Background: Electronic health records (EHRs) are commonplace in industrialized countries. Many hospitals are granting their patients access to their medical information through online patient portals. In this report, we describe a retrospective analysis of patient access to diagnostic test results released through the patient portal (MyChart; Epic, Inc.) at a state academic medical center. Methods: We analyzed 6 months of data for anatomic pathology, clinical laboratory, and radiology test results to evaluate variations in results release (automated vs. manual) and subsequent patient access to the institutional patient portal. During this period, diagnostic test results were released for all patient encounters including inpatient units, outpatient clinics, and the emergency department. Results: Manual results release by providers before automated release time occurred most commonly in the outpatient setting. The highest rates of access of diagnostic test results occurred for outpatients (about 30% overall view rate), females (two times or more compared to males in nearly every age bracket), and 20–45-year-old. Access rates of diagnostic tests in the emergency department or inpatient units were <10% across all populations. Access of diagnostic test results was very low for 12–17-year-old, likely influenced by institutional policies limiting parental proxy access within this pediatric age range. Approximately 20% of outpatient laboratory results were viewed by patients within 8 h of release from the EHR to the patient portal and 10% within 2 h of release. Conclusions: Patient accessing of diagnostic test results were generally higher for females, outpatients, and 20–45-year-old. Approximately, 20% of outpatient results were viewed quickly by patients after release to the EHR.

Introduction: Next-generation-sequencing (NGS) is increasingly used in clinical and research protocols for patients with cancer. NGS assays are routinely used in clinical laboratories to detect mutations bearing on cancer diagnosis, prognosis and personalized therapy. A typical assay may interrogate 50 or more gene targets that encompass many thousands of possible gene variants. Analysis of NGS data in cancer is a labor-intensive process that can become overwhelming to the molecular pathologist or research scientist. Although commercial tools for NGS data analysis and interpretation are available, they are often costly, lack key functionality or cannot be customized by the end user. Methods: To facilitate NGS data analysis in our clinical molecular diagnostics laboratory, we created a custom bioinformatics tool termed Houston Methodist Variant Viewer (HMVV). HMVV is a Java-based solution that integrates sequencing instrument output, bioinformatics analysis, storage resources and end user interface. Results: Compared to the predicate method used in our clinical laboratory, HMVV markedly simplifies the bioinformatics workflow for the molecular technologist and facilitates the variant review by the molecular pathologist. Importantly, HMVV reduces time spent researching the biological significance of the variants detected, standardizes the online resources used to perform the variant investigation and assists generation of the annotated report for the electronic medical record. HMVV also maintains a searchable variant database, including the variant annotations generated by the pathologist, which is useful for downstream quality improvement and research projects. Conclusions: HMVV is a clinical grade, low-cost, feature-rich, highly customizable platform that we have made available for continued development by the pathology informatics community.

Background: Observational data and funnel plots are routinely used outside of pathology to understand trends and improve performance. Objective: Extract diagnostic rate (DR) information from free text surgical pathology reports with synoptic elements and assess whether inter-rater variation and clinical history completeness information useful for continuous quality improvement (CQI) can be obtained. Methods: All in-house prostate biopsies in a 6-year period at two large teaching hospitals were extracted and then diagnostically categorized using string matching, fuzzy string matching, and hierarchical pruning. DRs were then stratified by the submitting physicians and pathologists. Funnel plots were created to assess for diagnostic bias. Results: 3,854 prostate biopsies were found and all could be diagnostically classified. Two audits involving the review of 700 reports and a comparison of the synoptic elements with the free text interpretations suggest a categorization error rate of <1%. Twenty-seven pathologists each read >40 cases and together assessed 3,690 biopsies. There was considerable inter-rater variability and a trend toward more World Health Organization/International Society of Urologic Pathology Grade 1 cancers in older pathologists. Normalized deviations plots, constructed using the median DR, and standard error can elucidate associated over- and under-calls for an individual pathologist in relation to their practice group. Clinical history completeness by submitting medical doctor varied significantly (100% to 22%). Conclusion: Free text data analyses have some limitations; however, they could be used for data-driven CQI in anatomical pathology, and could lead to the next generation in quality of care.