Performance of a rule-based semi-automated method to optimize chart abstraction for surveillance imaging among patients treated for non-small cell lung cancer.

BACKGROUND: We aim to develop and test performance of a semi-automated method (computerized query combined with manual review) for chart abstraction in the identification and characterization of surveillance radiology imaging for post-treatment non-small cell lung cancer patients. METHODS: A gold standard dataset consisting of 3011 radiology reports from 361 lung cancer patients treated at the Veterans Health Administration from 2008 to 2016 was manually created by an abstractor coding image type, image indication, and image findings. Computerized queries using a text search tool were performed to code reports. The primary endpoint of query performance was evaluated by sensitivity, positive predictive value (PPV), and F1 score. The secondary endpoint of efficiency compared semi-automated abstraction time to manual abstraction time using a separate dataset and the Wilcoxon rank-sum test. RESULTS: Query for image type demonstrated the highest sensitivity of 85%, PPV 95%, and F1 score 0.90. Query for image indication demonstrated sensitivity 72%, PPV 70%, and F1 score 0.71. The image findings queries ranged from sensitivity 75-85%, PPV 23-25%, and F1 score 0.36-0.37. Semi-automated abstraction with our best performing query (image type) improved abstraction times by 68% per patient compared to manual abstraction alone (from median 21.5 min (interquartile range 16.0) to 6.9 min (interquartile range 9.5), p < 0.005). CONCLUSIONS: Semi-automated abstraction using the best performing query of image type improved abstraction efficiency while preserving data accuracy. The computerized query acts as a pre-processing tool for manual abstraction by restricting effort to relevant images. Determining image indication and findings requires the addition of manual review for a semi-automatic abstraction approach in order to ensure data accuracy.

Development and implementation of collaborative care for depression in HIV clinics.

We sought to develop and implement collaborative depression care in human immunodeficiency virus (HIV) clinics in a project called HIV Translating Initiatives for Depression into Effective Solutions (HITIDES). Here we describe: (i) the formative evaluation (FE) conducted prior to implementation; (ii) the process used to adapt the primary care collaborative care model for depression to specialty HIV clinics; and (iii) the intervention itself. The overall design of HITIDES was a multi-site randomized trial in United States Department of Veterans Affairs (VA) HIV clinics comparing the depression collaborative care intervention to usual depression care. Qualitative methods were used for the FEs and informed the evidence-based quality improvement (EBQI) methods that were used for adapting and implementing the intervention. Baseline assessments were completed by 249 depressed HIV participants. Summaries of respective key informant interviews with eight HIV patients who were receiving depression treatment and 25 HIV or mental health (MH) providers were presented to each site. EBQI methods were used to tailor the HITIDES intervention to each site while maintaining true to the evidence base for depression collaborative care. EBQI methods provided a useful framework for intervention adaptation and implementation. The HITIDES study provides the opportunity to evaluate collaborative depression care in a specialty physical health clinic setting with a population that has a high prevalence of depression and MH comorbidity.