Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.

BACKGROUND: Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive. OBJECTIVE: The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP. METHODS: Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR. RESULTS: Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria. CONCLUSIONS: Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.

Improvement Initiative: End-Tidal Carbon Monoxide Measurement in Newborns Receiving Phototherapy.

OBJECTIVES: To determine, as part of our Utah Newborn Nursery Bilirubin Management Program, whether end-tidal carbon monoxide concentration (ETCOc) measurements in all newborns in our nursery receiving phototherapy were associated with outcomes related to the management of hyperbilirubinemia, including time (hours after birth) when phototherapy was initiated, total duration of phototherapy during the nursery stay, repeat phototherapy treatments, and hospital readmission for phototherapy. STUDY DESIGN: We performed a planned interim analysis of a component of our program in which we measured ETCOc noninvasively using CoSense on each newborn in our nursery receiving phototherapy and recorded specific outcomes related to phototherapy management. RESULTS: Of 1856 newborns admitted to our nursery in a 6-month period in 2020, 170 (9.8%) were treated with phototherapy. An ETCOc reading was successfully obtained in 145 of 151 attempts (96%). Higher ETCOc values were associated with earlier institution of phototherapy and longer duration of phototherapy. For every 1-ppm increase in ETCOc, phototherapy was started 9 hours earlier (95% CI, 3.3-14.8; P = .002) and was administered for an additional 9.3 hours (95% CI, 4.1-14.6; P < .001). Three newborns were readmitted to the hospital for intensive phototherapy; while in the nursery, all 3 had an elevated ETCOc (2.2, 2.6, and 2.9 ppm). CONCLUSIONS: Our findings provide answers to questions raised in the 2004 American Academy of Pediatrics bilirubin guidelines. In our neonatal nursery, measuring ETCOc in all phototherapy recipients was feasible and safe, and the results were associated with multiple aspects of phototherapy management. Higher ETCOc values predicted earlier and longer phototherapy courses.

Standards-Based Clinical Decision Support Platform to Manage Patients Who Meet Guideline-Based Criteria for Genetic Evaluation of Familial Cancer.

PURPOSE: The ubiquitous adoption of electronic health records (EHRs) with family health history (FHH) data provides opportunities for tailoring cancer screening strategies to individuals. We aimed to enable a standards-based clinical decision support (CDS) platform for identifying and managing patients who meet guidelines for genetic evaluation of hereditary cancer. METHODS: The CDS platform (www.opencds.org) was used to implement algorithms based on the 2018 National Comprehensive Cancer Network guidelines for genetic evaluation of hereditary breast/ovarian and colorectal cancer. The platform was designed to be interfaced with different EHR systems via the Health Level Seven International Fast Healthcare Interoperability Resources standard. The platform was integrated with the Epic EHR and evaluated in a pilot study at an academic health care system. RESULTS: The CDS platform was executed against a target population of 143,012 patients; 5,245 (3.7%) met criteria for genetic evaluation based on the FHH recorded in the EHR. In a clinical pilot study, genetic counselors attempted to reach out to 71 of the patients. Of those patients, 25 (35%) scheduled an appointment, 10 (14%) declined, 2 (3%) did not need genetic counseling, 7 (10%) said they would consider it in the future, and 27 (38%) were unreachable. To date, 13 (52%) of the scheduled patients completed visits, and 2 (15%) of those were found to have pathogenic variants in cancer predisposition genes. CONCLUSION: A standards-based CDS platform integrated with EHR systems is a promising population-based approach to identify patients who are appropriate candidates for genetic evaluation of hereditary cancers.

Association of an Electronic Health Record Add-on App for Neonatal Bilirubin Management With Physician Efficiency and Care Quality.

Importance: The usefulness of electronic health record (EHR) systems could be significantly enhanced by innovative, third-party EHR add-on apps. Objective: To evaluate whether an EHR add-on app for neonatal bilirubin management can save clinicians time and improve patient care. Design, Setting, and Participants: This quality improvement study was conducted at the University of Utah Health Well Baby nursery and outpatient clinics and consisted of 4 substudies: (1) time savings were estimated in an experimental task-timing study comparing the time required for physicians to manage newborns' bilirubin levels with and without the add-on app, (2) app use was estimated from app logs, (3) health care use measures and guideline compliance were compared retrospectively before and after the intervention, and (4) clinician-perceived usability was measured through System Usability Scale surveys. The study took place between April 1, 2016, and September 3, 2019. Data analyses were conducted from October 30, 2018, to September 23, 2019. Interventions: At baseline, clinicians used a manual approach to ensure compliance with an evidence-based clinical guideline for neonatal bilirubin management. To facilitate guideline compliance, an EHR add-on app that automatically retrieves, organizes, and visualizes relevant patient data was developed. The app provides patient-specific assessments and recommendations, including the risk of rebound hyperbilirubinemia following phototherapy based on a predictive model. The add-on app was integrated with the University of Utah Health EHR on April 12, 2017. Main Outcomes and Measures: Clinician time savings, app use, health care use measures, guideline-compliant phototherapy ordering, and perceived usability as measured by the System Usability Scale survey. The survey is composed of 10 statements with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The survey results in a single score ranging from 0 to 100, with ratings described as worst imaginable (mean System Usability Scale score, 12.5), awful (20.3), poor (35.7), okay (50.9), good (71.4), excellent (85.5), and best imaginable (90.9). Results: In 2018, the application was used 20 516 times by clinicians for 91.84% of eligible newborns. Use of the app saved 66 seconds for bilirubin management tasks compared with a commonly used tool (95% CI, 53-79 seconds; P < .001). Following the intervention, health care use rates remained stable, while orders for clinically appropriate phototherapy during hospitalization increased for newborns with bilirubin levels above the guideline-recommended threshold (odds ratio, 1.84; 95% CI, 1.16-2.90; P = .009). Surveys indicated excellent usability (System Usability Scale score, 83.90; 95% CI, 81.49-86.31). Conclusions and Relevance: Well-designed EHR add-on apps may save clinicians time and improve patient care. If time-saving apps, such as the bilirubin app, were implemented widely across institutions and care domains, the potential association with improved patient care and clinician efficiency could be significant. The University of Utah Health bilirubin app is being prepared for release into EHR app stores as free-to-use software.