Stroebel, Robert J.

The optimal patient panel size (PPS) in primary care and the factors determining it remain unclear. We conducted a meta-narrative review of the literature to evaluate factors influencing PPS and assess its association with patient outcomes. A comprehensive search of electronic databases was performed from inception through December 2023, focusing on original studies reporting factors used to determine PPS and related outcomes (eg, clinical outcomes, process measures, and resource utilization). A total of 48 studies were included, identifying 7 key factors influencing PPS. Smaller panels were associated with improved patient satisfaction, continuity of care, and health promotion, while clinical outcomes, utilization, and costs showed minimal impact by PPS. Panel size was primarily associated with patient age, sex, comorbidities, and practice type and structure. Community-based centers typically managed smaller panels, often staffed by female clinicians and serving socioeconomically disadvantaged populations with greater health needs than hospital-based practices. Female clinicians were also independently associated with managing smaller panels, higher quality care indicators, fewer emergency department visits, and improved patient satisfaction. Determining the ideal PPS is a multifaceted process influenced by practice setting, patient demographics, and clinician characteristics. While practice-related factors showed limited association with PPS, patient-reported outcomes were more closely linked to it. Primary care practices should tailor panel sizes to their patient populations, emphasizing a patient-centered approach and ensuring adequate infrastructure support to optimize care delivery.

AbstractThe Adult Medical Care Coordination program (“the program”) was implemented at Mayo Clinic to promote patient self‐management and improve 30‐day unplanned readmission for patients with high risk for readmission after hospital discharge. This study aimed to evaluate the impact of the program compared to usual care using a pragmatic, stepped wedge cluster randomized trial (“stepped wedge trial”). However, several challenges arose including large differences between the study arms. Our goal is to describe the challenges and present lessons learned on how to overcome such challenges and generate evidence to support practice decisions. We describe the challenges encountered during the trial, the approach to addressing these challenges, and lessons learned for other learning health system researchers facing similar challenges. The trial experienced several challenges in implementation including several clinics dropping from the study and care disruptions due to COVID‐19. Additionally, there were large differences in the patient population between the program and usual care arms. For example, the mean age was 76.8 for the program and 68.1 for usual care. Due to these differences, we adapted the methods using the propensity score matching approach that is traditionally applied to observational designs and adjusted for differences in observable characteristics. When conducting pragmatic research, researchers will encounter factors beyond their control that may introduce bias. The lessons learned include the need to weigh the tradeoffs of pragmatic design elements and the potential value of adaptive designs for pragmatic trials. Applying these lessons would promote the successful generation of evidence that informs practice decisions.

Objective: The purpose of this study was to implement a 2-phase approach to rapidly increase the number of annual wellness visits (AWVs) and build a sustainable model at 3 study units (Mayo Clinic in Rochester, Minnesota, and clinics in 2 regions of Mayo Clinic Health System), which collectively serve approximately 80 000 patients who qualify for an AWV annually. Methods: In the rapid improvement phase, beginning in July 2022, goals at the facilities were reoriented to prioritize AWVs, educate staff on existing AWV resources, and create low-effort workflows so that AWVs could be incorporated into existing patient appointments. Staff at all 3 study units worked independently and iterated quickly. In the second phase, all study units collaborated to design and implement a best-practice solution while they leveraged the engagement and lessons learned from the first phase and invested in additional system elements and change management to codify long-term success. Results: The number of AWVs completed monthly increased in each study unit. In the rapid improvement phase, the number of AWVs increased but then plateaued (or decreased at some study units). In April 2023, the final scheduled outreach automation and visit tools were implemented, and the number of AWVs was sustained or increased, while outreach and scheduling times were decreased. The number of completed AWVs increased from 1148 across all study units in the first 6 months of 2022 to 14 061 during the first 6 months of 2023. Conclusions: The lessons learned from this project can be applied to other health systems that want to provide more patients with AWVs while improving operational efficiency. The keys are to have a clear vision of a successful outcome, engage all stakeholders, and iterate quickly to find what works best for the organization.

Background Patients often present to emergency departments (EDs) with concerns that do not require emergency care. Self-triage and other interventions may help some patients decide whether they should be seen in the ED. Symptoms associated with low risk of hospitalization can be identified in national ED data and can inform the design of interventions to reduce avoidable ED visits. Methods We used the National Hospital Ambulatory Medical Care Survey (NHAMCS) data from the United States National Health Care Statistics (NHCS) division of the Centers for Disease Control and Prevention (CDC). The ED datasets from 2011 through 2020 were combined. Primary reasons for ED visit and the binary field for hospital admission from the ED were used to estimate the proportion of ED patients admitted to the hospital for each reason for visit and age category. Results There were 221,027 surveyed ED visits during the 10-year data collection with 736 different primary reasons for visit and 23,228 hospitalizations. There were 145 million estimated hospitalizations from 1.37 billion estimated ED visits (10.6%). Inclusion criteria for this study were reasons for visit which had at least 30 ED visits in the sample; there were 396 separate reasons for visit which met this criteria. Of these 396 reasons for visit, 97 had admission percentages less than 2% and another 52 had hospital admissions estimated between 2% and 4%. However, there was a significant increase in hospitalizations within many of the ED reasons for visit in older adults. Conclusion Reasons for visit from national ED data can be ranked by hospitalization risk. Low-risk symptoms may help healthcare institutions identify potentially avoidable ED visits. Healthcare systems can use this information to help manage potentially avoidable ED visits with interventions designed to apply to their patient population and healthcare access.

Background Although online self-triage is easily accessible, little is known about the patients who use self-triage or their subsequent diagnoses. We compared ear/hearing self-triage subsequent diagnoses to ear/hearing visit diagnoses in emergency departments (ED) and ambulatory clinics across the United States. Methods We compared International Classification of Diseases version 10 (ICD10) coded diagnoses following online self-triage for ear/hearing concerns with those from national ED and ambulatory clinic samples. We used data from the Centers for Disease Control (CDC) National Hospital Ambulatory Medical Care Survey (NHAMCS) and National Ambulatory Medical Care Survey (NAMCS) for comparison. Using matched ear/hearing diagnostic categories for those aged 1 and over, we compared self-triage diagnosis frequencies with national ED and ambulatory diagnosis frequencies. Results Following ear/hearing self-triage, there were 1092 subsequent office visits with a primary diagnosis code. For five frequently diagnosed ear/hearing conditions (i.e., suppurative and nonsuppurative otitis media [OM], otalgia, otitis externa, and cerumen impaction), there was a strong correlation between diagnosis counts made following self-triage and estimated counts of national ED visit diagnoses (r = 0.94; CI 95% [0.37 to 0.99]; p = .016, adjusted r2 = 0.85). Seven diagnoses were available to compare with the national ambulatory sample; correlation was r = 0.79; CI 95% [0.08 to 0.97]; p = .037, adjusted r2 = 0.54. For ages 1 and over, estimated hospital admissions from the national ED visits for ear/hearing were 0.76%, CI 95% [0.28-2.1%]; estimated total national ear/hearing ED visits were 7.5 million (for 4 years, 2016 through 2019). Conclusion The strong correlation of ear-related self-triage diagnoses with national ED diagnoses and the low hospitalization risk for these diagnoses suggests that there is an opportunity for self-triage of ear/hearing concerns to decrease ED visits for these symptoms.

Background Self-triage is becoming more widespread, but little is known about the people who are using online self-triage tools and their outcomes. For self-triage researchers, there are significant barriers to capturing subsequent healthcare outcomes. Our integrated healthcare system was able to capture subsequent healthcare utilization of individuals who used self-triage integrated with self-scheduling of provider visits. Methods We retrospectively examined healthcare utilization and diagnoses after patients had used self-triage and self-scheduling for ear or hearing symptoms. Outcomes and counts of office visits, telemedicine interactions, emergency department visits, and hospitalizations were captured. Diagnosis codes associated with subsequent provider visits were dichotomously categorized as being associated with ear or hearing concerns or not. Nonvisit care encounters of patient-initiated messages, nurse triage calls, and clinical communications were also captured. Results For 2168 self-triage uses, we were able to capture subsequent healthcare encounters within 7 days of the self-triage for 80.5% (1745/2168). In subsequent 1092 office visits with diagnoses, 83.1% (891/1092) of the uses were associated with relevant ear, nose and throat diagnoses. Only 0.24% (4/1662) of patients with captured outcomes were associated with a hospitalization within 7 days. Self-triage resulted in a self-scheduled office visit in 7.2% (126/1745). Office visits resulting from a self-scheduled visit had significantly fewer combined non-visit care encounters per office visit (fewer combined nurse triage calls, patient messages, and clinical communication messages) than office visits that were not self-scheduled (−0.51; 95% CI, −0.72 to −0.29; P < .0001). Conclusion In an appropriate healthcare setting, self-triage outcomes can be captured in a high percentage of uses to examine for safety, patient adherence to recommendations, and efficiency of self-triage. With the ear or hearing self-triage, most uses had subsequent visit diagnoses relevant to ear or hearing, so most patients appeared to be selecting the appropriate self-triage pathway for their symptoms.

Objective: This article aims to illustrate the design considerations of team-based primary care clinics in response to the pandemic. Background: Due to COVID-19, physical distancing became a critical practice in our daily life, especially in healthcare settings where healthcare professionals must continue providing care to patients despite the manifold risks. Many healthcare facilities are implementing physical distancing in their clinic layouts, and healthcare professionals are adjusting their behaviors, so they can stay away from each other. Methods: A total of four team-based primary care clinics were studied to identify their lessons learned regarding safety measures and space usage during the pandemic. Results: The four team-based primary care clinics made changes to the clinic design (e.g., waiting areas, exam rooms, team workspaces), operational protocols (e.g., in-person huddles, social gatherings, staff work locations), and usage of spaces (e.g., outdoor spaces, utility rooms). Such changes enabled the implementation of safety measures during the pandemic. However, healthcare professionals also reported challenges regarding their team communication and coordination due to physical distancing and separation. Conclusions: Our findings suggest that the physical distancing may in fact contribute to less effective teamwork and patient care and negatively affect staff well-being. In this article, we ask healthcare system leaders and designers to continue supporting both safety and teamwork by paying attention to the flexibility and spatial relationships among healthcare professionals rather than fully sacrificing teamwork for safety. Also, now is the time when multidisciplinary collaborations are needed to establish and validate guidelines that can improve both factors.

Effective medical teamwork can improve the effectiveness and experience of care for staff and patients, including safety. Healthcare organizations, and especially primary care clinics, have sought to improve medical teamwork through improved layout and design, moving staff into shared multidisciplinary team rooms. While co-locating staff has been shown to increase communication, successful designs balance four teamwork needs: face-to-face communications; situational awareness; heads-down work; perception of teamness. However, precautions for COVID-19 make it more difficult to conduct face-to-face communications. In this paper we describe a model for understanding how layout affects these four teamwork needs and describe how the perception of teamwork by staff changed after COVID-19 precautions were put in place. Observations, interviews and two standard surveys were conducted in two primary care clinics before COVID-19 and again in 2021 after a year of precautions. In general, staff felt more isolated and found it more difficult to conduct brief consults, though these perceptions varied by role. RNs, who spent more time on the phone, found it convenient to work part time-from home, while medical assistants found it more difficult to find providers in the distanced clinics. These cases suggest some important considerations for future clinic designs, including greater physical transparency that also allow for physical separation and more spaces for informal communication that are distanced from workstations.

Objective: This study empirically investigates the relationships between visibility attributes and both patients’ and staff members’ teamwork experiences. Background: Teamwork among healthcare professionals is critical for the safety and quality of patient care. While a patient-centered, team-based care approach is promoted in primary care clinics, little is known about how clinic layouts can support the teamwork experiences of staff and patients in team-based primary clinics. Methods: This article measured teamwork perceptions of staff members and patients at four primary care clinics providing team-based care. Visual access to staff workstations from both staff and patient perspectives was analyzed using VisualPower tool(version 21). The relationships between teamwork perception and visibility attributes were analyzed for each entity: staff members and patients. Results: The results showed that the visual relationships among staff members and those between staff members and patients have significant associations with overall perceptions of teamwork. While clinics providing more visual connections between staff workstations reported higher teamwork perception of staff members, patient perceptions of staff teamwork were inversely related to the number of visual connections between patients and staff workstations. Conclusions: The findings of the study provide implications for designing team-based primary care clinics to enhance the teamwork experience of both staff members and patients, which is also applicable to teamwork perceptions in other settings where both inhabitants and visitors are main user groups of the spaces. This study illustrates the representational function of space: Organizations can emphasize their values via layout design by regulating what they show to inhabitants or visitors.

The importance of communication among healthcare providers has been long recognized, and many healthcare organizations are implementing team-based care, with emphasis on staff communication. While previous empirical studies in various settings illustrate the role of built environments in user communication, there is a lack of quantified interpersonal spatial metrics to predict interactions. This study investigates how interpersonal spatial metrics at different scales predict staff communication patterns by empirically studying four primary care clinics that provide team-based care. We found that staff members in clinics with higher visual connections among staff members reported more timely and frequent communication. We also found that staff members talked to each other more frequently when their workstations were visually connected. The findings of this study are expected to help designers and facility managers provide well-designed team-based clinic layouts, beyond just shared work spaces for team members, for improved staff communication.