The Vanderbilt Experience with Electronic Health Records

An Analysis of Electronic Health Record Work to Manage Asynchronous Clinical Messages among Breast Cancer Care Teams

OBJECTIVE

Asynchronous messaging is an integral aspect of communication in clinical settings, but imposes additional work and potentially leads to inefficiency. The goal of this study was to describe the time spent using the electronic health record (EHR) to manage asynchronous communication to support breast cancer care coordination.

METHODS

We analyzed 3 years of audit logs and secure messaging logs from the EHR for care team members involved in breast cancer care at Vanderbilt University Medical Center. To evaluate trends in EHR use, we combined log data into sequences of events that occurred within 15 minutes of any other event by the same employee about the same patient.

RESULTS

Our cohort of 9,761 patients were the subject of 430,857 message threads by 7,194 employees over a 3-year period. Breast cancer care team members performed messaging actions in 37.5% of all EHR sessions, averaging 29.8 (standard deviation [SD] = 23.5) messaging sessions per day. Messaging sessions lasted an average of 1.1 (95% confidence interval: 0.99-1.24) minutes longer than nonmessaging sessions. On days when the cancer providers did not otherwise have clinical responsibilities, they still performed messaging actions in an average of 15 (SD = 11.9) sessions per day.

CONCLUSION

At our institution, clinical messaging occurred in 35% of all EHR sessions. Clinical messaging, sometimes viewed as a supporting task of clinical work, is important to delivering and coordinating care across roles. Measuring the electronic work of asynchronous communication among care team members affords the opportunity to systematically identify opportunities to improve employee workload.

Impact of Clinician Recognition of Acute Respiratory Distress Syndrome on Evidenced-Based Interventions in the Medical ICU

Acute respiratory distress syndrome is underrecognized in the ICU, but it remains uncertain if acute respiratory distress syndrome recognition affects evidence-based acute respiratory distress syndrome care in the modern era. We sought to determine the rate of clinician-recognized acute respiratory distress syndrome in an academic medical ICU and understand how clinician-recognized-acute respiratory distress syndrome affects clinical care and patient-centered outcomes.

DESIGN

Observational cohort study.

SETTING

Single medical ICU at an academic tertiary-care hospital.

PATIENTS

Nine hundred seventy-seven critically ill adults (381 with expert-adjudicated acute respiratory distress syndrome) enrolled from 2006 to 2015.

INTERVENTIONS

Clinician-recognized-acute respiratory distress syndrome was identified using an electronic keyword search of clinical notes in the electronic health record. We assessed the classification performance of clinician-recognized acute respiratory distress syndrome for identifying expert-adjudicated acute respiratory distress syndrome. We also compared differences in ventilator settings, diuretic prescriptions, and cumulative fluid balance between clinician-recognized acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome.

MEASUREMENTS AND MAIN RESULTS

Overall, clinician-recognized-acute respiratory distress syndrome had a sensitivity of 47.5%, specificity 91.1%, positive predictive value 77.4%, and negative predictive value 73.1% for expert-adjudicated acute respiratory distress syndrome. Among the 381 expert-adjudicated acute respiratory distress syndrome cases, we did not observe any differences in ventilator tidal volumes between clinician-recognized-acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome, but clinician-recognized-acute respiratory distress syndrome patients had a more negative cumulative fluid balance (mean difference, -781 mL; 95% CI, [-1,846 to +283]) and were more likely to receive diuretics (49.3% vs 35.7%, p = 0.02). There were no differences in mortality, ICU length of stay, or ventilator-free days.

CONCLUSIONS

Acute respiratory distress syndrome recognition was low in this single-center study. Although acute respiratory distress syndrome recognition was not associated with lower ventilator volumes, it was associated with differences in behaviors related to fluid management. These findings have implications for the design of future studies promoting evidence-based acute respiratory distress syndrome interventions in the ICU.

Characterizing communication patterns among members of the clinical care team to deliver breast cancer treatment

OBJECTIVE

Research to date focused on quantifying team collaboration has relied on identifying shared patients but does not incorporate the major role of communication patterns. The goal of this study was to describe the patterns and volume of communication among care team members involved in treating breast cancer patients.

MATERIALS AND METHODS

We analyzed 4 years of communications data from the electronic health record between care team members at Vanderbilt University Medical Center (VUMC). Our cohort of patients diagnosed with breast cancer was identified using the VUMC tumor registry. We classified each care team member participating in electronic messaging by their institutional role and classified physicians by specialty. To identify collaborative patterns, we modeled the data as a social network.

RESULTS

Our cohort of 1181 patients was the subject of 322 424 messages sent in 104 210 unique communication threads by 5620 employees. On average, each patient was the subject of 88.2 message threads involving 106.4 employees. Each employee, on average, sent 72.9 messages and was connected to 24.6 collaborators. Nurses and physicians were involved in 98% and 44% of all message threads, respectively.

DISCUSSION AND CONCLUSION

Our results suggest that many providers in our study may experience a high volume of messaging work. By using data routinely generated through interaction with the electronic health record, we can begin to evaluate how to iteratively implement and assess initiatives to improve the efficiency of care coordination and reduce unnecessary messaging work across all care team roles.

Creating an immunization content database for knowledge management across clinical systems

Purpose

A initiative at an academic medical center to create a single database of immunization-related content to inform the build and configuration of immunization-related knowledge assets across multiple clinical systems is described.

Methods

Semistructured expert interviews were conducted to ascertain the immunization information needs of the institution’s clinical systems. Based on those needs, an immunization domain model constructed with data available from the Centers for Disease Control and Prevention (CDC) website was developed and used to analyze and compare current immunization-related content from CDC data sources with the content of the institution’s clinical systems.

Results

Five identified clinical systems that used immunization-related content collectively required 22 unique information concepts, 11 of which were obtainable from CDC vaccine code sets. The proportion of vaccines designated by CDC as active products (i.e., currently available administrable vaccines) that were included in the 5 clinical systems ranged from 59% to 95%; in addition, some non–active-status vaccines were listed as active-status products in the various clinical systems. Upon further review, updates to immunization-related content in the 5 clinical systems were implemented.

Conclusion

Creating a single database for immunization-related content based on CDC data facilitated an explicit and tractable knowledge management process and helped ensure that clinical systems had correct and current content. The immunization domain model created has the potential to assist in the automated detection of updates and relaying those updates to the applicable clinical systems.

Next-generation long-term transplant clinics: improving resource utilization and the quality of care through health information technology

By the year 2020, potentially one-half a million hematopoietic cell transplant (HCT) recipients will need long-term follow-up care to address not only chronic GvHD but also multiple other late consequences of transplant. Despite this increase in patients, there will not be a concomitant increase in the HCT workforce. Thus, the future of long-term patient management will require a new 'next-generation' clinical model that utilizes technological solutions to make the care of the HCT patient efficient, safe and cost-effective. Guideline-based decision support will be embedded in clinical workflows. Documentation requirements will be reduced as automated data collection from electronic medical records (EMRs) will populate registries and provide feedback for a rapid learning health system. Interoperable EMRs will disseminate treatment protocols to multiple care providers in a distributed long-term clinic model, such that providers outside of the transplant center can provide services closer to the patient. Patients will increase their participatory role through patient portals and mobile devices. At Vanderbilt, we have responded to some of these future challenges by embedding guideline-based decision support, structuring clinical documentation and being early adopters of communication technology. This manuscript describes the current state of some of these innovations, and a vision for the future of the long-term transplant clinic.

Coordinating e-health systems with TuCSoN semantic tuple centres

Open and distributed application scenarios like e-Health systems mandate for new coordination models and technologies. In particular, they require middleware providing coordination and security services modelled with abstractions promoting run-time observability and adaptation.

Along this line, in this paper we describe the architecture of the TuCSoN coordination infrastructure, and show its application to an e-Health application scenario.