BACKGROUND/RATIONALE:
Many missed and delayed cancer diagnoses result from breakdowns in communication and coordination of abnormal findings suspicious for cancer, which often first emerge in the primary care setting. Our previous work in the VA has shown that delays in the follow-up of abnormal test results persist despite reliable delivery of test results through the electronic health record. Methods to detect these delays and identify "high risk" patients are underdeveloped and need to be optimized for use within Patient Aligned Care Teams (PACTs). We conducted pilot work to determine whether the use of electronic queries, or "triggers," can proactively identify patients at risk of delayed cancer diagnosis. Triggers prompted review of selected medical records with evidence of possible care delays (e.g., a chart with no documented follow-up of an abnormal chest X-ray after 30 days). More than half the charts identified by the triggers were confirmed on chart review to have missed follow-up (positive predictive values [PPVs] >50%). However, the processes by which our team confirmed these delays and communicated them to providers were inefficient and resource intensive.

OBJECTIVE(S):
Building on our pilot work, we propose to develop and test an innovative automated surveillance intervention to improve timely diagnosis and follow-up of five common cancers in primary care practice (colorectal, bladder, lung, hepatocellular, and breast). Our methodology will use the VA Informatics and Computing Infrastructure (VINCI) to trigger medical records with evidence of potential delays in follow-up of abnormal test results. To guide our work, we will use Sittig and Singh's 8-Dimension, Socio-Technical Model built on principles from clinical informatics and human factors. Our specific aims are to: 1) Evaluate the accuracy of a VINCI-based "real-time" automated surveillance system to identify patients at risk of missed or delayed diagnosis of 5 common cancers. 2) Establish how to integrate "real-time" surveillance and communication of information about at-risk patients into the point of PACT care through adoption of informatics and human factors engineering principles. 3) Evaluate effects of the automated surveillance intervention on timeliness of the diagnostic process.

METHODS:
Study sites include 3 facilities in VISN 12 (Hines VA Hospital, Jesse Brown VA Medical Center, and Madison VA Hospital) and 1 facility in VISN 16 (Houston VA Medical Center). In Aim 1, we will use an iterative approach to develop and test algorithms to "trigger" records lacking documented follow-up action after pre-defined diagnostic clues for cancer. Data elements needed to operationalize our triggers already exist as part of the Corporate Data Warehouse. We will apply trigger algorithms to test cohorts, compare their output against manual chart reviews to confirm delays, and use these data to modify the algorithms to improve trigger PPVs. The finalized triggers will be applied to validation cohorts to determine the final PPVs through the same methods. In Aim 2 we will use interviews, task analysis, participatory design techniques, and usability testing to ensure that the automated intervention will fit within the workflow of real-world clinical practice. We will determine the technical requirements to transmit data to the PACTs and explore the best ways of communicating the information to the PACT team. In Aim 3 we will conduct a randomized controlled trial with PACT providers in VISN 12 and VISN 16 randomly assigned to intervention or usual care. Intervention development is ongoing, and will consist of: 1) weekly/biweekly identification of patients at risk of diagnostic delays and making this data accessible to a designated facility-level recipient; and 2) communication to PACT team providers in VISN 12 and 16 about their patients that are experiencing potential delays. Our outcomes are the median time in days from diagnostic clue to follow-up action (e.g., time to colonoscopy after a positive hemoccult test) and the proportion of patients receiving appropriate and timely follow-up care.

FINDINGS/RESULTS:
Aim 1 Findings:

We have completed the development and validation of all 5 trigger algorithms. Overall, the triggers were successful in identification of delays in diagnostic evaluation after an abnormal test results suggestive of cancer. Four publications detailing the processes for the 5 trigger algorithms and each trigger's specific findings are detailed later in the Impact section:

- We developed and validated a trigger for diagnostic evaluation delays in lung cancer diagnosis. This trigger found that among 1,847 results coded by radiologists as "suspicious for malignancy" between January to December 2012, 635 (35%), were flagged by the trigger to have a potential delay in subsequent follow-up action. Review of 400 of these records confirmed 242 patients experienced delays (PPV=60.5%). Thus, application of triggers to the VA's corporate data warehouse enabled efficient identification of patients experiencing delays in follow up of possible lung cancer.

-We developed and validated a trigger for diagnostic evaluation delays in bladder cancer diagnosis. Among 5,857 patients with high-grade hematuria suggestive of possible bladder cancer between January 2012 to December 2014, this trigger found 495 patients potentially experiencing a delay in follow-up evaluation. Review of 400 of these records confirmed 232 experienced delays (PPV=58.0%). The findings indicate that a hematuria-based trigger could aid in the reduction of delays in bladder cancer diagnosis.

-We developed and validated a trigger for diagnostic evaluation delays in gastrointestinal (colorectal and hepatocellular) cancer diagnosis. The trigger found 1,073 patients with suspected colorectal cancer seen between January to December 2013. Review of 400 of these records confirmed 224 experienced delays (PPV=56.0%). The trigger identified 130 patients with suspected hepatocellular cancer that were seen between January 2011 to December 2014. Review of all 130 records confirmed delays in diagnostic evaluation for 107 of these patients (PPV=82.3%). The application of these triggers provides a more efficient way to detect delays in GI cancer diagnosis.

-We developed and validated a trigger for diagnostic evaluation delays in breast cancer diagnosis. Among 2,129 patients seen between January 2010 to May of 2015 with abnormal mammograms, this trigger identified 552 potentially experiencing a follow-up. Review of 400 of these records confirmed 283 experienced delays (PPV=70.8%). The mammography-related trigger has the potential to aid in timely follow up to prevent delays in diagnosis.

Aim 2 Findings:

Our Team traveled to VISN 12 twice in 2014 and completed 37 in-person interviews with PACT members and 2 in-person interviews with facility safety personnel. Formal analysis of interview data (collected for the most part during the trips to VISN 12 in 2014) is now complete. Due to the turnover of one of the experts, there were some delays in analysis of the cognitive task interviews. Content analysis was performed by using a distributed cognition approach to identify patterns of information transmission across people and artifacts. The findings from this data shows several challenges to test-result management and identifies team-based strategies to overcome these challenges. The challenges include information overload, coordination across distributed care, and demands on prospective memory. In order to deal with information overload, intermediaries, such as nurses, can aid in information exchange by filtering information to prevent the primary care provider (PCP) from being overburdened by low priority or irrelevant messages. Distribution of healthcare teams requires the use of various communication methods such as asynchronous communication. Aim 2 data has shown that test result follow-up processes are quite fragmented and non-standardized at the PACT level, and we need much more leadership support and organization-level interventions to improve follow-up.

IMPACT:
Thus, through this research we aim to improve the safety and quality of care that veterans receive. Our findings provide important information on the effectiveness and value of "trigger-based" interventions to identify and reduce cancer-related diagnostic delays.

The following excerpts are from the conclusions of the Aim 1-related papers:

Lung Cancer Trigger Paper (published in Chest): An algorithm designed to identify patients at risk for delays in follow-up of abnormal imaging from a large national dataset performed with reasonable accuracy for use in the clinical setting. Future research to develop and refine similar algorithms more widely can potentially reduce delays in diagnostic evaluation and improve quality and safety of patient care.

Gastrointestinal (GI) Cancer Trigger Paper [contains data from colorectal and hepatocellular cancer triggers] (published in Clinical Gastroenterology and Hepatology): We developed and tested an algorithm to identify patients at risk for delayed diagnostic evaluation for GI cancers. Prospective use of such triggers can improve care delivery related to diagnostic evaluation of cancer.

Bladder Cancer Trigger Paper (published in Applied Clinical Informatics): We developed and tested an algorithm to identify patients at risk for delayed diagnostic evaluation after lab findings of high-grade hematuria and found a performance level conducive for future application in clinical practice. Such triggers may serve as a resource for clinicians, informaticians and patient safety professionals to help reduce delays in cancer care.

Breast Cancer Trigger Paper (published in Journal of the American College of Radiology): In this study, we developed and tested an algorithm to detect delays in follow-up of externally-performed mammography. We discovered that despite legal requirements regarding reporting and current processes for communicating mammography results to patients, a small percentage of patients continue to lack timely follow-up. Thus, clinical application of such triggers could track and reduce missed opportunities to act early on abnormal mammogram results.

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External Links for this Project

NIH Reporter

Grant Number: I01HX000993-01
Link: https://reporter.nih.gov/project-details/8399241

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PUBLICATIONS:

None at this time.

DRA: Cancer
DRE: Diagnosis, Treatment - Comparative Effectiveness
Keywords: none
MeSH Terms: none