Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review.

BACKGROUND: Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. METHODS: We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. RESULTS: Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments' data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians' reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. CONCLUSIONS: Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs' method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

"They say it's fentanyl, but they honestly look like Perc 30s": Initiation and use of counterfeit fentanyl pills.

BACKGROUND: Worsening of the overdose crisis in the USA has been linked to the continuing proliferation of non-pharmaceutical fentanyl (NPF). The recent wave of NPF spread in the USA has been fueled by an increased presence of counterfeit pills that contain NPF. This qualitative study aims to characterize the motivation and practices of counterfeit NPF pill initiation and use among individuals using illicit opioids in Arizona. METHODS: Between October 2020 and May 2021, semi-structured interviews were conducted with 22 individuals meeting the following eligibility criteria: (1) 18 years or older; (2) residence in Arizona; and (3) use of illicit opioids in the past 30 days and/or opioid use disorder treatment in the past 12 months. Participants were recruited through referrals by a harm reduction organization, craigslist ads, and referrals by other participants. Interviews were conducted virtually via Zoom. Qualitative interviews were transcribed and analyzed thematically using NVivo. RESULTS: Out of 22 participants, 64% were male, and 45% were ethnic minorities. Age ranged between 25 and 51 years old. Participants noted significant recent increases in the availability of counterfeit NPF pills ("blues," "dirty oxys") that were most commonly used by smoking. The majority indicated first trying NPF pills in the past year, and the first use often occurred in situations of reduced access to heroin or pharmaceutical opioids. Participant decisions to switch over to more frequent NPF pill use or to maintain some levels of heroin use were shaped by local drug availability trends and personal experiences with NPF effects. They were also influenced by conflicting views of social acceptability of pharmaceutical-like drugs, perceived harms of NPF in terms of overdose risks and increased difficulty of quitting, and perceived benefits of switching to the non-injection route of opioid administration (e.g., from injecting heroin to smoking NPF pills). CONCLUSION: Our findings highlight the need for the implementation of novel policy, treatment, and harm reduction approaches to address the growing unpredictability of drug supply and NPF pill-specific risks, attitudes, and behaviors.

Aging through the time of COVID-19: a survey of self-reported healthcare access.

BACKGROUND: Chronic conditions are common and require ongoing continuous management and preventive measures. The COVID-19 pandemic may have affected the management of chronic conditions by delaying care. We sought to understand the impact of personal characteristics (i.e., age) and healthcare factors (i.e., access to a provider) on healthcare access in a sample of Americans 50 years of age or older during COVID-19. METHOD: Participants completed an online survey at the start of the COVID-19 pandemic - the Aging in the Time of COVID Survey. Questions focused on health status, health care access, COVID-19 fear, and social connectedness. Participants were recruited through social media advertisements, list serves, and snowball sampling. Data collection started in early April 2020 and concluded in late May 2020. Logistic regression models examined the results of two key access points: healthcare provider/doctor (n = 481) and medication (n = 765), with 56 and 93% of participants reporting access to a provider and medications, respectively. RESULTS: Individuals with an established primary care provider were much more likely to obtain access to a healthcare provider, OR = 3.81 (95% CI: 1.69, 8.77), and to receive medication, OR = 4.48 (95% CI: 1.61, 11.48), during the time of COVID-19. In addition, access to medication was (a) higher for those who were older, OR = 1.05 (95% CI: 1.01, 1.09), had a higher income (greater than 100 k compared to less than 50 k, OR = 3.04 (95% CI: 1.11, 8.98), and (b) lower for those having caregiving responsibilities, OR = 0.41 (95% CI: 0.21, 0.78), or greater social isolation, OR = 0.93 (95% CI: 0.87, 0.98). CONCLUSIONS: Although most participants had access to medication, just over half had access to a healthcare provider when needed. Notably, health-seeking behaviors for individuals who do not have an established primary care providers as well as those who provide unpaid care, are socially isolated, and younger may require more proactive approaches to care monitoring, management, and maintenance.

Study of EHR-mediated workflows using ethnography and process mining methods.

Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow.

A micro-analytic approach to understanding electronic health record navigation paths.

Clinician task performance is significantly impacted by the navigational efficiency of the system interface. Here we propose and evaluate a navigational complexity framework useful for examining differences in electronic health record (EHR) interface systems and their impact on task performance. The methodological approach includes 1) expert-based methods-specifically, representational analysis (focused on interface elements), keystroke level modeling (KLM), and cognitive walkthrough; and 2) quantitative analysis of interactive behaviors based on video-captured observations. Medication administration record (MAR) tasks completed by nurses during preoperative (PreOp) patient assessment were studied across three Mayo Clinic regional campuses and three different EHR systems. By analyzing the steps executed within the interfaces involved to complete the MAR tasks, we characterized complexities in EHR navigation. These complexities were reflected in time spent on task, click counts, and screen transitions, and were found to potentially influence nurses' performance. Two of the EHR systems, employing a single screen format, required less time to complete (mean 101.5, range 106-97 s), respectively, compared to one system employing multiple screens (176 s, 73% increase). These complexities surfaced through trade-offs in cognitive processes that could potentially influence nurses' performance. Factors such as perceptual-motor activity, visual search, and memory load impacted navigational complexity. An implication of this work is that small tractable changes in interface design can substantially improve EHR navigation, overall usability, and workflow.

A Task-Analytic Framework Comparing Preoperative Electronic Health Record-Mediated Nursing Workflow in Different Settings.

Preoperative care is a critical, yet complex, time-sensitive process. Optimization of workflow is challenging for many reasons, including a lack of standard workflow analysis methods. We sought to comprehensively characterize electronic health record-mediated preoperative nursing workflow. We employed a structured methodological framework to investigate and explain variations in the workflow. Video recording software captured 10 preoperative cases at Arizona and Florida regional referral centers. We compared the distribution of work for electronic health record tasks and off-screen tasks through quantitative analysis. Suboptimal patterns and reasons for variation were explored through qualitative analysis. Although both settings used the same electronic health record system, electronic health record tasks and off-screen tasks time distribution and patterns were notably different across two sites. Arizona nurses spent a longer time completing preoperative assessment. Electronic health record tasks occupied a higher proportion of time in Arizona, while off-screen tasks occupied a higher proportion in Florida. The contextual analysis helped to identify the variation associated with the documentation workload, preparation of the patient, and regional differences. These findings should seed hypotheses for future optimization efforts and research supporting standardization and harmonization of workflow across settings, post-electronic health record conversion.

We're Lost, But We are Making Good Time: Navigating Complex Pathways in a Patient-Order Management Task.

Patient order management (POM) is a mission-critical task for perioperative workflow. Interface complexity within different EHR systems result in poor usability, increasing documentation burden. POM interfaces were compared across two systems prior to (Cerner SurgiNet) and subsequent to an EHR conversion (Epic). Here we employ a navigational complexity framework useful for examining differences in EHR interface systems. The methodological approach includes 1) expert-based methods-specifically, functional analysis, keystroke level model (KLM) and cognitive walkthrough, and 2) quantitative analysis of observed interactive user behaviors. We found differences in relation to navigational complexity with the SurgiNet interface displaying a higher number of unused POM functions, with 12 in total whereas Epic displayed 7 total functions. As reflected in all measures, Epic facilitated a more streamlined task-focused user experience. The approach enabled us to scrutinize the impact of different EHR interfaces on task performance and usability barriers subsequent to system implementation.

Mayo Clinic Registry of Operational Tasks (ROOT): A Paradigm Shift in Electronic Health Reco Implementation Evaluation.

OBJECTIVE: To systematically examine clinical workflows before and after a major electronic health record (EHR) implementation, we performed this study. EHR implementation and/or conversion are associated with many challenges, which are barriers to optimal care. Clinical workflows may be significantly affected by EHR implementations and conversions, resulting in provider frustration and reduced efficiency. PATIENTS AND METHODS: Our institution completed a large EHR conversion and workflow standardization converting from 3 EHRs (GE Centricity and 2 versions of Cerner) to a system-wide Epic platform. To study this quantitatively and qualitatively, we collected and curated clinical workflows through rapid ethnography, workflow observation, video ethnography, and log-file analyses of hundreds of providers, patients, and more than 100,000 log files. The study included 5 geographic sites in 4 states (Arizona, Minnesota, Florida, and Wisconsin). This project began in April 2016, and will be completed by December 2019. Our study began on May 1, 2016, and is ongoing. RESULTS: Salient themes include the importance of prioritizing clinical areas with the most intensive EHR use, the value of tools to identify bottlenecks in workflow that cause delays, and desire for additional training to optimize navigation. Video microanalyses identified marked differences in patterns of workflow and EHR navigation patterns across sites. Log-file analyses and social network analyses identified differences in personnel roles, which led to differences in patient-clinician interaction, time spent using the EHR, and paper-based artifacts. CONCLUSION: Assessing and curating workflow data before and after EHR conversion may provide opportunities for unexpected efficiencies in workflow optimization and information-system redesign. This project may be a model for capturing significant new knowledge in using EHRs to improve patient care, workflow efficiency, and outcomes.

Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers.

Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater "in-CHC" data analytic capabilities to re-design care delivery processes for improving access and efficiency.

Process Mining and Ethnography Study of Medication Reconciliation Tasks.

We studied the medication reconciliation (MedRec) task through analysis of computer logs and ethnographic data. Time spent by healthcare providers performing MedRec was compared between two different EHR systems used at four different regional perioperative settings. Only one of the EHRs used at two settings generated computer logs that supported automatic discovery of the MedRec task. At those two settings, 53 providers generated 383 MedRec instances. Findings from the computer logs were validated with ethnographic data, leading to the identification and removal of 47 outliers. Without outliers, one of the settings had slightly smaller mean (SD) time in seconds 67.3 (40.2) compared with the other, 92.1 (25). The difference in time metrics was statistically significant (p<.001). Reusability of an existing task-based analytic method allowed for rapid study of EHR-based workflow and task.

Data Analytics and Modeling for Appointment No-show in Community Health Centers.

OBJECTIVES: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. METHODS AND MATERIALS: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models' ability to identify patients missing their appointments. RESULTS: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). DISCUSSION: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. CONCLUSION: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.

Using a Positive Deviance Approach to Influence the Culture of Patient Safety Related to Infection Prevention.

BACKGROUND: Health care-associated infections (HAIs) are a socio-technical problem. We evaluated the impact of a social change intervention on health care personnel (HCP), called "positive deviance" (PD), on patient safety culture related to infection prevention among HCP. METHODS: This observational study was done in 6 medical wards at an 800-bed public academic hospital in the United States. Three of these wards were randomly assigned to receive PD intervention on HCP. After a retrospective 6-month baseline period, PD was implemented over 9 months, followed by 9 months of follow-up. Patient safety culture and social networks among HCP were surveyed at 6, 15, and 24 months. Rates of HAI were measured among patients. RESULTS: The measured patient safety culture was steady over time at 69% aggregate percent positive responses in wards with PD vs decline from 79% to 75% in wards without PD (F statistic 10.55; P = .005). Social network maps suggested that nurses, charge nurses, medical assistants, ward managers, and ward clerks play a key role in preventing infections. Fitted time series of monthly HAI rates showed a decrease from 4.8 to 2.8 per 1000 patient-days (95% confidence interval [CI], 2.1 to 3.5) in wards without PD, and 5.0 to 2.1 per 1000 patient-days (95% CI, -0.4 to 4.5) in wards with PD. CONCLUSIONS: A positive deviance approach appeared to have a significant impact on patient safety culture among HCP who received the intervention. Social network analysis identified HCP who are likely to help disseminate infection prevention information. Systemwide interventions independent of PD resulted in HAI reduction in both intervention and control wards.

Identifying Successful Practices to Overcome Access to Care Challenges in Community Health Centers: A "Positive Deviance" Approach.

BACKGROUND: Despite health care access challenges among underserved populations, patients, providers, and staff at community health clinics (CHCs) have developed practices to overcome limited access. These "positive deviant" practices translate into organizational policies to improve health care access and patient experience. OBJECTIVE: To identify effective practices to improve access to health care for low-income, uninsured or underinsured, and minority adults and their families. PARTICIPANTS: Seven CHC systems, involving over 40 clinics, distributed across one midwestern state in the United States. METHODS: Ninety-two key informants, comprised of CHC patients (42%) and clinic staff (53%), participated in semi-structured interviews. Interview transcripts were subjected to thematic analysis to identify patient-centered solutions for managing access challenges to primary care for underserved populations. Transcripts were coded using qualitative analytic software. RESULTS: Practices to improve access to care included addressing illiteracy and low health literacy, identifying cost-effective resources, expanding care offerings, enhancing the patient-provider relationship, and cultivating a culture of teamwork and customer service. Helping patients find the least expensive options for transportation, insurance, and medication was the most compelling patient-centered strategy. Appointment reminders and confirmation of patient plans for transportation to appointments reduced no-show rates. CONCLUSION: We identified nearly 35 practices for improving health care access. These were all patient-centric, uncovered by both clinic staff and patients who had successfully navigated the health care system to improve access.

Designing Colorectal Cancer Screening Decision Support: A Cognitive Engineering Enterprise.

Adoption of clinical decision support has been limited. Important barriers include an emphasis on algorithmic approaches to decision support that do not align well with clinical work flow and human decision strategies, and the expense and challenge of developing, implementing, and refining decision support features in existing electronic health records (EHRs). We applied decision-centered design to create a modular software application to support physicians in managing and tracking colorectal cancer screening. Using decision-centered design facilitates a thorough understanding of cognitive support requirements from an end user perspective as a foundation for design. In this project, we used an iterative design process, including ethnographic observation and cognitive task analysis, to move from an initial design concept to a working modular software application called the Screening & Surveillance App. The beta version is tailored to work with the Veterans Health Administration's EHR Computerized Patient Record System (CPRS). Primary care providers using the beta version Screening & Surveillance App more accurately answered questions about patients and found relevant information more quickly compared to those using CPRS alone. Primary care providers also reported reduced mental effort and rated the Screening & Surveillance App positively for usability.

Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation.

OBJECTIVE: To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors. MATERIALS AND METHODS: We performed a scenario-based simulation study using a counterbalanced, crossover design with 20 Veterans Affairs prescribers to compare original versus redesigned alerts. We redesigned drug-allergy, drug-drug interaction, and drug-disease alerts based upon human factors principles. We assessed usability (learnability of redesign, efficiency, satisfaction, and usability errors), perceived workload, and prescribing errors. RESULTS: Although prescribers received no training on the design changes, prescribers were able to resolve redesigned alerts more efficiently (median (IQR): 56 (47) s) compared to the original alerts (85 (71) s; p=0.015). In addition, prescribers rated redesigned alerts significantly higher than original alerts across several dimensions of satisfaction. Redesigned alerts led to a modest but significant reduction in workload (p=0.042) and significantly reduced the number of prescribing errors per prescriber (median (range): 2 (1-5) compared to original alerts: 4 (1-7); p=0.024). DISCUSSION: Aspects of the redesigned alerts that likely contributed to better prescribing include design modifications that reduced usability-related errors, providing clinical data closer to the point of decision, and displaying alert text in a tabular format. Displaying alert text in a tabular format may help prescribers extract information quickly and thereby increase responsiveness to alerts. CONCLUSIONS: This simulation study provides evidence that applying human factors design principles to medication alerts can improve usability and prescribing outcomes.

Sources of variation in primary care clinical workflow: implications for the design of cognitive support.

This article identifies sources of variation in clinical workflow and implications for the design and implementation of electronic clinical decision support. Sources of variation in workflow were identified via rapid ethnographic observation, focus groups, and interviews across a total of eight medical centers in both the Veterans Health Administration and academic medical centers nationally regarded as leaders in developing and using clinical decision support. Data were reviewed for types of variability within the social and technical subsystems and the external environment as described in the sociotechnical systems theory. Two researchers independently identified examples of variation and their sources, and then met with each other to discuss them until consensus was reached. Sources of variation were categorized as environmental (clinic staffing and clinic pace), social (perception of health information technology and real-time use with patients), or technical (computer access and information access). Examples of sources of variation within each of the categories are described and discussed in terms of impact on clinical workflow. As technologies are implemented, barriers to use become visible over time as users struggle to adapt workflow and work practices to accommodate new technologies. Each source of variability identified has implications for the effective design and implementation of useful health information technology. Accommodating moderate variability in workflow is anticipated to avoid brittle and inflexible workflow designs, while also avoiding unnecessary complexity for implementers and users.

Efficiency strategies for facilitating computerized clinical documentation in ambulatory care.

Most providers have experienced increased documentation demands with the use of electronic health records (EHRs). We sought to identify efficiency strategies that providers use to complete clinical documentation tasks in ambulatory care. Two observers performed ethnographic observations and interviews with 22 ambulatory care providers in a U.S. Veterans Affairs Medical Center. Observation notes and interview transcripts were coded for recurrent strategies relating to completion of the EHR progress notes. Findings included: the use of paper artifacts for handwritten notations; electronic templates for automation of certain parts of the note; use of shorthand and phrases rather than narrative writing; copying and pasting from previous EHR notes; directly entering information into the EHR note during the patient encounter; reliance on memory; and pre-populating an EHR note prior to seeing the patient. We discuss the findings in the context of distributed cognition to understand how clinical information is propagated and represented toward completion of a progress note. The study findings have important implications for improving and streamlining clinical documentation related to human factors workload management strategies.

The next-generation electronic health record: perspectives of key leaders from the US Department of Veterans Affairs.

The rapid change in healthcare has focused attention on the necessary development of a next-generation electronic health record (EHR) to support system transformation and more effective patient-centered care. The Department of Veterans Affairs (VA) is developing plans for the next-generation EHR to support improved care delivery for veterans. To understand the needs for a next-generation EHR, we interviewed 14 VA operational, clinical and informatics leaders for their vision about system needs. Leaders consistently identified priorities for development in the areas of cognitive support, information synthesis, teamwork and communication, interoperability, data availability, usability, customization, and information management. The need to reconcile different EHR initiatives currently underway in the VA, as well as opportunities for data sharing, will be critical for continued progress. These findings may support the VA's effort for evolutionary change to its information system and draw attention to necessary research and development for a next-generation information system and EHR nationally.

A regional informatics platform for coordinated antibiotic-resistant infection tracking, alerting, and prevention.

BACKGROUND: We developed and assessed the impact of a patient registry and electronic admission notification system relating to regional antimicrobial resistance (AMR) on regional AMR infection rates over time. We conducted an observational cohort study of all patients identified as infected or colonized with methicillin-resistant Staphylococcus aureus (MRSA) and/or vancomycin-resistant enterococci (VRE) on at least 1 occasion by any of 5 healthcare systems between 2003 and 2010. The 5 healthcare systems included 17 hospitals and associated clinics in the Indianapolis, Indiana, region. METHODS: We developed and standardized a registry of MRSA and VRE patients and created Web forms that infection preventionists (IPs) used to maintain the lists. We sent e-mail alerts to IPs whenever a patient previously infected or colonized with MRSA or VRE registered for admission to a study hospital from June 2007 through June 2010. RESULTS: Over a 3-year period, we delivered 12 748 e-mail alerts on 6270 unique patients to 24 IPs covering 17 hospitals. One in 5 (22%-23%) of all admission alerts was based on data from a healthcare system that was different from the admitting hospital; a few hospitals accounted for most of this crossover among facilities and systems. CONCLUSIONS: Regional patient registries identify an important patient cohort with relevant prior antibiotic-resistant infection data from different healthcare institutions. Regional registries can identify trends and interinstitutional movement not otherwise apparent from single institution data. Importantly, electronic alerts can notify of the need to isolate early and to institute other measures to prevent transmission.

Paper- and computer-based workarounds to electronic health record use at three benchmark institutions.

BACKGROUND: Healthcare professionals develop workarounds rather than using electronic health record (EHR) systems. Understanding the reasons for workarounds is important to facilitate user-centered design and alignment between work context and available health information technology tools. OBJECTIVE: To examine both paper- and computer-based workarounds to the use of EHR systems in three benchmark institutions. METHODS: Qualitative data were collected in 11 primary care outpatient clinics across three healthcare institutions. Data collection methods included direct observation and opportunistic questions. In total, 120 clinic staff and providers and 118 patients were observed. All data were analyzed using previously developed workaround categories and examined for potential new categories. Additionally, workarounds were coded as either paper- or computer-based. RESULTS: Findings corresponded to 10 of 11 workaround categories identified in previous research. All 10 of these categories applied to paper-based workarounds; five categories also applied to computer-based workarounds. One new category, no correct path (eg, a desired option did not exist in the computer interface, precipitating a workaround), was identified for computer-based workarounds. The most consistent reasons for workarounds across the three institutions were efficiency, memory, and awareness. CONCLUSIONS: Consistent workarounds across institutions suggest common challenges in outpatient clinical settings and failures to accommodate these challenges in EHR design. An examination of workarounds provides insight into how providers adapt to limiting EHR systems. Part of the design process for computer interfaces should include user-centered methods particular to providers and healthcare settings to ensure uptake and usability.