Brian W. Pickering

The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance*

Objectives:The care of critically ill patients generates large quantities of data. Increasingly, these data are presented to the provider within an electronic medical record. The manner in which data are organized and presented can impact on the ability of users to synthesis that data into meaningful information. The objective of this study was to test the hypothesis that novel user interfaces, which prioritize the display of high-value data to providers within system-based packages, reduce task load, and result in fewer errors of cognition compared with established user interfaces that do not. Design:Randomized crossover study. Setting:Academic tertiary referral center. Subjects:Attending, resident and fellow critical care physicians. Interventions:Novel health care record user interface. Measurement:Subjects randomly assigned to either a standard electronic medical record or a novel user interface, were asked to perform a structured task. The task required the subjects to use the assigned electronic environment to review the medical record of an intensive care unit patient said to be actively bleeding for data that formed the basis of answers to clinical questions posed in the form of a structured questionnaire. The primary outcome was task load, measured using the paper version of the NASA-task load index. Secondary outcome measures included time to task completion, number of errors of cognition measured by comparison of subject to post hoc gold standard questionnaire responses, and the quantity of information presented to subjects by each environment. Main Results:Twenty subjects completed the task on eight patients, resulting in 160 patient–provider encounters (80 in each group). The standard electronic medical record contained a much larger data volume with a median (interquartile range) number of data points per patient of 1008 (895–1183) compared with 102 (77–112) contained within the novel user interface. The median (interquartile range) NASA-task load index values were 38.8 (32–45) and 58 (45–65) for the novel user interface compared with the standard electronic medical record (p < .001). The median (interquartile range) times in seconds taken to complete the task for four consecutive patients were 93 (57–132), 60 (48–71), 68 (48–80), and 54 (42–64) for the novel user interface compared with 145 (109–201), 125 (113–162), 129 (100–145), and 112 (92–123) for the standard interface (p < .0001), respectively. The median (interquartile range) number of errors per provider was 0.5 (0–1) and two (0.25–3) for the novel user interface and standard electronic medical record interface, respectively (p = .007). Conclusions:A novel user interface was designed based on the information needs of intensive care unit providers with a specific goal of development being the reduction of task load and errors of cognition associated with filtering, extracting, and using medical data contained within a comprehensive electronic medical record. The results of this simulated clinical experiment suggest that the configuration of the intensive care unit user interface contributes significantly to the task load, time to task completion, and number of errors of cognition associated with the identification, and subsequent use, of relevant patient data. Task-specific user interfaces, developed from an understanding of provider information requirements, offer advantages over interfaces currently available within a standard electronic medical record.

Derivation and Validation of Automated Electronic Search Strategies to Extract Charlson Comorbidities From Electronic Medical Records

OBJECTIVE To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities. PATIENTS AND METHODS The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities. RESULTS In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS. CONCLUSION Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.

Limiting ventilator-induced lung injury through individual electronic medical record surveillance

Background:To improve the safety of ventilator care and decrease the risk of ventilator-induced lung injury, we designed and tested an electronic algorithm that incorporates patient characteristics and ventilator settings, allowing near-real-time notification of bedside providers about potentially injurious ventilator settings. Methods:Electronic medical records of consecutive patients who received invasive ventilation were screened in three Mayo Clinic Rochester intensive care units. The computer system alerted bedside providers via the text paging notification about potentially injurious ventilator settings. Alert criteria included a Pao2/Fio2 ratio of <300 mm Hg, free text search for the words “edema” or “bilateral + infiltrates” on the chest radiograph report, a tidal volume of >8 mL/kg predicted body weight (based on patient gender and height), a plateau pressure of >30 cm H2O, and a peak airway pressure of >35 cm H2O. Respiratory therapists answered a brief online satisfaction survey. Ventilator-induced lung injury risk was compared before and after the introduction of ventilator-induced lung injury alert. Findings:The prevalence of acute lung injury was 42% (n = 490) among 1,159 patients receiving >24 hrs of invasive ventilation. The system sent 111 alerts for 80 patients, with a positive predictive value of 59%. The exposure to potentially injurious ventilation decreased after the intervention from 40.6 ± 74.6 hrs to 26.9 ± 77.3 hrs (p = .004). Interpretations:Electronic medical record surveillance of mechanically ventilated patients accurately detects potentially injurious ventilator settings and is able to influence bedside practice at moderate costs. Its implementation is associated with decreased patient exposure to potentially injurious mechanical ventilation settings.

Novel Representation of Clinical Information in the ICU: Developing User Interfaces which Reduce Information Overload.

The introduction of electronic medical records (EMR) and computerized physician order entry (CPOE) into the intensive care unit (ICU) is transforming the way health care providers currently work. The challenge facing developers of EMRs is to create products which add value to systems of health care delivery. As EMRs become more prevalent, the potential impact they have on the quality and safety, both negative and positive, will be amplified. In this paper we outline the key barriers to effective use of EMR and describe the methodology, using a worked example of the output. AWARE (Ambient Warning and Response Evaluation), is a physician led, electronic-environment enhancement program in an academic, tertiary care institutions ICU. The development process is focused on reducing information overload, improving efficiency and eliminating medical error in the ICU.

Data Utilization for Medical Decision Making at the Time of Patient Admission to Icu

Objectives:Information overload in electronic medical records can impede providers’ ability to identify important clinical data and may contribute to medical error. An understanding of the information requirements of ICU providers will facilitate the development of information systems that prioritize the presentation of high-value data and reduce information overload. Our objective was to determine the clinical information needs of ICU physicians, compared to the data available within an electronic medical record. Design:Prospective observational study and retrospective chart review. Setting:Three ICUs (surgical, medical, and mixed) at an academic referral center. Subjects:Newly admitted ICU patients and physicians (residents, fellows, and attending staff). Measurements and Main Results:The clinical information used by physicians during the initial diagnosis and treatment of admitted patients was captured using a questionnaire. Clinical information concepts were ranked according to the frequency of reported use (primary outcome) and were compared to information availability in the electronic medical record (secondary outcome). Nine hundred twenty-five of 1,277 study questionnaires (408 patients) were completed. Fifty-one clinical information concepts were identified as being useful during ICU admission. A median (interquartile range) of 11 concepts (6–16) was used by physicians per patient admission encounter with four used greater than 50% of the time. Over 25% of the clinical data available in the electronic medical record was never used, and only 33% was used greater than 50% of the time by admitting physicians. Conclusions:Physicians use a limited number of clinical information concepts at the time of patient admission to the ICU. The electronic medical record contains an abundance of unused data. Better electronic data management strategies are needed, including the priority display of frequently used clinical concepts within the electronic medical record, to improve the efficiency of ICU care.

Delayed Rapid Response Team Activation Is Associated With Increased Hospital Mortality, Morbidity, and Length of Stay in a Tertiary Care Institution.

Objective:To identify whether delays in rapid response team activation contributed to worse patient outcomes (mortality and morbidity). Design:Retrospective observational cohort study including all rapid response team activations in 2012. Setting:Tertiary academic medical center. Patients:All those 18 years old or older who had a rapid response team call activated. Vital sign data were abstracted from individual patient electronic medical records for the 24 hours before the rapid response team activation took place. Patients were considered to have a delayed rapid response team activation if more than 1 hour passed between the first appearance in the record of an abnormal vital sign meeting rapid response team criteria and the activation of an rapid response team. Interventions:None. Measurements and Main Results:A total of 1,725 patients were included in the analysis. Data were compared between those who had a delayed rapid response team activation and those who did not. Fifty seven percent patients met the definition of delayed rapid response team activation. Patients in high-frequency physiologic monitored environments were more likely to experience delay than their floor counterparts. In the no-delay group, the most common reasons for rapid response team activation were tachycardia/bradycardia at 29% (217/748), respiratory distress/low SpO2 at 28% (213/748), and altered level of consciousness at 23% (170/748) compared with respiratory distress/low SpO2 at 43% (423/977), tachycardia/bradycardia at 33% (327/977), and hypotension at 27% (261/977) in the delayed group. The group with no delay had a higher proportion of rapid response team calls between 8:00 and 16:00, whereas those with delay had a higher proportion of calls between midnight and 08:00. The delayed group had higher hospital mortality (15% vs 8%; adjusted odds ratio, 1.6; p = 0.005); 30-day mortality (20% vs 13%; adjusted odds ratio, 1.4; p = 0.02); and hospital length of stay (7 vs 6 d; relative prolongation, 1.10; p = 0.02) compared with the no-delay group. Conclusions:Delays in rapid response team activation occur frequently and are independently associated with worse patient mortality and morbidity outcomes.

The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: A pilot step-wedge cluster randomized trial

OBJECTIVES AWARE (Ambient Warning and Response Evaluation) is a novel electronic medical record (EMR) dashboard designed by clinicians to support bedside clinical information management in the ICU. AWARE sits on top of pre-existing, comprehensive EMR systems. The purpose of the study was to test the acceptance and impact of AWARE on data management in live clinical ICU settings. The primary outcome measure was observed efficiency of data utilization as determined by time spent in data gathering before morning rounds. DESIGN Step wedge cluster randomization trial. SETTING Four ICUs (surgical, medical, and mixed) at an academic referral center. SUBJECTS All members of the critical care team participating in morning ICU rounds. INTERVENTION Pilot implementation of a novel EMR interface with direct observation and survey. MEASUREMENTS AND MAIN RESULTS The study took place between April and July 2012. A total of 80 and 63 direct observations were made in the pre- and post-implementation study periods respectively. The time spent on pre-round data gathering per patient decreased from 12 (10-15) to 9 (7.3-11) min for pre- and post-implementation phases respectively (p=0.03). Compared to the existing EMR, information management (data presentation format, efficiency of data access) was reported to be better after AWARE implementation. AWARE made the task of gathering data for rounds significantly less difficult and mentally demanding. CONCLUSIONS The introduction of a novel, patient-centered EMR viewer for the ICU was associated with improved efficiency and ease of clinical data management compared to the standard EMR.

Impact of the Electronic Medical Record on Mortality, Length of Stay, and Cost in the Hospital and ICU: A Systematic Review and Metaanalysis.

Objective:To evaluate effects of health information technology in the inpatient and ICU on mortality, length of stay, and cost. Methodical evaluation of the impact of health information technology on outcomes is essential for institutions to make informed decisions regarding implementation. Data Sources:EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science were searched from database inception through July 2013. Manual review of references of identified articles was also completed. Study Selection:Selection criteria included a health information technology intervention such as computerized physician order entry, clinical decision support systems, and surveillance systems, an inpatient setting, and endpoints of mortality, length of stay, or cost. Studies were screened by three reviewers. Of the 2,803 studies screened, 45 met selection criteria (1.6%). Data Extraction:Data were abstracted on the year, design, intervention type, system used, comparator, sample sizes, and effect on outcomes. Studies were abstracted independently by three reviewers. Data Synthesis:There was a significant effect of surveillance systems on in-hospital mortality (odds ratio, 0.85; 95% CI, 0.76–0.94; I2 = 59%). All other quantitative analyses of health information technology interventions effect on mortality and length of stay were not statistically significant. Cost was unable to be quantitatively evaluated. Qualitative synthesis of studies of each outcome demonstrated significant study heterogeneity and small clinical effects. Conclusions:Electronic interventions were not shown to have a substantial effect on mortality, length of stay, or cost. This may be due to the small number of studies that were able to be aggregately analyzed due to the heterogeneity of study populations, interventions, and endpoints. Better evidence is needed to identify the most meaningful ways to implement and use health information technology and before a statement of the effect of these systems on patient outcomes can be made.

Clinical review: the hospital of the future - building intelligent environments to facilitate safe and effective acute care delivery.

The translation of knowledge into rational care is as essential and pressing a task as the development of new diagnostic or therapeutic devices, and is arguably more important. The emerging science of health care delivery has identified the central role of human factor ergonomics in the prevention of medical error, omission, and waste. Novel informatics and systems engineering strategies provide an excellent opportunity to improve the design of acute care delivery. In this article, future hospitals are envisioned as organizations built around smart environments that facilitate consistent delivery of effective, equitable, and error-free care focused on patient-centered rather than provider-centered outcomes.

Connecting the dots: rule-based decision support systems in the modern EMR era

The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and “sniffers” within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.