Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit

Standardized Comparison of Voice-Based Information and Documentation Systems to Established Systems in Intensive Care: Crossover Study

BACKGROUND

The medical teams in intensive care units (ICUs) spend increasing amounts of time at computer systems for data processing, input, and interpretation purposes. As each patient creates about 1000 data points per hour, the available information is abundant, making the interpretation difficult and time-consuming. This data flood leads to a decrease in time for evidence-based, patient-centered care. Information systems, such as patient data management systems (PDMSs), are increasingly used at ICUs. However, they often create new challenges arising from the increasing documentation burden.

OBJECTIVE

New concepts, such as artificial intelligence (AI)-based assistant systems, are hence introduced to the workflow to cope with these challenges. However, there is a lack of standardized, published metrics in order to compare the various data input and management systems in the ICU setting. The objective of this study is to compare established documentation and retrieval processes with newer methods, such as PDMSs and voice information and documentation systems (VIDSs).

METHODS

In this crossover study, we compare traditional, paper-based documentation systems with PDMSs and newer AI-based VIDSs in terms of performance (required time), accuracy, mental workload, and user experience in an intensive care setting. Performance is assessed on a set of 6 standardized, typical ICU tasks, ranging from documentation to medical interpretation.

RESULTS

A total of 60 ICU-experienced medical professionals participated in the study. The VIDS showed a statistically significant advantage compared to the other 2 systems. The tasks were completed significantly faster with the VIDS than with the PDMS (1-tailed t59=12.48; Cohen d=1.61; P<.001) or paper documentation (t59=20.41; Cohen d=2.63; P<.001). Significantly fewer errors were made with VIDS than with the PDMS (t59=3.45; Cohen d=0.45; P=.03) and paper-based documentation (t59=11.2; Cohen d=1.45; P<.001). The analysis of the mental workload of VIDS and PDMS showed no statistically significant difference (P=.06). However, the analysis of subjective user perception showed a statistically significant perceived benefit of the VIDS compared to the PDMS (P<.001) and paper documentation (P<.001).

CONCLUSIONS

The results of this study show that the VIDS reduced error rate, documentation time, and mental workload regarding the set of 6 standardized typical ICU tasks. In conclusion, this indicates that AI-based systems such as the VIDS tested in this study have the potential to reduce this workload and improve evidence-based and safe patient care.

"Everything's so Role-Specific": VA Employee Perspectives' on Electronic Health Record (EHR) Transition Implications for Roles and Responsibilities

BACKGROUND

Electronic health record (EHR) transitions are increasingly widespread and often highly disruptive. It is imperative we learn from past experiences to anticipate and mitigate such disruptions. Veterans Affairs (VA) is undergoing a large-scale transition from its homegrown EHR (CPRS/Vista) to a commercial EHR (Cerner), creating a unique opportunity of shedding light on large-scale EHR-to-EHR transition challenges.

OBJECTIVE

To explore one facet of the organizational impact of VA's EHR transition: its implications for employees' roles and responsibilities at the first VA site to implement Cerner Millennium EHR.

DESIGN

As part of a formative evaluation of frontline staff experiences with VA's EHR transition, we conducted brief (~ 15 min) and full-length interviews (~ 60 min) with clinicians and staff at Mann-Grandstaff VA Medical Center in Spokane, WA, before, during, and after transition (July 2020-November 2021).

PARTICIPANTS

We conducted 111 interviews with 26 Spokane clinicians and staff, recruited via snowball sampling.

APPROACH

We conducted audio interviews using a semi-structured guide with grounded prompts. We coded interview transcripts using a priori and emergent codes, followed by qualitative content analysis.

KEY RESULTS

Unlike VA's previous EHR, Cerner imposes additional restrictions on access to its EHR functionality based upon "roles" assigned to users. Participants described a mismatch between established institutional duties and their EHR permissions, unanticipated changes in scope of duties brought upon by the transition, as well as impediments to communication and collaboration due to different role-based views.

CONCLUSIONS

Health systems should anticipate substantive impacts on professional workflows when EHR role settings do not reflect prior workflows. Such changes may increase user error, dissatisfaction, and patient care disruptions. To mitigate employee dissatisfaction and safety risks, health systems should proactively plan for and communicate about expected modifications and monitor for unintended role-related consequences of EHR transitions, while vendors should ensure accurate role configuration and assignment.

The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence

Critical care practice has been embodied in the healthcare system since the institutionalization of intensive care units (ICUs) in the late '50s. Over time, this sector has experienced many changes and improvements in providing immediate and dedicated healthcare as patients requiring intensive care are often frail and critically ill with high mortality and morbidity rates. These changes were aided by innovations in diagnostic, therapeutic, and monitoring technologies, as well as the implementation of evidence-based guidelines and organizational structures within the ICU. In this review, we examine these changes in intensive care management over the past 40 years and their impact on the quality of care available to patients. Moreover, the current state of intensive care management is characterized by a multidisciplinary approach and the use of innovative technologies and research databases. Advancements such as telecritical care and artificial intelligence are being increasingly explored, especially since the COVID-19 pandemic, to reduce the length of hospitalization and ICU mortality. With these advancements in intensive care and ever-changing patient needs, critical care experts, hospital managers, and policymakers must also explore appropriate organizational structures and future enhancements within the ICU.

Impacts of an Electronic Health Record Transition on Veterans Health Administration Health Professions Trainee Experience

BACKGROUND

Adoption of electronic health care records (EHRs) has proliferated since 2000. While EHR transitions are widely understood to be disruptive, little attention has been paid to their effect on health professions trainees' (HPTs) ability to learn and conduct work. Veterans Health Administration's (VA) massive transition from its homegrown EHR (CPRS/Vista) to the commercial Oracle Cerner presents an unparalleled-in-scope opportunity to gain insight on trainee work functions and their ability to obtain requisite experience during transitions.

OBJECTIVE

To identify how an organizational EHR transition affected HPT work and learning at the third VA go-live site.

DESIGN

A formative mixed-method evaluation of HPT experiences with VHA's EHR transition including interviews with HPTs and supervisors at Chalmers P. Wylie VA Outpatient Clinic in Columbus, OH, before (~60 min), during (15-30 min), and after (~60 min) go-live (December 2021-July 2022). We also conducted pre- (March 2022-April 2022) and post-go live (May 2022-June 2022) HPT and employee surveys.

PARTICIPANTS

We conducted 24 interviews with HPTs (n=4), site leaders (n=2), and academic affiliates (n=2) using snowball sampling. We recruited HPTs in pre- (n=13) and post-go-live (n=10) surveys and employees in pre- (n=408) and post-go-live (n=458) surveys.

APPROACH

We conducted interviews using a semi-structured guide and grounded prompts. We coded interviews and survey free text data using a priori and emergent codes, subsequently conducting thematic analysis. We conducted descriptive statistical analysis of survey responses and merged interview and survey data streams.

KEY RESULTS

Our preliminary findings indicate that the EHR transition comprehensively affected HPT experiences, disrupting processes from onboarding and training to clinical care contributions and training-to-career retention.

CONCLUSIONS

Understanding HPTs' challenges during EHR transitions is critical to effective training. Mitigating the identified barriers to HPT training and providing patient care may lessen their dissatisfaction and ensure quality patient care during EHR transitions.

Examining Pediatric Resident Electronic Health Records Use During Prerounding: Mixed Methods Observational Study

BACKGROUND

Electronic health records (EHRs) play a substantial role in modern health care, especially during prerounding, when residents gather patient information to inform daily care decisions of the care team. The effective use of the EHR system is crucial for efficient and frustration-free prerounding. Ideally, the system should be designed to support efficient user interactions by presenting data effectively and providing easy navigation between different pages. Additionally, training on the system should aim to make user interactions more efficient by familiarizing the users with best practices that minimize interaction time while using the full potential of the system's capabilities. However, formal training on EHR systems often falls short of providing residents with all the necessary EHR-related skills, leading to the adoption of inefficient practices and the underuse of the system's full range of capabilities.

OBJECTIVE

This study aims to examine the efficiency of EHR use during prerounding among pediatric residents, assess the effect of experience level on EHR use, and identify areas for improvement in EHR design and training.

METHODS

A mixed methods approach was used, involving a self-reported survey and video analysis of prerounding practices of the entire population of pediatric residents from a large teaching hospital in the South Atlantic Region. The residents were stratified by experience level by postgraduate year. Data were collected on the number of pages accessed, duration of prerounding, task completion rates, and effective use of data sources. Observational and qualitative data complemented the quantitative analysis. Our study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines, ensuring completeness and transparency of reporting.

RESULTS

Of the 30 pediatric residents, 20 were included in the analyses; of these, 16 (80%) missed at least 1 step during prerounding. Although more experienced residents on average omitted fewer steps, 4 (57%) of the 7 most experienced residents still omitted at least 1 step. On average, residents took 6.5 minutes to round each patient and accessed 21 pages within the EHR during prerounding; no statistically significant differences were observed between experience levels for prerounding times (P=.48) or number of pages accessed (P=.92). The use of aggregated data pages within the EHR system neither seem to improve prerounding times nor decrease the number of pages accessed.

CONCLUSIONS

The findings suggest that EHR design should be improved to better support user needs, and hospitals should adopt more effective training programs to familiarize residents with the system's capabilities. We recommend implementing prerounding checklists and providing ongoing EHR training programs for health care practitioners. Despite the generalizability of limitations of our study in terms of sample size and specialization, it offers valuable insights for future research to investigate the impact of EHR use on patient outcomes and satisfaction, as well as identify factors that contribute to efficient and effective EHR usage.

A qualitative study of the dark and bright sides of physicians' electronic health record work outside work hours

BACKGROUND

The introduction of electronic health records (EHRs) has contributed considerably to EHR work outside work (WOW) hours for physicians. Prior research has identified the pressures associated with stress resulting from EHR WOW, yet developing a nuanced understanding of how physicians appraise and respond to this stress, and the resulting impacts, remains absent from the literature.

PURPOSE

Grounded in the technostress model, this study takes a qualitative approach to explore both the pressures and opportunities associated with EHR WOW.

METHODS

Thematic analysis of data from semistructured interviews was utilized to examine the pressures and opportunities associated with EHR WOW among primary care pediatricians (n = 15) affiliated with a large Midwestern pediatric health system.

RESULTS

The physicians in this study regularly spent time working in the EHR outside work hours. They felt the EHR contributed to their documentation burden, which ultimately increased their EHR WOW, and reported a sense of burden from ubiquitous EHR availability. Conversely, they appreciated the flexibility the EHR provided in terms of work-life balance. Suggestions for improvement under the direct purview of practice management included enhanced EHR usability, improvements in workflow during work hours to free up time to document, and more training on both EHR documentation strategies and ongoing software upgrades.

CONCLUSION

Physicians perceive that the EHR exerts certain pressures while affording new opportunities and conveniences. This study provides evidence of both the pressures and opportunities of EHR WOW and their effect on physician well-being.

PRACTICE IMPLICATIONS

Specific opportunities are identified for health administrators to enable physicians to better manage EHR WOW.

Optimising the educational value of indirect patient care

INTRODUCTION

Indirect patient care activities (IPCA) such as documentation, reviewing investigations and filling out forms require an increasing amount of physician time. While an essential part of patient care, rising rates of IPCA work correspond with increases in physician burnout and job dissatisfaction. It is not known how best to prepare residents in IPCA-heavy specialties (e.g. family medicine) for this aspect of their career. This study investigates how educators and residency programmes can optimise IPCA work during residency to best prepare residents for future practice.

METHODS

Using Constructivist Grounded Theory, we conducted focus groups and individual interviews with 42 clinicians (19 family medicine residents, 16 family physicians in the first 5 years of practice and 7 family physician educators). All participants were connected to one family medicine residency programme. We analysed interview data iteratively, using a staged approach to constant comparative analysis.

RESULTS

While residents, early career physicians and educators perceived the educational value of IPCAs differently, they all reported IPCAs as a necessary weight that family physicians carry throughout their career. Some residents described IPCAs as a burden, creating inequities in workload and interfering with other learning and personal opportunities. In contrast, educators conceptualised IPCAs as an opportunity to build and develop the skills required to carry the weight of IPCAs throughout their career. We make specific recommendations for helping residents recognise this educational opportunity, such as clarifying expectations, navigating equity, understanding purpose and maintaining consistency when teaching IPCAs.

CONCLUSION

IPCAs are a key competency for many medical residents but require explicit pedagogical attention. If the educational opportunities are not made explicit, residents may miss the opportunity to develop strategies for practice management, professional boundaries, and administrative efficiencies.

Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review

BACKGROUND

Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit.

OBJECTIVE

To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes.

METHODS

The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay.

RESULTS

A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data.

CONCLUSION

With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.

A mixed-methods study of quality differences between applied documentation approaches in nursing homes

BACKGROUND

Several approaches to nursing documentation exist. Some address standardised terminology and daily monitoring, whereas the structural model approach focuses on open-ended text information and special incidents. This study aims to identify quality differences between available documentation approaches from the perspectives of nursing professionals in Germany.

METHODS

Between October 2018 and May 2019, a convenience sample of German nursing home practitioners was surveyed concerning the quality of their documentation techniques. The quality measurement was developed from the findings of a literature review on indicators that define successful nursing documentation. Selected indicators were structured according to Donabedian's quality dimensions of structure, process, and outcome. A mean score was calculated for each quality dimension. Non-parametric tests were employed to discover whether organisational and person-related conditions affect score values. The framework method was used to analyse textual data.

RESULTS

Responses from 250 nursing care practitioners show significant differences between users of different documentation approaches in the outcome dimension. Nurses who worked with the structural model were slightly more satisfied with their documentation approach than users of other approaches. In addition, differences between subgroups were identified depending on the mode of the tools employed for nursing documentation, participation in training, and length of time spent using the present documentation tool. Qualitative data reveal that digitalisation, unequal task distribution, and appreciation and motivation are critical topics in nursing homes.

CONCLUSIONS

The results indicate that regular opportunities to reflect on challenges in documentation activities might increase nurses' perceptions of documentation as a valuable part of nursing care. Training might serve this purpose for users of non-structural model approaches. Regardless of the specific recording techniques employed, more investment in digital infrastructure is required.

Technology Acceptance and Usability of a Mobile Application to Support the Workflow of Health Care Aides who Provide Services to older adults residing in a care facility: A Pilot Mixed Methods Study (Preprint)

Background

Health care aides are unlicensed support personnel who provide direct care, personal assistance, and support to people with health conditions. The shortage of health care aides has been attributed to recruitment challenges, high turnover, an aging population, the COVID-19 pandemic, and low retention rates. Mobile apps are among the many information communication technologies that are paving the way for eHealth solutions to help address this workforce shortage by enhancing the workflow of health care aides. In collaboration with Clinisys EMR Inc, we developed a mobile app (Mobile Smart Care System [mSCS]) to support the workflow of health care aides who provide services to older adult residents of a long-term care facility.

Objective

The purpose of this study was to investigate the technology acceptance and usability of a mobile app in a real-world environment, while it is used by health care aides who provide services to older adults.

Methods

This pilot study used a mixed methods design: sequential mixed methods (QUANTITATIVE, qualitative). Our study included a pre– and post–paper-based questionnaire with no control group (QUAN). Toward the end of the study, 2 focus groups were conducted with a subsample of health care aides (qual, qualitative description design). Technology acceptance and usability questionnaires used a 5-point Likert scale ranging from disagree (1) to agree (5). The items included in the questionnaires were validated in earlier research as having high levels of internal consistency for the Unified Theory of Acceptance and Use of Technology constructs. A total of 60 health care aides who provided services to older adults as part of their routine caseloads used the mobile app for 1 month. Comparisons of the Unified Theory of Acceptance and Use of Technology constructs’ summative scores at pretest and posttest were calculated using a paired t test (2-tailed). We used the partial least squares structural regression model to determine the factors influencing mobile app acceptance and usability for health care aides. The α level of significance for all tests was set at P≤.05 (2-tailed).

Results

We found that acceptance of the mSCS was high among health care aides, performance expectancy construct was the strongest predictor of intention to use the mSCS, intention to use the mSCS predicted usage behavior. The qualitative data support the quantitative findings and showed health care aides’ strong belief that the mSCS was useful, portable, and reliable, although there were still opportunities for improvement, especially with regard to the mSCS user interface.

Conclusions

Overall, these results support the assertion that mSCS technology acceptance and usability are high among health care aides. In other words, health care aides perceived that the mSCS assisted them in addressing their workflow issues.

Design, Implementation, Utilization, and Sustainability of a Fast Healthcare Interoperability Resources-Based Inpatient Rounding List

OBJECTIVE

We designed and implemented an application programming interface (API)-based electronic health record (EHR)-integrated rounding list and evaluated acceptability, clinician satisfaction, information accuracy, and efficiency related to the application.

METHODS

We developed and integrated an application, employing iterative design techniques with user feedback. EHR and application user action logs, as well as hospital safety reports, were evaluated. Rounding preparation characteristics were obtained through surveys before and after application integration. To evaluate usability, inpatient providers, including residents, fellows, and attendings were surveyed 2 weeks prior to and 6 months after enterprise-wide EHR application integration. Our primary outcome was provider time savings measured by user action logs; secondary outcomes include provider satisfaction.

RESULTS

The application was widely adopted by inpatient providers, with more than 69% of all inpatients queried by the application within 6 months of deployment. Application utilization was sustained throughout the study period with 79% (interquartile range [IQR]: 76, 82) of enterprise-wide unique patients accessed per weekday. EHR action logs showed application users spent -3.24 minutes per day (95% confidence interval [CI]: -6.8, 0.33), p = 0.07 within the EHR compared with nonusers. Median self-reported chart review time for attendings decreased from 30 minutes (IQR: 15, 60) to 20 minutes (IQR: 10, 45) after application integration (p = 0.04). Self-reported sign-out preparation time decreased by a median of 5 minutes (p < 0.01), and providers were better prepared for hand-offs (p = 0.02). There were no increased safety reports during the study period.

CONCLUSION

This study demonstrates successful integration of a rounding application within a commercial EHR using APIs. We demonstrate increasing both provider-reported satisfaction and time savings. Rounding lists provided more accurate and timely information for rounds. Application usage was sustained across multiple specialties at 42 months. Other application designers should consider data density, optimization of provider workflows, and using real-time data transfer using novel tools when designing an application.

Acute Care Surgeons Spend More Time than General Surgeons on the Electronic Health Record (EHR)

BACKGROUND

The paradigm of Acute Care Surgery (ACS) emerged in response to decreasing operative opportunities for trauma surgeons and increasing need for surgical coverage in disciplines to which the expertise of trauma surgeons naturally extends. While the continued evolution of this specialty remains largely beneficial, unintended consequences may have arisen along the way. One aspect of ACS that remains to be thoroughly investigated is the impact of the electronic health record (EHR). The purpose of this study is to objectively quantify EHR usage for ACS and compare it to other general surgery specialties.

METHODS

EHR user data were collected for fifteen ACS attendings and thirty-seven general surgery attendings from October 2014 to September 2019. Comparative analysis was conducted using two-tailed t-tests. Subgroup analysis was conducted for subspecialties included in the general surgery group.

RESULTS

ACS attendings opened almost 3 times as many charts as general surgery attendings per month (180 vs 64 charts/month, P < .0001), and ultimately spent more time on the EHR as a result (10 vs 6.4 hours/month, P < .0001). Documentation was the most time consuming EHR task for both groups. Although ACS attendings spent less overall time per patient chart, the proportion of time spent on certain EHR tasks was similar to that of general surgery colleagues.

DISCUSSION

The EHR imposes a disproportionate burden on ACS attendings compared to their general surgery counterparts, and additional study is warranted to improve usage. EHR usage burden has workforce implications for trainees considering a career in ACS.

Characterization of Electronic Health Record Use Outside Scheduled Clinic Hours among Primary Care Pediatricians: A Retrospective Descriptive Task Analysis of Electronic Health Record Access Log Data (Preprint)

Background

Many of the benefits of electronic health records (EHRs) have not been achieved at expected levels because of a variety of unintended negative consequences such as documentation burden. Previous studies have characterized EHR use during and outside work hours, with many reporting that physicians spend considerable time on documentation-related tasks. These studies characterized EHR use during and outside work hours using clock time versus actual physician clinic schedules to define the outside work time.

Objective

This study aimed to characterize EHR work outside scheduled clinic hours among primary care pediatricians using a retrospective descriptive task analysis of EHR access log data and actual physician clinic schedules to define work time.

Methods

We conducted a retrospective, exploratory, descriptive task analysis of EHR access log data from primary care pediatricians in September 2019 at a large Midwestern pediatric health center to quantify and identify actions completed outside scheduled clinic hours. Mixed-effects statistical modeling was used to investigate the effects of age, sex, clinical full-time equivalent status, and EHR work during scheduled clinic hours on the use of EHRs outside scheduled clinic hours.

Results

Primary care pediatricians (n=56) in this study generated 1,523,872 access log data points (across 1069 physician workdays) and spent an average of 4.4 (SD 2.0) hours and 0.8 (SD 0.8) hours per physician per workday engaged in EHRs during and outside scheduled clinic hours, respectively. Approximately three-quarters of the time working in EHR during or outside scheduled clinic hours was spent reviewing data and reports. Mixed-effects regression revealed no associations of age, sex, or clinical full-time equivalent status with EHR use during or outside scheduled clinic hours.

Conclusions

For every hour primary care pediatricians spent engaged with the EHR during scheduled clinic hours, they spent approximately 10 minutes interacting with the EHR outside scheduled clinic hours. Most of their time (during and outside scheduled clinic hours) was spent reviewing data, records, and other information in EHR.

Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete

Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.

Interaction Time with Electronic Health Records: A Systematic Review

BACKGROUND

The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood.

OBJECTIVE

This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR).

METHODS

Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS.

STUDY ELIGIBILITY CRITERIA

Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021.

PARTICIPANTS

All physicians and nurses involved in inpatient and outpatient settings.

STUDY APPRAISAL AND SYNTHESIS METHODS

A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs.

RESULTS

Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity.

CONCLUSION

This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants

BACKGROUND

In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs.

OBJECTIVES

This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC.

METHODS

Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the radiologist's textual report of the PICC line's tip. After categorization, we performed a 70/30 train/test split and benchmarked the performance of seven different (neural network, support vector machine, the naïve Bayes, decision tree, random forest, AdaBoost, and K-nearest neighbors) supervised ML algorithms. After optimization, we calculated accuracy, precision, and recall of each algorithm's ability to correctly categorize the stated location of the PICC tip.

RESULTS

A total of 17,337 radiographs met criteria for inclusion and were labeled manually. Interrater agreement was 99.1%. Support vector machines and neural networks yielded accuracies as high as 98% in identifying PICC tips in central versus noncentral position (binary outcome) and accuracies as high as 95% when attempting to categorize the individual anatomical location (12-category outcome).

CONCLUSION

Our study shows that ML classifiers can automatically extract the anatomical location of PICC tips from radiology reports. Two ML classifiers, support vector machine (SVM) and a neural network, obtained top accuracies in both binary and multiple category predictions. Implementing these algorithms in a neonatal intensive care unit as a clinical decision support system may help clinicians address PICC line position.

A Scoping Review of Health Information Technology in Clinician Burnout

BACKGROUND

Clinician burnout is a prevalent issue in healthcare, with detrimental implications in healthcare quality and medical costs due to errors. The inefficient use of health information technologies (HIT) is attributed to having a role in burnout.

OBJECTIVE

This paper seeks to review the literature with the following two goals: (1) characterize and extract HIT trends in burnout studies over time, and (2) examine the evidence and synthesize themes of HIT's roles in burnout studies.

METHODS

A scoping literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with two rounds of searches in PubMed, IEEE Xplore, ACM, and Google Scholar. The retrieved papers and their references were screened for eligibility by using developed inclusion and exclusion criteria. Data were extracted from included papers and summarized either statistically or qualitatively to demonstrate patterns.

RESULTS

After narrowing down the initial 945 papers, 36 papers were included. All papers were published between 2013 and 2020; nearly half of them focused on primary care (n = 16; 44.4%). The most commonly studied variable was electronic health record (EHR) practices (e.g., number of clicks). The most common study population was physicians. HIT played multiple roles in burnout studies: it can contribute to burnout; it can be used to measure burnout; or it can intervene and mitigate burnout levels.

CONCLUSION

This scoping review presents trends in HIT-centered burnout studies and synthesizes three roles for HIT in contributing to, measuring, and mitigating burnout. Four recommendations were generated accordingly for future burnout studies: (1) validate and standardize HIT burnout measures; (2) focus on EHR-based solutions to mitigate clinician burnout; (3) expand burnout studies to other specialties and types of healthcare providers, and (4) utilize mobile and tracking technology to study time efficiency.

Electronic health record usability and workload changes over time for provider and nursing staff following transition to new EHR

The ubiquity of EHRs in healthcare means that small EHR inefficiencies can have a major impact on clinician workload. We conducted a sequential explanatory mixed methods study to: 1) identify EHR-associated workload and usability effects for clinicians following an EHR change over time, 2) determine workload and usability differences for providers (MD and Advance Practice Nurses) versus nurses (RNs and MAs), 3) determine if usability predicts workload, 4) identify potential sources of EHR design flaws. Workload (NASA-Task Load Index) and usability (System Usability Scale) measures were administered pre, 6-8 month and 30-32 months post-implementation. We found significant increase in perceived workload post-implementation that persisted for 2.5 years (p < .001). The workload increase was associated with usability ratings, which in turn may relate to EHR interface design violations identified by a heuristic evaluation. Our findings suggest further innovation and attention to interface design flaws are needed to improve EHR usability and reduce clinician workload.

Development and validation of high definition phenotype-based mortality prediction in critical care units

Objectives

The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings.

Materials and Methods

A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long–short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II.

Results

A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM.

Conclusions and Relevance

The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP–based clinical decision tools to detect the early onset of neonatal morbidities.

Evaluating the effects of electronic health records system adoption on the performance of Malaysian health care providers

BACKGROUND

The Ministry of Health of Malaysia has invested significant resources to implement an electronic health record (EHR) system to ensure the full automation of hospitals for coordinated care delivery. Thus, evaluating whether the system has been effectively utilized is necessary, particularly regarding how it predicts the post-implementation primary care providers' performance impact.

METHODS

Convenience sampling was employed for data collection in three government hospitals for 7 months. A standardized effectiveness survey for EHR systems was administered to primary health care providers (specialists, medical officers, and nurses) as they participated in medical education programs. Empirical data were assessed by employing partial least squares-structural equation modeling for hypothesis testing.

RESULTS

The results demonstrated that knowledge quality had the highest score for predicting performance and had a large effect size, whereas system compatibility was the most substantial system quality component. The findings indicated that EHR systems supported the clinical tasks and workflows of care providers, which increased system quality, whereas the increased quality of knowledge improved user performance.

CONCLUSION

Given these findings, knowledge quality and effective use should be incorporated into evaluating EHR system effectiveness in health institutions. Data mining features can be integrated into current systems for efficiently and systematically generating health populations and disease trend analysis, improving clinical knowledge of care providers, and increasing their productivity. The validated survey instrument can be further tested with empirical surveys in other public and private hospitals with different interoperable EHR systems.

Association between ICU interruptions and physicians trainees' electronic health records efficiency

The intensive care unit (ICU) is a stressful and complex environment in due to its dynamic nature and severity of admitted patients. EHR interface design can be cumbersome and lead to prolonged times to complete tasks. This paper investigated the relationship between a prominent EHR interface design and interruptions with physician's efficiency during patient chart review at ICU Pre-Rounds. We conducted a live observation of ICU physicians in a 30-bed MICU at a tertiary, southeastern medical center. Directly after the observation sessions, the physicians completed a modified System Usability Scale (SUS) survey. A total of 52 EHR patient chart reviews were observed at the MICU Pre-rounds. There was statistically significant positive correlation between time spent to review patient EHR with both number of scrolling(p-value<0.0001) across EHR interface; and with number of visited EHR  screens (p-value=0.0444). There was positive correlation between number of interruptions with time spent to review patient EHR during ICU prerounds. EHR design and the occurrence of interruptions lead to reduced physician-EHR efficiency levels. We report that the number of scrolling and visited screens executed by physicians to gather the required information was associated with increased screen time and consequently decreased physician efficiency.

Physicians' gender and their use of electronic health records: findings from a mixed-methods usability study

OBJECTIVE

Physician burnout associated with EHRs is a major concern in health care. A comprehensive assessment of differences among physicians in the areas of EHR performance, efficiency, and satisfaction has not been conducted. The study sought to study relationships among physicians' performance, efficiency, perceived workload, satisfaction, and usability in using the electronic health record (EHR) with comparisons by age, gender, professional role, and years of experience with the EHR.

MATERIALS AND METHODS

Mixed-methods assessments of the medical intensivists' EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews. NASA Task Load Index (TLX), System Usability Scale (SUS), and Questionnaire on User Interface Satisfaction surveys were deployed.

RESULTS

The study enrolled 25 intensive care unit (ICU) physicians (11 residents, 9 fellows, 5 attendings); 12 (48%) were men, with a mean age of 33 (range, 28-55) years and a mean of 4 (interquartile range, 2.0-5.5) years of Epic experience. Overall task performance scores were similar for men (90% ± 9.3%) and women (92% ± 4.4%), with no statistically significant differences (P = .374). However, female physicians demonstrated higher efficiency in completion time (difference = 7.1 minutes; P = .207) and mouse clicks (difference = 54; P = .13). Overall, men reported significantly higher perceived EHR workload stress compared with women (difference = 17.5; P < .001). Men reported significantly higher levels of frustration with the EHR compared with women (difference = 33.15; P < .001). Women reported significantly higher satisfaction with the ease of use of the EHR interface than men (difference = 0.66; P =.03). The women's perceived overall usability of the EHR is marginally higher than that of the men (difference = 10.31; P =.06).

CONCLUSIONS

Among ICU physicians, we measured significant gender-based differences in perceived EHR workload stress, satisfaction, and usability-corresponding to objective patterns in EHR efficiency. Understanding the reasons for these differences may help reduce burnout and guide improvements to physician performance, efficiency, and satisfaction with EHR use.

DESIGN

Mixed-methods assessments of the medical intensivists' EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews.

EHR Conversion on the PreOp Care: A Pre-Post Workflow Comparison

The impact of EHRs conversion on clinicians' daily work is crucial to evaluate the success of the intervention for Hospitals and to yield valuable insights into quality improvement. To assess the impact of different EHR systems on the preoperative nursing workflow, we used a structured framework combining quantitative time and motion study and qualitative cognitive analysis to characterize, visualize and explain the differences before and after an EHR conversion. The results showed that the EHR conversion brought a significant decrease in the patient case time and a reduced percentage of time using EHR. PreOp nurses spent a higher proportion of time caring for the patient, while the important tasks were completed in a more continuous pattern after the EHR conversion. The workflow variance was due to different nurse's cognitive process and the task time change was reduced because of some new interface features in the new EHR systems.

Quantified electronic health record (EHR) use by academic surgeons

BACKGROUND

The electronic health record has improved medical billing, research, and sharing of patient data, but its clinical use by physicians has been linked to rising physician burnout leading to numerous subjective editorials about the electronic health record inefficiencies and detriment to frontline caregivers. This study aimed to quantify electronic health record use by surgeons.

METHODS

The study is a retrospective review and descriptive analysis of deidentified electronic health record data from September 2016 to June 2017. A binary time series was created for each attending to calculate electronic health record system login times. The primary outcome was the total amount of time a surgeon logged into the electronic health record system during the study period.

RESULTS

Fifty-one general surgery attendings (31 males, 20 females), spanning 9 specialties spent a mean of 2.0 hours per day and 13.8 hours per week logged into the electronic health record. The top 15% of users were logged in for an average of 4.6 hours per weekday. Sixty-five percent of overall electronic health record use occurred on-site, and 35% was remote. A greater proportion of remote use occurred during nighttime hours and Sundays. Clinic days required the largest amount of electronic health record use time compared with operating room and administrative days.

CONCLUSION

General surgery attendings spend a considerable amount of time using the electronic health record. Ultimately, the goal of these quantitative electronic health record results is to correlate with burnout and job satisfaction data to facilitate the implementation of programs to improve efficiency and decrease the burden of charting. Further investigation needs to focus on subgroups who are high electronic health record users to better identify the barriers to efficient electronic health record use.

Usability evaluation of an integrated electronic medication management system implemented in an oncology setting using the unified theory of the acceptance and use of technology

BACKGROUND

Medication management processes in an Oncology setting are complex and difficult to examine in isolation from interrelated processes and contextual factors. This qualitative study aims to evaluate the usability of an Electronic Medication Management System (EMMS) implemented in a specialised oncology unit using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework.

METHODS

The study was conducted in a 12-bed outpatient Oncology unit of a major teaching hospital 6 months following implementation of a commercial EMMS. In-depth semi-structured interviews were conducted with doctors, nurses and pharmacists using the system to assess usability. The UTAUT framework was used to analyse the results, which facilitated evaluation of interrelated aspects and provided a structured summary of user experience and usability factors.

RESULTS

Direct cross-comparison between user groups illustrated that doctors and pharmacists were generally satisfied with the facilitating conditions (hardware and training), but had divergent perceptions of performance (automation, standardised protocols and communication and documented) and effort (mental and temporal demand) expectancy. In counterpoint, nurses were generally satisfied across all constructs. Prior experience using an alternative EMMS influenced performance and effort expectancy and was related to early dissatisfaction with the EMMS. Furthermore, whilst not originally designed for the healthcare setting, the flexibility of the UTAUT allowed for translation to the hospital environment.

CONCLUSION

Nurses demonstrated overall satisfaction with the EMMS, whilst doctors and pharmacists perceived usability problems, particularly related to restricted automaticity and system complexity, which hindered perceived EMMS success. The study demonstrates the feasibility and utility of the UTAUT framework to evaluate usability of an EMMS for multiple user groups in the Oncology setting.

Reduction of phone interruptions post implementation of a central call center in community pharmacies of an academic health system

PURPOSE

A pharmacy services call center (PSCC) was implemented to centralize pharmacy phone calls and reduce interruptions of dispensing activities in 7 community pharmacies of an academic health center. An evaluation was conducted to define, quantify, and compare the numbers and types of phone interruptions before and 3 months after PSCC implementation.

METHODS

Through structured, direct observation of pharmacy staff, the numbers and types of "breaks in task" (BIT) due to phone interruptions and other distractions were identified. A standardized data collection tool formatted on tablet computers was used by trained observers to document BIT for 3-hour time blocks on 5 consecutive business days (2 days of pharmacist observation and 3 days of technician observation, for a total of 10 observation days per pharmacy).

RESULTS

Over 5,000 prescriptions were processed during 414 hours of observation (13.3 prescriptions per observation hour). Overall, BIT due to phone interruptions totaled 2.2 BIT per observation hour, with those interruptions reduced by 46.4% overall after PSCC implementation (by 30.0% in 4 small pharmacies and by 57.5% in 3 large pharmacies). Technicians were more likely than pharmacists to be interrupted by phone vs nonphone BIT (eg, distraction by another technician, pharmacist, or patient). Comparison of phone vs nonphone BIT suggested an overall 46.0% reduction in phone BIT in all pharmacies (reductions of 42.4% and 45.0% in large and small pharmacies, respectively).

CONCLUSION

PSCC implementation noticeably decreased the amount of phone interruptions and distractions for employees.

Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study

Our objective in this study was to determine if machine learning (ML) can automatically recognize neonatal manipulations, along with associated changes in physiological parameters. A retrospective observational study was carried out in two Neonatal Intensive Care Units (NICUs) between December 2019 to April 2020. Both the video and physiological data (heart rate (HR) and oxygen saturation (SpO₂)) were captured during NICU hospitalization. The proposed classification of neonatal manipulations was achieved by a deep learning system consisting of an Inception-v3 convolutional neural network (CNN), followed by transfer learning layers of Long Short-Term Memory (LSTM). Physiological signals prior to manipulations (baseline) were compared to during and after manipulations. The validation of the system was done using the leave-one-out strategy with input of 8 s of video exhibiting manipulation activity. Ten neonates were video recorded during an average length of stay of 24.5 days. Each neonate had an average of 528 manipulations during their NICU hospitalization, with the average duration of performing these manipulations varying from 28.9 s for patting, 45.5 s for a diaper change, and 108.9 s for tube feeding. The accuracy of the system was 95% for training and 85% for the validation dataset. In neonates <32 weeks’ gestation, diaper changes were associated with significant changes in HR and SpO₂, and, for neonates ≥32 weeks’ gestation, patting and tube feeding were associated with significant changes in HR. The presented system can classify and document the manipulations with high accuracy. Moreover, the study suggests that manipulations impact physiological parameters.

Effects of Electronic Health Record Implementation and Barriers to Adoption and Use: A Scoping Review and Qualitative Analysis of the Content

Despite the great advances in the field of electronic health records (EHRs) over the past 25 years, implementation and adoption challenges persist, and the benefits realized remain below expectations. This scoping review aimed to present current knowledge about the effects of EHR implementation and the barriers to EHR adoption and use. A literature search was conducted in PubMed, Web of Science, IEEE Xplore Digital Library and ACM Digital Library for studies published between January 2005 and May 2020. In total, 7641 studies were identified of which 142 met the criteria and attained the consensus of all researchers on inclusion. Most studies (n = 91) were published between 2017 and 2019 and 81 studies had the United States as the country of origin. Both positive and negative effects of EHR implementation were identified, relating to clinical work, data and information, patient care and economic impact. Resource constraints, poor/insufficient training and technical/educational support for users, as well as poor literacy and skills in technology were the identified barriers to adoption and use that occurred frequently. Although this review did not conduct a quality analysis of the included papers, the lack of uniformity in the use of EHR definitions and detailed contextual information concerning the study settings could be observed.

Workflow Analysis Driven Recommendations for Integration of Electronically-Enhanced Sexually Transmitted Infection Screening Tools in Pediatric Emergency Departments

Adolescents are disproportionately affected by sexually transmitted infections (STIs). Failure to diagnose and treat STIs in a timely manner may result in serious sequelae. Adolescents frequently access the emergency department (ED) for care. Although ED-based STI screening is acceptable to both patients and clinicians, understanding how best to implement STI screening processes into the ED clinical workflow without compromising patient safety or efficiency is critical. The objective of this study was to conduct direct observations documenting current workflow processes and tasks during patient visits at six Pediatric Emergency Care Applied Research Network (PECARN) EDs for site-specific integration of STI electronically-enhanced screening processes. Workflow observations were captured via TaskTracker, a time and motion electronic data collection application that allows researchers to categorize general work processes and record multitasking by providing a timestamp of when tasks began and ended. Workflow was captured during 118 patient visits across six PECARN EDs. The average time to initial assessment by the most senior provider was 76 min (range 59-106 min, SD = 43 min). Care teams were consistent across sites, and included attending physicians, advanced practice providers, nurses, registration clerks, technicians, and students. A timeline belt comparison was performed. Across most sites, the most promising implementation of a STI screening tool was in the patient examination room following the initial patient assessment by the nurse.

Impact of an electronic health record on task time distribution in a neonatal intensive care unit

BACKGROUND

An Electronic Health Record (EHR) has been introduced to four Irish maternity units, with further implementations planned. Previous studies indicate that healthcare professionals are concerned that EHRs may increase time spent on documentation and medication-related tasks.

OBJECTIVE

To determine the impact of an EHR on task time distribution in a Neonatal Intensive Care Unit (NICU).

METHODS

A pre-post, time and motion study. An electronic data collection tool was used to collate time spent on direct care, professional communication, reviewing charts, documentation, and medication-related tasks. Interruptions and contact with the patient zone were quantified. Statistical significance was assessed using two-sample proportion tests, two-sample t-tests, and two-sample Wilcoxon rank-sum tests. A Bonferroni correction set significance at p ≤ 0.0025.

RESULTS

63 doctors and nurses participated, with 169.23 h of data collected. There were no significant changes to nurses' task time distribution. The proportion of time spent by doctors on professional communication increased from 15.4% to 26.0% (p < 0.001). Significant increases to median task times were seen for both doctors and nurses. Interruptions to tasks decreased post-implementation (p < 0.001), as did frequency of contact with the patient zone (p < 0.001).

CONCLUSION

The EHR did not redistribute time towards documentation and medication-related tasks. A reduction in interruptions to tasks may streamline workflow. Decreased contact with the patient zone may improve patient safety through reduced potential for pathogen transmission.

Characterizing and Visualizing Display and Task Fragmentation in the Electronic Health Record: Mixed Methods Design

BACKGROUND

The complexity of health care data and workflow presents challenges to the study of usability in electronic health records (EHRs). Display fragmentation refers to the distribution of relevant data across different screens or otherwise far apart, requiring complex navigation for the user's workflow. Task and information fragmentation also contribute to cognitive burden.

OBJECTIVE

This study aims to define and analyze some of the main sources of fragmentation in EHR user interfaces (UIs); discuss relevant theoretical, historical, and practical considerations; and use granular microanalytic methods and visualization techniques to help us understand the nature of fragmentation and opportunities for EHR optimization or redesign.

METHODS

Sunburst visualizations capture the EHR navigation structure, showing levels and sublevels of the navigation tree, allowing calculation of a new measure, the Display Fragmentation Index. Time belt visualizations present the sequences of subtasks and allow calculation of proportion per instance, a measure that quantifies task fragmentation. These measures can be used separately or in conjunction to compare EHRs as well as tasks and subtasks in workflows and identify opportunities for reductions in steps and fragmentation. We present an example use of the methods for comparison of 2 different EHR interfaces (commercial and composable) in which subjects apprehend the same patient case.

RESULTS

Screen transitions were substantially reduced for the composable interface (from 43 to 14), whereas clicks (including scrolling) remained similar.

CONCLUSIONS

These methods can aid in our understanding of UI needs under complex conditions and tasks to optimize EHR workflows and redesign.

A novel tool for patient data management in the ICU-Ensuring timely and accurate vital data exchange among ICU team members

•

COVID-19 displaced many healthcare providers to intensive care units to meet the demand of incoming COVID-19 patients.

•

The infrastructure and IT support costs needed to establish EMRs are barriers to underserved regions adopting EMR technology.

•

Inexpensive implementation of this tool may allow for better patient care and data collection in certain regions.

•

Users can manage patient information electronically with less data overload and a more intuitive user experience.

Objective

The coronavirus pandemic has highlighted the need to simplify data collection for critically-ill patients, particularly for physicians relocated to the ICU setting. Herein we present a simple, reproducible, and highly-customizable manual-entry tool to track ICU patients using new HIPAA-compliant Google Big Query technology for parsing large datasets. This innovative flow chart is useful and could be modified to serve the particular needs of different sub-specialists, particularly those that either rely heavily on hand-written notes or experience poor electronic medical record (EMR) penetration.

Methods

The tool was developed using a combination of three Google Enterprise features: Google Forms for data input, Google Sheets for data output, and Google Big Query for data parsing. Code was written in SQL. Sheets functions were used to transpose and filter parsed data. White and black box tests were performed to examine functionality.

Results

Our tool was successfully able to collect and output fictional patient data across all 57 data points specified by the intensivists and surgeons of Cardiovascular Department of Mt. Sinai Morningside Hospital.

Conclusion

The functional tests performed demonstrate use of the tool. Though originally conceived to simplify patient data collection for newly relocated physicians to the ICU, our tool also overcomes financial and technological barriers previously described in low-income countries that could dramatically improve patient care and provide data to power future studies in these regions. With the original code provided, implementers may adapt our tool to best meet the requirements of their clinical setting and protocols during this very challenging time.

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.

Nurse workarounds in the electronic health record: An integrative review

OBJECTIVE

The study sought to synthesize published literature on direct care nurses' use of workarounds related to the electronic health record.

MATERIALS AND METHODS

We conducted an integrative review of qualitative and quantitative peer-reviewed research through a structured search of Academic Search Complete, EBSCO Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, Engineering Village, Ovid Medline, Scopus, and Web of Science. We systematically applied exclusion rules at the title, abstract, and full article stages and extracted and synthesized their research methods, workaround classifications, and probable causes from articles meeting inclusion criteria.

RESULTS

Our search yielded 5221 results. After removing duplicates and applying rules, 33 results met inclusion criteria. A total of 22 articles used qualitative approaches, 10 used mixed methods, and 1 used quantitative methods. While researchers may classify workarounds differently, they generally fit 1 of 3 broad categories: omission of process steps, steps performed out of sequence, and unauthorized process steps. Each study identified probable causes, which included technology, task, organizational, patient, environmental, and usability factors.

CONCLUSIONS

Extensive study of nurse workarounds in acute settings highlights the gap in ambulatory care research. Despite decades of electronic health record development, poor usability remains a key concern for nurses and other members of care team. The widespread use of workarounds by the largest group of healthcare providers subverts quality health care at every level of the healthcare system. Research is needed to explore the gaps in our understanding of and identify strategies to reduce workaround behaviors.

Analysing EHR navigation patterns and digital workflows among physicians during ICU pre-rounds

Background:

Some physicians in intensive care units (ICUs) report that electronic health records (EHRs) can be cumbersome and disruptive to workflow. There are significant gaps in our understanding of the physician–EHR interaction.

Objective:

To better understand how clinicians use the EHR for chart review during ICU pre-rounds through the characterisation and description of screen navigation pathways and workflow patterns.

Method:

We conducted a live, direct observational study of six physician trainees performing electronic chart review during daily pre-rounds in the 30-bed medical ICU at a large academic medical centre in the Southeastern United States. A tailored checklist was used by observers for data collection.

Results:

We observed 52 distinct live patient chart review encounters, capturing a total of 2.7 hours of pre-rounding chart review activity by six individual physicians. Physicians reviewed an average of 8.7 patients (range = 5–12), spending a mean of 3:05 minutes per patient (range = 1:34–5:18). On average, physicians visited 6.3 (±3.1) total EHR screens per patient (range = 1–16). Four unique screens were viewed most commonly, accounting for over half (52.7%) of all screen visits: results review (17.9%), summary/overview (13.0%), flowsheet (12.7%), and the chart review tab (9.1%). Navigation pathways were highly variable, but several common screen transition patterns emerged across users. Average interrater reliability for the paired EHR observation was 80.0%.

Conclusion:

We observed the physician–EHR interaction during ICU pre-rounds to be brief and highly focused. Although we observed a high degree of “information sprawl” in physicians’ digital navigation, we also identified common launch points for electronic chart review, key high-traffic screens and common screen transition patterns.

Implications:

From the study findings, we suggest recommendations towards improved EHR design.

Autopilots in the Operating Room

Automated medical technology is becoming an integral part of routine anesthetic practice. Automated technologies can improve patient safety, but may create new workflows with potentially surprising adverse consequences and cognitive errors that must be addressed before these technologies are adopted into clinical practice. Industries such as aviation and nuclear power have developed techniques to mitigate the unintended consequences of automation, including automation bias, skill loss, and system failures. In order to maximize the benefits of automated technology, clinicians should receive training in human–system interaction including topics such as vigilance, management of system failures, and maintaining manual skills. Medical device manufacturers now evaluate usability of equipment using the principles of human performance and should be encouraged to develop comprehensive training materials that describe possible system failures. Additional research in human–system interaction can improve the ways in which automated medical devices communicate with clinicians. These steps will ensure that medical practitioners can effectively use these new devices while being ready to assume manual control when necessary and prepare us for a future that includes automated health care.

Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow

The overwhelming number of medication alerts generated by clinical decision support systems (CDSS) has led to inappropriate alert overrides, which may lead to unintended patient harm. This review highlights the factors affecting the alert appropriateness of CDSS and barriers to the fit of CDSS alert with clinical workflow. A literature review was conducted to identify features and functions pertinent to CDSS alert appropriateness using the five rights of CDSS. Moreover, a process improvement method, namely, Lean, was used as a tool to optimise clinical workflows, and the appropriate design for CDSS alert using a human automation interaction (HAI) model was recommended. Evaluating the appropriateness of CDSS alert and its impact on workflow provided insights into how alerts can be designed and triggered effectively to support clinical workflow. The application of Lean methods and tools to analyse alert efficiencies in supporting workflow in this study provides an in-depth understanding of alert-workflow fit problems and their root cause, which is required for improving CDSS design. The application of the HAI model is recommended in the design of CDSS alerts to support various levels and stages of alert automations, namely, information acquisition and analysis, decision action and action implementation.

Impact of Patient Census and Admission Mortality on Pediatric Intensive Care Unit Attending Electronic Health Record Activity: A Preliminary Study

BACKGROUND

Physicians may spend a significant amount of time using the electronic health record (EHR), but this is understudied in the pediatric intensive care unit (PICU). The objective of this study is to quantify PICU attending physician EHR usage and determine its association with patient census and mortality scores.

METHODS

During the year 2016, total EHR, chart review, and documentation times of 7 PICU physicians were collected retrospectively utilizing an EHR-embedded time tracking software package. We examined associations between documentation times and patient census and maximum admission mortality scores. Odds ratios (ORs) are reported per 1-unit increase in patient census and mortality scores.

RESULTS

Overall, total daily EHR usage time (median time [hh:mm] [25th, 75th percentile]) was 2:10 (1:31, 3:08). For all hours (8 a.m.-8 a.m.), no strong association was noted between total EHR time, chart review, and documentation times and patient census, Pediatric Index of Mortality 2 (PIM2), or Pediatric Risk of Mortality 3 (PRISM3) scores. For regular hours (8 a.m.-7 p.m.), no strong association was noted between total EHR, chart review, and documentation times and patient census, PIM2, or PRISM3 scores. When patient census was higher, the odds of EHR after-hour usage (7 p.m.-8 a.m.) was higher (OR 1.262 [1.135, 1.403], p < 0.0001), but there were no increased odds with PIM2 (OR 1.090 [0.956, 1.242], p = 0.20) and PRISM3 (OR 1.010 [0.984, 1.036], p = 0.47) scores. A subset of physicians spent less time performing EHR-related tasks when patient census and admission mortality scores were elevated.

CONCLUSION

We performed a novel evaluation of physician EHR workflow in our PICU. Our pediatric critical care physicians spend approximately 2 hours (out of an expected 10-hour shift) each service day using the EHR, but there was no strong or consistent association between EHR usage and patient census or mortality scores. Future larger scale studies are needed to ensure validity of these results.

Health information technology:Fallacies and Sober realities - Redux A homage to Bentzi Karsh and Robert Wears

Since the publication of "Health Information Technology: Fallacies and Sober Realities" in 2010, health information technology (HIT) has become nearly ubiquitous in US healthcare facilities. Yet, HIT has yet to achieve its putative benefits of higher quality, safer, and lower cost care. There has been variable but largely marginal progress at addressing the 12 HIT fallacies delineated in the original paper. Here, we revisit several of the original fallacies and add five new ones. These fallacies must be understood and addressed by all stakeholders for HIT to be a positive force in achieving the high value healthcare system the nation deserves. Foundational cognitive and human factors engineering research and development continue to be essential to HIT development, deployment, and use.

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

BACKGROUND

Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR.

OBJECTIVES

This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard.

METHODS

We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR.

RESULTS

By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5).

CONCLUSION

At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

Growing Disparities in Patient-Provider Messaging: Trend Analysis Before and After Supportive Policy

BACKGROUND

Public policy introduced since 2011 has supported provider adoption of electronic medical records (EMRs) and patient-provider messaging, primarily through financial incentives. It is unclear how disparities in patients' use of incentivized electronic health (eHealth) tools, like patient-provider messaging, have changed over time relative to disparities in use of eHealth tools that were not directly incentivized.

OBJECTIVE

This study examines trends in eHealth disparities before and after the introduction of US federal financial incentives. We compare rates of patient-provider messaging, which was directly incentivized, with rates of looking for health information on the Web, which was not directly incentivized.

METHODS

We used nationally representative Health Information National Trends Survey data from 2003 to 2018 (N=37,300) to describe disparities in patient-provider messaging and looking for health information on the Web. We first reported the percentage of individuals across education and racial and ethnic groups who reported using these tools in each survey year and compared changes in unadjusted disparities during preincentive (2003-2011) and postincentive (2011-2018) periods. Using multivariable linear probability models, we then examined adjusted effects of education and race and ethnicity in 3 periods-preincentive (2003-2005), early incentive (2011-2013), and postincentive (2017-2018)-controlling for sociodemographic and health factors. In the postincentive period, an additional model tested whether internet adoption, provider access, or providers' use of EMRs explained disparities.

RESULTS

From 2003 to 2018, overall rates of provider messaging increased from 4% to 36%. The gap in provider messaging between the highest and lowest education groups increased by 10 percentage points preincentive (P<.001) and 22 additional points postincentive (P<.001). The gap between Hispanics and non-Hispanic whites increased by 3.2 points preincentive (P=.42) and 11 additional points postincentive (P=.01). Trends for blacks resembled those for Hispanics, whereas trends for Asians resembled those for non-Hispanic whites. In contrast, education-based disparities in looking for health information on the Web (which was not directly incentivized) did not significantly change in preincentive or postincentive periods, whereas racial disparities narrowed by 15 percentage points preincentive (P=.008) and did not significantly change postincentive. After adjusting for other sociodemographic and health factors, observed associations were similar to unadjusted associations, though smaller in magnitude. Including internet adoption, provider access, and providers' use of EMRs in the postincentive model attenuated, but did not eliminate, education-based disparities in provider messaging and looking for health information on the Web. Racial and ethnic disparities were no longer statistically significant in adjusted models.

CONCLUSIONS

Disparities in provider messaging widened over time, particularly following federal financial incentives. Meanwhile, disparities in looking for health information on the Web remained stable or narrowed. Incentives may have disproportionately benefited socioeconomically advantaged groups. Future policy could address disparities by incentivizing providers treating these populations to adopt messaging capabilities and encouraging patients' use of messaging.

Why Residency Programs Should Not Ignore the Electronic Heath Record after Adoption

During residency training, one of the tools residents learn to use is the electronic health record (EHR). The EHR contains up-to-date medical data that are crucial to the care of the patient; thus the provider must know what is pertinent, where to locate it, and how to efficiently document the data for ongoing communication of patient care. Because institutions may have different EHR vendors, EHR workflow study data are often obtained in single institutions, with a limited number of participants and specialties. Increasing our understanding of the subtleties of residents' EHR usage not only can help educators understand how residents use the EHR but also may provide information on another cognitive factor to assess residents' performance. This, however, will only occur when EHR skills are considered an important part of residency training and we ask our EHR vendors to help us develop validated electronic tools to assess EHR performance.

Ontology-based clinical information extraction from physician's free-text notes

Documenting clinical notes in electronic health records might affect physician's workflow. In this paper, an Ontology-based clinical information extraction system, OB-CIE, has been developed. OB-CIE system provides a method for extracting clinical concepts from physician's free-text notes and converts the unstructured clinical notes to structured information to be accessed in electronic health records. OB-CIE system can help physicians to document visit notes without changing their workflow. For recognizing named entities of clinical concepts, ontology concepts have been used to construct a dictionary of semantic categories, then, exact dictionary matching method has been used to match noun phrases to their semantic categories. A rule-based approach has been used to classify clinical sentences to their predefined categories. The system evaluation results have achieved an F-measure of 94.90% and 97.80% for concepts classification and sentences classification, respectively. The results have showed that OB-CIE system performed well on extracting clinical concepts compared with data mining techniques. The system can be used in another field by adapting its ontology and extraction rule set.

Visualising clinical work in the emergency department: Understanding interleaved patient management

We present a unique data visualisation approach, called workflow time charts, to illustrate the sequential and multi-dimensional nature of work in emergency departments. Using 40 h of data from direct observations of emergency physicians, we applied the charts to visualise patient-stratified physicians' work as a continuous temporal process, including distinguishing tasks of different types and representing external prompts (similar to interruptions) and multitasking performance. The charts showed frequent changes in the nature of observed activities, with interleaved multitasking a constant feature and external prompts often clustered in time. Evidence of seniority-related differences in work were apparent with consultants switching between more concurrent patients and receiving more frequent clinical prompts than junior physicians, illustrating their overseeing and advice-giving role. The ubiquity of interleaved multitasking suggests a need to focus on developing individual strategies to support frequent cognitive switching. Work that appears fragmented at physician level may form part of a flexible and robust system, rather than an error-prone set of isolated individual behaviours.

Describing drug and fluid therapy in the paediatric intensive care unit: A pilot study

PURPOSE

Care in the paediatric intensive care unit (PICU) involves many clinical activities. The objectives of this study were to evaluate the feasibility of a novel observation method, the reliability of data abstraction, and to report the initial findings from application of this approach.

MATERIALS AND METHODS

Bedside activities of patients and clinical staff were recorded by direct observational study using video recording and audio annotation. Data were abstracted into 9 broad clinical activities and 12 specific drug-fluid activities. Enrolment rates, agreement between abstractors, clinical activity durations and interruptions are reported.

RESULTS

We enrolled 42 healthcare professionals, 12 family members of 13 patients, and recorded 12 patients (consent rates of 70%-92%). There were 884 clinical activity episodes. Each hour was comprised of a median (IQR) of 11.9 (4.8-16.5) minutes of drug and fluid related tasks. The 682 drug and fluid related activities were mainly preparation and administration. Interruptions occurred on average 7 times per hour. Data abstraction for 8 h had intra-class correlation co-efficient (95% CI) of 0.91 (079-0.96).

CONCLUSIONS

Real-time recording of clinical tasks in the PICU using a direct observation model combined with video recording is feasible. Preliminary results suggest abundant and diverse activity is routine.