The "Difficult" Inpatient, a Qualitative Study of Physician Perspectives.

BACKGROUND: Previous studies exploring difficult inpatients have mostly focused on psychiatric inpatients. OBJECTIVE: To explore the characteristics of difficult medicine inpatients. DESIGN: Qualitative study using focus groups and semi-structured interviews. Transcripts were recorded, transcribed, and coded (MAXQDA) using thematic content analysis. PARTICIPANTS: Medicine inpatient providers at a tertiary care facility. KEY RESULTS: Our sample consisted of 28 providers (6 hospitalists, 10 medicine attendings, 6 medicine residents, and 6 interns). Theme 1: Provider experience: Difficult inpatients were time-consuming and evoked emotional responses including frustration and dysphoria. Theme 2: Patient characteristics: Included having personality disorders or mental health issues, being uncooperative, manipulative, angry, demanding, threatening, or distrustful. Difficult patients also had challenging social situations and inadequate support, unrealistic care expectations, were self-destructive, tended to split care-team messages, and had unclear diagnoses. Theme 3: Difficult families: Shared many characteristics of difficult patients including being distrustful, demanding, manipulative, threatening, or angry. Difficult families were barriers to care, disagreed with the treatment plan and each other, did not act in the patient's best interest, suggested inappropriate treatment, or had unrealistic expectations. STRATEGIES: Approaches to dealing with difficult patients or families included building trust, being calm, and having a consistent message. Communication approaches included naming the emotion, empathetic listening, identifying patient priorities and barriers, and partnering. CONCLUSIONS: Difficult patients induced emotional responses, dysphoria, and self-doubt among providers. Underlying personality disorders were often mentioned. Difficult patients and families shared many characteristics. Communication and training were highlighted as key strategies.

Buprenorphine Use in the United States, 2010-2019.

BACKGROUND: Buprenorphine is effective for the treatment of opioid use disorder and chronic pain, has a safer pharmacological profile than full mu-opioid agonists, and can now be prescribed by any US provider with a Drug Enforcement Administration license. This study aimed to examine a decade of buprenorphine prescribing patterns in the United States. METHODS: We abstracted opioid and buprenorphine prescribing patterns, including patient characteristics, from the 2010-2019 National Ambulatory Medical Care Survey, a national probability sample of non-federal, ambulatory encounters. DISCUSSION: Among 248,164 ambulatory encounters, opioids were prescribed 2.6%-4.3% of the time with a rate that peaked in 2013 and has been steadily declining. Buprenorphine was infrequently prescribed. Patients receiving buprenorphine were predominantly male (59%), white (70%), younger in age, and had higher rates of substance use disorder (72%). CONCLUSION: Buprenorphine is infrequently used, despite being effective for pain and safer than full mu-opioid agonists. The Drug Enforcement Administration recently ended the requirement for prescribers to obtain an X-waiver, which may increase the rate of buprenorphine use among US practitioners.

A Model of Burnout Among Healthcare Professionals.

BACKGROUND: Burnout is common and can lead to worse outcomes for both healthcare workers and patients. Our study purpose was to assess the structural relationship among factors that protect against or worsen burnout. DATA SOURCES AND STUDY SETTING: We surveyed healthcare professionals in 15 different Japanese intensive care units during the 3rd wave of the COVID-19 pandemic (March 2021). Surveys assessed burnout (Mini Z 2.0), resilience (Brief Resilience Scale), depressive (PHQ-9) and anxiety (GAD-7) symptoms, job and work environment characteristics, and personal experience with COVID. We explored survey domains with principal component factor analysis and modeled our results using structural equation modeling. PRINCIPAL FINDINGS: Among 936 ICU professionals, 24.3% met criteria for burnout. Our model suggested that resilience (ß = - 0.26, 95% CI - 0.32 to - 0.20), teamwork (ß = - 0.23, 95% CI - 0.30 to - 0.16), and feeling safe (ß = - 0.11, 95% CI - 0.18 to - 0.04) reduced burnout. Depression (ß = - 0.32, 95% CI - 0.41 to - 0.23) and anxiety (ß = - 0.20, 95% CI - 0.29 to - 0.10) both decreased resilience as did COVID fear (ß = 0.08, 95% CI - 0.14 to - 0.02). In addition to directly reducing resilience, anxiety also indirectly reduced resilience by increasing COVID fear (0.23, 95% CI 0.17 to 0.23), which decreased resilience (ß - 0.08, 95% CI - 0.14 to - 0.02). CONCLUSIONS: Burnout is common among Japanese ICU professionals. Resilience, teamwork, and safety are all correlated with reduced burnout. Those who had depression or anxiety or COVID fear had higher degrees of burnout, an effect that appears to be mediated by reduced resilience. These are potential targets for interventions to reduce burnout.

Burnout and Turnover Intention in Critical Care Professionals During the COVID-19 Pandemic in Japan: A Cross-sectional Survey.

Rationale: The prevalence of burnout among critical care professionals during the coronavirus disease (COVID-19) pandemic varies in different countries. Objectives: To investigate the prevalence of burnout and turnover intention in Japanese critical care professionals in March 2021. Methods: This cross-sectional study used a web-based survey of Japanese critical care professionals working in 15 intensive care units in 15 prefectures. Burnout was measured using the Mini Z 2.0 Survey. Intention to leave (turnover intention) was assessed by survey. Resilience was measured using the Brief Resilience Scale (Japanese version). Demographics and personal and workplace characteristics were also collected. Results: Of 1,205 critical care professionals approached, 936 (77.6%) completed the survey. Among these, 24.3%, 20.6%, and 14.2% reported symptoms of burnout, depression, and anxiety, respectively. A total of 157 respondents (16.8%) reported turnover intention. On multivariate analysis, higher resilience scores (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.84-0.95; and OR, 0.94; 95% CI, 0.91-0.96) and perceived support from the hospital (OR, 0.64; 95% CI, 0.44-0.93; and OR, 0.54; 95% CI, 0.40-0.73) were associated with a lower odds of burnout and turnover intention, respectively. Conclusions: Approximately 24% and 17% of the Japanese critical care professionals surveyed had symptoms of burnout and turnover intention from critical care, respectively, during the COVID-19 pandemic. Such professionals require organizational support to cultivate both individual and organizational resilience to reduce burnout and turnover intention.

Depression in Primary Care, 2010-2018.

BACKGROUND: Depression is common in primary care and significantly reduces quality of life. Our study aimed to examine the prevalence of depression in primary care visits, examine patterns of depression treatment and referral, and determine how often depression screening occurred over an 8-year timespan. METHODS: From the 2010-2018 National Ambulatory Medical Care Survey, a national probability sample of non-federal, ambulatory encounters, we identified adults being seen in a primary care clinic. We assessed the prevalence of depression screening, diagnosis, and treatment. RESULTS: During these 8 years, 13.1% of primary care encounters involved a patient with a diagnosis of depression. The prevalence of depression did not change over time. Patients were screened for depression 4.1% of the time, with screening increasing over time. Depression was more likely to be diagnosed when screening occurred (odds ratio 9.9; 95% confidence interval, 6.8-14.5%). Most patients were treated with a selective serotonin reuptake inhibitor. CONCLUSION: Depression is common in primary care, though screening was infrequent. Practices should consider instituting universal screening.

The Effect of Weather and Sporting Events on Ambulatory Visits and Hospitalizations at a Veterans Affairs Medical Center.

BACKGROUND: We explored the impact of weather and sporting events on clinic and emergency department (ED) visits and hospitalization. METHODS: Weather, dates of sporting events, ED visits, hospitalizations, clinic visits, no-shows, and same-day patient cancellations from 2015 to 2018 were abstracted from Milwaukee's Veterans Affairs (VA) medical system. RESULTS: Inclement precipitation increased clinic cancellations. Snowfall reduced ED visits. Green Bay Packer football games, University of Wisconsin-Madison football games, and the Super Bowl reduced ED visits but not hospitalizations. Milwaukee Brewers baseball home games didn't affect ED visits, but hospital admissions increased slightly. Basketball events (Milwaukee Bucks, University of Wisconsin-Madison, University of Wisconsin-Milwaukee, Marquette University) had no impact. CONCLUSION: Inclement weather increased clinic cancellations, and snow reduced ED visits. Football games reduced ED visits, while baseball increased hospitalizations, probably because the Milwaukee Brewers stadium is located on the VA property.

Risk Factors for Climate-Related Health Effects in an Ambulatory Population.

BACKGROUND: Climate change is a global health crisis. In most regions, heat waves are expected to become longer and more frequent and air quality is expected to worsen. Few physicians discuss climate and health with patients, and related guidelines are lacking. Our objective was to quantify the prevalence of risk factors for illness related to climate change in the U.S. ambulatory setting. METHODS: From the 2018 National Ambulatory Medical Care Survey, a national probability sample of nonfederal, ambulatory encounters, we identified adults with risk factors for illness related to heat or air pollution exposure. RESULTS: We found 91.4% of encounters involved a patient with at least 1 risk factor, while 46.7% had 2 or more. CONCLUSION: A high prevalence of patients with climate-related health risk factors exists in the ambulatory setting, representing a significant opportunity for evidence-based climate and health patient education and preventative care.

I don't think that means what you think it means: Why precision in lifelong learning terminology matters to medical education.

ISSUE: Medical educators share the belief that fostering the development of lifelong learning skills is a fundamental task for teachers and learners in all stages of a physician's education: undergraduate medical education, graduate medical education, and continuing medical education. A significant challenge to developing and implementing best practices in lifelong learning is the varied interpretation and application of its related terminology, such as 'self-directed learning' in this context. EVIDENCE: This paper discusses the scholarly origins of key terms in lifelong learning ('self-directed learning' and 'self-regulated learning') and explores their commonalities and their common conflation. IMPLICATION: The authors propose a renewed attention to precision in use of lifelong learning terminology in medical education across the spectrum as a way to best design and deploy impactful educational experiences for learners at all levels.

Tool to improve patient-provider interactions in adult primary care: Randomized controlled pilot study.

OBJECTIVE: To assess whether an intervention to help patients prioritize goals for their visit would improve patient-provider communication and clinical outcomes. DESIGN: Randomized controlled pilot study. SETTING: Primary care clinic. PARTICIPANTS: There were 120 adult hypertensive patients enrolled. INTERVENTION: Patients were randomized to receive either usual care or a previsit patient activation card developed through a series of focus groups that prompted patients to articulate their needs and set priorities for their clinic visit. Encounters were audiorecorded, transcribed, and assessed using duplicate ratings of patient activation and decision making. MAIN OUTCOME MEASURES: The primary outcome was change in medication adherence as measured by pill count at 4 and 12 weeks after the initial visit. Secondary outcomes evaluated patient-provider interaction quality (patient satisfaction, patient activation, shared decision making, patient trust, and physicians' perceived difficulty of the encounter), functional status, and blood pressure control. RESULTS: Of the 120 enrolled patients, 106 completed the baseline visit (mean age of 66 years, 53% women, 57% Black, 36% White). Participants had multiple comorbidities (median number of medications = 8). During the visit, there was greater patient activation in the intervention arm than in the control arm (4.4 vs 3.8, P = .047; ratings were based on a scale from 1 to 10). However, after the visit there were no differences in medication adherence (4 weeks: 45.8% vs 49.5%; 12 weeks: 49.4% vs 51.1%), blood pressure control (4 weeks: 133/78 mm Hg vs 131/77 mm Hg; 12 weeks: 129/77 mm Hg vs 129/76 mm Hg), or encounter satisfaction (78.6% vs 73.8% fully satisfied; P = .63). There were also no differences in shared decision making, patients' trust, or perceived difficulty of the encounter. CONCLUSION: A single previsit tool designed to prompt patients to set a prioritized agenda improved patient activation during the visit, but did not affect the quality of the interaction or postvisit patient-centred outcomes.

Approaches to Assessing and Adjusting for Selective Outcome Reporting in Meta-analysis.

BACKGROUND: Selective or non-reporting of study outcomes results in outcome reporting bias. OBJECTIVE: We sought to develop and assess tools for detecting and adjusting for outcome reporting bias. DESIGN: Using data from a previously published systematic review, we abstracted whether outcomes were reported as collected, whether outcomes were statistically significant, and whether statistically significant outcomes were more likely to be reported. We proposed and tested a model to adjust for unreported outcomes and compared our model to three other methods (Copas, Frosi, trim and fill). Our approach assumes that unreported outcomes had a null intervention effect with variance imputed based on the published outcomes. We further compared our approach to these models using simulation, and by varying levels of missing data and study sizes. RESULTS: There were 286 outcomes reported as collected from 47 included trials: 142 (48%) had the data provided and 144 (52%) did not. Reported outcomes were more likely to be statistically significant than those collected but for which data were unreported and for which non-significance was reported (RR, 2.4; 95% CI, 1.9 to 3.0). Our model and the Copas model provided similar decreases in the pooled effect sizes in both the meta-analytic data and simulation studies. The Frosi and trim and fill methods performed poorly. LIMITATIONS: Single intervention of a single disease with only randomized controlled trials; approach may overestimate outcome reporting bias impact. CONCLUSION: There was evidence of selective outcome reporting. Statistically significant outcomes were more likely to be published than non-significant ones. Our simple approach provided a quick estimate of the impact of unreported outcomes on the estimated effect. This approach could be used as a quick assessment of the potential impact of unreported outcomes.