Jennifer Freytag

Objective Due to an increase in healthcare quality research and QI projects, there is a high demand for clinicians’ time to serve as research participants. Traditional focus group methods are time demanding, posing a barrier to collect data from busy clinicians. We show that asynchronous online focus groups (AOFGs) are a viable method for increasing participation from busy clinicians. Materials and Methods Recruitment metrics were computed by counting the number of enrollees and sites of provenance during the period when traditional focus groups and AOFGs were offered, respectively. Then, using Microsoft Teams, private channels served as forums for primary care staff at Veterans Health Administration to share their experiences as part of two healthcare projects. Results 82 primary care staff from 17 VA sites individually participated. Pivoting to AOFGs helped increase acceptance rates by 429% and 116% in each respective project. Offering AOFGs helped decrease non-response rates across both projects by 35% and 16.34% on both projects. Notably, Care Managers (RNs) were the most well-represented, constituting nearly 40% of participants on each project. Discussion This paper explores metrics that can be tracked and measured for future research. AOFGs replace the coordination work of gathering participants in one place in real time with the protracted coordination work of keeping participants engaged. AOFGs thus require consistent communication between recruiters and participants as well as active moderation to sustain engagement. Conclusions By providing a flexible alternative that accommodates the unpredictable demands clinicians face in primary care, AOFGs help address the barrier of time demand and increase participation in quality improvement and healthcare research projects.

Objectives: Inpatient rounding is a foundational component of medical education in academic hospitals. The coronavirus 2019 (COVID-19) pandemic disrupted traditional inpatient rounding practices. The objectives of this study were to describe how Internal Medicine inpatient team rounding changed because of COVID-19-related precautions and the effect of these changes on education during...

Aligning treatment with patients’ self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4–6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient’s priorities.

BackgroundDespite a growing call to train providers in interpersonal communication skills, communication training is either not offered or is minimally effective, if at all. A critical need exists to develop new ways of teaching communication skills that are effective and mindful of provider time pressures. We propose a program that includes real-time observation and video-based coaching to teach provider communication skills. In this study, we assess acceptability and feasibility of the program using provider interviews and surveys. MethodsThe coaching intervention involved training providers to use five patient-centered communication behaviors. Two coaches were trained to observe and record live video feeds of provider visits. As coaches observed provider visits, they prepared feedback, which they delivered to the provider. During coaching sessions, coaches gave feedback while showing clips from the visit in which patient-centered communication behaviors could be included. Coaches practiced communication skills with providers using role-plays during coaching sessions. Providers included residents (n=15), fellows (n=3), attending physicians (n=3), and a nurse practitioner (n=1) from a VA clinic and county clinic specializing in care for patients with HIV. We report descriptive results from a survey taken by providers participating in the program. The survey was adapted from validated items used in I/O psychology to assess the feedback environment, including questions about the intervention’s feasibility and acceptability. A qualitative analysis was also conducted on transcripts of provider interviews following the intervention. We used rapid analysis to identify themes within the interviews.ResultsSurvey measures on showed high feasibility and acceptability ratings from providers, with mean item scores ranging from 6.2 to 6.8 on a 7-point scale. Qualitative analysis revealed that providers found that 1) coaches were credible and supportive, 2) feedback was useful, 3) video-clips allowed for self-reflection, 4) getting feedback on the same day was useful, and 5) use of real patients preferred over standardized patients. ConclusionsVideo-based coaching can help providers learn new communication skills in a way that is provider-centered, brief and timely. Our study demonstrates that real-time coaching using live feed and video technology is an acceptable and feasible way of teaching communication skills.

Background: Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient’s electronic health record (EHR). Objective: Our goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. Methods: This is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient’s free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. Results: Of 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P<.001). The NLP model in the training stage had an accuracy of 0.98 (95% CI 0.98-0.99), a recall of 0.98 (95% CI 0.98-0.99), and precision of 0.98 (95% CI 0.97-1.00). The NLP model in the validation stage had an accuracy of 0.92 (95% CI 0.90-0.94), recall of 0.84 (95% CI 0.79-0.89), and precision of 0.84 (95% CI 0.77-0.91). In contrast, an approach using simple search terms for PPC only had a precision of 0.757. Conclusions: An automated NLP model can reliably measure with high precision, recall, and accuracy when clinicians document patient priorities as a key step in the adoption of PPC.

BACKGROUND/OBJECTIVES: Aligning healthcare decisions with patients’ priorities may improve care for older adults with multiple chronic conditions (MCCs). We conducted a pilot study to assess the feasibility of identifying patient priorities in routine geriatrics care and to compare clinicians’ recommendations for patients who did or did not have their priorities identified. DESIGN: Retrospective chart review. SETTING: Veterans Administration Medical Center Geriatrics Clinic. PARTICIPANTS: Older adults with MCCs receiving Patient Priorities Care (PPC; n = 35) were matched with patients receiving usual care (UC; n = 35). Both PPC and UC patients were cared for by three primary care providers (PCPs) in an ambulatory geriatric clinic. INTERVENTION: In the PPC group, a clinician facilitator met with each patient to identify their healthcare priorities and transmitted patients’ priorities in the electronic health record (EHR). Trained PCPs then sought to align healthcare decisions with patients’ priorities. In the UC group, patients received usual care from the same PCPs. MEASUREMENTS: We matched patients by clinician seen, patient’s age, number of active conditions, medications, hospitalizations, functional status, and prior hospitalizations. EHRs were reviewed to identify care decisions including medications added or stopped, referrals and consults added or avoided, referrals to community services and supports, self‐management activities added or avoided, and total number of changes to care. Mean differences in recommended care between PPC and UC patients from the same PCPs were examined. RESULTS: Clinician facilitators could identify patient priorities during routine clinic encounters. Compared with patients in the UC group, those in the PPC group had, on average, fewer medications added ( P = .05), more referrals to community services and supports ( P = .03), and more priorities‐aligned self‐management tasks added ( P = .005). CONCLUSION: These findings support the feasibility of identifying and documenting patient priorities during routine encounters. Results also suggest that clinicians use patient priorities in recommending care.

BACKGROUND Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient’s electronic health record (EHR). OBJECTIVE Our goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. METHODS This is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient’s free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. RESULTS Of 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P<.001). The NLP model in the training stage had an accuracy of 0.98 (95% CI 0.98-0.99), a recall of 0.98 (95% CI 0.98-0.99), and precision of 0.98 (95% CI 0.97-1.00). The NLP model in the validation stage had an accuracy of 0.92 (95% CI 0.90-0.94), recall of 0.84 (95% CI 0.79-0.89), and precision of 0.84 (95% CI 0.77-0.91). In contrast, an approach using simple search terms for PPC only had a precision of 0.757. CONCLUSIONS An automated NLP model can reliably measure with high precision, recall, and accuracy when clinicians document patient priorities as a key step in the adoption of PPC.

BACKGROUND/OBJECTIVES: A patient‐directed, online program (PREPARE for Your Care [PREPARE]; prepareforyourcare.org ) has been shown to increase advance care planning (ACP) documentation. However, the mechanisms underlying PREPARE are unknown. Our objectives were to compare the efficacy of PREPARE plus an easy‐to‐read advance directive (AD) vs an AD alone to increase active patient participation in ACP discussions during clinic visits and to examine effects of active patient participation on ACP documentation. DESIGN: Audio recordings of postintervention primary care visits from two randomized trials (2013‐2016). SETTING: Seven primary care clinics at a veterans affair and safety‐net hospital in San Francisco, CA. PARTICIPANTS: English‐ and Spanish‐speaking adults, aged 55 years and older, with two or more chronic/serious conditions. INTERVENTION: PREPARE plus an easy‐to‐read AD or an AD alone. MEASUREMENTS: The primary outcome was the number of active patient participation utterances about ACP (eg, asking questions, stating preferences) measured by the validated Active Patient Participation Coding Scheme. We examined differences in utterances by study arm using mixed effects negative binomial models and utterances as a mediator of PREPARE's effect on documentation using adjusted logistic regression. Models were adjusted for health literacy, prior care planning, and clinician. RESULTS: Among 393 participants, the mean (SD) age was 66 (8.1) years, 120 (30.5%) had limited health literacy, and 99 (25.2%) were Spanish speaking. PREPARE plus the AD resulted in 41% more active patient participation in ACP discussions compared with the AD alone (mean [SD] = 10.1 [16.8] vs 6.6 [13.4] utterances; incidence rate ratio = 1.41; 95% confidence interval = 1.00‐1.98). For every additional utterance, participants had 15% higher odds of ACP documentation, and active patient participation accounted for 16% of PREPARE's effect on documentation. CONCLUSIONS: The PREPARE program and easy‐to‐read AD empowered patients to actively participate in ACP discussions during clinical visits more than the AD alone. Increased activation was associated with increased ACP documentation. Therefore, PREPARE may mitigate barriers to ACP among English‐ and Spanish‐speaking older adults. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: “Improving Advance Care Planning by Preparing Diverse Seniors for Decision Making (PREPARE)” NCT01990235 and “Preparing Spanish‐Speaking Older Adults for Advance Care Planning and Medical Decision Making (PREPARE)” NCT02072941. J Am Geriatr Soc 68:1210–1217, 2020.

Family communication environments can be a facilitator or barrier to family cooperation and communication in collecting family health history (FHH) information, which can facilitate disease prevention. This study examined the direct and indirect effects of family communicative environments on whether individuals actively collected FHH information, as well as how age and sex differences complicate this relationship. Participants (N = 203) completed online surveys, answering close-ended questions about their family’s communication patterns, how open their family is to communicating about FHH, and whether they have actively collected FHH information. Results show there was a direct effect between open family communicative environments and active collection, and found FHH communication openness was a positive partial mediator. Conversely, family environments stressing hierarchy and homogeneity of beliefs inhibit open communication about and collection of a FHH. Analysis of age and sex as moderators in the models showed a significant conditional indirect effects, which grew stronger as participants’ age increased. Furthermore, results showed open family communicative environments lead to active collection of FHH for women, but not for men. Results confirm the importance of family communicative environments in facilitating or inhibiting FHH collection. Findings from the current study provide intervention points for practitioners to advise patients on the importance of collecting a FHH and guide behaviors to collect FHH information based on the family communicative environment.