Psychological reactance, misinformation, and distrust: A mixed methods analysis of COVID-19 vaccine uptake.

Background: Assessing perceptions of the COVID-19 vaccines is essential for understanding vaccine hesitancy and for improving uptake during public health emergencies. In the complicated landscape of COVID-19 vaccine mandates and rampant misinformation, many individuals faced challenges during vaccination decision-making. The purpose of our mixed methods study is to elucidate factors affecting vaccine decision-making and to highlight the discourse surrounding the COVID-19 vaccines in diverse and underserved communities. Methods: This mixed methods study was conducted in Arizona, Florida, Minnesota, and Wisconsin between March and November 2021, combining a cross-sectional survey (n = 3593) and focus groups (n = 47). Results: The groups least likely to report receiving a vaccination were non-Hispanic Whites, Indigenous people, males, and those with moderate socioeconomic status (SES). Those indicating high and low SES reported similar vaccination uptake. Focus group data highlighted resistance to mandates, distrust, misinformation, and concerns about the rapid development surrounding the COVID-19 vaccines. Psychological reactance theory posits that strongly persuasive messaging and social pressure can be perceived as a threat to freedom, encouraging an individual to take action to restore that freedom. Conclusion: Our findings indicate that a subsection of participants felt pressured to get the vaccine, which led to weaker intentions to vaccinate. These results suggest that vaccine rollout strategies should be reevaluated to improve and facilitate informed decision-making.

A discrete event simulation to evaluate impact of radiology process changes on emergency department computed tomography access.

BACKGROUND: Hospitals face the challenge of managing demand for limited computed tomography (CT) resources from multiple patient types while ensuring timely access. METHODS: A discrete event simulation model was created to evaluate CT access time for emergency department (ED) patients at a large academic medical center with six unique CT machines that serve unscheduled emergency, semi-scheduled inpatient, and scheduled outpatient demand. Three operational interventions were tested: adding additional patient transporters, using an alternative creatinine lab, and adding a registered nurse dedicated to monitoring CT patients in the ED. RESULTS: All interventions improved access times. Adding one or two transporters improved ED access times by up to 9.8 minutes (Mann-Whitney (MW) CI: [-11.0,-8.7]) and 10.3 minutes (MW CI [-11.5, -9.2]). The alternative creatinine and RN interventions provided 3-minute (MW CI: [-4.0, -2.0]) and 8.5-minute (MW CI: [-9.7, -8.3]) improvements. CONCLUSIONS: Adding one transporter provided the greatest combination of reduced delay and ability to implement. The projected simulation improvements have been realized in practice.

Extended Patient Alone Time in Emergency Department Leads to Increased Risk of 30-Day Hospitalization.

OBJECTIVES: This study was conducted to describe patients at risk for prolonged time alone in the emergency department (ED) and to determine the relationship between clinical outcomes, specifically 30-day hospitalization, and patient alone time (PAT) in the ED. METHODS: An observational cohort design was used to evaluate PAT and patient characteristics in the ED. The study was conducted in a tertiary academic ED that has both adult and pediatric ED facilities and of patients placed in an acute care room for treatment between May 1 and July 31, 2016, excluding behavioral health patients. Simple linear regression and t tests were used to evaluate the relationship between patient characteristics and PAT. Logistic regression was used to evaluate the relationship between 30-day hospitalization and PAT. RESULTS: Pediatric patients had the shortest total PAT compared with all older age groups (86.4 minutes versus 131 minutes, P < 0.001). Relationships were seen between PAT and patient characteristics, including age, geographic region, and the severity and complexity of the health condition. Controlling for Charlson comorbidity index and other potentially confounding variables, a logistic regression model showed that patients are more likely to be hospitalized within 30 days after their ED visit, with an odds ratio (95% confidence interval) of 1.056 (1.017-1.097) for each additional hour of PAT. CONCLUSIONS: Patient alone time is not equal among all patient groups. Study results indicate that PAT is significantly associated with 30-day hospitalization. This conclusion indicates that PAT may affect patient outcomes and warrants further investigation.

Improving patient access in nuclear medicine: a case study of PET scanner scheduling.

We used the systems engineering technique of discrete event simulation modeling to assist in increasing patient access to positron emission tomographic examinations in the Department of Nuclear Medicine at Mayo Clinic, Rochester. The model was used to determine the best universal slot length to address the specific access challenges of a destination medical center such as Mayo Clinic. On the basis of the modeling, a new schedule was implemented in April 2012 and our before and after data analysis shows an increase of 2.4 scans per day. This was achieved without requiring additional resources or negatively affecting patient waiting, staff satisfaction (as evaluated by day length), or examination quality.

Recovery bed planning in cardiovascular surgery: a simulation case study.

Recovery beds for cardiovascular surgical patients in the intensive care unit (ICU) and progressive care unit (PCU) are costly hospital resources that require effective management. This case study reports on the development and use of a discrete-event simulation model used to predict minimum bed needs to achieve the high patient service level demanded at Mayo Clinic. In addition to bed predictions that incorporate surgery growth and new recovery protocols, the model was used to explore the effects of smoothing surgery schedules and transferring long-stay patients from the ICU. The model projected bed needs that were 30 % lower than the traditional bed-planning approach and the options explored by the practice could substantially reduce the number of beds required.

Use of a registry-generated audit, feedback, and patient reminder intervention in an internal medicine resident clinic--a randomized trial.

BACKGROUND: Disease registries, audit and feedback, and clinical reminders have been reported to improve care processes. OBJECTIVE: To assess the effects of a registry-generated audit, feedback, and patient reminder intervention on diabetes care. DESIGN: Randomized controlled trial conducted in a resident continuity clinic during the 2003-2004 academic year. PARTICIPANTS: Seventy-eight categorical Internal Medicine residents caring for 483 diabetic patients participated. Residents randomized to the intervention (n = 39) received instruction on diabetes registry use; quarterly performance audit, feedback, and written reports identifying patients needing care; and had letters sent quarterly to patients needing hemoglobin A1c or cholesterol testing. Residents randomized to the control group (n = 39) received usual clinic education. MEASUREMENTS: Hemoglobin A1c and lipid monitoring, and the achievement of intermediate clinical outcomes (hemoglobin A1c <7.0%, LDL cholesterol <100 mg/dL, and blood pressure <130/85 mmHg) were assessed. RESULTS: Patients cared for by residents in the intervention group had higher adherence to guideline recommendations for hemoglobin A1c testing (61.5% vs 48.1%, p = .01) and LDL testing (75.8% vs 64.1%, p = .02). Intermediate clinical outcomes were not different between groups. CONCLUSIONS: Use of a registry-generated audit, feedback, and patient reminder intervention in a resident continuity clinic modestly improved diabetes care processes, but did not influence intermediate clinical outcomes.

Recollection of previous colon cancer screening in Minnesota adults.

Regularly scheduled screening of patients has been shown to be effective for decreasing morbidity and mortality from colorectal cancer. We used a questionnaire to assess patients' ability to accurately recall the date and type of their most recent colorectal cancer screening. The study included 200 consecutive patients whose records indicated they had undergone previous colorectal cancer screening (8 patients were later excluded because they had been screened more than 5 years earlier). Overall, 32.3% of patients could not recall the date of their last colorectal cancer screening, 34.3% recalled the date inaccurately, and 33.3% recalled the date accurately within 1 month. The results suggest that recall alone is not sufficient to determine the date and type of previous colorectal cancer screening, especially if the screening was performed more than a year earlier.

Distinguishing hospital complications of care from pre-existing conditions.

OBJECTIVE: To compare cases identified through the Complications Screening Program (CSP) as complications with cases using the same ICD-9 secondary diagnosis codes, where the identifying diagnosis is also indicated as not present at admission. DESIGN: Observational study comparing two sources of potential hospital complications: published computer algorithms applied to coded diagnosis data versus a secondary diagnosis indicator, which distinguishes pre-existing from hospital-developed conditions. SETTING: All patients discharged from Mayo Clinic Rochester hospitals during 1998 and 1999. The Mayo Clinic is a large integrated delivery system in southeastern Minnesota, USA, providing services ranging from local, primary care to tertiary care for referral patients. Approximately 35% of Mayo patients travel >200 km for medical care. STUDY PARTICIPANTS: Hospital patients (total = 84 436). The numbers of cases with complications ranged from 0 to 2444 per algorithm. MAIN OUTCOME MEASURES: Percent of algorithm complication cases indicated as developing in the hospital, and percent of acquired conditions of that type detected by the computer algorithms. Incremental hospital charges, length of stay (LOS) and mortality associated with acquired complications. RESULTS: The percent of cases identified through the computer algorithm that were also coded as acquired varied from 8.8% to 100%. The ability of the computer algorithms to detect acquired conditions of that type also varied greatly, from 2% to 99%. Incremental charges and LOS were significant for patients with acquired complications except for hip fracture/falls. Many acquired complications also increased hospital mortality. CONCLUSIONS: Complication rates based strictly on standard discharge abstracts have limited use for inter-hospital comparisons due to large variability in coding across hospitals and the insensitivity of existing computer algorithms to exclude conditions present on admission from true complications. However, complications do carry high costs, including extended stays and increased hospital mortality. Enhancing secondary diagnoses with a simple indicator identifying which diagnoses were present on admission greatly increases the accurate identification of complications for internal quality and patient safety improvements.

Do complication screening programs detect complications present at admission?

BACKGROUND: A study was undertaken to verify the accuracy of computer algorithms on administrative data to identify hospital complications. The assessment was based on a medical records indicator that differentiated hospital-acquired conditions from preexisting comorbidities. METHODS: The indicators for identifying potential hospital complications were applied to all secondary diagnoses to distinguish hospital-acquired from preexisting conditions for all 1997-1998 discharges. RESULTS: Of the 95 defined complication types, cases were found with secondary diagnoses that met the criteria for 71 different complications. Sixty-nine of these complications had one or more cases with the trigger diagnosis coded as an acquired condition. Thirty-five complications had at least 30 cases with acquired conditions. Hospital complications add greatly to costs; for example, postoperative septicemia increased the hospital bill by more $25,000, added 13 hospital days to the stay, and increased hospital mortality by 16.6%. CONCLUSIONS: Current complication algorithms identify many cases where the condition was actually present on hospital admission. This fact, coupled with the known variability in coding between institutions, makes comparisons between hospitals on many of the complications problematic. Collection of the present-on-admission flag significantly reduces the noise in monitoring complication rates.