Understanding affective trust in AI: The effects of perceived benevolence

The primary objective of this research was to gain understanding of affective trust in AI (how comfortable individuals feel with various AI applications). This dissertation tested a model for affective trust in AI grounded in interpersonal trust theories with a focus on the effects of perceived benevolence of AI—an overlooked factor in AI trust research. In Study 1a, online survey participants evaluated 20 AI applications with single-item measures. In Study 1b, four AI applications were evaluated with multi-item measures. Perceived benevolence was significantly, positively associated with affective trust over and above cognitive trust and familiarity in 21 of 24 AI tests. Confirmatory factor analysis suggested four factors, supporting the theory that cognitive trust and affective trust in AI are distinct factors. The secondary objective was to test the utility of manipulating perceived benevolence of AI. In Study 2, online survey participants were randomly assigned to one of two groups with 10 AI applications described as “augmented intelligence” that “collaborates with” a specific or exact same AI described as “artificial intelligence.” The augmentation manipulation did not matter; there were no significant direct or indirect effects to benevolence or affective trust. These results imply that “Augmented Intelligence” positioning has no significant effect on affective trust, counter to practitioners’ beliefs. In Study 3, online survey participants were randomly assigned to one of two groups—one that received benevolence messaging (a message informing the participant that the AI was intended for human welfare) for five AI applications and the other did not.Benevolence messaging was also tested to see if it moderated contexts expected to diminish affective trust (likelihood of worker replacement and likelihood of death from error). Benevolence was not influenced by the manipulation. Surprisingly, likelihood of worker replacement had no significant association with affective trust, and likelihood of death from error had only one significant association. People may be more ambivalent about these contexts than previously thought. Understanding affective trust in AI was expanded by identifying the importance of perceived benevolence. Until benevolence messaging can boost perceptions of benevolence, the success of that strategy remains unknown

Free rides: Patient and clinic benefits in a rural pediatric clinic

In rural Oklahoma, lack of transportation is a significant barrier to health care. Rural patients, who are sicker (CDC), face an even higher burden of illness and disease with decreased access to care. Rural clinics, which are frequently financially fragile, in turn bear the brunt of lost productivity and interrupted workflow due to patients without transportation creating no shows, cancellations, rescheduled appointments, and late arrivals.The Oklahoma State University Center for Health Systems Innovation (CHSI) partnered with a Rural Oklahoma Network (ROK-Net) pediatric member clinic to innovate and implement a rurally viable model for rural clinics to provide transportation to patients who need a ride to scheduled appointments.Robust data about volume of transportation need, patient benefits and satisfaction, clinic workflow and financial implications were gathered. Both patients and the clinic benefited from the program. Transportation need was intermittent and by far greatest for sick patients. While all patients in need of transport had SoonerCare, SoonerRide restrictions such as a three day notice required for ride scheduling left patients, mostly sick patients, and patients with inability to predict their need for ride on the day of appointment without a ride to the clinic. Revenue collected for participant care far exceeded the cost of transporting them, creating a model for rural clinic transportation solutions where the clinic itself provides their patients a free ride

Strengthening rural primary care clinics using the 3i's

Most urban designed and tested health care solutions fail to consider rural primary care challenges. Rural practices face multiple hurdles and a lack of resources compound their barriers. This effort supports rural primary care clinics located in shortage areas by transforming operations to strengthen financial viability and enhance patient experience.Oklahoma State University's Center for Health Systems Innovation (CHSI) supports rural primary care clinics located in Health Resource and Services Administration (HRSA) defined health professional shortage areas by transforming practice operations to strengthen financial viability, operational efficiency and enhance patient experience. Rural clinics serve mostly Medicaid and Medicare patients in areas with high poverty rates and low educational and health literacy levels. Limited access to billing vendors and a well-trained workforce create additional barriers.CHSI has spent 2 years laying the foundation for a solution that aims to improve quality of life in rural Oklahoma by strengthening clinic workflow efficiency to increase access to and quality of primary care. The OSU Rural Clinic Efficiency Program (RCEP) was developed. This program incorporates utilizing the 3i's to develop solutions for clinics inevitably optimizing throughout and delivering quality patient centered care

CHSI/VA collaborative development of an asynchronous digital solution to increase veteran access to smoking cessation medications

Introduction: Smoking is a major modifiable health risk factor in the United States especially for veterans. Approximately, 10 million adults search online for assistance in quitting smoking each year. These signify a need for decreased barriers to smoking cessation medications, especially for smokers with a desire to quit.Research Question or Hypothesis: The aim of this study is to develop a digital solution to increase veteran access to smoking cessation medications.Study Design: Quality Improvement StudyMethods: Design and development of an online smoking cessation platform that allows online request for smoking cessation medications. This platform will also measure adoption and use by smokers when offered by physicians.Results: We successfully developed an asynchronous online platform that allows the following: (1) secure invitation from a physician; (2) a quick consultation in form of a questionnaire filled out by the patient; (3) review of patient profile by physician; (4) prescription of medication and (5) continuous evaluation of patients as they progress through the program.Conclusion: This platform was successfully developed and is in the process of being tested for effectiveness at the VA Hospital in Muskogee, Oklahoma

Projection of Oklahoma's pediatric transportation need and financial benefits of transporting them to primary care

Children that do not have access or transportation to the doctor's office tend to miss appointments. Often they end up in the emergency room (ER) which results in avoidable ER cost which is incurred by the insurance companies and state and federal payers. The hypothesis is: transporting pediatric patients in need of same or next day primary care would create cost savings from preventable ER visits. This study examines the results from a pilot transportation project conducted at a rural Oklahoma pediatric clinic and attempts to extrapolate these results to predict Oklahoma's statewide Medicaid pediatric no-show rate from a confirmed no-ride rate. Using rural clinic data and publicly available state Medicaid data, we predicted the number of statewide pediatric missed visits, the number of sick children visits, and incurred avoidable ER visit costs. These data suggest there are 52,386 missed visits which totals to $5,066,808 of lost clinic revenue. Of these, 36,908 are estimated to be sick visits and 16.7$1,316 per visit according the Oklahoma Health Care Authority published data. This translates into $8,095,279 in avoidable ER visit costs. The results suggest the lack of transportation for sick Medicaid children in need of same day or next day primary care generates higher health care cost. Moreover, a sick child's lack of health care will result in long term secondary and tertiary impacts and implications on family, community, and state

Estimating a rural-urban PCP workload disparity: caring for smokers

Background: Smokers are concentrated in rural America. CDC reports 28.5% of rural Americans smoke versus 25.1% of urban Americans. The workload impact of those additional smokers in a rural primary care practice has not been investigated. We hypothesize that workload difference associated with caring for rural smokers will be greater than the 3.4% suggested by the smoking rate difference. We will calculate primary care physician workload differences based on number of rural versus urban smoker comorbidities. Defining physician workload by number of comorbidities being managed is novel. Given that payers are associating disease management metrics to payment, calculating primary care workload by comorbidities managed is salient and illuminates real-world primary care workload differences.Methods: We hold constant the number of patients in a typical primary care panel (2500) to estimate the volume of smokers in a rural practice (28.5% of 2500 = 712.5) and in an urban practice (25.1% of 2500 = 627.5). We use the Cerner Health Facts Data Base to determine rates of comorbidities among patients designated as smokers from 1/1/2010 to 9/18/2017 (n = 7,757,949; rural = 1,337,423, urban = 6,420,526). We estimate smoker-related comorbidities using the rates of rural and urban patients with 1, 2, 3 or 4+* comorbidities and multiply the rate by rural/urban smoker volume. For example, of the 712.5 patients in a rural practice, 14.73% of them have 3 comorbidities, resulting in 314.85 comorbidities (712.5 * .1473 * 3 = 314.85). We total all estimated number of comorbidities and compare rural and urban.Results: Using 2500 patients in a patient panel, we estimate that rural primary care physicians care for 85 more smokers than urban counterparts. Due to higher comorbidity rates of those smokers, it is estimated that rural primary care physicians manage 319.54 more comorbidities (2,367.07 rural smoker comorbidities, 2,047.53 urban smoker comorbidities), constituting a 15.6% comorbidity management workload increase associated with caring for smokers.Conclusions: The 3.5% rural-urban smoking rate difference falls short of telling the story of how smokers impact physician workload differently in rural and urban practices. We estimate that the smoker associated physician workload (comorbidity management) in a rural primary care practice is 16% greater than urban practice. This demonstrates a sizeable workload disparity between rural and urban primary care physicians. We encourage the review of other patient populations to better understand rural primary care workload inflation.*Patients with more than 4 comorbidities were aggregated to the 4 conditions. Even if they had more comorbidities only 4 were calculated per patient. Therefore, comorbidity rate differences may be greater or less than reported. Since rural patients are sicker, the assumption is that comorbidity management differences are likely underreported in this study

Identifying barriers to healthcare as reported by rural and medically underserved patients in Oklahoma

OBJECTIVE: The Rural Patient Experience survey seeks to identify barriers to healthcare faced by patients in rural Oklahoma. Through the administration of a survey directly to patients, this study will analyze the current status of healthcare access, availability, and usage among rural Oklahoma populations. Results can be used to implement effective improvements in healthcare access tailored to specific patient-identified barriers.METHODS: Surveys will be distributed to individuals residing in rural communities and Health Professional Shortage Areas in the state of Oklahoma. The study involves patients of healthcare facilities in partnerships with Oklahoma State University's Center for Health System Innovation, and the facilities that agree to participate in the study will allow access to their patient panel. Patients residing in rural zip codes will be pooled into a randomly sampled population for survey distribution. Two-thirds (67%) of qualifying patients from each patient panel will be randomly selected to receive a survey in order to achieve a sample of adequate size.Responses will be analyzed using summary statistics, descriptive statistics, and significance testing.RESULTS & CONCLUSIONS: The development of the survey is being conducted and results are pending the distribution of the survey

Assessment of rural primary care clinics through the Patient Evaluation Advisory Tool

OBJECTIVE:To identify inefficiencies in a medical practice with the goal of creating a more efficient workflow from the perspective of the patient.METHODS:Human: Survey distribution and sentiment identificationSoftware: Statistical analysisRESULTS & CONCLUSIONS:According to the responses of patients of two rural primary care clinics, the results revealed areas of weakness and improvement centered around patient satisfaction. In addition to clinical staff, patients submitted their rating regarding all aspects of the clinic. The patient perspective is not currently being analyzed to transform workplace efficiencies, but this study aims to use the patient perspective insight to identify inefficiencies as well as deliver more patient-centered healthcare through the distribution of surveys

Reduced Time to Admit Emergency Department Patients to Inpatient Beds Using Outflow Barrier Analysis and Process Improvement

Objective: Because admitted emergency department (ED) patients waiting for an inpatient bed contribute to dangerous ED crowding, we conducted a patient flow investigation to discover and solve outflow delays. After solution implementation, we measured whether the time admitted ED patients waited to leave the ED was reduced. Methods: In June 2022, a team using Lean Healthcare methodologies identified flow delays and underlying barriers in a Midwest, mid-sized hospital. We calculated barriers’ magnitudes of burden by the frequency of involvement in delays. During October–December 2022, solutions targeting barriers were implemented. In October 2023, we tested whether waiting time, defined as daily median time in minutes from admission disposition to departure (ADtoD), declined by conducting independent sample, single-tailed t-test comparing pre- to post-intervention time periods, January 1–September 30, 2022 (273 days) to January 1–September 30, 2023 (273 days). Additionally, we regressed ADtoD onto pre-/post period while controlling for ED volume (total daily admissions and ED daily encounters) and hospital occupancy. A run chart analysis of monthly median ADtoD assessed improvement sustainability. Results: Process mapping revealed that three departments (ED, environmental services [EVS], and transport services) co-produced the outflow of admitted ED patients wherein 18 delays were identified. The EVS-clinical care collaboration failures explained 61% (11/18) of delays. Technology contributed to 78% (14/18) of delays primarily because staff’s technology did not display needed information, a condition we coined “digital blindness.” Comparing pre- and post-intervention days (3,144 patients admitted pre-intervention and 3,256 patients post), the median minutes a patient waited (ADtoD) significantly decreased (96.4 to 87.1 minutes, P = 0.04), even while daily ED encounter volume significantly increased (110.7 to 117.3 encounters per day, P < 0.001). After controlling in regression for other factors associated with waiting, the intervention reduced ADtoD by 12.7 minutes per patient (standard error 5.10, P = 0.01; 95% confidence interval −22.7, −2.7). We estimate that the intervention translated to ED staff avoiding 689 hours of admitted patient boarding over nine months (ADtoD coefficient [−12.7 minutes] multiplied by post-intervention ED admissions [3,256] and divided by 60). Run chart analysis substantiated the intervention’s sustainability over nine months. Conclusion: After systemwide patient flow investigation, solutions resolving digital blindness and environmental services-clinical care collaboration failures significantly reduced ED admitted patient boarding.&nbsp

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements