Abstract
Background:
The volume of specialty care referrals often outstrips specialists’ capacity. The Department of Veterans Affairs launched a system of Referral Coordination to augment our workforce, empowering registered nurses to use decision support tools to triage specialty referrals. While task-shifting may improve access, there is limited evidence regarding the relative quality of nurses’ triage decisions to ensure such management is safe.
Objective:
Within the specialty of sleep medicine, we compared receipt of contraindicated testing for obstructive sleep apnea (OSA) between patients triaged to sleep testing by nurses in the Referral Coordination system (RCS) relative to our traditional specialist-led system (TSS).
Methods:
Patients referred for OSA evaluation can be triaged to either home sleep apnea testing (HSAT) or polysomnography (PSG), and existing guidelines specify patients for whom HSAT is contraindicated. In RCS, nurses used a decision support tool to make triage decisions for sleep testing but were instructed to seek specialist oversight in complex cases. In TSS, specialists made triage decisions themselves. We performed a single-center retrospective cohort study of patients without OSA who were referred to sleep testing between September 2018 to August 2019. Patients were assigned to triage by RCS or TSS in quasi-random fashion based on triager availability at time of referral. We compared receipt of contraindicated sleep tests between groups using a generalized linear model adjusted for day of the week and time of day of referral.
Results:
RCS triaged 793 referrals for OSA evaluation relative to 1,787 by TSS. Patients with RCS triages were at lower risk of receiving potentially contraindicated sleep tests RR 0.52 (95%CI 0.29-0.93).
Conclusion:
Our results suggest that incorporating registered nurses into triage decision-making may improve the quality of diagnostic care for OSA.
Introduction:
Referrals to specialty care have increased in recent years,1 outpacing the growth of our specialty care workforce. This mismatch contributes to service delays and a lack of longitudinal follow-up care.2–5 The field of sleep medicine is representative of this challenge, particularly in the diagnosis and management of obstructive sleep apnea (OSA). Approximately 1 billion individuals worldwide are estimated to have OSA, the majority of whom are undiagnosed.6 In the U.S. Department of Veterans Affairs (VA), nearly 1.3 million Veterans are diagnosed with OSA and up to 50% of Veterans are at high risk for OSA.7,8 Relative to this demand, VA has just over 300 sleep specialists nationwide, a mismatch that often contributes to poor access for initial and longitudinal management of OSA.2
Given the limited pool of trained specialists,9 attention has focused on augmenting the specialist workforce by incorporating alternate care providers into roles traditionally occupied by specialists.10 Within sleep medicine, several randomized trials with follow-up ranging 1-6 months found that alternate care providers (e.g. registered nurses, respiratory therapists) can deliver care for OSA and achieve comparable patient outcomes relative to specialists.11–15 However, more than a decade after the first of these trials were published, alternate care providers are still not incorporated into widespread clinical practice. This lack of implementation is due, in part, to remaining concerns about the relative quality of alternate care providers’ decision-making outside of carefully controlled clinical trials.16
To augment specialist capacity and improve delivery of recommended care, we initiated a new team-based approach to sleep medicine referrals at our VA facility known as the Referral Coordination System (RCS).17 RCS is an integrated practice unit dedicated to triaging new sleep referrals, incorporating registered nurses, administrative staff, and supervising specialists.18 RCS shifts the initial responsibility of triage away from specialists to registered nurses.(Figure 1) RCS nurses triage initial referrals, document relevant patient information, make initial management decisions, and request specialist review when indicated. Nurses also work directly with administrative staff embedded within the team to schedule patients.
Figure 1:
Process diagram of patients referred for evaluation of OSA.
TSS-Traditional Specialist-Led System, RCS-Referral Coordination System, OSA-Obstructive sleep apnea, MD-Medical Doctor, DO-Doctor of Osteopathy, NP-Nurse Practitioner, PA-Physician’s Assistant, RN-Registered Nurse, HSAT-Home Sleep Apnea Test, PSG-Polysomnogram. *-The 306 patients with triages completed independently by nurses were propensity matched to patients in the traditional pathway. **-Symptoms were ascertained by chart review which was only performed in the propensity-matched sample.
While shifting tasks to nurses has the potential to improve provider efficiency, it is essential that such management is safe and does not detract from service quality. In the context of a Donabedian model,19 our primary goal was to compare receipt of contraindicated home sleep apnea testing between patients whose referrals were triaged by RCS relative to the traditional specialist-led system (TSS). In secondary analyses, we compared receipt of contraindicated sleep testing orders and diagnostic outcomes among the subset of patients whom nurses triaged independently relative to comparable patients triaged by TSS.
Methods:
Our cohort included consecutive patients without previously diagnosed OSA (ICD-10 G47.33) who were referred to sleep medicine at our VA Medical Center for initial evaluation of OSA. Patients referred for initial evaluation of OSA are next triaged to diagnostic sleep testing with either home sleep apnea testing (HSAT) or polysomnography (PSG).20 We identified our cohort using the VA Corporate Data Warehouse (CDW), which includes information regarding patient demographics, vital signs, diagnoses, orders and appointments. We included patients referred for initial OSA evaluation between September 2018 and August 2019. We chose this period as it was the first year in which nurses had the opportunity to triage patients to sleep tests independently as part of the RCS. Previously, RCS had run as a four month pilot where all nurse triages underwent specialist supervision, as described elsewhere.17 The Office of Veterans Access to Care approved our data collection and analyses as part of a quality improvement evaluation, exempt from institutional review board approval.
Our main exposure of interest was whether the initial referral to sleep medicine was triaged by RCS or TSS. Patients referred to VA sleep medicine were triaged either by RCS or TSS based on nurse (RCS) or specialists (TSS) availability at the time of referral. Like most VA facilities, our center uses an e-consult based system to triage new sleep referrals.21 Instead of requiring face-to-face patient visits for each referral, e-consults allow specialists to make triage decisions based on information present within the electronic medical record. In RCS, nurses completed triages and decided on sleep testing with HSAT versus PSG using a decision support tool which they developed in collaboration with our site’s sleep specialists.17 Among patients referred for OSA evaluation, this tool included guidance about patients for whom secondary specialist review was indicated (Figure 1) and recommendations about when to refer patients to HSAT or PSG.(See Supplemental Table 1 for Tool) We based this tool’s guidance for HSAT vs. PSG on the 2017 AASM Sleep Testing guidelines,20 with adaptations for center-specific resources and practices as discussed below. In addition to RCS nurses, service-line leaders also encouraged specialists to use the same decision-support tool when they triaged by themselves as part of the TSS. Patients with referrals triaged by the RCS team had subsequent care scheduled by RCS administrators regardless of whether the triage was referred by the RCS for secondary specialist review. In accordance with our standard practice, patients with TSS had scheduling arranged by our existing pool of administrators who are shared among pulmonary and sleep specialties (Figure 1).
Contextualizing Quality within the Donabedian Framework
The Donabedian model views quality in health care as being defined by three concepts: structures, processes, and outcomes.19 Structures refer to the equipment, facilities, personnel, and decision support tools that support care, which are outlined in Figure 1. Processes are the care delivery activities (e.g. testing, diagnosing) that healthcare structures perform in the delivery of care. Outcomes refer to the impact of healthcare structures and processes on patients’ health. Within this model, our primary goal in these analyses was to assess the processes of care that arise from the novel RCS structure, and use existing guidelines for OSA testing to define these assessments.20 We do not directly assess patient outcomes in this manuscript, but prior research has shown close connections between the receipt of quality-based sleep testing and improvements in patient outcomes, namely sleepiness and sleep related quality of life.22
Receipt of Potentially Contraindicated Sleep Tests in the Overall Sample
We define outcomes used to compare the quality and safety of triage between RCS and TSS groups in Table 1. Our primary outcome was receipt of potentially contraindicated home sleep apnea testing (HSAT) within 6 months of referral. Consistent with existing guidelines, we defined potential contraindications to HSAT as comorbidities or medications that increase the risk of central sleep apnea or sleep related hypoventilation.20 Furthermore, our center exclusively used peripheral arterial tonometry HSAT devices during this time period, which carry additional potential contraindications, including comorbidities and medications that reduce accuracy of these specific devices.23 We defined tests as potentially contraindicated in this setting based on diagnoses or medications outlined in Supplemental Table 2. Of note, while administrative data allows us to distinguish whether a performed sleep test was a HSAT or PSG, we cannot determine the type of test ordered during the triage process. We can only identify “potential contraindication” among those sleep tests that are actually performed. Therefore, we performed a sensitivity analysis comparing receipt of contraindicated sleep testing excluding patients who did not end up completing a sleep test.
Table 1:
Definition of primary and secondary outcomes.
|
Outcomes assessed in the overall sample (n=2580): These outcomes are defined entirely using administrative data. 1. Receipt of a potentially contraindicated sleep test (Home sleep apnea test, HSAT, despite the presence of an HSAT contraindication).* (As a sensitivity analysis, we also compared receipt of potentially contraindicated sleep testing where we excluded patients who did not complete testing) 2. Selection for independent review by referral coordination nurse.** |
|
Outcomes including the propensity-matched sub-sample (n=612): Patients with independent nurse review were propensity-matched to patients in the traditional pathway based on likelihood of independent review. Outcomes are defined by administrative data supplemented by chart reviews to determine initial orders, patient symptoms, and diagnostic impressions. 3. Receipt of a potentially contraindicated sleep test order (HSAT ordered despite the presence of an HSAT contraindication)** 4. Receipt of a potentially inappropriate sleep test order (HSAT ordered despite the presence of an HSAT contraindication OR without documentation of qualifying patient symptom)** (As a sensitivity analyses, we also compared eventual receipt of potentially contraindicated and potentially inappropriate sleep testing) 5. Patients’ eventual diagnosis (e.g. no OSA, mild OSA)** |
Legend: HSAT-Home Sleep Apnea Test, OSA-Obstructive Sleep Apnea.
-Primary Outcome.
-Secondary Outcome.
Selection for Specialist Oversight
As described in Figure 1, the decision support tool outlines indications for RCS nurses to send triages for secondary specialist review. As a secondary outcome examining quality in patient selection, we used administrative data to estimate the proportion of patients with potential indications for specialist review and whether patients were referred for such review. We defined potential indications for secondary review based on the decision-support tool, including prescriptions of long-acting opioids, body mass index (BMI) ≥ 45 kg/m2, and ICD-10 diagnosis codes for congestive heart failure, myocardial infarction, stroke, and chronic lung disease (Figure 1, Supplemental Table 1). In addition to the presence of these discrete items present in administrative data, we anticipated there would be reasons for specialist oversight documented in the chart and visible to the triaging nurse but not directly captured within our administrative dataset (e.g. severe insomnia). Finally, outside of indications outlined in the triage tool, we encouraged nurses to defer to specialists if there was ambiguity in documentation, or if nurses had subjective concerns about conditions impairing the feasibility of either HSAT or PSG (e.g. limited mobility, drive time to medical center).(Figure 1)
Quality of Nurse’s Independent Triages in a Propensity-Matched Sub-sample
A major departure from standard clinical practice in the RCS model is the option for nurses to triage patients to sleep tests independently without specialist oversight. Therefore, we paid particular attention to quality and safety in this subgroup of patients. We conducted chart reviews in this sample to more fully assess the quality of nurses’ independent triage decisions relative to comparable patients with TSS triage. To identify comparable patients with TSS triages, we propensity matched patients between groups based on a patient’s likelihood of having a nurse complete the triage independently (i.e. no secondary review by provider). Propensity models included demographics and diagnoses present within the electronic medical record hypothesized to affect patient complexity and RCS nurse’s likelihood of completing a triage independently in accordance with the decision support tool (Figure 1, Supplemental Table 1). These propensity models are described in further detail below, and include demographics (e.g. age, drive time to medical center), medical comorbidities (e.g. stroke, congestive heart failure, Charlson comorbidity index), comorbid sleep conditions (e.g. insomnia, restless leg syndrome), and medications outlined in Supplemental Table 3.
Our secondary outcomes analyzed using the propensity matched sub-sample included whether a triage order for sleep testing was potentially inappropriate in comparable patients (Table 1).20 Consistent with existing guidelines20 we recommend patients only be referred to HSAT if they are at moderate to high risk of OSA, defined by presence of snoring or witnessed apneas. We defined potentially inappropriate sleep tests as either 1) HSATs that were potentially contraindicated based on administrative data as defined in Supplemental Table 2, or 2) HSATs that were performed among patients without high-risk symptoms based on chart review (Tables 2 and 3). As the type of sleep test ordered and patient symptoms are not documented within administrative data, chart reviews were necessary. We compared outcomes among completed sleep tests similar to the methods described for the full sample above (Table 1).
Table 2:
Characteristics of patients triaged in both groups.
| TSS Triage | RCS Triage | ||
|---|---|---|---|
| n=1787 | n=793 | Standardized differences | |
| Mean (SD) / N (%) | |||
| Age (years) | 52.1 (16.0) | 52.5 (15.8) | 0.03 |
| Male (%) | 1653 (92.5) | 725 (91.4) | 0.04 |
| White (%) | 1256 (70.3) | 543 (68.5) | 0.04 |
| Hispanic (%) | 118 (6.6) | 58 (7.3) | 0.03 |
| BMI Category (%) | |||
| <30 kg/m2 | 596 (33.4) | 273 (34.4) | 0.09 |
| 30-34.9 kg/m2 | 497 (27.8) | 193 (24.3) | |
| 35-39.9 kg/m2 | 257 (14.4) | 114 (14.4) | |
| ≥40 kg/m2 | 145 (8.1) | 67 (8.4) | |
| Missing BMI | 292 (16.3) | 146 (18.4) | |
| BMI (kg/m2)** | 32.0 (6.2) | 32.0 (6.5) | 0.00 |
| Hypertension (%) | 647 (36.2) | 265 (33.4) | 0.06 |
| Myocardial Infarction (%) | 23 (1.3) | 16 (2.0) | 0.06 |
| Congestive Heart Failure (%) | 74 (4.1) | 36 (4.5) | 0.02 |
| Stroke (%) | 100 (5.6) | 42 (5.3) | 0.01 |
| Chronic Lung Disease (%) | 237 (13.3) | 85 (10.7) | 0.08 |
| Sleep Disturbance NOS (%) | 100 (5.6) | 43 (5.4) | 0.01 |
| Restless Leg Syndrome (%) | 16 (0.9) | 7 (0.9) | 0.00 |
| Insomnia (%) | 195 (10.9) | 114 (14.4) | 0.10 |
| Atrial Fibrillation (%) | 89 (5.0) | 39 (4.9) | 0.00 |
| Any Opioid Rx (%) | 91 (5.1) | 36 (4.5) | 0.03 |
| Alpha Blocker Rx (%) | 143 (8.0) | 61 (7.7) | 0.01 |
| PDE-5 Inhibitor Rx (%) | 22 (1.2) | 7 (0.9) | 0.03 |
| Charlson Score (points) | 1.9 (2.3) | 1.9 (2.3) | 0.02 |
| Drive Time (minutes) | 55.6 (34.2) | 58.2 (34.1) | 0.08 |
Legend: TSS-Traditional Specialist-Led System, RCS-Referral Coordination System, BMI-Body Mass Index Rx-Prescription.; NOS-Not Otherwise Specified, Rx-Prescription, PDE-5-Phosphodiesterase.
Table 3.
Patients selected for independent review managed by RCS nurses with potential indications for specialist oversight.
| Selected for Specialist Oversight n = 487 | Triaged Independently n = 306 | ||
|---|---|---|---|
| n (%) / Mean (SD) | Std Diff | ||
| One or More Potential Indications for specialist review (%) | 156 (32.0) | 22 (7.2) | 0.66 |
| Chronic Lung Disease (%) | 71 (14.6) | 14 (4.6) | 0.34 |
| Congestive Heart Failure (%) | 34 (7.0) | 2 (0.7) | 0.33 |
| Myocardial Infarction (%) | 14 (2.9) | 2 (0.7) | 0.17 |
| Atrial Fibrillation (%) | 39 (8.0) | 0 (0.0) | 0.42 |
| Stroke (%) | 38 (7.8) | 4 (1.3) | 0.32 |
| BMI ≥ 45 kg/m2 | 16 (3.3) | 2 (0.7) | 0.19 |
| Long Acting Opioids (%) | 7 (1.4) | 0 (0.0) | 0.17 |
| Age (years) | 56.1 (16.0) | 46.8 (13.7) | 0.63 |
| Charlson Comorbidity Score | 2.4 (2.5) | 1.0 (1.5) | 0.68 |
Legend: Std Diff- Standardized difference, BMI-Body Mass Index.
Diagnostic Impressions in Propensity Matched Sub-sample
Ultimately, guidelines around triage are intended to maximize the likelihood of achieving a successful diagnostic outcome among patients referred for evaluation of OSA. Therefore, understanding the diagnostic impressions of sleep testing is an important downstream measure of triage quality. Like symptoms, the diagnostic impressions of sleep tests were not recorded within administrative data and are therefore only present for the propensity-matched sub-sample where we conducted chart reviews. VA Puget Sound sleep specialists interpret all sleep tests consistent with the American Academy of Sleep Medicine’s recommended scoring criteria, with OSA diagnosis and severity centered around the number of respiratory events per hour of sleep (Apnea Hypopnea Index, AHI).24 Diagnostic outcomes were as follows: No OSA, AHI 0-4.9 events/hour; Mild OSA, 5.0-14.9 events/hour; Moderate OSA 15.0-29.9 events/hour; Severe OSA 30.0 or more events/hour. As HSAT is inadequate to rule out OSA, those patients with AHI < 5.0 on HSAT are labeled as indeterminant and typically require re-testing.20
Statistical analyses:
We used generalized linear models with log link to compare outcomes of interest between groups (Table 1). We used an intention-to-treat approach, including all triaged patients regardless of whether they eventually received a sleep study as intended. Given that patient allocation was quasi-random based on triager availability at timing of referral, we adjusted comparisons between RCS and TSS in the overall patient cohort based on day of week and hour of referral. As outcomes are not rare, we report relative risks in lieu of odds ratios.
For the propensity matched sub-sample, we calculated propensity score for RCS nurses completing a triage independently using the variables listed in Supplemental Table 3. Of note, we omitted atrial fibrillation and phosphodiesterase inhibitor prescriptions due to collinearity, and continuous measures of BMI and drive time due to missingness. Excluding patients with supervised nurse triage in RCS, we matched patients based on propensity scores 1:1 to nearest neighbors using a caliper width of 0.2 standard deviations of propensity score logit. Models of the propensity-matched cohort also included propensity score to account for residual imbalance between groups. Among patients in this propensity-matched sub-sample, we incorporated a similar intention-to-treat approach as outlined above. A histogram of propensity score distribution is included in supplement (Supplemental Figure 1). We performed all analyses in Stata (Version 16.0; College Station, TX).
Results:
Overall Sample
From September 2018 to August 2019, RCS completed triages for 793 patients referred for evaluation of OSA relative to 1787 triages completed by TSS. Characteristics of these patients are included in Table 2 and overall appeared similar between groups with standardized differences <0.1 for nearly all characteristics.
Eventually, 3.3% of patients with TSS triage received a potentially contraindicated HSAT relative to 1.8% of patients with RCS triage. The remaining patients received an HSAT without potential contraindication (TSS: 27.7%, RCS: 31.5%), PSG (TSS: 19.8%, RCS: 24.3%), or no testing (TSS: 49.2%, RCS: 42.4%). Adjusting for timing of referral, patients with RCS triage had lower risk of receiving a potentially contraindicated HSAT (Adjusted RR 0.52, 95% CI 0.29-0.93; Unadjusted RR 0.53, 95% CI 0.30-0.95). This finding persisted when we restricted analyses to the 1,364 patients who completed sleep testing in both groups (Adjusted RR 0.46, 95% CI 0.26-0.83; Unadjusted RR 0.47, 95% CI 0.27-0.83). The numerically higher proportion of patients who did not receive sleep testing in the TSS group is notable as all patients in our sample were triaged to sleep testing. Differential completion of sleep tests potentially reflects differences in scheduling in the two groups. Within our center, we expect specialists to respond to triage new referrals and make decisions regarding next steps in care (i.e. orders for sleep tests) within 48 hours of referral. This time standard was met for 96.1% of patients in TSS and 91.8% of patients in RCS. However, timeliness of triage completion did not necessarily translate into timely scheduling. Only 35.4% of patients with TSS triage were scheduled for sleep tests within 28 days relative to 60.3% of patients with triages completed by RCS.
Independent Nurse Decision-Making
Among the RCS nurses’ 793 triages, 38.6% (n=306) were completed independently without specialist oversight, a proportion that shifted over time. After being given the opportunity to triage independently, nurses completed just 25.3% of triages independently during the following 3 months. However, this proportion grew to 55.3% after 3 months. Among RCS triages, 32.0% of the triages that nurses referred for specialist oversight had one or more potential indications for specialist oversight, relative to 7.2% of the patients nurses triaged independently (Table 3). In general nurses tended to complete triages independently among younger patients with fewer comorbidities (Table 3).
Propensity-Matched Sub-sample
To specifically address the implications of independent nurse decision-making versus that of specialists, we propensity matched the 306 patients with independent RCS nurse triage to 306 patients in the TSS group. Overall, all variables achieved standardized difference of less than 0.1 between groups (Supplemental Figure 1, Supplemental Table 3). In our propensity matched sub-sample, RCS nurses ordered HSAT in 74.2% (n=227) of cases relative to 64.4% (n=197) in TSS (Table 4). We did not detect a difference between groups with regard to ordering potentially contraindicated HSATs based on patient diagnoses or prescriptions (RR adjusted for propensity score 0.91, 95% 0.41-1.98; Unadjusted RR 0.91, 95%CI 0.39-2.11). When we incorporated additional information about patient symptoms indicating high risk for OSA, we also did not detect a difference between groups with regard to potentially inappropriate HSAT orders (Adjusted RR 0.66, 95% CI 0.36-1.24; Unadjusted RR 0.65, 95% CI 0.35-1.23). Similarly, we did not observe differences between groups when we compared contraindicated and inappropriate test completion (data not shown).
Table 4:
Sleep test triage orders, completed tests, and eventual diagnostic outcomes after TSS or RCS triage in the propensity matched sub-sample.
| TSS Triage n=306 | RCS Triage n=306 | |
|---|---|---|
| Sleep Test Order Placed at Triage* | N (%) | |
| Potentially contraindicated HSAT order* (contraindicated diagnosis or prescriptions) | 11 (3.6) | 10 (3.3) |
| Potentially inappropriate HSAT order (contraindicated diagnosis/prescription, OR lack of symptoms indicating high risk) | 23 (7.5) | 15 (4.9) |
| Potentially appropriate HSAT order (symptoms indicating high risk AND no potential contraindication) | 174 (56.9) | 212 (69.3) |
| PSG | 109 (35.6) | 79 (25.8) |
| Completed Sleep Test* | ||
| Potentially contraindicated HSAT* | 8 (2.6) | 6 (2.0) |
| Potentially inappropriate HSAT | 14 (4.6) | 10 (3.3) |
| Potentially appropriate HSAT | 105 (34.3) | 146 (47.7) |
| PSG | 50 (16.3) | 50 (16.3) |
| No test | 137 (44.8) | 100 (32.7) |
| Diagnostic Outcome | ||
| Test with diagnosis | 165 (53.9) | 197 (64.4) |
| Indeterminant sleep test | 4 (1.3) | 9 (2.9) |
| No test | 137 (44.8) | 100 (32.7) |
Legend: TSS-Traditional Specialist-Led System, RCS-Referral Coordination System, HSAT-Home Sleep Apnea Test; PSG- Polysomnogram.
-Note proportions do not add up to 100% as ‘potentially contraindicated’ is a subset of ‘potentially inappropriate’.
Ultimately, patients with RCS triage in the propensity matched sub-sample were more likely to have a conclusive diagnosis (Adjusted RR 1.20, 95% CI 1.05-1.37; Unadjusted RR 1.19, 95% CI 1.05-1.36). Supplemental Figure 2 outlines sleep testing outcome at 6 months including severity of diagnosed OSA, lack of testing, or testing that was indeterminant (i.e. HSAT that did not meet criteria for OSA without confirmatory lab testing). As illustrated in Supplemental Figure 2, this difference appears to be driven by a higher proportion of patients in the TSS group who did not complete testing (44.8 % vs. 32.7%). Similar to the overall sample, this difference potentially reflects differences in the timeliness of scheduling between the two groups. Only 34.0% of TSS group patients were scheduled for sleep tests within 28 days relative to 69.6% in RCS.
Discussion:
Our results suggest that a nurse-led system of referral coordination can triage referrals for initial diagnosis of OSA in a way that reduces risk of potentially contraindicated sleep testing. Our findings reinforce the safety of incorporating nurses as clinical decision-makers as we augment our specialty care workforce. Although carefully controlled randomized trials have demonstrated comparable outcomes between registered nurse and specialist-led management for initial OSA care, these findings have not shifted general practice.11–16 Our results help to address an important barrier to widespread integration by providing additional evidence of real-world quality and safety. Our results reinforce that nurses are able to enact guideline-based care when equipped with decision-support tools and expand our understanding of nurses’ comparative decision-making to specialists within an e-consult based system. Furthermore, although both specialists and nurses were encouraged by center leadership to use the decision-support tool, our results suggest the referral coordination system had greater adherence to sleep testing guidelines than the traditional system. This finding may reflect the impact of having multiple individuals working on the same referral, acting as a second check on accuracy. It is also possible that as learners, nurses were more likely to follow the guidelines outlined in the decision-support tool rather than lean on past experience or personal opinion.
Our findings also suggest that there are limits to our approach. First, the need for specialist providers can be reduced, but not completely subsumed by nurses. Across our sample, nurses only triaged 38.6 % of cases independently. Although this proportion grew to 55.3 % after the first 3 months of experience, our findings suggest the need for a team-based approach where specialists and nurses work together. Second, while nurses appeared to select more complex cases for specialist oversight, we found 7.2 % of patients triaged independently by nurses had potential indications for specialist oversight. However, these cases did not translate into a greater likelihood of potentially contraindicated or potentially inappropriate sleep test orders. As we consider where nurse-led triage could be beneficial, it is incumbent on specialties to assess areas where specialist input is truly needed for patient safety, and optimize the decision support tools to make sure that instructions are as clear as possible such that this review occurs. Such decision support tools for nurse triages could also consider automated triggers from the electronic medical record based on discrete data elements (e.g. ICD-10 diagnoses).
In addition to nurse triage, our study offers insights into strategies to improve receipt of specialty care. Recent analyses outside of the VA find up to 65% of patients referred to specialty care overall do not receive services, and 50% of patients referred to sleep testing do not complete testing.5,25 While many referred patients will choose to not receive services due to lack of interest, low perceived importance, and competing demands, our results suggest that completion of specialty care services may be improved with prompt scheduling. As we reorganize referrals, it will be important to optimize staffing of administrative staff and processes to reach out to patients in a timely manner.
Our research has several limitations to acknowledge. First, the limited information present within administrative data prevents a full understanding of the quality of triage decisions and outcomes in the overall sample (e.g. symptoms). However, findings from our better characterized sub-sample provide reassurance. Second, our e-consult based approach may not be generalizable to centers with in-person or video-based consultation prior to testing. However, this concern is tempered somewhat by the results of randomized efficacy trials where nurses were incorporated into models of care delivery that included in-person consultations prior to testing.11–15 Matched with these limitations are a number of strengths, including our ability to assess sleep testing decisions among a large group of patients triaged by nurses in usual care. Our large volume of patient referrals allowed us to compare decision-making quality between nurses and specialists within the same health system in a contemporary time period.
Despite our results, additional barriers to widespread integration of nurses into specialty care remain. Perhaps the most formidable of these barriers concerns the generation of revenue and accepted scopes of practice. Capitated healthcare systems like VA are incentivized to integrate staff in the most efficient way possible, largely irrespective of billing revenue. However, this is not the case in traditional fee-for-service models, where billing models for consultation require face-to-face care with licensed independent providers (physicians or advanced care providers) as a first step in triage for specialty care services.26 A reassessment of payor reimbursement strategies will be needed in order to reimburse systems for decision-making made by registered nurses. Along with revenue, systems need to consider scope of practice. Systems considering nurse-led care will need to reassess policies that prevent nurses from ordering procedures like sleep tests independently. Finally, the impacts on ultimate patient-centered outcomes of interest will need to be considered.
Accommodating increasing demand for specialty services like sleep medicine will require a substantial redesign of clinical practices and our system of healthcare delivery. Supplementing evidence from randomized clinical trials, our results provide real-world evidence supporting greater quality of decision-making after inclusion of registered nurses. Future work will need to focus on the effectiveness and implications of such a system in diverse practice environments and healthcare systems.
Supplementary Material
Funding Information:
Office of Veteran’s Access to Care; Seattle-Denver HSR&D Center for Veteran-Centered and Value-Driven Care, Seattle, WA; VA HSR&D Career Development Award: CDA-18-187; NIH NHLBI K12 HL137940. The views expressed here are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. None of the funding sources were involved in the design, conduct or analysis of this project.
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