Enid Montague

Electronic medical records and physician stress in primary care: results from the MEMO Study.

BACKGROUND Little has been written about physician stress that may be associated with electronic medical records (EMR). OBJECTIVE We assessed relationships between the number of EMR functions, primary care work conditions, and physician satisfaction, stress and burnout. DESIGN AND PARTICIPANTS 379 primary care physicians and 92 managers at 92 clinics from New York City and the upper Midwest participating in the 2001-5 Minimizing Error, Maximizing Outcome (MEMO) Study. A latent class analysis identified clusters of physicians within clinics with low, medium and high EMR functions. MAIN MEASURES We assessed physician-reported stress, burnout, satisfaction, and intent to leave the practice, and predictors including time pressure during visits. We used a two-level regression model to estimate the mean response for each physician cluster to each outcome, adjusting for physician age, sex, specialty, work hours and years using the EMR. Effect sizes (ES) of these relationships were considered small (0.14), moderate (0.39), and large (0.61). KEY RESULTS Compared to the low EMR cluster, physicians in the moderate EMR cluster reported more stress (ES 0.35, p=0.03) and lower satisfaction (ES -0.45, p=0.006). Physicians in the high EMR cluster indicated lower satisfaction than low EMR cluster physicians (ES -0.39, p=0.01). Time pressure was associated with significantly more burnout, dissatisfaction and intent to leave only within the high EMR cluster. CONCLUSIONS Stress may rise for physicians with a moderate number of EMR functions. Time pressure was associated with poor physician outcomes mainly in the high EMR cluster. Work redesign may address these stressors.

Health and wellness technology use by historically underserved health consumers: systematic review.

Background The implementation of health technology is a national priority in the United States and widely discussed in the literature. However, literature about the use of this technology by historically underserved populations is limited. Information on culturally informed health and wellness technology and the use of these technologies to reduce health disparities facing historically underserved populations in the United States is sparse in the literature. Objective To examine ways in which technology is being used by historically underserved populations to decrease health disparities through facilitating or improving health care access and health and wellness outcomes. Methods We conducted a systematic review in four library databases (PubMed, PsycINFO, Web of Science, and Engineering Village) to investigate the use of technology by historically underserved populations. Search strings consisted of three topics (eg, technology, historically underserved populations, and health). Results A total of 424 search phrases applied in the four databases returned 16,108 papers. After review, 125 papers met the selection criteria. Within the selected papers, 30 types of technology, 19 historically underserved groups, and 23 health issues were discussed. Further, almost half of the papers (62 papers) examined the use of technology to create effective and culturally informed interventions or educational tools. Finally, 12 evaluation techniques were used to assess the technology. Conclusions While the reviewed studies show how technology can be used to positively affect the health of historically underserved populations, the technology must be tailored toward the intended population, as personally relevant and contextually situated health technology is more likely than broader technology to create behavior changes. Social media, cell phones, and videotapes are types of technology that should be used more often in the future. Further, culturally informed health information technology should be used more for chronic diseases and disease management, as it is an innovative way to provide holistic care and reminders to otherwise underserved populations. Additionally, design processes should be stated regularly so that best practices can be created. Finally, the evaluation process should be standardized to create a benchmark for culturally informed health information technology.

Modeling Eye Gaze Patterns in Clinician-Patient Interaction with Lag Sequential Analysis

Objective: The aim of this study was to examine whether lag sequential analysis could be used to describe eye gaze orientation between clinicians and patients in the medical encounter. This topic is particularly important as new technologies are implemented into multiuser health care settings in which trust is critical and nonverbal cues are integral to achieving trust. This analysis method could lead to design guidelines for technologies and more effective assessments of interventions. Background: Nonverbal communication patterns are important aspects of clinician–patient interactions and may affect patient outcomes. Method: The eye gaze behaviors of clinicians and patients in 110 videotaped medical encounters were analyzed using the lag sequential method to identify significant behavior sequences. Lag sequential analysis included both event-based lag and time-based lag. Results: Results from event-based lag analysis showed that the patient’s gaze followed that of the clinician, whereas the clinician’s gaze did not follow the patient’s. Time-based sequential analysis showed that responses from the patient usually occurred within 2 s after the initial behavior of the clinician. Conclusion: Our data suggest that the clinician’s gaze significantly affects the medical encounter but that the converse is not true. Application: Findings from this research have implications for the design of clinical work systems and modeling interactions. Similar research methods could be used to identify different behavior patterns in clinical settings (physical layout, technology, etc.) to facilitate and evaluate clinical work system designs.

Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Macroergonomics in Health Care Quality and Patient Safety

The US Institute of Medicine and healthcare experts have called for new approaches to manage healthcare quality problems. In this chapter, we focus on macroergonomics, a branch of human factors and ergonomics that is based on the systems approach and considers the organizational and sociotechnical context of work activities and processes. Selected macroergonomic approaches to healthcare quality and patient safety are described such as the SEIPS model of work system and patient safety and the model of healthcare professional performance. Focused reviews on job stress and burnout, workload, interruptions, patient-centered care, health IT and medical devices, violations, and care coordination provide examples of macroergonomics contributions to healthcare quality and patient safety. Healthcare systems and processes clearly need to be systematically redesigned; examples of macroergonomic approaches, principles and methods for healthcare system redesign are described. Further research linking macroergonomics and care processes/patient outcomes is needed. Other needs for macroergonomics research are highlighted, including understanding the link between worker outcomes (e.g., safety and well-being) and patient outcomes (e.g., patient safety), and macroergonomics of patient-centered care and care coordination.

Physician interactions with electronic health records in primary care

Objective: It is essential to design technologies and systems that promote appropriate interactions between physicians and patients. This study explored how physicians interact with Electronic Health Records (EHRs) to understand the qualities of the interaction between the physician and the EHR that may contribute to positive physician–patient interactions. Study Design: Video-taped observations of 100 medical consultations were used to evaluate interaction patterns between physicians and EHRs. Quantified observational methods were used to contribute to ecological validity. Methods: Ten primary care physicians and 100 patients from five clinics participated in the study. Clinical encounters were recorded with video cameras and coded using a validated coding methodology in order to examine how physicians interact with EHRs. Results: Three distinct styles were identified that characterize physician interactions with the EHR: technology-centered, human-centered, and mixed. Physicians who used a technology-centered style spent more time typing and gazing at the computer during the visit. Physicians who used a mixed style shifted their attention and body language between their patients and the technology throughout the visit. Physicians who used the human-centered style spent the least amount of time typing and focused more on the patient. Conclusion: A variety of EHR interaction styles may be effective in facilitating patient-centered care. However, potential drawbacks of each style exist and are discussed. Future research on this topic and design strategies for effective health information technology in primary care are also discussed.

Technology-mediated information sharing between patients and clinicians in primary care encounters

Objective: The aim of this study was to identify and describe the use of electronic health records (EHRs) for information sharing between patients and clinicians in primary-care encounters. This topic is particularly important as computers and other technologies are increasingly implemented in multi-user health-care settings where interactions and communication between patients and clinicians are integral to interpersonal and organisational outcomes. Method: An ethnographic approach was used to classify the encounters into distinct technology-use patterns based on clinicians’ interactions with the technology and patients. Each technology-use pattern was quantitatively analysed to assist with comparison. Quantitative analysis was based on duration of patient and clinician gaze at EHR. Findings: Physicians employed three different styles to share information using EHRs: (1) active information sharing, in which a clinician turns the monitor towards the patient and uses the computer to actively share information with the patient; (2) passive information sharing, when a clinician does not move the monitor, but the patient might see the monitor by leaning in if they choose and (3) technology withdrawal, when a clinician does not share the monitor with the patient. Conclusion: A variety of technology-mediated information-sharing styles may be effective in providing patient-centred care. New EHR designs may be needed to facilitate information sharing between patients and clinicians.

How different types of users develop trust in technology: A qualitative analysis of the antecedents of active and passive user trust in a shared technology

The aim of this study was to investigate the antecedents of trust in technology for active users and passive users working with a shared technology. According to the prominence-interpretation theory, to assess the trustworthiness of a technology, a person must first perceive and evaluate elements of the system that includes the technology. An experimental study was conducted with 54 participants who worked in two-person teams in a multi-task environment with a shared technology. Trust in technology was measured using a trust in technology questionnaire and antecedents of trust were elicited using an open-ended question. A list of antecedents of trust in technology was derived using qualitative analysis techniques. The following categories emerged from the antecedent: technology factors, user factors, and task factors. Similarities and differences between active users and passive user responses, in terms of trust in technology were discussed.

Understanding active and passive users: The effects of an active user using normal, hard and unreliable technologies on user assessment of trust in technology and co-user

The aim of this study was to understand how passive users perceive the trustworthiness of active users and technologies under varying technological conditions. An experimental study was designed to vary the functioning of technologies that active users interacted with, while passive users observed these interactions. Active and passive user ratings of technology and partner were collected. Exploratory data analysis suggests that passive users developed perceptions of technologies based on the functioning of the technology and how the active user interacted with the technology. Findings from this research have implications for the design of technologies in environments where active and passive users interact with technologies in different ways. Future work in this area should explore interventions that lead to enhanced affective engagement and trust calibration.

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research

OBJECTIVE To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development. MATERIALS AND METHODS Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. RESULTS Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. DISCUSSION Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. CONCLUSIONS Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.