Development and Validation of Electronic Health Record–based Triggers to Detect Delays in Follow-up of Abnormal Lung Imaging Findings

Abstract

Hardeep Singh, MD, MPH Purpose:To develop an electronic health record (EHR)-based trigger algorithm to identify delays in follow-up of patients with imaging results that are suggestive of lung cancer and to validate this trigger on retrospective data. Materials and Methods:The local institutional review board approved the study. A "trigger" algorithm was developed to automate the detection of delays in diagnostic evaluation of chest computed tomographic (CT) images and conventional radiographs that were electronically flagged by reviewing radiologists as being "suspicious for malignancy." The trigger algorithm was developed through literature review and expert input. It included patients who were alive and 40-70 years old, and it excluded instances in which appropriate timely follow-up (defined as occurring within 30 days) was detected (eg, pulmonary visit) or when follow-up was unnecessary (eg, in patients with a terminal illness). The algorithm was iteratively applied to a retrospective test cohort in an EHR data warehouse at a large Veterans Affairs facility, and manual record reviews were used to validate each individual criterion. The final algorithm aimed at detecting an absence of timely follow-up was retrospectively applied to an independent validation cohort to determine the positive predictive value (PPV). Trigger performance, time to follow-up, reasons for lack of follow-up, and cancer outcomes were analyzed and reported by using descriptive statistics. Results:The trigger algorithm was retrospectively applied to the records of 89 168 patients seen between January 1, 2009, and December 31, 2009. Of 538 records with an imaging report that was flagged as suspicious for malignancy, 131 were identified by the trigger as being high risk for delayed diagnostic evaluation. Manual chart reviews confirmed a true absence of follow-up in 75 cases (trigger PPV of 57.3% for detecting evaluation delays), of which four received a diagnosis of primary lung cancer within the subsequent 2 years. Conclusion:EHR-based triggers can be used to identify patients with suspicious imaging findings in whom follow-up diagnostic evaluation was delayed. cancer from failure to follow up patients with abnormal imaging results remains a common problem that contributes to both malpractice litigation and poor patient outcomes (1-6). These delays in care, which typically involve well-intentioned clinicians, also occur when clinicians have good access to electronic diagnostic information. Multiple factors likely contribute to the delays, including time pressures, information overload, heavy workloads, and a lack of robust test-result tracking systems (7-15). Currently, identification of such delays in care requires a review of the records of all patients who undergo imaging. However, this nonselective review is too inefficient for practical use given the number of records that would require review and the vast amounts of information contained in electronic health records (EHRs). Likewise, sending providers information created with simple...

Communicating Findings of Delayed Diagnostic Evaluation to Primary Care Providers

“…12,13 However, implementation of such interventions faces many challenges, such as the need for resources to develop and implement algorithms to extract EHR data and administrative or clinical personnel to deliver it to the point of care. 6 Additional research is needed to ensure such interventions are trans- lated successfully into practice. 14 Back-up strategies using organizational mechanisms (such as case coordinators) could also be considered to support PCPs in tracking and follow-up.…”

“…Electronic algorithms designed to identify patients with potential delays in diagnostic evaluation for lung, colorectal, or prostate cancer were applied to the electronic health records (EHRs) of all patients seen by these PCPs from April 20, 2011, to July 19, 2012 (approximately 118,400 unique patients). Algorithms included "red-flag" findings to identify patients with possible cancer, clinical exclusion criteria to eliminate patients for whom further evaluation was not warranted, and expected follow-up criteria to eliminate patients who already had follow-up (see Table 1; additional details are described elsewhere 2,5,6 ). The electronic algorithms identified 1,256 patients with possible delays in diagnostic evaluation.…”

Electronic Detection of Delayed Test Result Follow-Up in Patients with Hypothyroidism

“…23,33 Future prospective application of the TSH trigger algorithm can be used by clinical teams as a surveillance and quality improvement technique to monitor and improve follow-up.…”

“…Trigger-positive patient records were reviewed by a physician using a manual data collection instrument developed based on previous work on trigger development and evaluation. 23,24,33 The reviewer (AA) used the review instrument to evaluate whether a delay was truly experienced by the patient, collect reasons for false-positive results, and determine patient and provider characteristics that potentially impacted delays. Characteristics included patient age; gender; race; presence of comorbidities including diabetes, hypertension, hyperlipidemia, obesity, and depression; type of provider seen when being tested for the TSH results (primary care provider [PCP] vs. specialist); who was primarily managing the patient's thyroid disease (PCP vs. an endocrinologist); what type of PCP the patient regularly saw (physician vs. nurse practitioner or physician's assistant); and whether the patient had previous PCP or endocrine visits.…”

Challenges and opportunities from the Agency for Healthcare Research and Quality (AHRQ) research summit on improving diagnosis: a proceedings review

“…Currently, work is underway to understand how natural language processing can be employed to glean relevant information from the unstructured data within a patient's electronic health record and present the data in a usable way for the care team [20]. The evidence that electronic triggers can facilitate the receipt, acknowledgement, and follow-up of abnormal test results also is encouraging [21]. Understanding what works best to facilitate the timely collection and subsequent patient matching of HIE data is an ongoing endeavor at AHRQ.…”