Quality measures (QM) were developed by AASM in 2015 in order “to improve the quality of care for patients with sleep disorders” (AASM website) at sleep clinics and labs. Quality-of-life (QOL) measures are included as process and outcomes QMs for several sleep disorders. The Penn Sleep Center now collects the Epworth Sleepiness Scale (ESS), FOSQ-10 and PROMIS-10 Physical and Mental domains using an integrated EHR-based sleep questionnaire. EHR-based questionnaires are auto-linked to all new and return office visits and available for patients to complete from home or mobile using an EHR patient portal, or on waiting room tablets as part of the standard visit check-in process. EHR-based data were auto-exported, de-identified, and analyzed for benchmark QM. The aim was to determine the percentage of QOL data collected for all patients with sleep disorders at the Penn Sleep Center, and to evaluate QOL differences for new and returning patients. Of 2,844 patients seen over the 3 month study period, 700 (25%) were new and 2144 (75%) were returning patients. Nearly 80% of patients completed QOL metrics and >90% completed ESS. Each metric was better in all returning compared to all new patients, including the ESS (8.26 ± 5.26 vs. 9.03 ± 5.51, p=0.0016), FOSQ-10 (30.42 ± 7.56 vs. 29.16 ± 7.53, p=0.0003), and the PROMIS-10 Mental (13.03 ± 3.32 vs. 12.39 ± 3.36, p<0.0001) and Physical (13.87 ± 3.33 vs. 13.40 ± 3.25, p=0.0024) scores. Benchmark QOL data were successfully collected and exported using a recently developed EHR-based questionnaire in nearly 80% of patients. There were improved values for each metric in returning patients when compared to new patients during the same period. Obtaining these metrics systematically in a large majority of patients will allow for robust future analyses evaluating responses of QOL measures with therapy and comparisons between change of QOL for specific sleep disorders and therapies and for patient subgroups of interest. none.

CPAP and mask comfort is important to patient satisfaction and compliance. Yet the choice of the first mask varies across Sleep Centers and labs, DMEs, providers, patients, and coverage. We evaluated whether requesting masks changes after the initial CPAP fitting was associated with compliance or the type of medical insurance. We hypothesized that CPAP adherence would be higher in patients who changed their mask. Penn CPAP Program patients are all fitted by a lab technician, provider, or DME. We examined data on requests for replacement of the first mask among new CPAP prescriptions over a 6-month period (January - June 2017). We compared the rates of compliance in the first 90 days between those with and without mask replacements using chi-squared tests and whether these differences were dependent on specific insurers. Of 1065 new CPAP patients, 302 (28.4%) requested replacement of the first mask within the first 90 days. Rates of treatment compliance did not differ between those with (67.9%) or without (68.8%) CPAP mask replacements (p=0.7691). Patients with Medicaid and Medicare had a higher rate of mask replacements (31.1% and 32.3%, respectively) than patients with Commercial (25.9%) or other insurance (22.9%) on average, although the difference did not reach statistical significance (p=0.133). In this study of our patients with personal mask fittings, 28.4% requested a replacement of the first mask during the initial compliance period. At 90 days, their compliance was similar to those who did not change first masks. While changing a mask may take days/weeks out of the 90 day compliance window, compliance did not appear decreased by time taken to change to a second preferred mask. There was some evidence of increased mask replacement in Medicare or Medicaid patients. none.

The shift from volume-based to value-based payment models has increased the emphasis on quality measures (QM) when determining payment. Electronic health records (EHR) enable collection and use of large amounts of QM data, including patient reported outcomes (PRO). Benefits of EHR-based data include availability during individual patient visits and use for group-level data QM. However, efficient PRO collection into the EHR requires integration into the daily clinical workflow. We describe our experience in transitioning from paper-based to EHR-based collection of ESS for individual and group QM documentation. The ESS was administered with an EHR-based sleep questionnaire (SQ) to all new and returning patients at the Penn Sleep Center. Our goal was to collect ESS for >90% of all visits using the EHR. Originally, patients completed paper questionnaires in the waiting room and providers typed the ESS into EHR notes. Despite creation of an EHR provider digital data entry of ESS in 2010 and the use of PRO entry using the EHR-patient portal SQ in 2013, digital ESS collection remained inconsistent and <50%. In mid-April 2017, we introduced an additional ESS collection method using EHR-based waiting room tablets. Using auto-exported EHR-based ESS data, percent collection of EHR-based ESS were compared before (2014) and after (2017) tablet intervention. De-identified data were analyzed to compare percentages of ESS collection before and after the tablet intervention. EHR-based digital ESS data were collected for 1144 out of 2930 total visits (39.0%) from May-July 2014 (before tablets), and for 1776 out of the 1898 total visits (93.6%) from May-July 2017 (after tablets). Collection was significantly improved after tablets (p<0.0001). EHR-based ESS collection was improved from 2014 to 2017 with integration of tablets into the standard check-in workflow. Integration has enhanced digital EHR-based ESS collection to >90% of all clinic patients. ESS data auto-populate visit notes and save provider documentation time. Data also are available for EHR-based report exports to use for group PRO benchmarks and for AASM and CMS Outcomes QM. None.

We previously analyzed CPAP use times in our urban sleep clinic before and after the 2009 CMS compliance rules for ongoing PAP coverage >90 days. We found no difference in use times despite changes in insurance rules, workflow and adherence interventions or ongoing mask, unit, tracking technology changes (2011 APSS abstract: CPAP Treatment Adherence in CPAP Clinics: Schutte-Rodin et al). Here, we examine whether type of insurance is associated with CPAP adherence rates. CPAP compliance rates were evaluated over a 30-month period (January 2015 to June 2017) among patients with Commercial insurance, Medicaid, Medicare or other insurance. Compliance was defined using the standard CMS rule PAP use times. We compared compliance rates among and between insurance carriers using chi-squared tests. De-identified data were analyzed for 4,285 patients with new PAPs set-up by a DME between January 1, 2015 and June 30, 2017. A total of 1776 (41.4%) of patients had Commercial insurance, 641 (15.0%) Medicaid, 1251 (29.2%) Medicare, and 617 (14.4%) had another or no insurance. The average PAP compliance among all patients was 68.6%. There were significant differences in compliance rates among insurance carriers (p<0.0001), with compliance rates among Commercial (73.9%) and Medicare (73.7%) patients significantly higher than compliance rates among patients with Medicaid (55.1%) or other or no insurance (57.4%). The identification of groups at increased risk of non-adherence to PAP is important in improving overall treatment adherence and efficacy, and, relatedly, in identifying risk-specific interventions to improve patient outcomes. Our data indicate that insurance type is an important predictor of CPAP adherence. Further studies are needed to understand the related factors mediating these associations. Additionally, given the increased focus on quality measures and provider pay for performance, and the expected negative effect of lower adherence, these factors should also be taken into account when evaluating performance metrics of individual providers, hospitals and DMEs. none.