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OBJECTIVE: In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred. PATIENTS AND METHODS: We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days. RESULTS: Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70. CONCLUSION: Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems.

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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.

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BACKGROUND: Telehealth has been increasingly adopted by health care systems since the start of the COVID-19 pandemic. Although telehealth may provide convenience for patients and clinicians, there are several barriers to accessing it and using it effectively to provide high-quality patient care. OBJECTIVE: This study was part of a larger multisite community-engaged study conducted to understand the impact of COVID-19 on diverse communities. The work described here explored the perceptions of and experience with telehealth use among diverse and underserved community members during COVID-19. METHODS: We used mixed methods across three regions in the United States (Midwest, Arizona, and Florida) from January to November 2021. We promoted our study through social media and community partnerships, disseminating flyers in English and Spanish. We developed a moderator guide and conducted focus groups in English and Spanish, mostly using a videoconferencing platform. Participants were placed in focus groups with others who shared similar demographic attributes and geographic location. Focus groups were audio-recorded and transcribed. We analyzed our qualitative data using the framework analytic approach. We developed our broader survey using validated scales and with input from community and scientific leaders, which was then distributed through social media in both English and Spanish. We included a previously published questionnaire that had been used to assess perceptions about telehealth among patients with HIV. We analyzed our quantitative data using SAS software and standard statistical approaches. We examined the effect of region, age, ethnicity/race, and education on the use and perceptions of telehealth. RESULTS: We included data from 47 focus groups. Owing to our mode of dissemination, we were not able to calculate a response rate for the survey. However, we received 3447 English-language and 146 Spanish-language responses. Over 90% of participants had internet access and 94% had used telehealth. Approximately half of all participants agreed or strongly agreed that telehealth would be beneficial in the future because it better fit their schedules and they would not need to travel. However, approximately half of the participants also agreed or strongly agreed they would not be able to express themselves well and could not be examined when using telehealth. Indigenous participants were especially concerned about these issues when compared to other racial groups. CONCLUSIONS: This work describes findings from a mixed methods community-engaged research study about telehealth, including perceived benefits and concerns. Although participants enjoyed the benefits of telehealth (eg, not having to travel and easier scheduling), they also had concerns (eg, not being able to express themselves well and not having a physical exam) about telehealth. These sentiments were especially notable among the Indigenous population. Our work highlights the importance of fully understanding the impact of these novel health delivery modalities on the patient experience and actual or perceived quality of care received.

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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.

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Objective There is no coordinated cascade testing program for familial hypercholesterolemia (FH) in the U.S. We evaluated the contemporary cost-effectiveness of cascade genetic testing relatives of FH probands with a pathogenic variant. Methods A simulation model was created to simulate multiple family trees starting with progenitor individuals carrying a pathogenic variant for FH who were followed through several generations. This approach allowed us to examine a family tree that had grown sufficiently to have large numbers of relatives across multiple degrees of relatedness. The model estimated costs and life years gained (LYG) when cascade genetic testing was implemented for relatives of FH probands identified through standard care who were at or older than designated age thresholds (5, 10, 15, 20, 25, 30, 35, 40). Costs were valued in 2018 U.S. dollars. Future costs and LYG projected by the model were discounted at an annual rate of 3%. Results For 1st degree relatives, cascade testing at every age threshold resulted in a positive number of average LYG per person, though this number decreased as testing was started at higher age thresholds. Testing was not cost-effective if initiated at an age threshold of 40 and older but was cost-effective at younger age thresholds, with a discounted cost per LYG per person of less than $50,000. For 2nd degree relatives, testing was cost-effective with a screening age threshold of 10 but no longer cost-effective at a threshold of 15 or higher. In more distant relatives, cascade genetic testing was not beneficial or cost-effective. Conclusions Based on our simulation model, cascade genetic testing for FH in the U.S. is cost-effective if started before age 40 in 1st degree relatives and before age 15 in 2nd degree relatives.

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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.

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The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.

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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.

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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.

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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.

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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.

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This pilot project was designed to determine the feasibility and effectiveness of an adaptation of the chronic care model applied to uninsured patients in a free medical clinic staffed by volunteer physicians. Of the 149 enrolled patients, 117 had hypertension, 91 had diabetes, and 51 had hyperlipidemia. Patients were enrolled in a chronic disease registry from March 1, 2001 through September 30, 2002 at the Salvation Army Free Clinic (SAFC). Two part-time registered nurses served as care managers providing disease-specific management using evidence-based guidelines. Consistent specialty consultation was available via phone, e-mail, or physician visit. Patient self-management was encouraged through collaborative goal setting. There were 40 patients lost to follow-up; 109 completed the study. A clinically significant improvement was obtained in at least one chronic disease for 79 patients. The chronic care model was a useful template for the delivery of effective chronic disease care to a group of uninsured patients at a free medical clinic.

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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.

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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.

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