Elise Russo

The Burden of Inbox Notifications in Commercial Electronic Health Records

The Burden of Inbox Notifications in Commercial Electronic Health Records With wider use of electronic health records (EHRs), physicians increasingly receive notifications via EHR-based inboxes (eg, Epic’s In-Basket and General Electric Centricity’s Documents). Examples of types of notifications include test results, responses to referrals, requests for medication refills, and messages from physicians and other healthcareprofessionals.1,2 PreviousworkwithintheDepartment of Veterans Affairs found that health care professionals receive large quantities of EHR-based notifications, making it harder to discern important vs irrelevant information and increasing their risk of overlooking abnormal test results.3-6 Information overload is of emerging concern because new types of notifications and “FYI” (for your information) messages can be easily created in the EHR (vs in a paper-based system). Furthermore, the additional workload to read and process these messages remains uncompensated in an environment of reduced reimbursements for office-based care.1,2,4 Conversely, EHRs make it easier to measure the amount of information received. We quantified the notifications that physicians received via inboxes of commercial EHRs to estimate their burden.

Graphical display of diagnostic test results in electronic health Records: a comparison of 8 systems

Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x–y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

Challenges in patient safety improvement research in the era of electronic health records

Electronic health record (EHR) data repositories contain large volumes of aggregated, longitudinal clinical data that could allow patient safety researchers to identify important safety issues and conduct comprehensive evaluations of health care delivery outcomes. However, few health systems have successfully converted this abundance of data into useful information or knowledge for safety improvement. In this paper, we use a case study involving a project on missed/delayed follow-up of test results to discuss real-world challenges in using EHR data for patient safety research. We identify three types of challenges that pose as barriers to advance patient safety improvement research: 1) gaining approval to access/review EHR data; 2) interpreting EHR data; 3) working with local IT/EHR personnel. We discuss the complexity of these challenges, all of which are unlikely to be unique to this project, and outline some key next steps that must be taken to support research that uses EHR data to improve safety. We recognize that all organizations face competing priorities between clinical operations and research. However, to leverage EHRs and their abundant data for patient safety improvement research, many current data access and security policies and procedures must be rewritten and standardized across health care organizations. These efforts are essential to help make EHRs and EHR data useful for progress in our journey to safer health care.

Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers

BACKGROUND Electronic health records (EHRs) have been shown to increase physician workload. One EHR feature that contributes to increased workload is asynchronous alerts (also known as inbox notifications) related to test results, referral responses, medication refill requests, and messages from physicians and other health care professionals. This alert-related workload results in negative cognitive outcomes, but its effect on affective outcomes, such as burnout, has been understudied. OBJECTIVES To examine EHR alert-related workload (both objective and subjective) as a predictor of burnout in primary care providers (PCPs), in order to ultimately inform interventions aimed at reducing burnout due to alert workload. METHODS A cross-sectional questionnaire and focus group of 16 PCPs at a large medical center in the southern United States. RESULTS Subjective, but not objective, alert workload was related to two of the three dimensions of burnout, including physical fatigue (p = 0.02) and cognitive weariness (p = 0.04), when controlling for organizational tenure. To reduce alert workload and subsequent burnout, participants indicated a desire to have protected time for alert management, fewer unnecessary alerts, and improvements to the EHR system. CONCLUSIONS Burnout associated with alert workload may be in part due to subjective differences at an individual level, and not solely a function of the objective work environment. This suggests the need for both individual and organizational-level interventions to improve alert workload and subsequent burnout. Additional research should confirm these findings in larger, more representative samples.

Application of Electronic Algorithms to Improve Diagnostic Evaluation for Bladder Cancer

BACKGROUND Strategies to ensure timely diagnostic evaluation of hematuria are needed to reduce delays in bladder cancer diagnosis. OBJECTIVE To evaluate the performance of electronic trigger algorithms to detect delays in hematuria follow-up. METHODS We developed a computerized trigger to detect delayed follow-up action on a urinalysis result with high-grade hematuria (>50 red blood cells/high powered field). The trigger scanned clinical data within a Department of Veterans Affairs (VA) national data repository to identify all patient records with hematuria, then excluded those where follow-up was unnecessary (e.g., terminal illness) or where typical follow-up action was detected (e.g., cystoscopy). We manually reviewed a randomly-selected sample of flagged records to confirm delays. We performed a similar analysis of records with hematuria that were marked as not delayed (non-triggered). We used review findings to calculate trigger performance. RESULTS Of 310,331 patients seen between 1/1/2012-12/31/2014, the trigger identified 5,857 patients who experienced high-grade hematuria, of which 495 experienced a delay. On manual review of 400 randomly-selected triggered records and 100 non-triggered records, the trigger achieved positive and negative predictive values of 58% and 97%, respectively. CONCLUSIONS Triggers offer a promising method to detect delays in care of patients with high-grade hematuria and warrant further evaluation in clinical practice as a means to reduce delays in bladder cancer diagnosis.

Development and Validation of Trigger Algorithms to Identify Delays in Diagnostic Evaluation of Gastroenterological Cancer

BACKGROUND & AIMS: Colorectal cancer (CRC) and hepatocellular cancer (HCC) are common causes of death and morbidity, and patients benefit from early detection. However, delays in follow‐up of suspicious findings are common, and methods to efficiently detect such delays are needed. We developed, refined, and tested trigger algorithms that identify patients with delayed follow‐up evaluation of findings suspicious of CRC or HCC. METHODS: We developed and validated two trigger algorithms that detect delays in diagnostic evaluation of CRC and HCC using laboratory, diagnosis, procedure, and referral codes from the Department of Veteran Affairs National Corporate Data Warehouse. The algorithm initially identified patients with positive test results for iron deficiency anemia or fecal immunochemical test (for CRC) and elevated &agr;‐fetoprotein results (for HCC). Our algorithm then excluded patients for whom follow‐up evaluation was unnecessary, such as patients with a terminal illness or those who had already completed a follow‐up evaluation within 60 days. Clinicians reviewed samples of both delayed and nondelayed records, and review data were used to calculate trigger performance. RESULTS: We applied the algorithm for CRC to 245,158 patients seen from January 1, 2013, through December 31, 2013 and identified 1073 patients with delayed follow up. In a review of 400 randomly selected records, we found that our algorithm identified patients with delayed follow‐up with a positive predictive value of 56.0% (95% CI, 51.0%–61.0%). We applied the algorithm for HCC to 333,828 patients seen from January 1, 2011 through December 31, 2014, and identified 130 patients with delayed follow‐up. During manual review of all 130 records, we found that our algorithm identified patients with delayed follow‐up with a positive predictive value of 82.3% (95% CI, 74.4%–88.2%). When we extrapolated the findings to all patients with abnormal results, the algorithm identified patients with delayed follow‐up evaluation for CRC with 68.6% sensitivity (95% CI, 65.4%–71.6%) and 81.1% specificity (95% CI, 79.5%–82.6%); it identified patients with delayed follow‐up evaluation for HCC with 89.1% sensitivity (95% CI, 81.8%–93.8%) and 96.5% specificity (95% CI, 94.8%–97.7%). Compared to nonselective methods, use of the algorithm reduced the number of records required for review to identify a delay by more than 99%. CONCLUSIONS: Using data from the Veterans Affairs electronic health record database, we developed an algorithm that greatly reduces the number of record reviews necessary to identify delays in follow‐up evaluations for patients with suspected CRC or HCC. This approach offers a more efficient method to identify delayed diagnostic evaluation of gastroenterological cancers.

Electronic Triggers to Identify Delays in Follow-Up of Mammography: Harnessing the Power of Big Data in Health Care

PURPOSE We previously developed electronic triggers to automatically flag records for patients experiencing potential delays in diagnostic evaluation for certain cancers. Because of the unique clinical, logistic, and legal aspects of mammography, this study was conducted to evaluate the effectiveness of a trigger to flag delayed follow-up on mammography. METHODS An algorithm was developed to detect delays in follow-up of abnormal mammographic results (>60 days for BI-RADS® 0, 4, and 5 and >7 months for BI-RADS 3) using clinical data in the electronic health record. Flagged records were then manually reviewed to determine the triggers performance characteristics (positive and negative predictive value, sensitivity, and specificity). The frequency of delays and patient communication related to abnormal results, reasons for lack of follow-up, and whether patients were subsequently diagnosed with breast cancer were also assessed. RESULTS Of 365,686 patients seen between January 1, 2010, and May 31, 2015, the trigger identified 2,129 patients with abnormal findings on mammography, of whom it flagged 552 as having delays in follow-up. From these, review of 400 randomly selected records revealed 283 true delays (positive predictive value, 71%; 95% confidence interval, 66%-75%), including 280 records without any documented plan and three patients with plans that were not adhered to. Transcription and reporting inconsistencies were identified in 27% of externally performed mammographic reports. Only 335 records (84%) contained specific documentation that the patient was informed of the abnormal result. CONCLUSIONS Care delays appear to continue despite federal laws requiring patient notification of mammographic results within 30 days. Clinical application of mammography-related triggers could help detect these delays.

Electronic Health Record Features, Functions, and Privileges That Clinicians Need to Provide Safe and Effective Care for Adults and Children

This chapter will describe and discuss key requirements to enable clinician-users of electronic health records (EHRs) to deliver high-quality, safe, and effective care. We frame these requirements as “rights” and “responsibilities.” The “rights” represent not merely desirable, but also important EHR features, functions, and user privileges that clinicians need to perform their job. Each “right” is accompanied by a corresponding clinician duty or “responsibility,” without which the ultimate goal of improving healthcare quality might not be achieved. The issues discussed are generalizable to clinicians who care for adults and children using electronic health records across the globe. We recognize that healthcare presents complex and often unique challenges for the design and operation of health information technology-related facilities and EHRs worldwide. Addressing these rights and responsibilities comprehensively will be challenging, but we need to make the care delivered using electronic health record systems safer and more efficient.