Abstract
BACKGROUND:
Specialty medications pose unique prescribing challenges, including complexities with drug dosing and safety monitoring, need for prior authorization and patient financial assistance, use of payer-mandated specialty pharmacies, and special requirements for storage and handling. These challenges can lead to higher rates of represcribing for specialty clinic providers, who may be operating with inadequate information or limited resources. Multistep order transmittal (MSOT) is an electronic medical record (EMR) prescription work queue functionality used by a specialty pharmacy service to support select ambulatory clinics.
OBJECTIVE:
To explore the relationship between an MSOT specialty pharmacy support service and represcribing burden of specialty medications for providers in an ambulatory care setting.
METHODS:
A retrospective cohort pilot study was performed before and after MSOT implementation. Prescription data were queried from Sanford Health’s EMR for patients who were electronically issued at least 1 prescription for an injectable biologic medication at a dermatology and rheumatology clinic. The pre-intervention group included index prescriptions prescribed between October 1, 2017, and June 20, 2018. The post-intervention group included index prescriptions prescribed between October 1, 2018, and June 20, 2019. Retrospective EMR review was completed to identify any prescriptions that were represcribed and the reason for represcribing. The primary outcome was the rate of represcribing. Secondary endpoints explored reason for represcribing, when it occurred. Nominal data were compared using Pearson’s chi-square tests. Regressions were performed to account for potential confounders.
RESULTS:
The pre-intervention group included 880 index biologic prescriptions, and the post-intervention group included 941 index biologic prescriptions. The aggregate represcribing rate decreased from 12.73% in the pre-intervention group to 9.56% in the postintervention group (P = 0.03). Represcribing directly by providers as a result of needing to modify the destination pharmacy decreased significantly from 6.25% of the pre-intervention group to 0.64% of the post-intervention group (P < 0.01). However, represcribing due to patient preferences and prescribing errors increased significantly between the pre- and post-intervention groups, with patient preferences increasing from 0.91% to 2.55% (P = 0.01) and prescribing errors from 0.68% to 1.70% (P = 0.05), respectively. Represcribing due to provider preferences and payer restrictions remained similar between groups.
CONCLUSIONS:
Within 2 ambulatory care clinics in an integrated health care system, the use of pharmacist-managed MSOT reduced overall represcribing burden. The need to represcribe owing to wrong destination pharmacy was nearly eliminated, while appropriate represcribing to accommodate patient preferences and correct prescribing errors increased.
Plain language summary
Specialty drugs have become popular in recent years. Third-party payers require specific specialty pharmacies to fill these medications. A pharmacist-managed work queue functionality supports providers in the background by investigating insurance restrictions as soon as the prescription is generated to allow patients to know and decide which pharmacy to use for their prescription. This work decreased the need for providers to resend the prescription multiple times to out-ofnetwork pharmacies.
Implications for managed care pharmacy
Ambulatory care clinics operating with limited support staff may find it beneficial to implement a pharmacist-managed work queue functionality to assist the team with investigating third-party payer restrictions, which include payer-mandated specialty pharmacy network and preferred formulary medication(s). Decreased represcribing burden may reserve more resources available for quality patient care. This specialty pharmacy support service functionality may add value in the collaborative approach to foster a more seamless patient care continuum in the specialty landscape.
Innovative specialty therapies such as biologics have transformed the management of inflammatory autoimmune diseases, with associated drug spending that has grown rapidly in recent years. From 2006 to 2017, Medicare Part B drug spending for biologics has increased from 56% to 77%, accounting for 92% of overall drug spending growth during those years.1 These specialty medications present unique prescribing challenges to health care providers in terms of complex dosing and safety monitoring, requirements for prior authorizations and copay assistance, and payer restrictions for specific specialty pharmacies to acquire, handle, and dispense the prescription.2-4
Given the prescribing burdens associated with specialty therapies, specialized pharmacy support is needed for health care providers and their clinic staff who are tasked with navigating these complexities with limited resources. Previous studies have shown improved patient outcomes with specialty pharmacy support services to achieve higher rates of prior authorization approval, improve adherence and safety monitoring, and lower risk of disease relapse.5-12 A previously unstudied yet still burdensome aspect of specialty prescribing is that providers must often represcribe a medication with a modified destination pharmacy, medication type, dose, or quantity in order to gain insurance authorization.3,4 Although the concept of reducing represcribing appears to be highly valued by primary care physicians,13 data are lacking regarding the impact of specialty pharmacy support services on reducing provider represcribing of specialty medications.
Multistep order transmittal (MSOT) is an electronic medical record (EMR) prescription transmittal queue implemented by a specialty pharmacy service to support select ambulatory clinics within the Sanford Health integrated delivery network (IDN). When targeted specialty medications are prescribed electronically, the prescriptions are initially sent to an MSOT work queue for pharmacist review before being released to the destination pharmacy. Within the MSOT queue, the pharmacist reviews each prescription for errors, performs a pharmacy benefits investigation to detect the need for prior authorization or change in therapy due to formulary restrictions, and assesses whether the selected destination pharmacy is authorized by the third-party payer to dispense the specialty medication. To preserve the patient’s choice at all times, proposed changes are discussed thoroughly with the patient, who ultimately makes the informed decision on which destination pharmacy to receive their prescription. With patient consent, the pharmacist seamlessly modifies the destination pharmacy without needing the provider or their clinic staff to regenerate, or represcribe, another prescription.
We sought to determine the potential association between a pharmacist-managed MSOT specialty support service and represcribing burden for providers in an ambulatory care setting. By implementing an EMR work queue functionality that allows the pharmacy staff to investigate payer-mandated destination pharmacy restrictions prior to prescription transmittal, we hypothesized that the burden for clinic providers to represcribe would decrease.
Methods
A retrospective cohort study was performed before and after MSOT implementation. Prescription data between October 1, 2017, and June 20, 2019, were obtained from Sanford Health’s EMR database for patients who were electronically issued at least 1 prescription for an injectable biologic medication at the 2 Sanford Health dermatology and rheumatology ambulatory care clinics. To focus on unique implications of new therapy initiations rather than existing therapies, only the first encounter with the first biologic prescription for each patient within each study time frame was included for analysis, referred to as the index prescription. Index prescriptions were validated through manual chart review. Data collected included patient characteristics, prescription date, prescribed biologic name, dose, directions, quantity, refills, selected dispensing pharmacy, authorized provider, and ambulatory clinic (Table 1). For the patient corresponding to each index prescription, demographic characteristics collected were age, sex, race, and presence of comorbid conditions including hypertension, congestive heart failure, hyperlipidemia, diabetes mellitus, chronic kidney disease, depression, stroke, and cancer. Retrospective chart review was completed to identify the main represcribing reasons, including modification of destination pharmacy, patient preferences or factors, prescribing errors, payer or drug manufacturer requirements, and provider preferences. All represcribing reasons were reviewed by 2 study investigators to ensure interrater reliability. Destination pharmacy was defined as the specialty pharmacy to receive and dispense the prescribed specialty prescription to the patient. MSOT functionality only allowed pharmacy staff to modify the destination pharmacy, whereas all other changes to the prescription were strictly prohibited and would require the provider to represcribe.
TABLE 1.
Patient Characteristics and Index Biologic Prescriptions
| Pre-intervention group | Post-intervention group | P value | |
|---|---|---|---|
| Patient prescriptions eligible for study inclusion, n | 880 | 941 | |
| Rheumatology | 743 | 757 | |
| Dermatology | 137 | 184 | |
| Patient characteristics | |||
| Age, years, mean (SD) | 50.2 (12.8) | 50.3 (13.0) | |
| < 34, n (%) | 101 (11.5) | 109 (11.6) | |
| 34-49, n (%) | 276 (31.4) | 294 (31.2) | |
| 50-64, n (%) | 416 (47.3) | 434 (46.1) | |
| ≥ 65, n (%) | 87 (9.9) | 104 (11.1) | |
| Male sex, n (%) | 342 (38.9) | 375 (39.9) | |
| Race, n (%) | |||
| African American or Black | 5 (0.6) | 4 (0.4) | |
| American Indian or Alaska Native | 46 (5.2) | 44 (4.7) | |
| Asian | 11 (1.3) | 9 (1) | |
| Caucasian or White | 815 (92.6) | 881 (93.6) | |
| Hispanic or Latino | 1 (0.1) | 2 (0.2) | |
| Native Hawaiian or Pacific Islander | 1 (0.1) | 0 (0) | |
| Unknown | 1 (0.1) | 1 (0.1) | |
| Cancer, n (%) | 101 (11.5) | 94 (10) | |
| Chronic kidney disease, n (%) | 48 (5.5) | 44 (4.7) | |
| Congestive heart failure, n (%) | 29 (3.3) | 26 (2.8) | |
| Depression, n (%) | 252 (28.6) | 264 (28.1) | |
| Diabetes mellitus, n (%) | 128 (14.5) | 117 (12.4) | |
| Hyperlipidemia, n (%) | 288 (32.7) | 309 (32.8) | |
| Hypertension, n (%) | 280 (31.8) | 302 (32.1) | |
| Stroke, n (%) | 16 (1.8) | 18 (1.9) | |
| Index biologic prescription, n (%) | |||
| Abatacept | 70 (8.0) | 62 (6.6) | 0.73 |
| Adalimumab | 383 (43.5) | 408 (43.4) | |
| Anakinra | 3 (0.3) | 2 (0.2) | |
| Belimumab | 3 (0.3) | 5 (0.5) | |
| Certolizumab pegol | 20 (2.3) | 29 (3.1) | |
| Dupilumab | 10 (1.1) | 13 (1.4) | |
| Etanercept | 217 (24.7) | 211 (22.4) | |
| Golimumab | 17 (1.9) | 31 (3.3) | |
| Guselkumab | 4 (0.5) | 7 (0.7) | |
| Ixekizumab | 7 (0.8) | 11 (1.2) | |
| Mepolizumab | 0 (0.0) | 1 (0.1) | |
| Sarilumab | 6 (0.7) | 8 (0.9) | |
| Secukinumab | 71 (8.1) | 84 (8.9) | |
| Tocilizumab | 48 (5.5) | 46 (4.9) | |
| Ustekinumab | 21 (2.4) | 23 (2.4) | |
The primary outcome was the represcribing rate before and after implementation of pharmacist-managed MSOT for ambulatory care clinics. Represcribing was defined as another electronic prescription generated from the same ambulatory clinic for the same patient for any injectable biologic medication for the same therapeutic indication within 14 days following the index biologic prescription. Secondary endpoints explored represcribing rates related to 5 main represcribing reasons (Table 2, Table 3). The mean and SD were calculated for continuous measures. Pearson’s chi-square tests were performed for nonparametric statistical analyses of nominal data. Multiple logistic regression analyses were also performed to account for potential confounders in patient characteristics, comorbidities, and clinic site to confirm the findings from the chi-square tests performed on outcomes of interest. Data were analyzed using JMP software, version 15.1.0 (SAS Institute Inc., Cary, NC), with a P value of < 0.05 considered statistically significant.
TABLE 2.
Represcribing Rates by Ambulatory Clinic and Represcribing Reason
| Pre-intervention group | Post-intervention group | P value | |
|---|---|---|---|
| Patient prescriptions eligible for study inclusion, n | 880 | 941 | |
| Rheumatology | 743 | 757 | |
| Dermatology | 137 | 184 | |
| Represcribing rate by ambulatory clinic, n (%) | |||
| Rheumatology + dermatology | 112 (12.73) | 90 (9.56) | 0.03 |
| Rheumatology | 91 (12.25) | 64 (8.45) | 0.02 |
| Dermatology | 21 (15.33) | 26 (14.13) | 0.76 |
| Represcribing rate by represcribing reason, n (%) | |||
| Modification of destination pharmacy | 55 (6.25) | 6 (0.64) | < 0.01 |
| Patient preferences or factors | 8 (0.91) | 24 (2.55) | 0.01 |
| Prescribing errors | 6 (0.68) | 16 (1.70) | 0.05 |
| Provider preferences | 29 (3.30) | 37 (3.93) | 0.47 |
| Payer or drug manufacturer restrictions | 14 (1.59) | 7 (0.74) | 0.09 |
TABLE 3.
Represcribing Reasons
| Pre-intervention group | Post-intervention group | |
|---|---|---|
| Modification of destination pharmacy, n (%) | 55 (49.1) | 6 (6.7) |
| Biologic prescription initially sent to a non-payer-mandated pharmacy that was unable to process the claim; thus, the prescription was re-sent to a payer-mandated destination pharmacy | 55 | 6 |
| Patient preferences or factors, n (%) | 8 (7.1) | 24 (26.7) |
| Patient preference for a different biologic agent | 0 | 2 |
| Days supply (eg, preference for 30- vs 90-day supply) | 2 | 1 |
| Drug formulation (eg, preference for prefilled pen vs prefilled syringe) | 3 | 6 |
| Patient override for non-payer-mandated destination pharmacy | 1 | 11 |
| Change of prescription drug plans (eg, new plan selection for new year) | 2 | 4 |
| Prescribing errors, n (%) | 6 (5.4) | 16 (17.8) |
| Duplicate prescription requested (eg, transmittal error, prescription deleted erroneously, electronic prescription required instead of faxed) | 4 | 5 |
| Clarification for unclear or incorrect directions | 2 | 7 |
| Clarification for quantity written and refills authorized | 0 | 1 |
| Clarifications for dosing (eg, inappropriate loading vs maintenance doses ordered) | 0 | 3 |
| Provider preferences, n (%) | 29 (25.9) | 37 (41.1) |
| Dose changes | 3 | 1 |
| Therapy changes | 3 | 5 |
| Separate prescriptions for loading then maintenance doses | 14 | 19 |
| Bridge therapy while trying to get another biologic therapy approved | 0 | 1 |
| Small supply initially, then represcribed after follow-up office visit or lab(s) complete | 5 | 8 |
| Small supply initially, then represcribed once tolerability to therapy has been assessed | 4 | 3 |
| Payer or drug manufacturer restrictions, n (%) | 14 (12.5) | 7 (7.8) |
| Dosage restrictions (payer) | 3 | 0 |
| Drug restrictions (payer) | 5 | 5 |
| Product replacements (drug manufacturer) | 1 | 1 |
| Patient assistance programs (drug manufacturer) | 1 | 1 |
| PA process (eg, prescription re-sent with added PA details once PA approved) | 4 | 0 |
| Total represcribing incidents per group, n (%) | 112 (100) | 90 (100) |
PA = prior authorization.
The study was reviewed and approved by the health system institutional review board with a waiver for informed consent.
Results
The pre-intervention group included 880 index prescriptions, prescribed between October 1, 2017, and June 20, 2018. The post-intervention group included 941 index prescriptions, prescribed between October 1, 2018, and June 20, 2019. A full summary of results is included in Table 2. Represcribing with another electronic prescription for any biologic medication within 14 days from the index prescription decreased from 112/880 (12.73%) of the pre-intervention group to 90/941 (9.56%) of the postintervention group (P = 0.03). When separated by clinic, rheumatology had a statistically significant decrease in rate of represcribing, from 91/743 (12.25%) of pre-intervention to 64/757 (8.45%) of post-intervention (P = 0.01). Represcribing rates at the dermatology clinic, however, remained similar with 21/137 (15.33%) of pre-intervention compared with 26/184 (14.13%) of post-intervention (P = 0.76).
Regression analysis confirmed a relationship between group (preintervention vs post-intervention) and incidence of represcription (P = 0.02). The only other variables in the regression model indicating a significant association with the outcome of represcription were clinic site (dermatology vs rheumatology) and diagnosis of cancer (P = 0.02 and P = 0.03, respectively). Presence of a cancer diagnosis and rheumatology clinic site both appeared to be associated with a decreased represcription rate overall.
Secondary endpoints revealed that represcribing due to modification from nonpayer-mandated to payer-mandated specialty pharmacy decreased significantly from 55/880 (6.25%) of pre-intervention group to 6/941 (0.64%) of the post-intervention group (P < 0.01). Represcribing due to patient preferences increased significantly from 8/880 (0.91%) of the pre-intervention group to 24/941 (2.55%) of the post-intervention group (P = 0.01). Represcribing due to prescribing errors statistically increased from 6/880 (0.68%) in the pre-intervention group to 16/941 (1.70%) in the post-intervention group (P = 0.05). Represcribing due to provider preferences and payer restrictions were similar between groups, with provider preferences resulting in the need to represcribe in 29/880 (3.30%) of the pre-intervention group compared with 37/941 (3.93%) of the postintervention group (P = 0.47), and payer restrictions requiring represcription constituted 14/880 (1.59%) of the preintervention group compared with 7/941 (0.74%) of the post-intervention group (P = 0.09).
Descriptive statistics for specific represcribing reasons are depicted in Table 3. The greatest observed difference between subgroups of represcribing reasons appeared to be a higher number of patient overrides for non-payer-mandated destination pharmacies in the post-intervention group than in the pre-intervention group (11 incidents or 12.2% vs 1 incident or 0.9%, respectively).
Discussion
To our knowledge, this is the first report to describe the association between a pharmacist-managed EMR prescription transmittal work queue functionality on biologic agent represcribing. Implementation of pharmacist-managed MSOT for specialty electronic prescriptions appeared to be linked to a more streamlined prescribing process for providers and their support staff.
As suspected for our primary outcome, the statistically significant decrease in the overall rate of represcribing for providers was largely driven by the functionality to modify the destination pharmacy within the work queue. Prior to MSOT implementation, electronic specialty prescriptions at the 2 studied ambulatory care clinics were essentially issued to the patient’s desired local pharmacy, without knowing exactly which pharmacy was payer mandated to dispense. Over the next few days to weeks following the index prescription, clinic resources were dedicated to facilitate communication and documentation of encounters with the patient, involved pharmacies, and third-party payer to decipher the contracted specialty pharmacy to receive the reissued prescription. If the payer’s preferred formulary drug was known, additional provider time was required to clinically assess the need to represcribe to a different biologic agent or to continue pursuing prior authorization of the originally prescribed therapy. Based on nursing reports at the 2 studied clinics, the combined tasks encompassing the generation of a duplicate prescription to a payer-mandated pharmacy required 26 to 35 minutes, and the generation of a new biologic prescription based on insurance formulary requirements required 41 to 50 minutes, which we feel represents a meaningful time burden for providers, support staff, patients, and health systems alike.
This workflow not only added confusion for patients with a limited baseline understanding of the specialty prescription process but also raised the task burden and number of work interruptions for providers and clinic staff. In a time and motion study by Westbrook et al, clinicians appeared to spend reduced time on clinical tasks if they experience interruptions and may delay or fail to return to 18.5% of interrupted tasks.14 We believe that our observed reduction of represcribing events may allow providers and their staff to have fewer interruptions to direct patient care activities and other administrative tasks, helping to improve efficiency and provide quality patient care.
The operational hardships around specialty pharmacy medications already create dissatisfaction among patients and providers, and further separation of the patient with an out-of-state specialty pharmacy creates additional barriers to timely delivery and therapy initiation.15 To minimize interruptions for providers after prescription clarifications were made, represcriptions related to prescribing errors within our study were often queued by the pharmacist for the provider to review and release at their discretion. For future direction, a collaborative practice agreement could be incorporated with select ambulatory care clinic providers to delegate this task to specified pharmacists when appropriate represcribing is necessary.
Efforts to prevent prescription errors can have a profound impact on patient safety. Although prescription errors can occur during any stage of the medication use process, existing literature suggests it is most common at the ordering stage, which should be the most targeted stage for error prevention strategies.16 With a unique opportunity within the work queue for pharmacists to review the specialty prescription immediately after order placement, but prior to transmission to its intended destination, we observed a significant increase in capture of prescribing errors requiring clinical interventions with providers after MSOT implementation, thus resulting in necessary represcribing. Although not directly evaluated in this study, it could be argued that the increased rate of necessary represcribing due to errors captured has a potential to improve patient safety. Additionally, an order verification and preapproval process in our outpatient specialty pharmacy setting was not well established. By minimizing both the quantity of erroneous prescriptions and the transmission of prescriptions to nonpayer-mandated pharmacies, we feel it could lessen the burden for involved pharmacies to make necessary clarifications with providers as well as perform closed-loop communication with patients and clinics regarding the need for represcription. However, further studies are needed to confirm theses hypothesized benefits.
Of note, real-time patient discussions that took place to obtain consent to modify the destination pharmacy per payer restrictions resulted in a statistically significant increase in represcribing due to patient overrides to proceed with the originally selected pharmacy. Under these circumstances, the pharmacist would release the prescription as desired by the patient. However, subsequent prescriptions were often reissued directly to payer-mandated specialty pharmacies shortly after the patients were unsuccessful with their preferred pharmacies. Although these real-time patient discussions posed an increased risk for represcribing, we believe in the value of preserving patient choice and continue to fully support this practice within our IDN.
LIMITATIONS
Our study has limitations that should be considered. First, our cohort included only 2 ambulatory care clinics. To reflect most ambulatory care settings with varying levels of available resources, we specifically included 2 clinics that operated with contrasting practice models for patient access. Specifically, whereas the rheumatology clinic used a centralized practice model supported by an established patient access specialist dedicated to navigating prior authorizations and other specialty prescription-related needs, this work is delegated to individual nurses to manage independently at the dermatology clinic in a decentralized practice model.
Second, our control group was a historical cohort. Although no changes were made, we cannot exclude other factors introduced between the time frames that could have affected the represcribing rate. Regression analyses were performed to confirm our observed associations and assess for confounders, where diagnosis of cancer was identified as a potential contributor to decreased represcription rate. This finding is of unclear origin and requires further study. Additionally, we attempted to control for potential confounders by including the same time of year within each cohort, as represcribing may be influenced by early vs late in the year with insurance plan preferred formulary changes. We also specifically included index prescriptions for biologic agents to evaluate the common challenges of navigating payer restrictions when a patient is prescribed a specialty therapy for the first time. Even though this specialty pharmacy service was implemented seamlessly within our IDN, reproducibility and generalizability of these results may be limited in settings not supported by EPIC electronic health record software (Epic Systems Corporation) or with restricted access to patients’ EMRs in general. Although workflows should be adapted to each unique practice setting, patient choice should always be preserved.
Conclusions
Within an IDN, the use of pharmacist-managed MSOT for electronic specialty prescriptions reduced represcribing burden for health care providers at ambulatory care clinics, almost eliminating the need to represcribe due to an inappropriate destination pharmacy (payer-mandated pharmacy not selected). Future studies should examine the influence of collaborative practice agreements on represcribing patterns for specialty medications at the clinic or provider level, potential for advanced pharmacy technician roles to support specialty pharmacy services with MSOT functionality, MSOT functionality within additional disease states such as oncology, and the potential impact of an MSOT work queue for specialty prescriptions on patient safety.
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