Awareness of the Care Team in Electronic Health Records

Perspectives on the Intersection of Electronic Health Records and Health Care Team Communication, Function, and Well-being

Importance

Understanding of the interplay between the electronic health record (EHR), health care team relations, and physician well-being is currently lacking. Approaches to cultivate interpersonal interactions may be necessary to complement advancements in health information technology with high-quality team function.

Objective

To examine ways in which the EHR, health care team functioning, and physician well-being intersect and interact.

Design, Setting, and Participants

Secondary qualitative analysis of semistructured interview data from 2 studies used keyword-in-context approaches to identify excerpts related to teams. Thematic analysis was conducted using pattern coding, then organized using the relationship-centered organization model. Two health care organizations in California from March 16 to October 13, 2017, and February 28 to April 21, 2022, participated, with respondents including attending and resident physicians.

Main Outcome and Measures

Across data sets, themes centered around the interactions between the EHR, health care team functioning, and physician well-being. The first study data focused on EHR-related distressing events and their role in attending physician and resident physician emotions and actions. The second study focused on EHR use and daily EHR irritants.

Results

The 73 respondents included attending physicians (53 [73%]) and resident physicians (20 [27%]). Demographic data were not collected. Participants worked in ambulatory specialties (33 [45%]), hospital medicine (10 [14%]), and surgery (10 [14%]). The EHR was reported to be the dominant communication modality among all teams. Interviewees indicated that the EHR facilitates task-related communication and is well suited to completing simple, uncomplicated tasks. However, EHR-based communication limited the rich communication and social connection required for building relationships and navigating conflict. The EHR was found to negatively impact team function by promoting disagreement and introducing areas of conflict into team relationships related to medical-legal pressures, role confusion, and undefined norms around EHR-related communication. In addition, interviewees expressed that physician EHR-related distress affects interactions within the team, eroding team well-being.

Conclusions and Relevance

In this study, the EHR supported task-oriented and efficient communication among team members to get work done and care for patients; however, participants felt that the technology shifts attention away from the human needs of the care team that are necessary for developing relationships, building trust, and resolving conflicts. Interventions to cultivate interpersonal interactions and team function are necessary to complement the efficiency benefits of health information technology.

Characterizing intensive care unit rounding teams using meta-data from the electronic health record

PURPOSE

Teamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams.

METHODS

We analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization.

RESULTS

We identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0-706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0-99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07-0.23), degree centralization (0.50, 0.35-0.65) and closeness centralization (0.45, 0.11-0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics.

CONCLUSIONS

EHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.

The Use of Electronic Health Record Metadata to Identify Nurse-Patient Assignments in the Intensive Care Unit: Algorithm Development and Validation

Background

Nursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse’s unique skills are matched to each patient’s needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale.

Objective

This study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU.

Methods

We used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts.

Results

The analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions.

Conclusions

Metadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Why Should I Trust You? Supporting the Sharing of Health Data in the Interprofessional Space of Child Development

Many stakeholders can be involved in supporting a child's development, including parents, pediatricians, and educators. These stakeholders struggle to collaborate, and experts suggest that health information technology could improve their communication. Trust, based on perceptions of competence, benevolence, and integrity is fundamental to supporting information sharing, so information technologies should address trust between stakeholders. We engaged 75 parents and 60 healthcare workers with two surveys to explore this topic. We first elicited the types of information parents and healthcare workers use to form perceptions of competence, benevolence, and integrity. We then designed and tested user profile prototypes listing the elicited information to see if it builds trust in previously unknown professionals. We discovered that providing information related to personal characteristics, relationships, professional experience, and workplace practices can support trust and the sharing of information. This work has implications for designing informative electronic user interfaces to support interprofessional trust.

Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

OBJECTIVE

To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities.

MATERIALS AND METHODS

In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research.

RESULTS

Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy.

DISCUSSION

While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis.

CONCLUSION

EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

Clinical Research Integration Within the Electronic Health Record: A Literature Review

Clinical trials have become commonplace as a treatment option. As clinical trial participants are integrated into all healthcare delivery settings, organizations are tasked with sustaining specific care regimens with appropriate documentation and maintenance of participant protections within electronic health records. Our aim was to identify the common elements necessary for electronic health record integration of clinical research for optimal trial conduct and participant management. Review of literature was conducted utilizing PubMed and CINAHL to identify relevant publications that described use of the electronic health record to directly support trial conduct, with a total of 15 publications ultimately meeting inclusion criteria. Three thematic groupings emerged that categorized common aspects of clinical research integration: functional, structural, and procedural components. These components include technological requirements (platform/system), regulatory and legal compliance, and stakeholder involvement with clinical trial procedures (recruitment of participants). Without a centralized means of providing clinicians with current treatment and adverse event management information, participant injury or likelihood of withdrawal will increase. Further research is required to develop an optimal model of research-related integration within commercial electronic health records.

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

OBJECTIVE

Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs).

METHODS

We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow.

RESULTS

We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19-278.56; ED: OR = 19.06, 95% CI: 9.53-41.65' inpatient: OR = 2.95, 95% CI: 2.23-3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%).

CONCLUSION

Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

An investigation of the information technology needs associated with delivering chronic disease care to large clinical populations

BACKGROUND

The growing number of individuals with complex medical and social needs has motivated the adoption of care management (CM) - programs wherein multidisciplinary teams coordinate and monitor the clinical and non-clinical aspects of care for patients with chronic disease. Despite claims that health information technology (IT) is essential to CM, there has been limited research focused on the IT needs of clinicians providing care management to large groups of patients with chronic disease.

OBJECTIVE

To assess clinicians' needs pertaining to CM and to identify inefficiencies and bottlenecks associated with the delivery of CM to large groups of patients with chronic disease.

METHODS

A qualitative study of two HIV care programs. Methods included observations of multidisciplinary care team meetings and semi-structured interviews with physicians, care managers, and social workers. Thematic analysis was conducted to analyze the data.

RESULTS

CM was perceived by staff as requiring the development of novel strategies including patient prioritization and patient monitoring, which was supported by patient registries but also required the creation of additional homegrown tools. Common challenges included: limited ability to identify pertinent patient information, specifically in regards to social and behavioral determinants of health, limited assistance in matching patients to appropriate interventions, and limited support for communication within multidisciplinary care teams.

CONCLUSION

Clinicians delivering care management to chronic disease patients are not adequately supported by electronic health records and patient registries. Tools that better enable population monitoring, facilitate communication between providers, and help address psychosocial barriers to treatment could enable more effective care.

Failure to Rescue Event Mitigation System Assessment: A Mixed-methods Approach to Analysis of Complex Adaptive Systems

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

Understanding Interpersonal and Organizational Dynamics Among Providers Responding to Crisis

Patient rescue occurs in phases: recognizing the problem, communicating the concern, and treating the complication. To help improve rescue, we sought to understand facilitators and barriers to managing postoperative complications. We used a criterion-based sample from a large academic medical center. Semistructured interviews (n = 57) were conducted, which were audio-recorded and transcribed verbatim. Thematic analysis and consensus coding was performed using NVivo 11. We used a framework matrix approach to synthesize our coding and identify themes that facilitate or impede rescue. Clinicians identified root causes for delays in care, such as recognizing patient deterioration, knowing whom to contact and when, and reaching the correct decision-making provider. This study identified significant variation in communication processes across providers caring for surgical patients. Targeted interventions aimed at improving and standardizing these aspects of communication may significantly influence the ability to effectively identify and escalate care for postoperative complications.

Measuring electronic communication networks in virtual care teams using electronic health records access-log data

OBJECTIVE

To develop methods for measuring electronic communication networks in virtual care teams using electronic health records (EHR) access-log data.

METHODS

For a convenient sample of 100 surgical colorectal cancer patients, we used time-stamped EHR access-log data extracted from an academic medical center's EHR system to construct communication networks among healthcare professionals (HCPs) in each patient's virtual care team. We measured communication linkages between HCPs using the inverse of the average time between access events in which the source HCPs sent information to and the destination HCPs retrieved information from the EHR system. Social network analysis was used to examine and visualize communication network structures, identify principal care teams, and detect meaningful structural differences across networks. We conducted a non-parametric multivariate analysis of variance (MANOVA) to test the association between care teams' communication network structures and patients' cancer stage and site.

RESULTS

The 100 communication networks showed substantial variations in size and structures. Principal care teams, the subset of HCPs who formed the core of the communication networks, had higher proportions of nurses, physicians, and pharmacists and a lower proportion of laboratory medical technologists than the overall networks. The distributions of conditional uniform graph quantiles suggested that our network-construction technique captured meaningful underlying structures that were different from random unstructured networks. MANOVA results found that the networks' topologies were associated with patients' cancer stage and site.

CONCLUSIONS

This study demonstrates that it is feasible to use EHR access-log data to measure and examine communication networks in virtual care teams. The proposed methods captured salient communication patterns in care teams that were associated with patients' clinical differences.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

OBJECTIVES

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.

MATERIALS AND METHODS

A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared.

RESULTS

Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists.

DISCUSSION

The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay.

CONCLUSIONS

Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Automatic Detection of Front-Line Clinician Hospital Shifts: A Novel Use of Electronic Health Record Timestamp Data

OBJECTIVE

Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution.

METHODS

We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role.

RESULTS

We collected 6.3 × 10⁷ EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1).

CONCLUSION

Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions.

The use of model constructs to design collaborative health information technologies: A case study to support child development

OBJECTIVE

Health information technology could provide valuable support for inter-professional collaboration to address complex health issues, but current HIT systems do not adequately support such collaboration. Existing theoretical research on supporting collaborative work can help inform the design of collaborative HIT systems. Using the example of supporting collaboration between child development service providers, we describe a deductive approach that leverages concepts from the literature and analyzes qualitative user-needs data to aid in collaborative system design.

MATERIALS AND METHODS

We use the Collaboration Space Model to guide the deductive qualitative analysis of interviews focused on the use of information technology to support child development. We deductively analyzed 44 interviews from two separate research initiatives and included data from a wide range of stakeholder groups including parents and various service providers. We summarized the deductively coded interview excerpts using quantitative and qualitative methods.

RESULTS

The deductive analysis method provided a rich set of design data, highlighting heterogeneity in work processes, barriers to adequate communication, and gaps in stakeholder knowledge in supporting child development work.

DISCUSSION

Deductive qualitative analysis considering constructs from a literature-based model provided useful, actionable data to aid in design. Design implications underscore functions needed to adequately share data across many stakeholders. More work is needed to validate our design implications and to better understand the situations where specific system features would be most useful.

CONCLUSIONS

Deductive analysis considering model constructs provides a useful approach to designing collaborative HIT systems, allowing designers to consider both empirical user data and existing knowledge from the literature. This method has the potential to improve designs for collaborative HIT systems.

Formative Evaluation of Care Nexus: a Tool for the Visualization and Management of Care Teams of Complex Pediatric Patients

Complex and chronic conditions in pediatric patients with special needs often result in large and diverse patient care teams. Having a comprehensive view of the care teams is crucial to achieving effective and efficient care coordination for these vulnerable patients. In this study, we iteratively design and develop two alternative user interfaces (graphical and tabular) of a prototype of a tool for visualizing and managing care teams and conduct a formative assessment of the usability, usefulness, and efficiency of the tool. The median time to task completion for the 21 study participants was less than 7 seconds for 19 out of the 22 usability tasks. While both the prototype formats were well-liked in terms of usability and usefulness, the tabular format was rated higher for usefulness (p=0.02). Inclusion of CareNexus-like tools in electronic and personal health records has the potential to facilitate care coordination in complex pediatric patients.

Exploring "patient-centered" hospitals: a systematic review to understand change

Background

The healthcare scenario in developed countries is changing deeply: patients, who are frequently affected by multi-pathological chronic conditions, have risen their expectations. Simultaneously, there exist dramatic financial pressures which require healthcare organizations to provide more and better services with equal (or decreasing) resources. In response to these challenges, hospitals are facing radical transformations by bridging, redesigning and engaging their organization and staff.

Methods

This study has the ambitious aim to shed light and clearly label the trends of change hospitals are enhancing in developed economies, in order to fully understand the presence of common trends and which organizational models and features are inspiring the most innovative organizations. The purpose is to make stock of what is known in the field of hospital organization about how hospitals are changing, as well as of how such change may be implemented effectively through managerial tools. To do so the methodology adopted integrates a systematic literature review to a wider engaged research approach.

Results

Evidence suggests that the three main pillars of change of the system are given by the progressive patient care model, the patient-centered approach and the lean approach. However, there emerge a number of gaps in what is known about how to exploit drivers of change and their effects.

Conclusions

This study confirms that efforts in literature are concentrated in analyzing circumscribed experiences in the implementation of new models and approaches, failing therefore to extend the analysis at the organizational and inter-organizational level in order to legitimately draw consequences to be generalized. There seem to be a number of “gaps” in what is known about how to exploit drivers of change and their effects, suggesting that the research approach privileged till now fails in providing a clear guidance to policy makers and to organizations’ management on how to concretely and effectively implement new organizational models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-017-2306-0) contains supplementary material, which is available to authorized users.

Using telephony data to facilitate discovery of clinical workflows

BACKGROUND

Discovery of clinical workflows to target for redesign using methods such as Lean and Six Sigma is difficult. VoIP telephone call pattern analysis may complement direct observation and EMR-based tools in understanding clinical workflows at the enterprise level by allowing visualization of institutional telecommunications activity.

OBJECTIVE

To build an analytic framework mapping repetitive and high-volume telephone call patterns in a large medical center to their associated clinical units using an enterprise unified communications server log file and to support visualization of specific call patterns using graphical networks.

METHODS

Consecutive call detail records from the medical center's unified communications server were parsed to cross-correlate telephone call patterns and map associated phone numbers to a cost center dictionary. Hashed data structures were built to allow construction of edge and node files representing high volume call patterns for display with an open source graph network tool.

RESULTS

Summary statistics for an analysis of exactly one week's call detail records at a large academic medical center showed that 912,386 calls were placed with a total duration of 23,186 hours. Approximately half of all calling called number pairs had an average call duration under 60 seconds and of these the average call duration was 27 seconds.

CONCLUSIONS

Cross-correlation of phone calls identified by clinical cost center can be used to generate graphical displays of clinical enterprise communications. Many calls are short. The compact data transfers within short calls may serve as automation or re-design targets. The large absolute amount of time medical center employees were engaged in VoIP telecommunications suggests that analysis of telephone call patterns may offer additional insights into core clinical workflows.

The electronic health record audit file: the patient is waiting

Objective: We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC).

Materials/methods: We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features.

Results: On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time.

Conclusions: This study provides insights on uses of audit file data for workflow analysis during PC encounters.

Discussion: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

The electronic health record audit file: the patient is waiting

OBJECTIVE

We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC).

MATERIALS/METHODS

We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features.

RESULTS

On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time.

CONCLUSIONS

This study provides insights on uses of audit file data for workflow analysis during PC encounters.

DISCUSSION

Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

OBJECTIVE

Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.

METHODS

Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation.

RESULTS

EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation.

CONCLUSION

Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach

BACKGROUND

The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment.

METHODS AND RESULTS

We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine's Enterprise Data Warehouse. We then created a provider-patient network to identify healthcare providers who jointly participated in patient encounters and calculated satisfaction rates for provider-provider pairs. We demonstrated the application of a novel parameter, the shared positive outcome ratio, a measure that assesses the strength of a patient-sharing relationship between 2 providers based on risk-adjusted encounter outcomes. We compared an observed collaboration network of 334 providers and 3453 relationships to 1000 networks with shared positive outcome ratio scores based on randomized outcomes and found 188 collaborative relationships between pairs of providers that showed significantly higher than expected patient satisfaction ratings. A group of 22 providers performed exceptionally in terms of patient satisfaction. Our results indicate high variability in collaboration scores across the network and highlight our ability to identify relationships with both higher and lower than expected scores across a set of shared patient encounters.

CONCLUSIONS

Satisfaction rates seem to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform quality improvement strategies such as optimizing team staffing, identifying characteristics and practices of high-performing teams, developing evidence-based team guidelines, and redesigning inpatient care processes.

An Outcome-Weighted Network Model for Characterizing Collaboration

Shared patient encounters form the basis of collaborative relationships, which are crucial to the success of complex and interdisciplinary teamwork in healthcare. Quantifying the strength of these relationships using shared risk-adjusted patient outcomes provides insight into interactions that occur between healthcare providers. We developed the Shared Positive Outcome Ratio (SPOR), a novel parameter that quantifies the concentration of positive outcomes between a pair of healthcare providers over a set of shared patient encounters. We constructed a collaboration network using hospital emergency department patient data from electronic health records (EHRs) over a three-year period. Based on an outcome indicating patient satisfaction, we used this network to assess pairwise collaboration and evaluate the SPOR. By comparing this network of 574 providers and 5,615 relationships to a set of networks based on randomized outcomes, we identified 295 (5.2%) pairwise collaborations having significantly higher patient satisfaction rates. Our results show extreme high- and low-scoring relationships over a set of shared patient encounters and quantify high variability in collaboration between providers. We identified 29 top performers in terms of patient satisfaction. Providers in the high-scoring group had both a greater average number of associated encounters and a higher percentage of total encounters with positive outcomes than those in the low-scoring group, implying that more experienced individuals may be able to collaborate more successfully. Our study shows that a healthcare collaboration network can be structurally evaluated to characterize the collaborative interactions that occur between healthcare providers in a hospital setting.

Care team identification in the electronic health record: A critical first step for patient-centered communication

Patient-centered communication is essential to coordinate care and safely progress patients from admission through discharge. Hospitals struggle with improving the complex and increasingly electronic conversation patterns among care team members, patients, and caregivers to achieve effective patient-centered communication across settings. Accurate and reliable identification of all care team members is a precursor to effective patient-centered communication and ideally should be facilitated by the electronic health record. However, the process of identifying care team members is challenging, and team lists in the electronic health record are typically neither accurate nor reliable. Based on the literature and on experience from 2 initiatives at our institution, we outline strategies to improve care team identification in the electronic health record and discuss potential implications for patient-centered communication. Journal of Hospital Medicine 2016;11:381-385. © 2016 Society of Hospital Medicine.

A Design Methodology for Medical Processes

BACKGROUND

Healthcare processes, especially those belonging to the clinical domain, are acknowledged as complex and characterized by the dynamic nature of the diagnosis, the variability of the decisions made by experts driven by their experiences, the local constraints, the patient's needs, the uncertainty of the patient's response, and the indeterminacy of patient's compliance to treatment. Also, the multiple actors involved in patient's care need clear and transparent communication to ensure care coordination.

OBJECTIVES

In this paper, we propose a methodology to model healthcare processes in order to break out complexity and provide transparency.

METHODS

The model is grounded on a set of requirements that make the healthcare domain unique with respect to other knowledge domains. The modeling methodology is based on three main phases: the study of the environmental context, the conceptual modeling, and the logical modeling.

RESULTS

The proposed methodology was validated by applying it to the case study of the rehabilitation process of stroke patients in the specific setting of a specialized rehabilitation center. The resulting model was used to define the specifications of a software artifact for the digital administration and collection of assessment tests that was also implemented.

CONCLUSIONS

Despite being only an example, our case study showed the ability of process modeling to answer the actual needs in healthcare practices. Independently from the medical domain in which the modeling effort is done, the proposed methodology is useful to create high-quality models, and to detect and take into account relevant and tricky situations that can occur during process execution.

Engaging hospitalized patients in clinical care: Study protocol for a pragmatic randomized controlled trial

BACKGROUND

Patients who are better informed and more engaged in their health care have higher satisfaction with health care and better health outcomes. While patient engagement has been a focus in the outpatient setting, strategies to engage inpatients in their care have not been well studied. We are undertaking a study to assess how patients' information needs during hospitalization can be addressed with health information technologies. To achieve this aim, we developed a personalized inpatient portal that allows patients to see who is on their care team, monitor their vital signs, review medications being administered, review current and historical lab and test results, confirm allergies, document pain scores and send questions and comments to inpatient care providers. The purpose of this paper is to describe the protocol for the study.

METHODS/DESIGN

This pragmatic randomized controlled trial will enroll 426 inpatient cardiology patients at an urban academic medical center into one of three arms receiving: 1) usual care, 2) iPad with general internet access, or 3) iPad with access to the personalized inpatient portal. The primary outcome of this trial is patient engagement, which is measured through the Patient Activation Measure. To assess scalability and potential reach of the intervention, we are partnering with a West Coast community hospital to deploy the patient engagement technology in their environment with an additional 160 participants.

CONCLUSION

This study employs a pragmatic randomized control trial design to test whether a personalized inpatient portal will improve patient engagement. If the study is successful, continuing advances in mobile computing technology should make these types of interventions available in a variety of clinical care delivery settings.

Who's My Doctor? Using an Electronic Tool to Improve Team Member Identification on an Inpatient Pediatrics Team

OBJECTIVES

Increase parent/caregiver ability to correctly identify the attending in charge and define terminology of treatment team members (TTMs). We hypothesized that correct TTM identification would increase with use of an electronic communication tool. Secondary aims included assessing subjects' satisfaction with and trust of TTM and interest in computer activities during hospitalization.

METHODS

Two similar groups of parents/legal guardians/primary caregivers of children admitted to the Pediatric Hospital Medicine teaching service with an unplanned first admission were surveyed before (Phase 1) and after (Phase 2) implementation of a novel electronic medical record (EMR)-based tool with names, photos, and definitions of TTMs. Physicians were also surveyed only during Phase 1. Surveys assessed TTM identification, satisfaction, trust, and computer use.

RESULTS

More subjects in Phase 2 correctly identified attending physicians by name (71% vs. 28%, P < .001) and correctly defined terms intern, resident, and attending (P ≤ .03) compared with Phase 1. Almost all subjects (>79%) and TTMs (>87%) reported that subjects' ability to identify TTMs moderately or strongly impacted satisfaction and trust. The majority of subjects expressed interest in using computers to understand TTMs in each phase.

CONCLUSIONS

Subjects' ability to correctly identify attending physicians and define TTMs was significantly greater for those who used our tool. In our study, subjects reported that TTM identification impacted aspects of the TTM relationship, yet few could correctly identify TTMs before tool use. This pilot study showed early success in engaging subjects with the EMR in the hospital and suggests that families would engage in computer-based activities in this setting.

Designing a system for patients controlling providers' access to their electronic health records: organizational and technical challenges

BACKGROUND

Electronic health records (EHRs) are proliferating, and financial incentives encourage their use. Applying Fair Information Practice principles to EHRs necessitates balancing patients' rights to control their personal information with providers' data needs to deliver safe, high-quality care. We describe the technical and organizational challenges faced in capturing patients' preferences for patient-controlled EHR access and applying those preferences to an existing EHR.

METHODS

We established an online system for capturing patients' preferences for who could view their EHRs (listing all participating clinic providers individually and categorically-physicians, nurses, other staff) and what data to redact (none, all, or by specific categories of sensitive data or patient age). We then modified existing data-viewing software serving a state-wide health information exchange and a large urban health system and its primary care clinics to allow patients' preferences to guide data displays to providers.

RESULTS

Patients could allow or restrict data displays to all clinicians and staff in a demonstration primary care clinic, categories of providers (physicians, nurses, others), or individual providers. They could also restrict access to all EHR data or any or all of five categories of sensitive data (mental and reproductive health, sexually transmitted diseases, HIV/AIDS, and substance abuse) and for specific patient ages. The EHR viewer displayed data via reports, data flowsheets, and coded and free text data displayed by Google-like searches. Unless patients recorded restrictions, by default all requested data were displayed to all providers. Data patients wanted restricted were not displayed, with no indication they were redacted. Technical barriers prevented redacting restricted information in free textnotes. The program allowed providers to hit a "Break the Glass" button to override patients' restrictions, recording the date, time, and next screen viewed. Establishing patient-control over EHR data displays was complex and required ethical, clinical, database, and programming expertise and difficult choices to overcome technical and health system constraints.

CONCLUSIONS

Assessing patients' preferences for access to their EHRs and applying them in clinical practice requires wide-ranging technical, clinical, and bioethical expertise, to make tough choices to overcome significant technical and organization challenges.

Coordination of care for complex pediatric patients: perspectives from providers and parents

Coordinators help patients requiring complex chronic care manage frequent ambulatory visits and services received at home or from community-based agencies. EHRs directly support only a few of the required tasks as they do not allow access to all parties involved in care. Our goal was to examine how technology was used to coordinate efforts and to describe common barriers and facilitators. Insights may inform the design of tools that would effectively support identified goals. We conducted five hours of interviews with sixteen parents and six clinicians and characterized emergent themes from transcripts. Situational awareness, care and visit planning, document aggregation, abstraction and interpretation were tasks essential to coordination yet generally poorly supported by EHRs. Providers communicated primarily by email, telephone and by exchanging paper and scanned documents. A preliminary model of coordination that could be used in the planning and testing stages of a User Centered Design process is described.

Using EHRs to integrate research with patient care: promises and challenges

Clinical research is the foundation for advancing the practice of medicine. However, the lack of seamless integration between clinical research and patient care workflow impedes recruitment efficiency, escalates research costs, and hence threatens the entire clinical research enterprise. Increased use of electronic health records (EHRs) holds promise for facilitating this integration but must surmount regulatory obstacles. Among the unintended consequences of current research oversight are barriers to accessing patient information for prescreening and recruitment, coordinating scheduling of clinical and research visits, and reconciling information about clinical and research drugs. We conclude that the EHR alone cannot overcome barriers in conducting clinical trials and comparative effectiveness research. Patient privacy and human subject protection policies should be clarified at the local level to exploit optimally the full potential of EHRs, while continuing to ensure participant safety. Increased alignment of policies that regulate the clinical and research use of EHRs could help fulfill the vision of more efficiently obtaining clinical research evidence to improve human health.