;2013(12):CD007458.
8. Chen, P. S., Hong, I. H., Hou, Y., et al., (2019). Healthcare scheduling policies in a sequence-number-based appointment system for outpatients’ arrivals: Early, on time, or late?. Computers & Industrial Engineering, 130, 298-308.
9. Kasle, M, (2018). Improving Online Self-Scheduling with Analytics. Blog post: https://www.chiefhealthcareexecutive.com/view/improving-online-selfscheduling-with-analytics.
10. North F, Nelson M, Majerus R, et al., (2021). Impact of Web-Based Self-Scheduling on Finalization of Well-Child Appointments in a Primary Care Setting: Retrospective Comparison Study. JMIR Med Inform, 9(3), e23450.
11. Interactions LLC, (2022).  Self-scheduling can Reduce Patient Leakage and Improve Patient Experience. Blog post: https://www.interactions.com/blog/industry/self-scheduling-can-reduce-patient-leakage-and-improve-patient-experience/
12. Lee J, (2023). The impact of conversational AI on healthcare outcomes and patient satisfaction. Blog post: https://www.datasciencecentral.com/the-impact-of-conversational-ai-on-healthcare-outcomes-and-patient-satisfaction/
13. Hall JL, McGraw D., (2014). For telehealth to succeed, privacy and security risks must be identified and addressed. Health Aff (Millwood), 33(2):216-221.
14. Unitek College, (2022). Step-by-Step Guide to Medical Appointment Scheduling. Blog post: (https://www.unitekcollege.edu/blog/a-step-by-step-guide-to-medical-appointment-scheduling/).
15. Medical Group Management Association, Performance and Practices. Accessed 26/06/2023 3:38 pm :https://www.mgma.com/

A Future Book Publication Note:

This article is a chapter in the forthcoming 2nd Edition book “Medical Billing Networks and Processes,” authored by Dr. Yuval Lirov and planned for publication in 2024. We will post more chapters on this blog soon.