Revealing complex interdependencies in surgical instrument reprocessing using SEIPS 101 tools.

Sterile Processing Departments (SPDs) must clean, maintain, store, and organize surgical instruments which are then delivered to Operating Rooms (ORs) using a Courier Network, with regular coordination occurring across departmental boundaries. To represent these relationships, we utilized the Systems Engineering Initiative for Patient Safety (SEIPS) 101 Toolkit, which helps model how health-related outcomes are affected by healthcare work systems. Through observations and interviews which built on prior work system analyses, we developed a SEIPS 101 journey map, PETT scan, and tasks matrices to represent the instrument reprocessing work system, revealing complex interdependencies between the people, tools, and tasks occurring within it. The SPD, OR and Courier teams are found to have overlapping responsibilities and a clear co-dependence, with critical implications for the successful functioning of the whole hospital system.

Patient-driven decisions and perceptions of the 'safest possible choice': insights from patient-provider conversations about how some breast cancer patients choose contralateral prophylactic mastectomy.

OBJECTIVE: Observe patient-clinician communication to gain insight about the reasons underlying the choice of patients with unilateral breast cancer to undergo contralateral prophylactic mastectomy (CPM), despite lack of survival benefit, risk of harms, and cautions expressed by surgical guidelines and clinicians. METHODS & MEASURES: WORDS is a prospective study that explored patient-clinician communication and patient decision making. Participants recorded clinical visits through a downloadable mobile application. We analyzed 44 recordings from 22 patients: 9 who chose CPM, 8 who considered CPM but decided against it, and 5 who never considered CPM. We used abductive analysis combined with constructivist grounded theory methods. RESULTS: Decisions to undergo CPM are patient-driven and motivated by perceptions that CPM is the most aggressive, and therefore safest, treatment option available. These decisions are shaped not primarily by the content of conversations with clinicians, but by the history of cancer in patients' families, their own first-hand experiences with cancers among loved ones, fear for their children, and anxiety about cancer recurrence. CONCLUSION: The perception that CPM is the safest, most aggressive option strongly influences patients, despite scientific evidence to the contrary. Future efforts to address high CPM rates should focus on patient-driven decision making and cancer-related fears.

Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.

BACKGROUND: The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. OBJECTIVE: This study's objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. METHODS: The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation. RESULTS: Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. CONCLUSIONS: This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.

Toward Joint Activity Design: Augmenting User-Centered Design with Heuristics for Supporting Joint Activity.

From their common roots in Human Factors Engineering, Human-Centered Design and Cognitive Systems Engineering have drifted into distinct fields over the past three decades, each developing beneficial heuristics, design patterns, and evaluation methods for designing for individuals and teams, respectively. GeoHAI, a clinical decision support application for preventing hospital-acquired infection, has yielded positive results in early usability testing and is expected to test positively in supporting joint activity, which will be measured through the novel implementation of Joint Activity Monitoring . The design and implementation of this application provide a demonstration of the possibilities and necessities to unify the work of Human-Centered Design and Cognitive Systems Engineering when designing technologies that are usable and useful to individuals engaged in joint activity with machine counterparts and other people. We are calling this unified process Joint Activity Design, which supports designing for machines to be good team players.

Perceptually Discriminating the Highest Priority Alarms Reduces Response Time: A Retrospective Pre-Post Study at Four Hospitals.

OBJECTIVE: Reduce nurse response time for emergency and high-priority alarms by increasing discriminability between emergency and all other alarms and suppressing redundant and likely false high-priority alarms in a secondary alarm notification system (SANS). BACKGROUND: Emergency alarms are the most urgent, requiring immediate action to address a dangerous situation. They are clinician-triggered and have higher positive predictive value (PPV). High-priority alarms are automatically triggered and have lower PPV. METHOD: We performed a retrospective pre-post study, analyzing data 15 months before and 25 months after a SANS redesign was implemented in four hospitals. For emergency alarms, we incorporated digitized human speech to distinguish them from automatically triggered alarms, leaving their onset and escalation pathways unchanged. For automatically triggered alarms, we suppressed some by delaying initial onset and escalation by 20 s. We used linear mixed models to assess the change in response time, Fisher's exact test for the proportion of response times longer than 120 s, and control charts for process stability. RESULTS: Response time for emergency alarms decreased at all hospitals (main, from 26.91 s to 22.32 s, p < .001; cardiac, from 127.10 s to 52.43 s, p < .001; cancer, from 18.03 s to 15.39 s, p < .001). Improvements were sustained. Automatically triggered alarms decreased 25.0%. Response time for the three automatically triggered cardiac alarms increased at the four hospitals. CONCLUSION: Auditory sound disambiguation was associated with a sustained reduced nurse response time for emergency alarms, but suppressing some high-priority automatically triggered alarms was not. APPLICATION: Distinguishing and escalating urgent, actionable alarms with higher PPV improves response time.

Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators.

Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers.

Exploring the relationships between computer task characteristics, mental workload, and computer users' biomechanical responses.

Previous biomechanics studies suggest that higher cognitive mental workload when performing office computer tasks may increase the risk of MSDs among office workers. Cognitive workload can be interpreted in terms of task factors (e.g. task complexity and time pressure) and mental workload factors which include mental demand and mental effort. A laboratory study was conducted to further explore how the task and mental workload factors affected computer users' biomechanical responses, specifically the muscle activation levels and sitting postures. Data were collected as 20 participants worked on computer tasks which varied in their levels of task complexity and time pressure. Visual analog scales were used for assessing mental workload factors. Results indicated that the level of mental effort reported, as opposed to the level of task complexity, was associated with changes in participants' biomechanical responses, but primarily occurred when the chair's backrest was not used. Practitioner summary: A study was conducted to investigate the association between computer users' cognitive workload and biomechanical responses when performing computer task. While task complexity was not directly associated with the changes in participants' biomechanical responses, higher reported mental effort was associated with increased biomechanical responses, but only when the participants did not use the backrest on the chair.

The effects of using a footrest during computer tasks varying in complexity and temporal demands: A postural and electromyographic analysis.

Prior research has found that office workers may not be fully utilizing their chair's back support. This may be due in part to cognitive demands or other psychological stressors. Not using the back support may increase the muscle tension and contribute to muscle fatigue and discomfort. Historically, footrests have been advocated to address anthropometric disparities in office settings. In this laboratory study, it was hypothesized that a footrest may facilitate the use of the backrest and mediate the biomechanical demands on the back and neck muscles, especially when cognitive workload is elevated. Twenty participants performed computer tasks, which varied in their complexity levels, both with and without an angled footrest. Using a footrest increased workers' use of a chair's backrest, increased pelvic rotation towards the backrest, and had a corresponding change in spine flexion. However, no changes were found in the sampled electromyographic activities due to the footrest.

Simulation Platform Development for Diabetes and Technology Self-Management.

BACKGROUND: Specialized education is critical for optimal insulin pump use but is not widely utilized or accessible. We aimed to (1) test the usability and acceptability of A1Control, a simulation platform supporting insulin pump education, and (2) determine predictors of performance. METHOD: Rural adult insulin pump users with type 1 diabetes (T1D) participated in a mixed methods usability study in 2 separate rounds. Participants navigated 3 simulations (ie, infusion site occlusion, hypoglycemia, exercise). Net Promoter Score (NPS) and Systems Usability Scale (SUS) were administered. Semi-structured interviews and direct observation were used to assess perceived usability, acceptability and performance. Synthetic Minority Oversampling Technique was used to fit predictive models for visualization of patterns leading to good or poor A1Control performance. RESULTS: Participants (N = 13) were 28-70 years old, 10 used automated insulin delivery and 12 used continuous glucose monitoring (CGM). Mean NPS was 9.5 (range 9-10) and positive sentiment during interviews indicated very high acceptability. SUS (mean 88.5, range 70-100) indicted a high perceived usability. CGM percent wear ≥ 94%, time spent in hypoglycemia ≤ 54 mg/dl of <0.01%, and <70 mg/dl of 0.5% predicted successful site-occlusion scenario performance with 100% accuracy. BOLUS score ≥ 2, TDD ≥ 34, and technology brand predicted exercise scenario success with 100% accuracy. There were an insufficient number of failed hypoglycemia scenarios to assess predictors. CONCLUSION: A1Control shows potential to increase access and frequency of self-management and technology education. Additional study is needed to determine sustained engagement and benefit.

Applying Human Factors Engineering to Address the Telemetry Alarm Problem in a Large Medical Center.

OBJECTIVE: Address the alarm problem by redesigning, reorganizing, and reprioritizing to better discriminate alarm sounds and displays in a hospital. BACKGROUND: Alarms in hospitals are frequently misunderstood, disregarded, and overridden. METHOD: Discovery-oriented, intervention, and translational studies were conducted. Study objectives and measures varied, but had the shared goals of increasing positive predictive value (PPV) of critical alarms by reducing low-PPV alarms in the background, prioritizing alarms, redesigning alarm sounds to increase information content, and transparently conveying who initiated alarms. An alarm ontology was iteratively generated and refined until consensus was achieved. RESULTS: The ontology distinguishes five levels of urgency that incorporate likely PPV, three categories for who initiates the alarm (hospital staff, patient, or machine), whether it is clinical or technical, and clinical functions. CONCLUSION: This unique collaboration allowed us to make progress on the alarm problem by making unintuitive leaps, avoiding common missteps, and refuting conventional healthcare approaches. APPLICATION: Hospitals can consistently redesign, reorganize, reprioritize, and better discriminate alarms by priority, PPV, and content to reduce nurse response times.

From Reactive to Proactive Safety: Joint Activity Monitoring for Infection Prevention.

Despite the promise of a proactive approach to safety, a lack of resources and tangible measures have limited its implementation in organizations. We are exploring Joint Activity Monitoring (JAM) as one key component of a proactive safety program within the domain of infection prevention. However, despite a conceptual alignment to the requirements of a proactive monitoring capability, our experiences instrumenting daily work tools with the capabilities to support continuous, unobtrusive, real-time monitoring have revealed additional organizational and technological requirements. In this paper, we describe our strategies and challenges in developing this capability and discuss implications for supporting successful proactive safety implementations.

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers' allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods-bandit algorithms-similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

Symbiotic Design Application in Healthcare: Preventing Hospital Acquired Infections.

Any clinical decision support (CDS) design project integrating computational technologies with clinician workflows will require the merging of multiple perspectives and fields of expertise in multidisciplinary teams. Much like the tools these teams aim to create, the team itself will need to continuously build, monitor, and repair a mutually beneficial relationship between each of its members. From our experience during the early development stages of an AI-enabled CDS tool for hospital-acquired infection (HAI) prevention, we abstract three central tenets of a symbiotic design process we have found to be vital for aligning goals, priorities, mental models, and techniques among a multidisciplinary team: (1) recurrent bottom-up feedback, (2) continual model (re-)alignment, and (3) openness to co-direction. With regards to these tenets, we discuss the successes and challenges our team has faced during the symbiotic design process through a series of vignettes and how these experiences coalescing diverse human design teams can influence the design of human-machine teams.

Using timbre to improve performance of larger auditory alarm sets.

Identifiability and perceived urgency were compared for two sets of alarms in a healthcare inpatient setting. One contained currently used alarms where possible, with new sounds added as needed. The other was designed together, was more heterogenous, used timbre to encode intended similarities and explicitly encoded intended urgency across the set. Twenty nurses reported the identity and perceived urgency of the sounds in each set. Participants correctly identified the sound (0.89 vs. 0.77) and alarm category (0.93 vs. 0.82) more often in the new set than in the baseline set. In addition, multiple sounds in the new set were more identifiable. The new sounds also had a larger range of perceived urgency and better urgency match. The results indicate that timbre is well-suited to encode alarm groupings in larger alarm sets and that this, along with increased heterogeneity and explicit urgency mapping, improves alarm set performance. Practitioner summary: Clinical alarms are frequently misidentified. We found that making alarms more acoustically rich, using timbre to convey alarm groups, and explicitly encoding intended urgency improved identifiability and urgency match. These findings can be used to improve alarm performance across all safety-critical industries.

Continuous Cardiac Monitoring Policy Implementation: Three-year Sustained Decrease of Hospital Resource Utilization.

Inappropriate cardiac monitoring leads to increased hospital resource utilization and alarm fatigue, which is ultimately detrimental to patient safety. Our institution implemented a continuous cardiac monitoring (CCM) policy that focused on selective monitoring for patients based on the American Heart Association (AHA) guidelines. The primary goal of this study was to perform a three-year median follow-up review on the longitudinal impact of a selective CCM policy on usage rates, length of stay (LOS), and mortality rates across the medical center. A secondary goal was to determine the effect of smaller-scale interventions focused on reeducating the nursing population on the importance of cardiac alarms. A system-wide policy was developed at The Ohio State University in December 2013 based on guidelines for selective CCM in all patient populations. Patients were stratified into Critical Class I, II, and III with 72 hours, 48 hours, or 36 hours of CCM, respectively. Pre- and post-implementation measures included average cardiac monitoring days (CMD), emergency department (ED) boarding rate, mortality rates, and LOS. A 12-week evaluation period was analyzed prior to, directly after, and three years after implementation. There was an overall decrease of 53.5% CMDs directly after implementation of selective CCM. This had remained stable at the three-year follow-up with slight increase of 0.5% (p = 0.2764). Subsequent analysis by hospital type revealed that the largest and most stable reductions in CMD were in noncardiac hospitals. The cardiac hospital CMD reduction was stable for roughly one year, then dipped into a lower stable level for nine months, then returned to the previous post-implementation levels. This change coincided with a smaller intervention to further reduce CMD in the cardiac hospital. There was no significant change in mortality rates with a slight decrease of 3.1% at follow-up (p = 0.781). Furthermore, there was no significant difference in LOS with a slight increase of 1.1% on follow-up (p = 0.649). However, there was a significant increase in ED boarding rate of 7.7% (p < 0.001) likely due to other hospital factors altering boarding times. Implementing selective CCM decreases average cardiac monitoring rate without affecting LOS or overall mortality rate. Selective cardiac monitoring is also a sustainable way to decrease overall hospital resource utilization and more appropriately focus on patient care.

A qualitative analysis of clinical decompensation in the surgical patient: Perceptions of nurses and physicians.

BACKGROUND: There is little knowledge on how health care providers individually interpret and communicate early warning signs to other providers. The aim of the study described here was to qualitatively assess the similarities and differences in how nurses and physicians perceive early warning signs that potentially predict clinical decompensation, changes in clinical acuity in surgical patients, and need for escalation of care. METHODS: Ethnographic interviews were conducted with nurses, surgical residents, and attending surgeons on an acute care medical-surgical unit. Constant comparative analysis was used to analyze and draw conclusions from the interview data. RESULTS: There were many areas of strong agreement across all care providers including the same data analyzed, importance of temporal trends, and lower acuity level for an established patient. However, physicians differed from nurses in that their primary indicator of patient stability was their level of confidence in the current diagnosis. Nurses, however, deemed patients to be stable only when their symptoms resolved. Other differences were the methods and frequency they used to monitor unstable patients. CONCLUSION: Differences in the type of communication and clinician's mental models of acuity and stability could lead to coordination failures and adverse events. Understanding and addressing these differences has the potential to improve outcomes.

PARTICIPATORY BULLSEYE TOOLKIT INTERVIEW: IDENTIFYING PHYSICIANS' RELATIVE PRIORITIZATION OF DECISION FACTORS WHEN ORDERING RADIOLOGIC IMAGING IN A HOSPITAL SETTING.

Critical Decision Method (CDM), a popular cognitive task analysis (CTA) method, is an in-depth retrospective interview that uses a historical non-routine incident to identify experts' decision-making factors in complex socio-technical settings with high consequences for failure. However, it is challenging to use CDM to make comparisons, including those between experts and trainees. We describe an alternative CTA method used to study physicians' decision making for ordering diagnostic imaging. After being primed with 11 simulated patient scenarios, nine attending and 11 resident physicians were asked to map out and present their decision-making process with a bullseye participatory design toolkit. Interviews were analyzed qualitatively, revealing four common decision factors: diagnostic efficacy, patient safety, organizational constraints, and patient comfort. The bullseye maps were used to quantitatively measure priority differences between these decision factors. Attending and resident physicians both prioritized diagnostic efficacy over the other factors (2.38 vs. 3.71, p <.01, and 2.59 vs. 3.52, p<.01, respectively), but attending physicians' decisions had a higher proportion of non-diagnostic items (65% vs. 50%, p = .008). Our results demonstrate the usefulness of this method in eliciting decision factors for a complex, face-valid task and for identifying differences due to levels of expertise and training.

Patient-Centered Handovers: Ethnographic Observations of Attending and Resident Physicians: Ethnographic Observations of Attending and Resident Physicians.

Handover communication improvement initiatives typically employ a "one size fits all" approach. A human factors perspective has the potential to guide how to tailor interventions to roles, levels of experience, settings, and types of patients. We conducted ethnographic observations of sign-outs by attending and resident physicians in 2 medical intensive care units at one institution. Digitally audiotaped data were manually analyzed for content using codes and time spent using box plots for emergent categories. A total of 34 attending and 58 resident physician handovers were observed. Resident physicians spent more time for "soon to be discharged" and "higher concern" patients than attending physicians. Resident physicians spent less time discussing patients which they had provided care for within the last 3 days ("handbacks"). The study suggested differences for how handovers were conducted for attending and resident physicians for 3 categories of patients; handovers differ on the basis of role or level of expertise, patient type, and amount of prior knowledge of the patient. The findings have implications for new directions for subsequent research and for how to tailor quality improvement interventions based upon the role, level of experience, level of prior knowledge, and patient categories.