Longitudinal Monitoring of Clinician-Patient Video Visits During the Peak of the COVID-19 Pandemic: Adoption and Sustained Challenges in an Integrated Health Care Delivery System.

BACKGROUND: Numerous prior opinion papers, administrative electronic health record data studies, and cross-sectional surveys of telehealth during the pandemic have been published, but none have combined assessments of video visit success monitoring with longitudinal assessments of perceived challenges to the rapid adoption of video visits during the pandemic. OBJECTIVE: This study aims to quantify (1) the use of video visits (compared with in-person and telephone visits) over time during the pandemic, (2) video visit successful connection rates, and (3) changes in perceived video visit challenges. METHODS: A web-based survey was developed for the dual purpose of monitoring and improving video visit implementation in our health care system during the COVID-19 pandemic. The survey included questions regarding rates of in-person, telephone, and video visits for clinician-patient encounters; the rate of successful connection for video visits; and perceived challenges to video visits (eg, software, hardware, bandwidth, and technology literacy). The survey was distributed via email to physicians, advanced practice professionals, and clinicians in May 2020. The survey was repeated in March 2021. Differences between the 2020 and 2021 responses were adjusted for within-respondent correlation across surveys and tested using generalized estimating equations. RESULTS: A total of 1126 surveys were completed (511 surveys in 2020 and 615 surveys in 2021). In 2020, only 21.7% (73/336) of clinicians reported no difficulty connecting with patients during video visits and 28.6% (93/325) of clinicians reported no difficulty in 2021. The distribution of the percentage of successfully connected video visits ("Over the past two weeks of scheduled visits, what percentage did you successfully connect with patients by video?") was not significantly different between 2020 and 2021 (P=.74). Challenges in conducting video visits persisted over time. Poor connectivity was the most common challenge reported by clinicians. This response increased over time, with 30.5% (156/511) selecting it as a challenge in 2020 and 37.1% (228/615) in 2021 (P=.01). Patients not having access to their electronic health record portals was also a commonly reported challenge (109/511, 21.3% in 2020 and 137/615, 22.3% in 2021, P=.73). CONCLUSIONS: During the pandemic, our health care delivery system rapidly adopted synchronous patient-clinician communication using video visits. As experience with video visits increased, the reported failure rate did not significantly decline, and clinicians continued to report challenges related to general network connectivity and patient access to technology.

Creating research-ready partnerships: the initial development of seven implementation laboratories to advance cancer control.

BACKGROUND: In 2019-2020, with National Cancer Institute funding, seven implementation laboratory (I-Lab) partnerships between scientists and stakeholders in 'real-world' settings working to implement evidence-based interventions were developed within the Implementation Science Centers in Cancer Control (ISC3) consortium. This paper describes and compares approaches to the initial development of seven I-Labs in order to gain an understanding of the development of research partnerships representing various implementation science designs. METHODS: In April-June 2021, members of the ISC3 Implementation Laboratories workgroup interviewed research teams involved in I-Lab development in each center. This cross-sectional study used semi-structured interviews and case-study-based methods to collect and analyze data about I-Lab designs and activities. Interview notes were analyzed to identify a set of comparable domains across sites. These domains served as the framework for seven case descriptions summarizing design decisions and partnership elements across sites. RESULTS: Domains identified from interviews as comparable across sites included engagement of community and clinical I-Lab members in research activities, data sources, engagement methods, dissemination strategies, and health equity. The I-Labs use a variety of research partnership designs to support engagement including participatory research, community-engaged research, and learning health systems of embedded research. Regarding data, I-Labs in which members use common electronic health records (EHRs) leverage these both as a data source and a digital implementation strategy. I-Labs without a shared EHR among partners also leverage other sources for research or surveillance, most commonly qualitative data, surveys, and public health data systems. All seven I-Labs use advisory boards or partnership meetings to engage with members; six use stakeholder interviews and regular communications. Most (70%) tools or methods used to engage I-Lab members such as advisory groups, coalitions, or regular communications, were pre-existing. Think tanks, which two I-Labs developed, represented novel engagement approaches. To disseminate research results, all centers developed web-based products, and most (n = 6) use publications, learning collaboratives, and community forums. Important variations emerged in approaches to health equity, ranging from partnering with members serving historically marginalized populations to the development of novel methods. CONCLUSIONS: The development of the ISC3 implementation laboratories, which represented a variety of research partnership designs, offers the opportunity to advance understanding of how researchers developed and built partnerships to effectively engage stakeholders throughout the cancer control research lifecycle. In future years, we will be able to share lessons learned for the development and sustainment of implementation laboratories.

Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics.

BACKGROUND: Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias. OBJECTIVE: This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I). METHODS: We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption. RESULTS: The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden. CONCLUSIONS: EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.

Use of phentermine-topiramate extended release in combination with sleeve gastrectomy in patients with BMI 50 kg/m2 or more.

BACKGROUND: Super obesity (body mass index [BMI] ≥50 kg/m2) treatment can be complicated and high risk. OBJECTIVES: We studied whether the pre and postoperative use of phentermine and topiramate (phen/top) combined with laparoscopic sleeve gastrectomy (LSG) in super obesity increases the odds of achieving a BMI <40 at 2 years postoperatively. SETTING: Academic medical center in Winston Salem, North Carolina. METHODS: We recruited patients between 2014 and 2016 who had a BMI ≥50 and planned to undergo LSG (n = 25) to participate in an open-label trial. Participants took phen/top (7.5/46-15/92 mg/d) for at least 3 months preoperatively and 2 years postoperatively. We compared weight loss, BMI changes, and odds for achieving BMI <40 for phen/top + LSG to historical controls. Controls had an initial BMI ≥50 and underwent LSG, without phen/top, at our center during the same timeframe (n = 40). RESULTS: Of the 25 participants recruited, 13 completed LSG. Phen/top participants had a baseline BMI of 61.2 ± 7.1 kg/m2 compared with 57.0 ± 5.6 kg/m2 for control participants. Percent initial weight loss was -39.3% (phen/top + LSG) versus -31.4% (control) at 12 months, P = .018; by 24 months, phen/top + LSG had an 11.2% greater initial weight loss, P = .007. At 24 months, the mean BMI was 33.8 kg/m2 for phen/top versus 42 kg/m2 for controls. The odds ratio for achieving a BMI <40 at 2 years with phen/top + LSG versus LSG alone was 4.1 (95% confidence interval, 0.8-21). CONCLUSIONS: Compared with LSG alone, phen/top combined with LSG may help patients with a BMI >50 achieve greater weight loss and reach a BMI <40. Long-term, controlled trials are needed to follow up these results.

Skin in the game: Does paying for obesity treatment out of pocket lead to better outcomes compared to insurance coverage?

OBJECTIVE: To determine whether insurance coverage for medical weight loss treatment was associated with different program engagement and weight loss outcomes compared to those who paid out of pocket. METHODS: One-year outcomes from an academic medical weight management program were used to compare two groups: employees (n = 480) with insurance coverage ("covered") versus nonemployees (n = 463) who paid out of pocket ("self-pay"). Demographics and weight were abstracted from medical records. Socioeconomic status was estimated using neighborhood demographics. Group differences in weight were analyzed using generalized linear modeling adjusted for age, baseline BMI, sex, program type, and neighborhood socioeconomic status. RESULTS: Covered patients were younger (46.5 ± 10.6 vs. 51.6 ± 12.5) with a lower BMI (38.5 ± 7.5 vs. 41.3 ± 9.9) compared to self-pay (P < 0.001). Self-pay patients resided in higher annual per capita income neighborhoods (+$4,545, P < 0.001). Program dropout was lower for covered patients (12.7% vs. 17.6%, P = 0.03). There was no significant difference in 12-month weight loss between groups in adjusted models; covered patients lost 13.4%, compared to 13.6% for self-pay. CONCLUSIONS: Data from an academic medical weight management program suggest that individuals with access to insurance coverage for nonsurgical obesity treatment have lower levels of attrition and similar levels of participation and outcomes as those who pay out of pocket.

Relationship of weekly activity minutes to metabolic syndrome in prediabetes: the healthy living partnerships to prevent diabetes.

BACKGROUND: Physical inactivity contributes to metabolic syndrome (MetS) in overweight/obesity. However, little is known about this relationship in prediabetes. METHODS: The study purpose is to examine relationships between physical activity (PA) and MetS in prediabetes. The Healthy Living Partnerships to Prevent Diabetes tested a community translation of the Diabetes Prevention Program (DPP). Three hundred one overweight/obese prediabetics provided walking minutes/week (WM) and total activity minutes/week (AM) via the International Physical Activity Questionnaire. MetS was at least 3 of waist (men ≥ 102 cm, women ≥ 88 cm), triglycerides (≥150 mg·dl), blood pressure (≥130·85 mm Hg), glucose (≥100 mg·dl), and HDL (men < 40 mg·dl, women < 50 mg·dl). RESULTS: The sample was 57.5% female, 26.7% nonwhite/Hispanic, 57.9 ± 9.5 years and had a body mass index (BMI) 32.7 ± 4 kg·m². Sixty percent had MetS. Eighteen percent with MetS reported at least 150 AM compared with 29.8% of those without MetS. The odds of MetS was lower with greater AM (P(trend) = .041) and WM (P(trend) = .024). Odds of MetS with 0 WM were 2.08 (P = .046) and with no AM were 2.78 (P = .009) times those meeting goal. One hour additional WM led to 15 times lower MetS odds. CONCLUSIONS: Meeting PA goals reduced MetS odds in this sample, which supported PA for prediabetes to prevent MetS.