The Impact of Feedback Modalities and the Influence of Cognitive Load on Interpersonal Communication in Nonclinical Settings: Experimental Study Design.

BACKGROUND: The escalating demands of modern health care systems, combined with the emotional toll of patient care, have led to an alarming increase in physician burnout rates. This burnout, characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, can hinder doctors' ability to connect with patients effectively. Moreover, the cognitive load arising from information overload and the need for multitasking can further hinder doctors' ability to connect with patients effectively. Understanding the complex relationship between physician burnout and cognitive load is crucial for devising targeted interventions that enhance physician well-being and promote effective physician-patient interactions. Implementing strategies to alleviate burnout and cognitive load can lead to improved health care experiences and patient outcomes. OBJECTIVE: Our study explores the interplay between physician burnout and its potential impact on interpersonal communication, particularly focusing on the role of cognitive load using a pilot study in a nonclinical setting involving nonclinical participants. METHODS: This study uses an experimental design to evaluate 3 feedback tools (haptic, visual, and postvisit summary) and measure the cognitive load they impose on nonclinical participants in a nonclinical environment. The NASA Task Load Index, a widely accepted measure of cognitive load, was used to quantify the cognitive load associated with the feedback tools. The study used a within-subject design, meaning participants experienced all 3 feedback methods. A sample of 18 nonclinical participants was selected using counterbalancing techniques. RESULTS: Postsession feedback not only enhancing performance but also mitigating the influence of cognitive load as compared with real-time feedback (haptic+visual). Participants with interview experience showed lower cognitive load levels when exposed to real-time feedback as compared with novice users. In contrast, postsession feedback was more effective for novice users. In addition, cognitive workload emerged as a moderating factor in the relationship between feedback tools and their impact on performance, particularly in terms of speaking balance and pace. This moderating effect suggests that the correlation between feedback tool efficacy and performance varies based on an individual's cognitive load while using the feedback tool. The comparison of postfeedback with haptic feedback yielded a Z score of -3.245 and a P value of .001, while the comparison with visual feedback resulted in a Z score of -2.940 and a P value of .003. These outcomes underscore a significant disparity in the means between postsession feedback and real-time feedback (haptic+visual), with postsession feedback indicating the lowest mean score. CONCLUSIONS: Through the examination of various feedback tools, this study yields significant and insightful comparisons regarding their usability and appropriateness in nonclinical settings. To enhance the applicability of these findings to clinical environments, further research encompassing diverse participant cohorts and clinical scenarios is warranted.

Evaluation of Eye Gaze Dynamics During Physician-Patient-Computer Interaction in Federally Qualified Health Centers: Systematic Analysis.

BACKGROUND: Understanding the communication between physicians and patients can identify areas where they can improve and build stronger relationships. This led to better patient outcomes including increased engagement, enhanced adherence to treatment plan, and a boost in trust. OBJECTIVE: This study investigates eye gaze directions of physicians, patients, and computers in naturalistic medical encounters at Federally Qualified Health Centers to understand communication patterns given different patients' diverse backgrounds. The aim is to support the building and designing of health information technologies, which will facilitate the improvement of patient outcomes. METHODS: Data were obtained from 77 videotaped medical encounters in 2014 from 3 Federally Qualified Health Centers in Chicago, Illinois, that included 11 physicians and 77 patients. Self-reported surveys were collected from physicians and patients. A systematic analysis approach was used to thoroughly examine and analyze the data. The dynamics of eye gazes during interactions between physicians, patients, and computers were evaluated using the lag sequential analysis method. The objective of the study was to identify significant behavior patterns from the 6 predefined patterns initiated by both physicians and patients. The association between eye gaze patterns was examined using the Pearson chi-square test and the Yule Q test. RESULTS: The results of the lag sequential method showed that 3 out of 6 doctor-initiated gaze patterns were followed by patient-response gaze patterns. Moreover, 4 out of 6 patient-initiated patterns were significantly followed by doctor-response gaze patterns. Unlike the findings in previous studies, doctor-initiated eye gaze behavior patterns were not leading patients' eye gaze. Moreover, patient-initiated eye gaze behavior patterns were significant in certain circumstances, particularly when interacting with physicians. CONCLUSIONS: This study examined several physician-patient-computer interaction patterns in naturalistic settings using lag sequential analysis. The data indicated a significant influence of the patients' gazes on physicians. The findings revealed that physicians demonstrated a higher tendency to engage with patients by reciprocating the patient's eye gaze when the patient looked at them. However, the reverse pattern was not observed, suggesting a lack of reciprocal gaze from patients toward physicians and a tendency to not direct their gaze toward a specific object. Furthermore, patients exhibited a preference for the computer when physicians directed their eye gaze toward it.

A Call to Action: Using and Extending Human-Centered Design Methodologies to Improve Mental and Behavioral Health Equity.

Mental health disparities directly tie to structural racism. Digital mental health (DMH), the use of technologies to deliver services, have been touted as a way to expand access to care and reduce disparities. However, many DMH fail to mitigate the persistent disparities associated with structural racism that impact delivery (e.g., costs, dependable internet access)-and may even exacerbate them. Human-centered design (HCD) may be uniquely poised to design and test interventions alongside, rather than "for," marginalized individuals. In employing HCD methodologies, developers may proceed with a vested interest in understanding and establishing empathy with users and their needs, behaviors, environments, and constraints. As such, HCD used to mindfully address structural racism in behavioral health care may address shortcomings of prior interventions that have neglected to elevate the voices of marginalized individuals. We argue that a paradigm shift in behavioral health services research is critically needed-one that embraces HCD as a key methodological framework for developing and evaluating interventions with marginalized communities, to ultimately promote more accessible, useful, and equitable care. The current commentary illustrates practical examples of the use of HCD methodologies to develop and evaluate DMH designed with marginalized populations, while also highlighting its limitations and need for even greater inclusivity. Following this, calls to action to learn from and improve upon HCD methodologies will be detailed. Acknowledging potential limitations of current design practices, methodologies must ultimately engage representative voices beyond research participation and invest in their active role as compensated and true collaborators to intervention design.

Using a sociotechnical systems analysis to evaluate an intervention to improve opioid prescribing in emergency medicine.

The United States is facing an unprecedented epidemic of opioid addiction and death due to opioid overdose. In an effort to improve patient knowledge and safe use about opioids, an Electronic Medication Complete Communication (EMC2) opioid strategy was developed targeting opioid naïve patients in the Emergency Department (ED). We conducted pre and post sociotechnical systems analyses to evaluate the variance between the process before the intervention and whether or not the process changed as expected with the new intervention. Results were analyzed using thematic qualitative analysis. Sociotechnical systems modeling illustrates the complexity of designing interventions for emergency medicine that affect multiple patients, providers, work systems, technologies, and processes. The post work systems model illustrates that several elements in the external ED environment can affect the effectiveness of the intervention. Sociotechnical systems analysis is an effective tool to illustrate the opportunities for designing health system interventions and evaluating the fidelity of such interventions.

A Multifaceted Intervention to Improve Patient Knowledge and Safe Use of Opioids: Results of the ED EMC2 Randomized Controlled Trial.

OBJECTIVES: Despite increased focus on opioid prescribing, little is known about the influence of prescription opioid medication information given to patients in the emergency department (ED). The study objective was to evaluate the effect of an Electronic Medication Complete Communication (EMC2 ) Opioid Strategy on patients' safe use of opioids and knowledge about opioids. METHODS: This was a three-arm prospective, randomized controlled pragmatic trial with randomization occurring at the physician level. Consecutive discharged patients at an urban academic ED (>88,000 visits) with new hydrocodone-acetaminophen prescriptions received one of three care pathways: 1) usual care, 2) EMC2 intervention, or 3) EMC2  + short message service (SMS) text messaging. The ED EMC2 intervention triggered two patient-facing educational tools (MedSheet, literacy-appropriate prescription wording [Take-Wait-Stop]) and three provider-facing reminders to counsel (directed to ED physician, dispensing pharmacist, follow-up physician). Patients in the EMC2  + SMS arm additionally received one text message/day for 1 week. Follow-up at 1 to 2 weeks assessed "demonstrated safe use" (primary outcome). Secondary outcomes including patient knowledge and actual safe use (via medication diaries) were assessed 2 to 4 days and 1 month following enrollment. RESULTS: Among the 652 enrolled, 343 completed follow-up (57% women; mean ± SD age = 42 ± 14.0 years). Demonstrated safe opioid use occurred more often in the EMC2 group (adjusted odds ratio [aOR] = 2.46, 95% confidence interval [CI] = 1.19 to 5.06), but not the EMC2  + SMS group (aOR = 1.87, 95% CI = 0.90 to 3.90) compared with usual care. Neither intervention arm improved medication safe use as measured by medication diary data. Medication knowledge, measured by a 10-point composite knowledge score, was greater in the EMC2  + SMS group (ß = 0.57, 95% CI = 0.09 to 1.06) than usual care. CONCLUSIONS: The study found that the EMC2 tools improved demonstrated safe dosing, but these benefits did not translate into actual use based on medication dairies. The text-messaging intervention did result in improved patient knowledge.

Implementation fidelity of patient-centered prescription label to promote opioid safe use.

PURPOSE: Patient-centered labels may improve safe medication use, but implementation challenges limit use. We assessed implementation of a patient-centered "PRN" (as needed) label entitled "Take-Wait-Stop" (TWS) with three deconstructed steps replacing traditional wording. METHODS: As part of a larger investigation, patients received TWS prescriptions (eg, Take: 1 pill if you have pain; Wait: at least 4 h before taking again; Stop: do not take more than 6 pills in 24 h). Prescriptions labels recorded at follow-up were classified into three categories: (1) one-step wording (Take 1 pill every 4 h [without daily limits]), (2) two-step wording (Take 1 pill every 4 h; do not exceed 6 pills/day), and (3) three-step wording. There were three subtypes of three-step wording: (3a) three-step, not TWS (three deconstructed steps, not necessarily TWS wording), (3b) TWS format, employing three steps with leading verbs, but "with additions or replacements" (eg, replaced "do not take" with "do not exceed"), and (3c) verbatim TWS. RESULTS: Two hundred eleven participants completed follow-up. Mean age was 44.3 years (SD 14.3); 44% were male. One-step bottles represented 12% (n = 25) of the sample, whereas 26% (n = 55) had two-step wording. The majority (44%, n = 93) had three-deconstructed steps, not TWS (3a); 16% (n = 34) retained TWS structure, but not verbatim (3b). Only 2% (n = 4) displayed verbatim TWS wording (3c). All category three labels (utilizing deconstructed instructions) were considered adequate implementation (62%). CONCLUSIONS: Exact intervention adherence was not achieved in the majority of cases, limiting impact. Nonetheless, community pharmacies were responsive to new instructions, but higher implementation reliability requires additional supports.

An experimental study on individual and group affect in multi-tasking teams.

Research has shown that affect can influence human performance in various settings. This study aimed to explore the effects of individual and group affect on task perceptions and performance under different stress conditions (task demand and technology reliability) and team training methods in multi-tasking environments. Seventy-two participants participated in the experiment in two-person teams. The participants' affect was measured using facial expression recognition technology. Affect aggregated mean and affect similarity were used as indicators of group affect. Self-reports of workload, teamwork satisfaction, trust in team, and trust in technology were collected. The results demonstrated that different stress conditions and training methods altered individual and group affect. Individual affect did not significantly correlate with the task perception measures, while group affect was related to performance in the team-oriented task but not the individual-oriented tasks. These findings suggested that group affect may have an important role in both multi-tasking and team coordination. Practitioner summary: Affect can influence human performance in various settings. This study explored how affect influences task perceptions and performance for teams in multi-tasking environments. The results demonstrated that individual affect was not correlated with self-reported task perceptions. Group affect was related to team performance in a team-oriented task.

Behavioral and cognitive intervention strategies delivered via coached apps for depression: Pilot trial.

Depression is common in primary care settings, but barriers prevent many primary care patients from initiating treatment. Smartphone apps stand as a possible means to overcome such barriers. However, there is limited evidence to understand the use and efficacy of these apps. The purpose of the current study was to pilot an evaluation of the usage and efficacy of apps for depression based upon behavioral or cognitive intervention skills, compared to a wait-list control. Thirty adults with depression were randomized to the use of either a behavioral app (Boost Me) or a cognitive app (Thought Challenger) or to a wait-list control. Boost Me and Thought Challenger participants received 6 weeks of the respective intervention along with weekly coaching sessions, with a 4-week follow-up period; wait-list control participants received no interventions for 10 weeks. A repeated-measures analysis of variance was conducted to examine depression over time and across treatment groups; t tests compared app usage across groups. Depression scores changed significantly over time (p < .001), with group differences occurring between Thought Challenger and wait-list control participants (p = .03). Boost Me was used significantly more than was Thought Challenger (p = .02); however, there was no evidence to suggest correlations between usage and changes in depression (ps > .05). The present study provides initial support that intervention strategies for depression delivered via apps with human support can impact symptoms and may promote continued use over 6 weeks. This pilot also demonstrates the feasibility of future research regarding the delivery of behavioral and cognitive intervention strategies via apps. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Redesigning Transplant Organ Labeling to Prevent Patient Harm and Organ Loss.

BACKGROUND: In 2012, the Health Resources and Services Administration and the United Network for Organ Sharing launched the "Electronic Tracking and Transportation" (ETT) project, in response to "labeling and packaging issues" being a frequently reported safety incident. This article describes an improvement project conducted as part of this United Network for Organ Sharing project. METHODS: An interdisciplinary team conducted a Process Failure Modes and Effects Analysis, laboratory simulations of organ labeling during procurement, and a heuristic evaluation of a label software application to inform the design of TransNet, a system that uses barcode technology at the point of organ recovery. A total of 42 clinicians and staff from 10 organ procurement organizations and 2 transplant centers in the United States participated. Processes Addressed: Key features of the redesigned labeling system include independent, double entry of label information into the software application, a machine-readable barcode on each organ's label, and a handheld printer for at "point of use" label printing. OUTCOMES: The new labeling system, TransNet, has become mandatory since June 2017. A survey conducted on early adopters (N = 11), after 1 year of use, indicates the process is safer and more efficient. IMPLICATIONS FOR PRACTICE: The findings from this study suggest that the application of quality planning methods, common in other industries, when redesigning a health-care process, are valuable and revelatory and should be adopted more extensively. Future evaluation of TransNet effectiveness to reduce safety incidents is critical.

Exploring User Learnability and Learning Performance in an App for Depression: Usability Study.

BACKGROUND: Mental health apps tend to be narrow in their functioning, with their focus mostly being on tracking, management, or psychoeducation. It is unclear what capability such apps have to facilitate a change in users, particularly in terms of learning key constructs relating to behavioral interventions. Thought Challenger (CBITs, Chicago) is a skill-building app that engages users in cognitive restructuring, a core component of cognitive therapy (CT) for depression. OBJECTIVE: The purpose of this study was to evaluate the learnability and learning performance of users following initial use of Thought Challenger. METHODS: Twenty adults completed in-lab usability testing of Thought Challenger, which comprised two interactions with the app. Learnability was measured via completion times, error rates, and psychologist ratings of user entries in the app; learning performance was measured via a test of CT knowledge and skills. Nonparametric tests were conducted to evaluate the difference between individuals with no or mild depression to those with moderate to severe depression, as well as differences in completion times and pre- and posttests. RESULTS: Across the two interactions, the majority of completion times were found to be acceptable (5 min or less), with minimal errors (1.2%, 10/840) and successful completion of CT thought records. Furthermore, CT knowledge and skills significantly improved after the initial use of Thought Challenger (P=.009). CONCLUSIONS: The learning objectives for Thought Challenger during initial uses were successfully met in an evaluation with likely end users. The findings therefore suggest that apps are capable of providing users with opportunities for learning of intervention skills.

Electronic medication complete communication strategy for opioid prescriptions in the emergency department: Rationale and design for a three-arm provider randomized trial.

BACKGROUND: Thousands of people die annually from prescription opioid overdoses; however there are few strategies to ensure patients receive medication risk information at the time of prescribing. OBJECTIVES: To compare the effectiveness of the Emergency Department (ED) Electronic Medication Complete Communication (EMC2) Opioid Strategy (with and without text messaging) to promote safe medication use and improved patient knowledge as compared to usual care. METHODS: The ED EMC2 Opioid Strategy consists of 5 automated components to promote safe medication use: 1) physician reminder to counsel, 2) inbox message sent on to the patient's primary care physician, 3) pharmacist message on the prescription to counsel, 4) MedSheet supporting prescription information, and 5) patient-centered Take-Wait-Stop wording of prescription instructions. This strategy will be assessed both with and without the addition of text messages via a three-arm randomized trial. The study will take place at an urban academic ED (annual volume>85,000) in Chicago, IL. Patients being discharged with a new prescription for hydrocodone-acetaminophen will be enrolled and randomized (based on their prescribing physician). The primary outcome of the study is medication safe use as measured by a demonstrated dosing task. Additionally actual safe use, patient knowledge and provider counseling will be measured. Implementation fidelity as well as costs will be reported. CONCLUSIONS: The ED EMC2 Opioid Strategy embeds a risk communication strategy into the electronic health record and promotes medication counseling with minimal workflow disruption. This trial will evaluate the strategy's effectiveness and implementation fidelity as compared to usual care. TRIAL REGISTRATION: This trial is registered on clinicaltrials.gov with identifier NCT02431793.

What might get in the way: Barriers to the use of apps for depression.

OBJECTIVE: Smartphones are being used with increasing frequency to deliver behavioral interventions for depression via apps. However, barriers specific to using an app for depression are poorly defined. The purpose of the current study is to identify barriers to the use of a mobile app to deliver treatment for depression. Secondarily, design implications will be provided based upon identified barriers. METHOD: A card sorting task that ranked and grouped barriers to the use of apps for depression was completed. Participants first completed a card sorting task identifying barriers to face-to-face treatment, as a primer to identification of treatment barriers. The sample consisted of those above (n = 9) and below (n = 11) the threshold for a referral to psychotherapy, to capture anticipated barriers for likely end users. Cluster analyses were conducted to analyze the card sorting data. Multiple analyses were conducted to identify: 1) the most important barriers, and 2) how consistently barriers were ranked as important. RESULT: The card sorting task identified a number of primary barriers to the use of apps for depression treatment, including concerns over intervention efficacy, app functioning, privacy, cost, and lack of guidance and tailored feedback. The top face-to-face treatment barrier was cost, overlapping with mobile barriers. CONCLUSION: This study identified perceived barriers to the use of mobile treatment apps. Identification of barriers implicates design recommendations for apps for depression.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution.

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

Impact of Work Conditions and Minority Patient Populations on Quality and Errors.

OBJECTIVES: To determine whether workplace conditions affect care quality and errors, especially in primary care clinics serving minority patients. METHODS: We conducted a 3-year assessment of work conditions and patient outcomes in 73 primary care clinics in the upper Midwest and New York City. Study participants included 287 physicians and 1204 patients with hypertension and/or diabetes. Chart audit data were contrasted between clinics with ≥30% minority patients (minority-serving clinics, or MSCs) and those with <30% (nonminority-serving clinics, or NMSCs). Physicians reported on time pressure, work control, clinical resources, and specialty referral access; managers described room availability; and chart audits determined care errors and quality. Two-level hierarchical models tested work conditions as mediators between MSC status and clinical outcomes. RESULTS: Error rates were higher in MSCs than NMSCs (29.6% vs 24.8%, P < .05). Lack of clinical resources explained 41% of the effect of MSC status on errors (P < .05). Diabetes control was poorer in MSCs than in NMSCs (53.8% controlled vs 76.1%, P < .05); lack of clinical resources explained 24% of this difference (P < .05). Room availability increased quality in both MSCs and NMSCs by 5.95% for each additional room per clinician per session. Lack of access to rooms and specialists decreased the likelihood of blood pressure control in MSCs. CONCLUSION: Work conditions such as clinical resources, examination room availability, and access to referrals are significantly associated with errors and quality, especially in MSCs.

MedLink: A Mobile Intervention to Address Failure Points in the Treatment of Depression in General Medicine.

Major depression is common, and imposes a high burden in terms of cost, morbidity, and suffering. Most people with depression are treated in general medicine using antidepressant medication. Outcomes are poor due to failure points across the care system, including patient non-adherence, failure of physicians to optimize the treatment regimens, and lack of patient-physician communication. This study reports on the 4-week pilot deployment of MedLink, a mobile intervention aimed at systemically addressing each of these failure points. A mobile app provides the patient with information and collects data on symptoms and side-effects. A cellularly enabled pill bottle monitors medication adherence. Data from these are provided to the physician and patient to foster communication and medication adjustments. Usability evaluation was generally favorable. Medication adherence rates in this first deployment were high with no patients discontinuing, and 84% of doses taken. Depressive symptom severity was significantly reduced. This study supports the use of a comprehensive, systemic approach to mHealth solutions to enhance processes of care for depression by general medicine physicians.

Portrayal of medical decision making around medical interventions life-saving encounters on three medical television shows.

INTRODUCTION: Previous literature has shown that patients obtain information about the medical system from television shows. Additionally, shared decision making is regularly cited as the ideal way to make decisions during a medical encounter. Little information exists surrounding the characteristics of medical decision-making, such as who makes the decision, on medical television shows. We evaluate the characteristics of medical decisions in lifesaving encounters on medical television shows and evaluate if these characteristics were different on staged and reality television shows. METHODS: We coded type of medical intervention, patient's ability to participate in decision, presence of patient advocate during decision, final decision maker, decision to use intervention, and controversy surrounding decision on three television shows. Frequencies by show were calculated and differences across the three television shows and between staged (ER) and reality (BostonMed and Hopkins) television shows were assessed with chi-square tests. RESULTS: The final data set included 37 episodes, 137 patients and 593 interventions. On ER, providers were significantly more likely to make the decision about the medical intervention without informing the patient when a patient was capable of making a decision compared to BostonMed or Hopkins (p<0.001). Across all shows, 99% of all decisions on whether to use a medical intervention resulted in the use of that intervention. DISCUSSION: Medical interventions are widely portrayed in the medical television shows we analyzed. It is possible that what patients see on television influences their expectations surrounding the decision making process and the use of medical interventions in everyday healthcare encounters.

Exploring Residents' Interactions With Electronic Health Records in Primary Care Encounters.

OBJECTIVES: Our objective was to explore and compare residents' interaction with electronic health records during primary care encounters and whether those behaviors differ by residency year level. METHODS: A field study was conducted in which 38 patient visits with 12 family medicine resident physicians were observed and video recorded in two primary care clinics under the umbrella of a university-based department of family medicine. Videos were then coded for gaze behaviors in which patients' and physicians' gazes at electronic health records and at each other were coded using a pre-established objective coding scheme. RESULTS: Third-year residents spent the greatest percentage of visit time looking at EHR screens when compared to first- and second-year residents. Third-year residents also spent significantly more time typing or inputting information into the records during the clinical visit when compared to first- and second-year residents. CONCLUSIONS: This study illustrated that patterns for using the EHR and interacting with patients may change over time for residents. These changes may be due to increased EHR proficiency or increased workload that incentivizes using the EHR to retrieve information and to complete charting in front of the patient.

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.

Quantitative ethnographic study of physician workflow and interactions with electronic health record systems.

This study explores the relationship between primary care physicians' interactions with health information technology and primary care workflow. Clinical encounters were recorded with high-resolution video cameras to capture physicians' workflow and interaction with two objects of interest, the electronic health record (EHR) system, and their patient. To analyze the data, a coding scheme was developed based on a validated list of primary care tasks to define the presence or absence of a task, the time spent on each task, and the sequence of tasks. Results revealed divergent workflows and significant differences between physicians' EHR use surrounding common workflow tasks: gathering information, documenting information, and recommend/discuss treatment options. These differences suggest impacts of EHR use on primary care workflow, and capture types of workflows that can be used to inform future studies with larger sample sizes for more effective designs of EHR systems in primary care clinics. Future research on this topic and design strategies for effective health information technology in primary care are discussed.

A Modular Architecture for Electronic Health Record-Driven Phenotyping.

Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.