Developing Software to “Track and Catch” Missed Follow-up of Abnormal Test Results in a Complex Sociotechnical Environment

Smith, Michael W.; Murphy, Daniel R.; Laxmisan, Archana; Sittig, Dean F.; Reis, Brian; Esquivel, Adol; Singh, Hardeep

doi:10.4338/aci-2013-04-ra-0019

Cited by 27 publications

(23 citation statements)

References 61 publications

(72 reference statements)

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“…Similar to the Breast Imaging Reporting and Data System coding scheme, which greatly reduced ambiguity of mammogram interpretation and communication of results, a set of structured codes to indicate imaging findings that are suspicious for malignancy could be developed. These codes would allow EHRs to "understand" radiology report interpretations in clinical decision support tools (41). For example, regardless of the initial test result communication, an application could alert providers when action on an abnormal chest imaging finding has not been completed within a certain number of days and enable other types of institution-level measurements for an absence of follow-up (42).…”

Section: Discussionmentioning

confidence: 99%

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

Murphy

Thomas

Meyer³

et al. 2015

Radiology

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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...

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Section: Discussionmentioning

confidence: 99%

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

Murphy

Thomas

Meyer³

et al. 2015

Radiology

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“…Just as was done in this case study, one might consider whether the software was well designed and standardized. For instance, electronic referral software that does not harness the benefits of EHR data to automatically pre-populate fields in the referral template when possible (e.g., demographic data, current medication list, recent relevant laboratory test results) 38 will be considered poorly designed, and will essentially increase the clinician's workload. The eReferral system automatically populated patient details from the EHR, thus making it easier to use.…”

Section: Teaching Point 2: Evaluation Must Determine Whether Technolomentioning

confidence: 99%

Implementation Science Workshop: Implementation of an Electronic Referral System in a Large Academic Medical Center

et al. 2015

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“…In this changing context it becomes financially important for organizations to optimize the overall health status for a population of patients. As a consequence of this broader motivation, efforts to reduce diagnostic error, while always the right thing to do, become financially aligned despite the complexity and likely costs [16,17]. Unfortunately, precisely where these investments should be made by organizations to minimize risks for an inaccurate or delayed diagnosis remains unknown.…”

Section: Electronic Health Record (Ehr) Adoption In the Unitedmentioning

confidence: 99%

“…Table 1 shows some of these possibilities and was created to complement the DEER taxonomy but focuses on EHR usage [20]. In particular, strategic use of EHRs in the following categories appears potentially beneficial to the diagnostic process: documentation, care coordination, patient engagement, automation, physician and patient checklists, next step guidance, redundancy avoidance, monitoring, and broadened differential diagnoses [10,16,[18][19][20]22]. For example, when applied judiciously and in the appropriate workflow context, diagnostic error triggers already have a growing body of evidence behind their benefits [18,23].…”

Section: Electronic Health Record (Ehr) Adoption In the Unitedmentioning

confidence: 99%

Next steps for electronic health records to improve the diagnostic process

Liebovitz

2015

Diagnosis

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Electronic health record (EHR) usage is accelerating while preventable diagnostic error persists. EHRs may even contribute to diagnostic error through several pathways including poor usability and an over reliance on electronic chart based communication. The changing context of healthcare delivery offers potential financial incentives for organizations to leverage EHRs to reduce diagnostic error. The lack of standard quality metrics for reporting rates of diagnostic error, a lack of diagnostic feedback systems for physicians and organizations, and a lack of compelling evidence for specific interventions underscore the need for further research in preventing diagnostic error. Many potential strategies exist for EHRs to reduce the likelihood of diagnostic error. Practical next steps for leveraging EHR systems to assist in the diagnostic process are suggested. These include patient engagement strategies, closed loop result tracking, targeted next step reminder systems, and expansion of a list of actionable patient states based on diagnosis triggers.

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Developing Software to “Track and Catch” Missed Follow-up of Abnormal Test Results in a Complex Sociotechnical Environment

Cited by 27 publications

References 61 publications

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

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

Implementation Science Workshop: Implementation of an Electronic Referral System in a Large Academic Medical Center

Next steps for electronic health records to improve the diagnostic process

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