Optimizing Revisit Intervals: Reducing Variability to Enhance Health Care Efficiency

ABSTRACT

Physicians often rely on follow-up appointments to help patients achieve their health care goals. This is particularly true of primary care, where physicians rely on longitudinal care practices to manage chronic illnesses such as diabetes and hypertension. However, although patients are often scheduled to return for a follow-up visit in 3 to 6 months, there is little evidence supporting these recommendations. In other words, revisit interval (RVI) assignment is often left exclusively to the provider’s discretion. The lack of standards means RVIs may vary from physician to physician, impacted by subjective factors such as provider sex, geographical location, clinical heuristics, and administrative practice patterns. This inconsistency has serious implications. Scheduling revisits too frequently may result in resource overuse and increased administrative burden. Conversely, waiting too long before revisits may result in discontinuity of treatment, decreased physician-patient rapport, and, subsequently, suboptimal patient outcomes. The first and foremost step in ameliorating this issue involves investigating the relationship among RVIs, patient outcomes, and cost of care.

Collecting data on the most efficacious RVIs for patients with varying disease states and severities will allow the development of evidence-based guidelines for RVI assignment. The garnered information could then be used to establish an algorithm capable of recommending optimal RVI based solely on patient characteristics. By eliminating variability in RVI assignment, unnecessary health care costs associated with resource overuse could be reduced and patient health outcomes enhanced.

Am J Manag Care. 2025;31(10):In Press

Takeaway Points

We explore the limitations of the current system of revisit interval (RVI) assignment and discuss the importance of standardization to optimize patient health care outcomes.

• Standardized RVI guidelines: Reducing variability in RVIs can optimize resource allocation and reduce administrative burden.
• Improved patient care: Evidence-based RVIs can ensure timely follow-ups, enhancing continuity of care and patient outcomes.
• Cost efficiency: Minimizing unnecessary visits lowers health care costs and improves system efficiency.
• Artificial intelligence implementation: Using algorithms for RVI assignment can standardize care, reduce biases, and streamline decision-making.

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A

lthough continuity of care has significant importance within the health care field, there is a lack of established guidelines for assigning revisit intervals (RVIs). Both medical training and literature are lacking in discussions regarding the ideal timing of RVIs for patients with varying conditions. The lack of guidelines leaves RVI assignment vulnerable to various external factors, including physician identity, availability, and care practices. This variability can lead to unnecessarily short RVIs, which increase costs without improving outcomes, or overly long intervals, which risk delayed care. Given the undeniable implications of RVI variability, the need for further research into standards is apparent. Objectifying the physician decision-making process using an algorithm-based approach may help optimize RVIs, ultimately improving patient outcomes and minimizing cost.

Factors That Contribute to RVI Assignment Variability

Given the absence of standardized recommendations, RVIs are subject to physician heuristics. Findings from a retrospective cohort study conducted by Frohlich et al revealed that non–patient-related factors, such as physician practice and referral patterns, accounted for approximately 80% of the explainable variance in RVI assignment by a cohort of 157 physicians.¹ They also found that more than 80% of patients visited their physicians more often than recommended by hypertension management guidelines, and this rate varied significantly among physicians.¹ Thus, it is evident that sole reliance on physician judgment can be unreliable.

Physician identity is an integral parameter contributing to RVI assignment variability. A provider’s personal values, clinical background, approach to decision-making, and demographic attributes can shape their approach to RVI assignment. One such critical demographic component is the physician’s sex. A univariate analysis, conducted as part of a 2003 study by DeSalvo et al, revealed a significant difference in RVI assignment between male and female practitioners, with male physicians assigning a mean of 13.1 weeks between visits and female physicians assigning a mean of 9.9 weeks.² The observed variance could be attributed to the tendency of women to utilize more involved preventive health care practices.²

Geography may also play a role in RVI assignment. Systemic health care overuse phenotypes have been exhibited in metropolitan areas, whereas their rural counterparts tend to experience low systemic overuse. It can be deduced that a provider would make clinical decisions reflective of the region in which they practice.³

Given the nationwide physician shortage in the US, it is key to consider the role of provider availability.⁴ For example, consider the bidirectional relationship between hectic days and RVIs. Physicians may feel strapped for time on hectic days and thus may set shorter RVIs to tie up loose ends with their patients and achieve health maintenance goals. Reciprocally, physicians who set shorter RVIs are more likely to fill their appointments and double-book patients, leading to burdensome schedules. Thus, varying administrative practices may interfere with an objective approach to RVI assignment.

Last-case bias may contribute to variance, as physicians are often influenced by the relative success of prior patient outcomes. For example, a negative outcome may influence the physician to adopt a different style of care for their next patient due to heightened concerns about malpractice litigation and altered tolerance to diagnostic uncertainty.⁵ This is an example of the ways in which medicolegal implications may affect physician decision-making.

The Role of Virtual Care

The rise of virtual medicine in recent years may significantly impact evidence-based RVIs. Physicians can now utilize telehealth encounters, electronic health record (EHR) portal messaging, and phone communication in addition to traditional in-person follow-up visits. Reducing the frequency of physical visits lowers administrative costs and overhead while ensuring continuity of treatment and bolstering physician-patient rapport.⁶ This approach also allows providers to manage a larger patient load while ensuring accessible, quality care. By substituting in-person visits with virtual alternatives, physicians can extend the time between physical appointments, increasing RVI. Further, EHR and telehealth platforms facilitate large-scale collection of patient data, including treatment response and visit frequency, allowing for continuous adjustment of RVI guidelines. However, inconsistent adoption of telehealth, where some physicians have integrated it and others remain more apprehensive, introduces variability in RVI assignment.

Implications of RVI Variability

The implications of varying RVIs are considerable (Table). From a health care delivery perspective, the quality of care offered to a patient is limited by the ratio between physician availability and scheduled patient visits. In other words, the amount of time physicians can devote per patient may be limited by the number and duration of appointment slots. Consider a physician who sets an RVI time of 6 months vs 12 months. In the first case, the physician will be able to see half the number of patients but, in theory, will be able to allocate twice as much time per patient. Although this method offers greater individual attention and improved physician-patient rapport, it is important to consider that a greater number of clinic visits may be associated with increased utilization of resource-intensive care practices, such as radiologic testing.⁵ In fact, regions with more frequent outpatient visits have been found to have higher expenditure indices than those with fewer outpatient visits per patient.⁷ Conversely, excessive time between visits can result in disrupted continuity of care and reduced emphasis on preventive measures and chronic disease management.⁸ Prolonging the time between routine primary care visits may also cause patients to develop an incorrect perception of reduced access to their provider. Consequently, they may seek medical care from an urgent or specialty care facility, leading to compromised health outcomes and increased burden on hospital systems.⁷

Recommendations

Considering the numerous factors that influence RVI assignment, there is a clear need for standardization to optimize this process. The first step toward accomplishing this objective involves investigating the correlation among RVIs, patient outcomes, and the associated costs and administrative burden. Conducting outcome and cost-benefit analysis as part of comparative patient cohort studies would provide us with the data needed to develop evidence-based guidelines for RVI assignment. Correlating these results with patient satisfaction surveys would provide a holistic, patient-centered perspective on the effectiveness of different RVIs for individuals in varying risk brackets.

An algorithm could then be employed to prioritize variables affecting RVI assignment, such as patient vital parameters, past medical history, and medical practitioner discretion. A similar computerized nonlinear optimization method was applied to a bladder cancer diagnosis setting by Kent et al.⁹ Analysis revealed that computer-calculated scheduling reduced the delay in detecting potentially malignant tumors by 2 weeks.⁹ Likewise, in their 2021 paper, Ala et al supported the use of the Whale Optimization Algorithm to streamline current hospital appointment scheduling models.¹⁰ It is thus evident that algorithms hold immense potential to beneficially standardize RVI assignments.

The clinical decision analysis model, using decision trees and Bayesian probabilities, provides a structured, evidence-based approach to RVI standardization.¹¹ The final step encompasses sensitivity analysis and an evaluation of variables pertinent to clinical decision-making. This framework furnishes a dependable and reproducible means of presenting clinicians with objective evidence essential for making well-informed clinical decisions, aligning with the principles of evidence-based medicine, which endeavors to reduce the uncertainties in clinical decision-making by utilizing tools such as risk-benefit analysis.¹² Physician recommendations remain essential and can be incorporated into the algorithm using a scoring system, similar to Apgar or APACHE II.

The algorithm can be integrated into existing health care software, such as Epic, making it a feasible addition, given the widespread use of these systems. Privacy, security, and alignment with Health Insurance Portability and Accountability Act standards can be ensured through encryption, audit trails, and breach notifications. Additionally, the growing role of artificial intelligence in health care presents promising opportunities for optimizing RVIs, provided similar safeguards are in place.

When setting the parameters for the model, we must consider the differing goals of various specialties. Internal medicine physicians are often tasked with chronic illness management, which leads to frequent visits. Gao et al found that primary care visits initially decreased costs—$3976 for the first, $1149 for the second, and $896 for the third—until diminishing returns occurred after 10 visits.¹³ Thus, in primary care, more frequent visits improve patient outcomes up to a point. However, this phenomenon may prove false in more specialized fields such as cardiology or neurology. A study by Oakes et al found that an average of 4% of commercially insured patients in the study cohort experienced at least 1 cardiac health care overuse event within the span of a single semiannual period.³ It is vital to include branch-based heuristics within the suggested framework.

Conclusions

The goal of implementing such approaches is to minimize the confounding variables that impact RVI assignment. We must establish gold standards for RVIs, given the repercussions associated with inappropriate frequency of visits. Scheduling visits too frequently presents challenges related to resource overutilization, increased health care costs, and heightened administrative burden, whereas infrequent visits may result in delayed treatment, suboptimal medication management, and reduced patient engagement. Although objectifying the clinical decision-making process does have its limitations, it gives us a general, evidence-based framework on which to base practice approaches. We believe that establishing gold standards will help eliminate biases and ultimately improve patient health outcomes while minimizing costs.

Author Affiliations: Medical College of Georgia AV, AB, PR), Augusta, GA; Central Michigan University (NJM), Mt Pleasant, MI.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (AV, AB, NJM); analysis and interpretation of data (AB); drafting of the manuscript (AV, AB, NJM); critical revision of the manuscript for important intellectual content (AV, PR); provision of patients or study materials (AV); administrative, technical, or logistic support (AV, PR, NJM); and supervision (PR, NJM).

Address Correspondence to: Archana Venkatesan, BS, Medical College of Georgia, Augusta University, 1120 15th St, Augusta, GA 30912. Email: arvenkatesan@augusta.edu.

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