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

The effect of teamwork, communication skills, and structural empowerment on the provision of patient-centered care among nurses in Eswatini: A cross-sectional study

AIMS

To identify the influence of teamwork, communication skills, and structural empowerment on providing patient-centered care among nurses in Eswatini.

DESIGN

A quantitative, cross-sectional survey was used.

METHODS

This survey conducted in Eswatini selected registered nurses working as full-time nurses with more than 3 months of working experience using convenience sampling. Different measures were adopted and used to collect data among 130 eligible nurses from February to March 2022. Data were analyzed using Statistical Program for Social Sciences version 26.0.

RESULTS

A total of 123 nurses responded in the study and the mean age was 31.05 years with a standard deviation of 5.49. The overall response rate was 94.6% in this study. Hierarchical multiple regression analysis revealed that teamwork (β = 0.236, p < 0.05) and communication skills (β = 0.328, p < 0.05) were significant predictors of the provision of patient-centered care among nurses.

CONCLUSION

Team-building exercises and management interventions to empower nurses should be established to improve the provision of patient-centered care among nurses. Nurses should be empowered to provide patient-centered care in health facilities by encouraging them to provide their input in management procedures and planning programs.

IMPACT

Clinical practice requires nurses to use effective communication skills and work as a team. Nursing care established on teamwork, respect, empathy, and communication skills allows patients to express their concerns and views in health facilities as they feel involved in care.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution in this paper. This study only involved registered nurses as health professionals.

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis

INTRODUCTION

Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning.

METHODS

We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone.

RESULTS

Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively.

CONCLUSIONS

EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.

Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting

BACKGROUND

Diagnostic errors (DEs) have been studied extensively in ambulatory care, but less work has been done in the acute care setting. In this study, the authors examined health care providers' and patients' perspectives about the classification of DEs, the main causes and scope of DEs in acute care, the main gaps in current systems, and the need for innovative solutions.

METHODS

A qualitative mixed methods study was conducted, including semistructured interviews with health care providers and focus groups with patient advisors. Using grounded theory approach, thematic categories were derived from the interviews and focus groups.

RESULTS

The research team conducted interviews with 17 providers and two focus groups with seven patient advisors. Both providers and patient advisors struggled to define and describe DEs in acute care settings. Although participants agreed that DEs pose a significant risk to patient safety, their perception of the frequency of DEs was mixed. Most participants identified communication failures, lack of comfort with diagnostic uncertainty, incorrect clinical evaluation, and cognitive load as key causes of DEs. Most respondents believed that non-information technology (IT) tools and processes (for example, communication improvement strategies) could significantly reduce DEs.

CONCLUSION

The study findings represent an important supplement to our understanding of DEs in acute care settings and the advancement of a culture of patient safety in the context of patient-centered care and patient engagement. Health care organizations should consider the key factors identified in this study when trying to create a culture that engages clinicians and patients in reducing DEs.

Where Is the Digitally Silent Provider? Development and Validation of a Team-Centered Electronic Health Record Attribution Model for Supervising Residents

PROBLEM

Providing trainees with data and benchmarks on their own patient populations is an Accreditation Council for Graduate Medical Education core residency requirement. Leveraging electronic health records (EHRs) for this purpose relies on correctly attributing patients to the trainees responsible for their care. EHR activity logs are useful for attributing interns to inpatients but not for attributing supervising residents, who often have no inpatient EHR usage obligations, and therefore may generate no digital "footprints" on a given patient-day from which to ascertain attribution.

APPROACH

The authors developed and tested a novel team-centered binary logistic regression model leveraging EHR activity logs from July 1, 2018, to June 30, 2019, for pediatric hospital medicine (PHM) supervising residents at the University of California, San Francisco. Unlike patient-centered models that determine daily attribution according to the trainee generating the greatest relative activity in individual patients' charts, the team-centered approach predicts daily attribution based on the trainee generating EHR activity across the greatest proportion of a team's patients. To assess generalizability, the authors similarly modeled supervising resident attribution in adult hospital medicine (AHM) and orthopedic surgery (OS).

OUTCOMES

For PHM, AHM, and OS, 1,100, 1,399, and 803 unique patient encounters and 29, 62, and 10 unique supervising residents were included, respectively. Team-centered models outperformed patient-centered models for the 3 specialties, with respective accuracies of 85.4% versus 72.4% (PHM), 88.7% versus 75.4% (AHM), and 69.3% versus 51.6% (OS; P < .001 for all). AHM and PHM models demonstrated relative generalizability to one another while OS did not.

NEXT STEPS

Validation at other institutions will be essential to understanding the potential for generalizability of this approach. Accurately attributed data are likely to be trusted more by trainees, enabling programs to operationalize feedback for use cases including performance measurement, case mix assessment, and postdischarge opportunities for follow-up learning.

nalyzing diagnostic errors in the acute setting: a process-driven approach

OBJECTIVES

We describe an approach for analyzing failures in diagnostic processes in a small, enriched cohort of general medicine patients who expired during hospitalization and experienced medical error. Our objective was to delineate a systematic strategy for identifying frequent and significant failures in the diagnostic process to inform strategies for preventing adverse events due to diagnostic error.

METHODS

Two clinicians independently reviewed detailed records of purposively sampled cases identified from established institutional case review forums and assessed the likelihood of diagnostic error using the Safer Dx instrument. Each reviewer used the modified Diagnostic Error Evaluation and Research (DEER) taxonomy, revised for acute care (41 possible failure points across six process dimensions), to characterize the frequency of failure points (FPs) and significant FPs in the diagnostic process.

RESULTS

Of 166 cases with medical error, 16 were sampled: 13 (81.3%) had one or more diagnostic error(s), and a total of 113 FPs and 30 significant FPs were identified. A majority of significant FPs (63.3%) occurred in "Diagnostic Information and Patient Follow-up" and "Patient and Provider Encounter and Initial Assessment" process dimensions. Fourteen (87.5%) cases had a significant FP in at least one of these dimensions.

CONCLUSIONS

Failures in the diagnostic process occurred across multiple dimensions in our purposively sampled cohort. A systematic analytic approach incorporating the modified DEER taxonomy, revised for acute care, offered critical insights into key failures in the diagnostic process that could serve as potential targets for preventative interventions.

Patient Perspectives of Inpatient Telemedicine During COVID-19: A Qualitative Assessment (Preprint)

Background

Telemedicine has been adopted in the inpatient setting to facilitate clinical interactions between on-site clinicians and isolated hospitalized patients. Such remote interactions have the potential to reduce pathogen exposure and use of personal protective equipment but may also pose new safety concerns given prior evidence that isolated patients can receive suboptimal care. Formal evaluations of the use and practical acceptance of inpatient telemedicine among hospitalized patients are lacking.

Objective

We aimed to evaluate the experience of patients hospitalized for COVID-19 with inpatient telemedicine introduced as an infection control measure during the pandemic.

Methods

We conducted a qualitative evaluation in a COVID-19 designated non–intensive care hospital unit at a large academic health center (Stanford Health Care) from October 2020 through January 2021. Semistructured qualitative interviews focused on patient experience, impact on quality of care, communication, and mental health. Purposive sampling was used to recruit participants representing diversity across varying demographics until thematic saturation was reached. Interview transcripts were qualitatively analyzed using an inductive-deductive approach.

Results

Interviews with 20 hospitalized patients suggested that nonemergency clinical care and bridging to in-person care comprised the majority of inpatient telemedicine use. Nurses were reported to enter the room and call on the tablet far more frequently than physicians, who typically entered the room at least daily. Patients reported broad acceptance of the technology, citing improved convenience and reduced anxiety, but preferred in-person care where possible. Quality of care was believed to be similar to in-person care with the exception of a few patients who wanted more frequent in-person examinations. Ongoing challenges included low audio volume, shifting tablet location, and inconsistent verbal introductions from the clinical team.

Conclusions

Patient experiences with inpatient telemedicine were largely favorable. Although most patients expressed a preference for in-person care, telemedicine was acceptable given the circumstances associated with the COVID-19 pandemic. Improvements in technical and care team use may enhance acceptability. Further evaluation is needed to understand the impact of inpatient telemedicine and the optimal balance between in-person and virtual care in the hospital setting.

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.

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.

The Impact of Automated Notification on Follow-up of Actionable Tests Pending at Discharge: a Cluster-Randomized Controlled Trial

BACKGROUND

Follow-up of tests pending at discharge (TPADs) is poor. We previously demonstrated a twofold increase in awareness of any TPAD by attendings and primary care physicians (PCPs) using an automated email intervention OBJECTIVE: To determine whether automated notification improves documented follow-up for actionable TPADs DESIGN: Cluster-randomized controlled trial SUBJECTS: Attendings and PCPs caring for adult patients discharged from general medicine and cardiology services with at least one actionable TPAD between June 2011 and May 2012 INTERVENTION: An automated system that notifies discharging attendings and network PCPs of finalized TPADs by email MAIN MEASURES: The primary outcome was the proportion of actionable TPADs with documented action determined by independent physician review of the electronic health record (EHR). Secondary outcomes included documented acknowledgment, 30-day readmissions, and adjusted median days to documented follow-up.

KEY RESULTS

Of the 3378 TPADs sampled, 253 (7.5%) were determined to be actionable by physician review. Of these, 150 (123 patients discharged by 53 attendings) and 103 (90 patients discharged by 44 attendings) were assigned to intervention and usual care groups, respectively, and underwent chart review. The proportion of actionable TPADs with documented action was 60.7 vs. 56.3% (p = 0.82) in the intervention vs. usual care groups, similar for documented acknowledgment. The proportion of patients with actionable TPADs readmitted within 30 days was 22.8 vs. 31.1% in the intervention vs. usual care groups (p = 0.24). The adjusted median days [95% CI] to documented action was 9 [6.2, 11.8] vs. 14 [10.2, 17.8] (p = 0.04) in the intervention vs. usual care groups, similar for documented acknowledgment. In sub-group analysis, the intervention had greater impact on documented action for patients with network PCPs compared with usual care (70 vs. 50%, p = 0.03).

CONCLUSIONS

Automated notification of actionable TPADs shortened time to action but did not significantly improve documented follow-up, except for network-affiliated patients. The high proportion of actionable TPADs without any documented follow-up (~ 40%) represents an ongoing safety concern.

CLINICAL TRIALS IDENTIFIER

NCT01153451.

A web-based and mobile patient-centered ''microblog'' messaging platform to improve care team communication in acute care

Communication in acute care settings is fragmented and occurs asynchronously via a variety of electronic modalities. Providers are often not on the same page with regard to the plan of care. We designed and developed a secure, patient-centered "microblog" messaging platform that identifies care team members by synchronizing with the electronic health record, and directs providers to a single forum where they can communicate about the plan of care. The system was used for 35% of patients admitted to a medical intensive care unit over a 6-month period. Major themes in messages included care coordination (49%), clinical summarization (29%), and care team collaboration (27%). Message transparency and persistence were seen as useful features by 83% and 62% of respondents, respectively. Availability of alternative messaging tools and variable use by non-unit providers were seen as main barriers to adoption by 83% and 62% of respondents, respectively. This approach has much potential to improve communication across settings once barriers are addressed.

Opportunities and Challenges for Improving the Patient Experience in the Acute and Postacute Care Setting Using Patient Portals: The Patient's Perspective

Efforts to improve the patient experience are increasingly focusing on engaging patients and their "care partners" by using patient portals. The Acute Care Patient Portal Task Force was supported by the Gordon and Betty Moore Foundation to convene a national meeting of an interdisciplinary group of stakeholders, including patient advocates, to consider how the acute and postacute care patient experience can be improved by using patient-facing technologies. We identified key opportunities and challenges for enhancing cognitive support, promoting respect while maintaining boundaries, and facilitating patient and family empowerment through the lens of the patient. Institutions, clinicians, and vendors would benefit tremendously by considering these 3 patient-centered themes when partnering with patients and family advisors to implement and realize the full potential of patient portals to enhance the acute and postacute care experience.

Protocol to describe the analysis of text-based communication in medical records for patients discharged from intensive care to hospital ward

INTRODUCTION

Effective communication during hospital transitions of patient care is fundamental to ensuring patient safety and continuity of quality care. This study will describe text-based communication included in patient medical records before, during and after patient transfer from the intensive care unit (ICU) to a hospital ward (n=10 days) by documenting (1) the structure and focus of physician progress notes within and between medical specialties, (2) the organisation of subjective and objective information, including the location and accessibility of patient data and whether/how this changes during the hospital stay and (3) missing, illegible and erroneous information.

METHODS

This study is part of a larger mixed methods prospective observational study of ICU to hospital ward transfer practices in 10 ICUs across Canada. Medical records will be collected and photocopied for each consenting patient for a period of up to 10 consecutive days, including the final 2 days in the ICU, the day of transfer and the first 7 days on the ward (n=10 days). Textual analysis of medical record data will be completed by 2 independent reviewers to describe communication between stakeholders involved in ICU transfer.

ETHICS AND DISSEMINATION

Research ethics board approval has been obtained at all study sites, including the coordinating study centre (which covers 4 Calgary-based sites; UofC REB 13-0021) and 6 additional study sites (UofA Pro00050646; UBC PHC Hi4-01667; Sunnybrook 336-2014; QCH 20140345-01H; Sherbrooke 14-172; Laval 2015-2171). Findings from this study will inform the development of an evidence-based tool that will be used to systematically analyse the series of notes in a patient's medical record.